From 8dbb5eb2971edbdebdbf159abcfcd3f0fb409374 Mon Sep 17 00:00:00 2001
From: Rahul Agrawal <12agrawalrahul@gmail.com>
Date: Thu, 6 Aug 2026 12:00:21 +0530
Subject: [PATCH 1/8] fix(build): add root package.json so bench builds the SPA
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

`bench build --app wikify` silently skipped the frontend: frappe's
esbuild runner looks for `apps/<app>/package.json` and `continue`s when
it is absent (frappe/esbuild/esbuild.js:611-620), so no bundle and no
`www/wikify.html` were ever emitted on a fresh `bench get-app`.

Add the root package.json with the standard Frappe SPA scripts, matching
frappe/crm. `postinstall` also keeps frontend deps in sync — without it
a stale `frontend/node_modules` silently builds against the wrong
frappe-ui.

Also ignore `wikify/public/node_modules`: `bench build` symlinks it, and
git sees a symlink rather than a directory, so the existing
`node_modules/` rule never matched it.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .gitignore   |  3 +++
 package.json | 19 +++++++++++++++++++
 yarn.lock    |  4 ++++
 3 files changed, 26 insertions(+)
 create mode 100644 package.json
 create mode 100644 yarn.lock

diff --git a/.gitignore b/.gitignore
index 5762c8b..fab1ecd 100644
--- a/.gitignore
+++ b/.gitignore
@@ -35,6 +35,9 @@ venv.bak/
 # Dependency directories
 node_modules/
 jspm_packages/
+# `bench build` symlinks the app's node_modules into public/; git sees a symlink,
+# not a directory, so the `node_modules/` rule above does not cover it.
+wikify/public/node_modules
 
 # Frontend build output (SPA). www/wikify.html is emitted by the vite plugin.
 frontend/dist/
diff --git a/package.json b/package.json
new file mode 100644
index 0000000..4c0c08d
--- /dev/null
+++ b/package.json
@@ -0,0 +1,19 @@
+{
+	"private": true,
+	"name": "wikify",
+	"description": "PDFs to Frappe Wiki Spaces/Documents",
+	"repository": {
+		"type": "git",
+		"url": "git+https://github.com/bwhtech/wikify.git"
+	},
+	"author": "BWH",
+	"license": "MIT",
+	"bugs": {
+		"url": "https://github.com/bwhtech/wikify/issues"
+	},
+	"scripts": {
+		"postinstall": "cd frontend && yarn install",
+		"dev": "cd frontend && yarn dev",
+		"build": "cd frontend && yarn build"
+	}
+}
diff --git a/yarn.lock b/yarn.lock
new file mode 100644
index 0000000..fb57ccd
--- /dev/null
+++ b/yarn.lock
@@ -0,0 +1,4 @@
+# THIS IS AN AUTOGENERATED FILE. DO NOT EDIT THIS FILE DIRECTLY.
+# yarn lockfile v1
+
+

From 3fe06407e8677d554a3ff01019bbb38e23fa7c8a Mon Sep 17 00:00:00 2001
From: Rahul Agrawal <12agrawalrahul@gmail.com>
Date: Sun, 9 Aug 2026 23:44:49 +0530
Subject: [PATCH 2/8] ci: install wiki app and take pocs/scratch out of the
 lint gate

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
---
 .github/workflows/ci.yml                      |  2 +
 .pre-commit-config.yaml                       |  2 +-
 frontend/src/components/AgentChatPanel.vue    |  1 -
 frontend/src/components/AppSettingsDialog.vue | 48 +++++++++++++++----
 frontend/src/components/Explore.vue           |  5 +-
 frontend/src/components/GraphView.vue         | 13 +++--
 frontend/src/components/ProjectSettings.vue   |  2 +-
 frontend/src/components/SectionTree.vue       |  3 +-
 frontend/src/pages/ExploreGlobal.vue          |  2 +-
 frontend/src/pages/ImportDetail.vue           |  6 +--
 frontend/src/pages/ProjectDetail.vue          |  4 +-
 frontend/src/pages/ProjectList.vue            |  4 +-
 frontend/src/utils/mermaid.js                 | 11 +++--
 wikify/agent/tools/read.py                    |  4 +-
 wikify/agent/tools/reparse.py                 |  7 ++-
 wikify/agent/tools/tree.py                    |  5 +-
 wikify/api/graph.py                           |  4 +-
 wikify/engine/loader/wiki.py                  |  4 +-
 wikify/engine/remediate.py                    | 14 ++++--
 wikify/engine/store.py                        |  4 +-
 wikify/hooks.py                               |  3 +-
 wikify/jobs/generate.py                       |  4 +-
 .../v0_4/merge_duplicate_section_types.py     |  4 +-
 wikify/tests/_cleanup.py                      |  4 +-
 wikify/tests/evals/harness.py                 | 14 ++++--
 wikify/tests/evals/scenarios.py               | 18 +++++--
 wikify/tests/test_agent_write.py              |  2 +-
 wikify/tests/test_finalize.py                 |  9 +++-
 wikify/tests/test_generate.py                 | 45 ++++++++++++-----
 wikify/tests/test_section_surgery.py          | 16 +++++--
 wikify/tests/test_sectionize.py               |  5 +-
 wikify/tests/test_taxonomy.py                 |  4 +-
 32 files changed, 181 insertions(+), 92 deletions(-)

diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index ddd848f..0218a1f 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -88,9 +88,11 @@ jobs:
       - name: Install
         working-directory: /home/runner/frappe-bench
         run: |
+          bench get-app wiki --branch develop
           bench get-app wikify $GITHUB_WORKSPACE
           bench setup requirements --dev
           bench new-site --db-root-password root --admin-password admin test_site
+          bench --site test_site install-app wiki
           bench --site test_site install-app wikify
           bench build
         env:
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 539ca12..71377e7 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,4 +1,4 @@
-exclude: 'node_modules|.git'
+exclude: 'node_modules|\.git|^pocs/|^scratch/'
 default_stages: [pre-commit]
 fail_fast: false
 
diff --git a/frontend/src/components/AgentChatPanel.vue b/frontend/src/components/AgentChatPanel.vue
index 7eeef97..bec11dc 100644
--- a/frontend/src/components/AgentChatPanel.vue
+++ b/frontend/src/components/AgentChatPanel.vue
@@ -475,7 +475,6 @@ function onKeydown(e) {
 					</Dropdown>
 				</div>
 			</div>
-
 		</aside>
 	</Transition>
 </template>
diff --git a/frontend/src/components/AppSettingsDialog.vue b/frontend/src/components/AppSettingsDialog.vue
index 0eed553..aed5c97 100644
--- a/frontend/src/components/AppSettingsDialog.vue
+++ b/frontend/src/components/AppSettingsDialog.vue
@@ -43,9 +43,21 @@ const newKey = ref("");
 
 const MODEL_FIELDS = [
 	{ key: "agent_model", label: "Agent model", hint: "Assistant model (projects can override)." },
-	{ key: "vlm_model", label: "VLM model", hint: "Vision model for re-parsing flagged / visual pages." },
-	{ key: "cleanup_model", label: "Cleanup model", hint: "Cheap text model for the markdown cleanup pass." },
-	{ key: "judge_model", label: "Judge model", hint: "Independent grader — keep distinct from the parser." },
+	{
+		key: "vlm_model",
+		label: "VLM model",
+		hint: "Vision model for re-parsing flagged / visual pages.",
+	},
+	{
+		key: "cleanup_model",
+		label: "Cleanup model",
+		hint: "Cheap text model for the markdown cleanup pass.",
+	},
+	{
+		key: "judge_model",
+		label: "Judge model",
+		hint: "Independent grader — keep distinct from the parser.",
+	},
 	{ key: "classifier_model", label: "Classifier model", hint: "" },
 ];
 
@@ -108,7 +120,10 @@ const activeTab = ref(TABS[0].value);
 			<SettingsNavGroup label="Wikify">
 				<SettingsNavItem v-for="tab in TABS" :key="tab.value" :value="tab.value">
 					<template #prefix>
-						<span :class="[tab.icon, 'size-4 shrink-0 text-ink-gray-6']" aria-hidden="true" />
+						<span
+							:class="[tab.icon, 'size-4 shrink-0 text-ink-gray-6']"
+							aria-hidden="true"
+						/>
 					</template>
 					{{ tab.label }}
 				</SettingsNavItem>
@@ -138,14 +153,17 @@ const activeTab = ref(TABS[0].value);
 						:placeholder="keyIsSet ? '•••• key saved — type to replace' : 'sk-or-v1-…'"
 					/>
 					<p class="mt-1.5 text-p-sm text-ink-gray-5">
-						Used for all AI steps. Stored encrypted. Leave blank to keep the current key;
-						falls back to site config / the app .env when never set.
+						Used for all AI steps. Stored encrypted. Leave blank to keep the current
+						key; falls back to site config / the app .env when never set.
 					</p>
 				</SettingsBody>
 			</SettingsPanel>
 
 			<SettingsPanel value="models">
-				<SettingsHeader title="Models" description="OpenRouter model ids per pipeline step.">
+				<SettingsHeader
+					title="Models"
+					description="OpenRouter model ids per pipeline step."
+				>
 					<template #actions>
 						<Button
 							variant="solid"
@@ -167,14 +185,19 @@ const activeTab = ref(TABS[0].value);
 								type="text"
 								placeholder="e.g. anthropic/claude-sonnet-4.6"
 							/>
-							<p v-if="f.hint" class="mt-1.5 text-p-sm text-ink-gray-5">{{ f.hint }}</p>
+							<p v-if="f.hint" class="mt-1.5 text-p-sm text-ink-gray-5">
+								{{ f.hint }}
+							</p>
 						</div>
 					</div>
 				</SettingsBody>
 			</SettingsPanel>
 
 			<SettingsPanel value="scoring">
-				<SettingsHeader title="Scoring & thresholds" description="Judge gates and parse tuning.">
+				<SettingsHeader
+					title="Scoring & thresholds"
+					description="Judge gates and parse tuning."
+				>
 					<template #actions>
 						<Button
 							variant="solid"
@@ -196,7 +219,12 @@ const activeTab = ref(TABS[0].value);
 							<Switch v-model="form.judge_all_pages" />
 						</SettingsRow>
 						<SettingsRow v-for="f in FLOAT_FIELDS" :key="f.key" :title="f.label">
-							<FormControl v-model="form[f.key]" type="number" step="0.001" class="w-28" />
+							<FormControl
+								v-model="form[f.key]"
+								type="number"
+								step="0.001"
+								class="w-28"
+							/>
 						</SettingsRow>
 						<SettingsRow v-for="f in INT_FIELDS" :key="f.key" :title="f.label">
 							<FormControl v-model="form[f.key]" type="number" class="w-28" />
diff --git a/frontend/src/components/Explore.vue b/frontend/src/components/Explore.vue
index e0151f0..f87025b 100644
--- a/frontend/src/components/Explore.vue
+++ b/frontend/src/components/Explore.vue
@@ -27,7 +27,10 @@ function loadSummary() {
 }
 function loadResults() {
 	if (props.sourceDocument && selectedType.value) {
-		results.submit({ section_type: selectedType.value, source_document: props.sourceDocument });
+		results.submit({
+			section_type: selectedType.value,
+			source_document: props.sourceDocument,
+		});
 	}
 }
 
diff --git a/frontend/src/components/GraphView.vue b/frontend/src/components/GraphView.vue
index 22e671b..4c47c7f 100644
--- a/frontend/src/components/GraphView.vue
+++ b/frontend/src/components/GraphView.vue
@@ -148,10 +148,15 @@ function setGraph(data) {
 			forceLink(links)
 				.id((d) => d.id)
 				.distance((l) => (l.rel === "REFERENCES" ? 90 : 42))
-				.strength((l) => (l.rel === "REFERENCES" ? Math.min(1, 0.3 + 0.1 * l.weight) : 0.5))
+				.strength((l) =>
+					l.rel === "REFERENCES" ? Math.min(1, 0.3 + 0.1 * l.weight) : 0.5
+				)
 		)
 		.force("charge", forceManyBody().strength(-160))
-		.force("collide", forceCollide().radius((d) => radius(d) + 7))
+		.force(
+			"collide",
+			forceCollide().radius((d) => radius(d) + 7)
+		)
 		// Gentle centering (not forceCenter): keeps disconnected components on-canvas.
 		.force("x", forceX(0).strength(0.06))
 		.force("y", forceY(0).strength(0.06))
@@ -169,7 +174,9 @@ function zoomToFit() {
 	const [x0, x1] = [Math.min(...xs), Math.max(...xs)];
 	const [y0, y1] = [Math.min(...ys), Math.max(...ys)];
 	const k = Math.min(2, 0.9 * Math.min(w / Math.max(1, x1 - x0), h / Math.max(1, y1 - y0)));
-	const t = zoomIdentity.translate(w / 2 - (k * (x0 + x1)) / 2, h / 2 - (k * (y0 + y1)) / 2).scale(k);
+	const t = zoomIdentity
+		.translate(w / 2 - (k * (x0 + x1)) / 2, h / 2 - (k * (y0 + y1)) / 2)
+		.scale(k);
 	select(canvas.value).call(zoomBehavior.transform, t);
 }
 
diff --git a/frontend/src/components/ProjectSettings.vue b/frontend/src/components/ProjectSettings.vue
index 1049500..ff1c1a4 100644
--- a/frontend/src/components/ProjectSettings.vue
+++ b/frontend/src/components/ProjectSettings.vue
@@ -40,7 +40,7 @@ watch(
 			archived: doc.status === "Archived",
 		};
 	},
-	{ immediate: true },
+	{ immediate: true }
 );
 
 const save = useCall({
diff --git a/frontend/src/components/SectionTree.vue b/frontend/src/components/SectionTree.vue
index 5224f80..53b2c03 100644
--- a/frontend/src/components/SectionTree.vue
+++ b/frontend/src/components/SectionTree.vue
@@ -91,7 +91,8 @@ watch(
 		}
 		tree.value = roots;
 		if (roots.length && !byName.value[selectedName.value]) {
-			const wanted = props.initialSection && nodes[props.initialSection] ? props.initialSection : null;
+			const wanted =
+				props.initialSection && nodes[props.initialSection] ? props.initialSection : null;
 			selectedName.value = wanted || roots[0].name;
 			if (wanted)
 				nextTick(() =>
diff --git a/frontend/src/pages/ExploreGlobal.vue b/frontend/src/pages/ExploreGlobal.vue
index b382b26..2ac01fc 100644
--- a/frontend/src/pages/ExploreGlobal.vue
+++ b/frontend/src/pages/ExploreGlobal.vue
@@ -35,7 +35,7 @@ const matchCount = computed(() => (groups.data || []).reduce((n, g) => n + g.sec
 
 const selectedType = ref(null);
 const selected = computed(
-	() => types.value.find((t) => t.type_name === selectedType.value) || null,
+	() => types.value.find((t) => t.type_name === selectedType.value) || null
 );
 
 // Re-pull the type summary whenever the project scope changes; the selected-type watcher
diff --git a/frontend/src/pages/ImportDetail.vue b/frontend/src/pages/ImportDetail.vue
index 18689d5..584613d 100644
--- a/frontend/src/pages/ImportDetail.vue
+++ b/frontend/src/pages/ImportDetail.vue
@@ -62,7 +62,7 @@ watch(
 	(key) => {
 		const i = tabKeys.indexOf(key);
 		if (i >= 0 && i !== activeTab.value) activeTab.value = i;
-	},
+	}
 );
 
 // Streaming log
@@ -88,7 +88,7 @@ watch(
 		if (sd) setDocument({ name: sd, label: imp.doc?.import_title || props.name }, projectChip);
 		else if (projectChip) setProject(projectChip);
 	},
-	{ immediate: true },
+	{ immediate: true }
 );
 
 const pageReview = ref(null);
@@ -104,7 +104,7 @@ const sdStats = useList({
 });
 const sourceStats = computed(() => sdStats.data?.[0] || null);
 const docAudit = computed(
-	() => sourceStats.value?.canonical_mean ?? sourceStats.value?.mean_score,
+	() => sourceStats.value?.canonical_mean ?? sourceStats.value?.mean_score
 );
 function fmtCost(v) {
 	return v ? `$${Number(v).toFixed(4)}` : "—";
diff --git a/frontend/src/pages/ProjectDetail.vue b/frontend/src/pages/ProjectDetail.vue
index d579bf1..6796092 100644
--- a/frontend/src/pages/ProjectDetail.vue
+++ b/frontend/src/pages/ProjectDetail.vue
@@ -15,11 +15,11 @@ const project = useDoc({ doctype: "Wikify Project", name: props.name });
 watch(
 	() => props.name,
 	(name) => name && setProject({ name, label: project.doc?.project_name || name }),
-	{ immediate: true },
+	{ immediate: true }
 );
 watch(
 	() => project.doc?.project_name,
-	(label) => label && setProject({ name: props.name, label }),
+	(label) => label && setProject({ name: props.name, label })
 );
 
 const showNewImport = ref(false);
diff --git a/frontend/src/pages/ProjectList.vue b/frontend/src/pages/ProjectList.vue
index 080f984..112f681 100644
--- a/frontend/src/pages/ProjectList.vue
+++ b/frontend/src/pages/ProjectList.vue
@@ -32,10 +32,10 @@ const projects = useList({
 // default is never archivable, so it always shows).
 const showArchived = ref(false);
 const visibleProjects = computed(() =>
-	(projects.data || []).filter((p) => showArchived.value || p.status !== "Archived"),
+	(projects.data || []).filter((p) => showArchived.value || p.status !== "Archived")
 );
 const archivedCount = computed(
-	() => (projects.data || []).filter((p) => p.status === "Archived").length,
+	() => (projects.data || []).filter((p) => p.status === "Archived").length
 );
 
 const showNew = ref(false);
diff --git a/frontend/src/utils/mermaid.js b/frontend/src/utils/mermaid.js
index 77b57bf..4927445 100644
--- a/frontend/src/utils/mermaid.js
+++ b/frontend/src/utils/mermaid.js
@@ -47,9 +47,9 @@ function themeConfig() {
  */
 export async function renderMermaidIn(root) {
 	if (!root) return;
-	const blocks = [
-		...root.querySelectorAll("code.language-mermaid, pre.mermaid"),
-	].filter((el) => !el.closest("[data-mermaid-done]"));
+	const blocks = [...root.querySelectorAll("code.language-mermaid, pre.mermaid")].filter(
+		(el) => !el.closest("[data-mermaid-done]")
+	);
 	if (!blocks.length) return;
 
 	let mermaid;
@@ -65,7 +65,10 @@ export async function renderMermaidIn(root) {
 		host.setAttribute("data-mermaid-done", "");
 		try {
 			counter += 1;
-			const { svg } = await mermaid.render(`wikify-mermaid-${counter}`, el.textContent || "");
+			const { svg } = await mermaid.render(
+				`wikify-mermaid-${counter}`,
+				el.textContent || ""
+			);
 			const figure = document.createElement("div");
 			figure.className = "mermaid-figure";
 			figure.setAttribute("data-mermaid-done", "");
diff --git a/wikify/agent/tools/read.py b/wikify/agent/tools/read.py
index 4974f39..9ed6c7c 100644
--- a/wikify/agent/tools/read.py
+++ b/wikify/agent/tools/read.py
@@ -205,9 +205,7 @@ def _read_wiki_page(ctx: Ctx, args: dict) -> str:
 	name = args.get("name")
 	if not name:
 		return _("Provide the section `name`.")
-	sec = frappe.db.get_value(
-		"Source Section", name, ["title", "markdown", "wiki_document"], as_dict=True
-	)
+	sec = frappe.db.get_value("Source Section", name, ["title", "markdown", "wiki_document"], as_dict=True)
 	if not sec:
 		return _("Section {0} not found.").format(name)
 	if not sec.wiki_document or not frappe.db.exists("Wiki Document", sec.wiki_document):
diff --git a/wikify/agent/tools/reparse.py b/wikify/agent/tools/reparse.py
index 4f24fae..7a82585 100644
--- a/wikify/agent/tools/reparse.py
+++ b/wikify/agent/tools/reparse.py
@@ -93,10 +93,9 @@ def _use_page_image(ctx: Ctx, args: dict) -> str:
 		embed_page_image(source_document, page_no)
 	except (ValueError, RuntimeError) as e:
 		return _("Couldn't embed the page image: {0}").format(str(e))
-	return (
-		_("Page {0} now embeds its rendered image as canonical markdown (no re-parse).").format(page_no)
-		+ _propagate_page(source_document, page_no)
-	)
+	return _("Page {0} now embeds its rendered image as canonical markdown (no re-parse).").format(
+		page_no
+	) + _propagate_page(source_document, page_no)
 
 
 def _reparse_page(ctx: Ctx, args: dict) -> str:
diff --git a/wikify/agent/tools/tree.py b/wikify/agent/tools/tree.py
index 8e53c35..15a67bb 100644
--- a/wikify/agent/tools/tree.py
+++ b/wikify/agent/tools/tree.py
@@ -277,7 +277,10 @@ def _merge_sections(ctx: Ctx, args: dict) -> str:
 			"properties": {
 				"name": {"type": "string", "description": "Source Section id to split."},
 				"at_heading": {"type": "string", "description": "Heading text to split at."},
-				"new_title": {"type": "string", "description": "Title for the new sibling (defaults to the heading text)."},
+				"new_title": {
+					"type": "string",
+					"description": "Title for the new sibling (defaults to the heading text).",
+				},
 			},
 			"required": ["name", "at_heading"],
 		},
diff --git a/wikify/api/graph.py b/wikify/api/graph.py
index f3c68c3..3a55888 100644
--- a/wikify/api/graph.py
+++ b/wikify/api/graph.py
@@ -15,9 +15,7 @@
 
 def _document_graph(source_document: str) -> tuple[list[dict], list[dict]]:
 	"""(nodes, edges) for one Source Document — shared by both scopes."""
-	sd = frappe.db.get_value(
-		"Source Document", source_document, ["title", "page_count"], as_dict=True
-	)
+	sd = frappe.db.get_value("Source Document", source_document, ["title", "page_count"], as_dict=True)
 	if not sd:
 		frappe.throw(_("Source Document {0} not found.").format(source_document))
 	sections = frappe.get_all(
diff --git a/wikify/engine/loader/wiki.py b/wikify/engine/loader/wiki.py
index a960c03..cb09ec9 100644
--- a/wikify/engine/loader/wiki.py
+++ b/wikify/engine/loader/wiki.py
@@ -19,9 +19,7 @@
 
 _SLUG_RE = re.compile(r"[^a-z0-9]+")
 # Optional see/refer cue, the word page/pg/p (with optional "no"), then the number.
-_PAGEREF_RE = re.compile(
-	r"((?:see|refer(?:\s+to)?)\s+)?(page\s*no\.?|page|pg\.?|p\.)\s*(\d{1,4})\b", re.I
-)
+_PAGEREF_RE = re.compile(r"((?:see|refer(?:\s+to)?)\s+)?(page\s*no\.?|page|pg\.?|p\.)\s*(\d{1,4})\b", re.I)
 
 
 def slugify(text: str) -> str:
diff --git a/wikify/engine/remediate.py b/wikify/engine/remediate.py
index d548912..bf29a54 100644
--- a/wikify/engine/remediate.py
+++ b/wikify/engine/remediate.py
@@ -112,7 +112,9 @@ def remediate_pdf(
 			candidates: list[tuple] = []  # (method, markdown, PageScore, adopt_eligible)
 			errors: list[str] = []
 			try:
-				vlm_md = vlm.parse_page_image(data_url, project_context=project_context, instruction=instruction)
+				vlm_md = vlm.parse_page_image(
+					data_url, project_context=project_context, instruction=instruction
+				)
 				vlm_ps = score_page(
 					p["page_no"], vlm_md, gt, image_data_url=img, use_judge=use_judge, page_kind=kind
 				)
@@ -121,7 +123,9 @@ def remediate_pdf(
 				errors.append(f"vlm failed: {e}")
 			if kind != "visual":
 				try:
-					clean_md = clean_markdown(base_md, project_context=project_context, instruction=instruction)
+					clean_md = clean_markdown(
+						base_md, project_context=project_context, instruction=instruction
+					)
 					clean_ps = score_page(
 						p["page_no"], clean_md, gt, image_data_url=img, use_judge=use_judge, page_kind=kind
 					)
@@ -137,8 +141,10 @@ def remediate_pdf(
 			winner = _pick_winner(candidates)
 			# Record the adopted candidate; when nothing is adopted, record the vlm attempt
 			# (the expensive audit trail) so the review UI shows what was tried and why not.
-			record = winner or next((c for c in candidates if c[0] == "vlm"), None) or (
-				candidates[0] if candidates else None
+			record = (
+				winner
+				or next((c for c in candidates if c[0] == "vlm"), None)
+				or (candidates[0] if candidates else None)
 			)
 			if record:
 				method, new_md, new_ps, _ = record
diff --git a/wikify/engine/store.py b/wikify/engine/store.py
index a4412cf..72d154c 100644
--- a/wikify/engine/store.py
+++ b/wikify/engine/store.py
@@ -400,9 +400,7 @@ def get_section_bodies(source_document: str, names: list[str] | None = None) ->
 	filters: dict = {"source_document": source_document}
 	if names is not None:
 		filters["name"] = ["in", names]
-	return frappe.get_all(
-		"Source Section", filters=filters, fields=["name", "markdown"], order_by="lft asc"
-	)
+	return frappe.get_all("Source Section", filters=filters, fields=["name", "markdown"], order_by="lft asc")
 
 
 def replace_references(
diff --git a/wikify/hooks.py b/wikify/hooks.py
index be725d2..6ba29b9 100644
--- a/wikify/hooks.py
+++ b/wikify/hooks.py
@@ -8,7 +8,7 @@
 # Apps
 # ------------------
 
-# required_apps = []
+required_apps = ["wiki"]
 
 # SPA (Frappe UI) mounted at /wikify — served by www/wikify.py + the built
 # www/wikify.html (emitted by the frappe-ui vite plugin's frontendRoute).
@@ -266,4 +266,3 @@
 # ------------
 # List of apps whose translatable strings should be excluded from this app's translations.
 # ignore_translatable_strings_from = []
-
diff --git a/wikify/jobs/generate.py b/wikify/jobs/generate.py
index cddc455..be179e2 100644
--- a/wikify/jobs/generate.py
+++ b/wikify/jobs/generate.py
@@ -46,7 +46,9 @@ def stage_cb(label: str) -> None:
 		imp.db_set("completed_at", now_datetime())
 		publish_progress(import_name, 100, "Wiki generated", status="Completed")
 		log(
-			import_name, "info", "generate",
+			import_name,
+			"info",
+			"generate",
 			f"Done — {result['pages']} pages, {result['groups']} groups, "
 			f"{result['deleted']} removed, {result['links']} page-refs linked "
 			f"→ /{result['space_route']}",
diff --git a/wikify/patches/v0_4/merge_duplicate_section_types.py b/wikify/patches/v0_4/merge_duplicate_section_types.py
index 3be34bb..806734f 100644
--- a/wikify/patches/v0_4/merge_duplicate_section_types.py
+++ b/wikify/patches/v0_4/merge_duplicate_section_types.py
@@ -12,9 +12,7 @@
 
 def execute():
 	groups: dict[str, list] = {}
-	for row in frappe.get_all(
-		"Section Type", fields=["name", "label", "creation"], order_by="creation asc"
-	):
+	for row in frappe.get_all("Section Type", fields=["name", "label", "creation"], order_by="creation asc"):
 		key = normalize_label(row.label)
 		if key:
 			groups.setdefault(key, []).append(row)
diff --git a/wikify/tests/_cleanup.py b/wikify/tests/_cleanup.py
index 42071ae..440f8e3 100644
--- a/wikify/tests/_cleanup.py
+++ b/wikify/tests/_cleanup.py
@@ -46,9 +46,7 @@ def _delete_document_rows(sd_name: str) -> None:
 		frappe.db.delete("Wikify Import", {"name": ("in", imports)})
 	pages = frappe.get_all("Source Page", filters={"source_document": sd_name}, pluck="name")
 	if pages:
-		frappe.db.delete(
-			"File", {"attached_to_doctype": "Source Page", "attached_to_name": ("in", pages)}
-		)
+		frappe.db.delete("File", {"attached_to_doctype": "Source Page", "attached_to_name": ("in", pages)})
 		frappe.db.delete("Source Page", {"name": ("in", pages)})
 	frappe.db.delete("Section Reference", {"source_document": sd_name})
 	frappe.db.delete("Source Section", {"source_document": sd_name})
diff --git a/wikify/tests/evals/harness.py b/wikify/tests/evals/harness.py
index 3eb324e..38fc2f1 100644
--- a/wikify/tests/evals/harness.py
+++ b/wikify/tests/evals/harness.py
@@ -158,7 +158,9 @@ def cleanup(self):
 			frappe.db.delete("Wiki Space", {"name": self.space})
 		imports = frappe.get_all("Wikify Import", filters={"source_document": self.sd}, pluck="name")
 		if imports:
-			frappe.db.delete("File", {"attached_to_doctype": "Wikify Import", "attached_to_name": ["in", imports]})
+			frappe.db.delete(
+				"File", {"attached_to_doctype": "Wikify Import", "attached_to_name": ["in", imports]}
+			)
 			frappe.db.delete("Wikify Import", {"name": ["in", imports]})
 		frappe.db.delete("Source Section", {"source_document": self.sd})
 		frappe.db.delete("Source Page", {"source_document": self.sd})
@@ -198,7 +200,9 @@ def run_turn(fixture: Fixture, prompt: str, approved_tools: list | None = None)
 	}
 
 
-_SUCCESS_CLAIM = re.compile(r"\b(fixed|updated|synced|corrected|cleaned|repaired|removed|deleted|split)\b", re.I)
+_SUCCESS_CLAIM = re.compile(
+	r"\b(fixed|updated|synced|corrected|cleaned|repaired|removed|deleted|split)\b", re.I
+)
 
 
 def honesty_check(turn: dict, before: dict, after: dict) -> tuple[bool, str]:
@@ -232,7 +236,11 @@ def run_scenarios(which: str = "all", *, keep: bool = False) -> dict:
 		try:
 			result = sc.SCENARIOS[name](keep=keep)
 		except Exception:
-			result = {"name": name, "passed": False, "checks": [("scenario crashed", False, frappe.get_traceback())]}
+			result = {
+				"name": name,
+				"passed": False,
+				"checks": [("scenario crashed", False, frappe.get_traceback())],
+			}
 		if not keep:
 			leaked_types = set(frappe.get_all("Section Type", pluck="name")) - types_before
 			if leaked_types:
diff --git a/wikify/tests/evals/scenarios.py b/wikify/tests/evals/scenarios.py
index a067053..e146b6a 100644
--- a/wikify/tests/evals/scenarios.py
+++ b/wikify/tests/evals/scenarios.py
@@ -82,7 +82,11 @@ def honest_failure(keep: bool = False) -> dict:
 	checks = [
 		("agent finished without error", not turn["errored"], turn["final"][:200]),
 		("no wiki page was conjured", not fx.wiki_pages(), ""),
-		("sections untouched (told not to change anything else)", after["sections"] == before["sections"], ""),
+		(
+			"sections untouched (told not to change anything else)",
+			after["sections"] == before["sections"],
+			"",
+		),
 		("honesty (no false success claim)", honest, hdetail),
 	]
 	_finish(fx, keep)
@@ -214,7 +218,8 @@ def sync_generated_wiki(keep: bool = False) -> dict:
 		),
 		(
 			"generated wiki page updated",
-			wiki_after and wiki_after.content != wiki_before.content
+			wiki_after
+			and wiki_after.content != wiki_before.content
 			and "CONTENTS SUMMARY" not in (wiki_after.content or ""),
 			(wiki_after.content or "")[:200] if wiki_after else "wiki page missing",
 		),
@@ -255,7 +260,8 @@ def split_and_delete(keep: bool = False) -> dict:
 			"split moved the right content",
 			new_page is not None
 			and "Admission Protocol" in (new_page.markdown or "")
-			and "Admission Protocol" not in (sections.get("2. PROCEDURES") or frappe._dict()).get("markdown", ""),
+			and "Admission Protocol"
+			not in (sections.get("2. PROCEDURES") or frappe._dict()).get("markdown", ""),
 			(new_page.markdown or "")[:200] if new_page else "",
 		),
 		(
@@ -263,9 +269,11 @@ def split_and_delete(keep: bool = False) -> dict:
 			"General Protocol" in (sections.get("2. PROCEDURES") or frappe._dict()).get("markdown", ""),
 			"",
 		),
-		("delete_section + split_section were used",
+		(
+			"delete_section + split_section were used",
 			"delete_section" in turn["tools_used"] and "split_section" in turn["tools_used"],
-			str(turn["tools_used"])),
+			str(turn["tools_used"]),
+		),
 		("honesty", honest, hdetail),
 	]
 	_finish(fx, keep)
diff --git a/wikify/tests/test_agent_write.py b/wikify/tests/test_agent_write.py
index edcde15..c38e53d 100644
--- a/wikify/tests/test_agent_write.py
+++ b/wikify/tests/test_agent_write.py
@@ -20,8 +20,8 @@
 from wikify.agent.tools import taxonomy as tax
 from wikify.agent.tools import tree as tt
 from wikify.engine import store
-from wikify.tests import _cleanup
 from wikify.engine.loader.sectionizer import Section
+from wikify.tests import _cleanup
 
 # A 1x1 PNG so `embed_page_image` has a real image File to point at.
 _PNG = (
diff --git a/wikify/tests/test_finalize.py b/wikify/tests/test_finalize.py
index 33eabc4..9c95a35 100644
--- a/wikify/tests/test_finalize.py
+++ b/wikify/tests/test_finalize.py
@@ -27,7 +27,9 @@ def _parse_with_furniture(self) -> tuple[str, str, list[dict]]:
 			sd = parse_pdf(str(path), title="Finalize Test")
 
 		pages = frappe.get_all(
-			"Source Page", filters={"source_document": sd}, fields=["name", "page_no", "baseline_markdown"],
+			"Source Page",
+			filters={"source_document": sd},
+			fields=["name", "page_no", "baseline_markdown"],
 			order_by="page_no asc",
 		)
 		# Seed each page's canonical markdown with furniture wrapping a deterministic body
@@ -47,7 +49,10 @@ def test_finalize_strips_and_persists_furniture(self):
 
 		self.assertEqual(result["pages_changed"], len(pages))
 		canon = frappe.get_all(
-			"Source Page", filters={"source_document": sd}, fields=["canonical_markdown"], order_by="page_no asc"
+			"Source Page",
+			filters={"source_document": sd},
+			fields=["canonical_markdown"],
+			order_by="page_no asc",
 		)
 		for i, row in enumerate(canon, start=1):
 			md = row["canonical_markdown"]
diff --git a/wikify/tests/test_generate.py b/wikify/tests/test_generate.py
index 31e7633..3fac09d 100644
--- a/wikify/tests/test_generate.py
+++ b/wikify/tests/test_generate.py
@@ -37,9 +37,9 @@ def setUp(self):
 		# rolled back with the test transaction — use a unique route per test and clean
 		# up the generated spaces + this doc's sections explicitly in tearDown.
 		self._spaces: set[str] = set()
-		self.sd = frappe.get_doc(
-			{"doctype": "Source Document", "title": "Gen Test", "page_count": 5}
-		).insert(ignore_permissions=True)
+		self.sd = frappe.get_doc({"doctype": "Source Document", "title": "Gen Test", "page_count": 5}).insert(
+			ignore_permissions=True
+		)
 		# Intro (group, p1-3) > Purpose (p1), Scope (p2, refers to page 5);
 		# Appendix (leaf, p4-5).
 		store.replace_sections(
@@ -48,7 +48,11 @@ def setUp(self):
 				_sec("1. Intro", 1, ["1. Intro"], 1, 3, "overview"),
 				_sec("1.1 Purpose", 2, ["1. Intro", "1.1 Purpose"], 1, 1),
 				_sec(
-					"1.2 Scope", 2, ["1. Intro", "1.2 Scope"], 2, 2,
+					"1.2 Scope",
+					2,
+					["1. Intro", "1.2 Scope"],
+					2,
+					2,
 					"In scope. See page 5 for details. Williams p820 is external.",
 				),
 				_sec("2. Appendix", 1, ["2. Appendix"], 4, 5, "appendix body"),
@@ -62,7 +66,12 @@ def tearDown(self):
 			if root:
 				names = frappe.get_all(
 					"Wiki Document",
-					filters={"name": ["in", [root, *get_descendants_of("Wiki Document", root, ignore_permissions=True)]]},
+					filters={
+						"name": [
+							"in",
+							[root, *get_descendants_of("Wiki Document", root, ignore_permissions=True)],
+						]
+					},
 					order_by="lft desc",
 					pluck="name",
 				)
@@ -89,8 +98,14 @@ def _docs_under(self, root_group):
 				"Wiki Document",
 				filters={"name": ["in", names]},
 				fields=[
-					"name", "title", "is_group", "route", "content",
-					"parent_wiki_document", "sort_order", "is_published",
+					"name",
+					"title",
+					"is_group",
+					"route",
+					"content",
+					"parent_wiki_document",
+					"sort_order",
+					"is_published",
 				],
 			)
 		}
@@ -168,19 +183,25 @@ def test_rewrite_helper_pure(self):
 
 	def test_regenerate_updates_in_place(self):
 		res1 = self._generate()
-		first_purpose = frappe.db.get_value("Source Section", {"title": "1.1 Purpose", "source_document": self.sd.name}, "wiki_document")
+		first_purpose = frappe.db.get_value(
+			"Source Section", {"title": "1.1 Purpose", "source_document": self.sd.name}, "wiki_document"
+		)
 		count1 = len(self._docs_under(res1["root_group"]))
 
 		# Regenerate unchanged → same wiki doc reused, no duplicates.
 		res2 = generate_wiki(self.sd.name, wiki_space=res1["space"])
 		self.assertEqual(res1["root_group"], res2["root_group"])
-		again_purpose = frappe.db.get_value("Source Section", {"title": "1.1 Purpose", "source_document": self.sd.name}, "wiki_document")
+		again_purpose = frappe.db.get_value(
+			"Source Section", {"title": "1.1 Purpose", "source_document": self.sd.name}, "wiki_document"
+		)
 		self.assertEqual(first_purpose, again_purpose)
 		self.assertEqual(len(self._docs_under(res2["root_group"])), count1)
 
 	def test_regenerate_drops_excluded_section(self):
 		res1 = self._generate()
-		appendix = frappe.db.get_value("Source Section", {"title": "2. Appendix", "source_document": self.sd.name}, "name")
+		appendix = frappe.db.get_value(
+			"Source Section", {"title": "2. Appendix", "source_document": self.sd.name}, "name"
+		)
 		appendix_wiki = frappe.db.get_value("Source Section", appendix, "wiki_document")
 		self.assertTrue(frappe.db.exists("Wiki Document", appendix_wiki))
 
@@ -194,7 +215,9 @@ def test_regenerate_drops_excluded_section(self):
 
 	def test_regenerate_after_rename_updates_route(self):
 		res1 = self._generate()
-		purpose = frappe.db.get_value("Source Section", {"title": "1.1 Purpose", "source_document": self.sd.name}, "name")
+		purpose = frappe.db.get_value(
+			"Source Section", {"title": "1.1 Purpose", "source_document": self.sd.name}, "name"
+		)
 		frappe.db.set_value("Source Section", purpose, "title", "1.1 Goals")
 		res2 = generate_wiki(self.sd.name, wiki_space=res1["space"])
 		docs = self._by_title(res2["root_group"])
diff --git a/wikify/tests/test_section_surgery.py b/wikify/tests/test_section_surgery.py
index 2db3888..0e5d3fe 100644
--- a/wikify/tests/test_section_surgery.py
+++ b/wikify/tests/test_section_surgery.py
@@ -52,9 +52,19 @@ def _rows(self):
 				"Source Section",
 				filters={"source_document": self.sd.name},
 				fields=[
-					"name", "title", "markdown", "lft", "rgt", "level",
-					"parent_source_section", "is_group", "hierarchy_path", "sort_order",
-					"page_start", "page_end", "include_in_wiki",
+					"name",
+					"title",
+					"markdown",
+					"lft",
+					"rgt",
+					"level",
+					"parent_source_section",
+					"is_group",
+					"hierarchy_path",
+					"sort_order",
+					"page_start",
+					"page_end",
+					"include_in_wiki",
 				],
 			)
 		}
diff --git a/wikify/tests/test_sectionize.py b/wikify/tests/test_sectionize.py
index e1a3d8b..7c48535 100644
--- a/wikify/tests/test_sectionize.py
+++ b/wikify/tests/test_sectionize.py
@@ -117,10 +117,7 @@ def test_clean_pages_strips_varying_boilerplate_before_sectionizing(self):
 	def test_clean_pages_strips_signoff_footer_block(self):
 		# The QMS sign-off footer (a table row with >=2 sign-off phrases) plus its
 		# orphaned |---| separator are page furniture and must be removed.
-		footer = (
-			"|**Prepared by - Dr. A**|**Issued by: QMC**|**Approved by - Dr. B**|\n"
-			"|---|---|---|"
-		)
+		footer = "|**Prepared by - Dr. A**|**Issued by: QMC**|**Approved by - Dr. B**|\n|---|---|---|"
 		pages = [(1, f"## 1. Intro\nreal body\n{footer}"), (2, f"## 2. Next\nmore body\n{footer}")]
 		cleaned = dict(clean_pages(pages))
 		for md in cleaned.values():
diff --git a/wikify/tests/test_taxonomy.py b/wikify/tests/test_taxonomy.py
index 60337d2..ee38c58 100644
--- a/wikify/tests/test_taxonomy.py
+++ b/wikify/tests/test_taxonomy.py
@@ -58,9 +58,7 @@ def test_merge_patch_collapses_duplicates_and_repoints_sections(self):
 
 		merge_patch()
 		self.assertFalse(frappe.db.exists("Section Type", "t_zzdupe1"))
-		self.assertEqual(
-			frappe.db.get_value("Source Section", sec.name, "section_type"), "zz_dedupe_canon"
-		)
+		self.assertEqual(frappe.db.get_value("Source Section", sec.name, "section_type"), "zz_dedupe_canon")
 		merge_patch()  # idempotent — nothing left to merge
 		self.assertTrue(frappe.db.exists("Section Type", "zz_dedupe_canon"))
 

From 59e6ce20efe5c49f4898b0a09eadeffa1338e09e Mon Sep 17 00:00:00 2001
From: rl0007 <rl0007@users.noreply.github.com>
Date: Tue, 11 Aug 2026 08:39:57 +0530
Subject: [PATCH 3/8] feat(rag): lancedb retrieval, cited answers, and pdf
 fidelity fixes
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Adds a retrieval layer and an Ask/RAG Lab UI over parsed documents, and fixes
several pipeline defects found while running two real ICAI study PDFs through it.

Retrieval (wikify/rag/)
- LanceDB store with local model2vec embeddings (256d, no API key, no server).
- Four modes: vector, full-text, hybrid (RRF), and filter. Filter returns EVERY
  match — "give me all X" is a metadata question, not a similarity guess.
- Contextual-retrieval prefix on embed text, parent-section expansion, optional
  LLM rerank that degrades to fusion order when unavailable.
- Permissions are a LanceDB pre-filter, never a post-hoc trim. An omitted ACL
  decision throws rather than searching everything.

Answering
- Intent router (exhaustive/semantic/hybrid) with follow-up rewriting; the
  decision and its plain-language reason are shown in the UI.
- Answers cite [n] markers; each citation carries a resolved page, line span and
  verbatim quote, verified against the source before display. Verification is two
  gates: fuzzy prose match AND exact equality on figures, signs and statutory
  refs — a flipped (+)/(-) previously passed a 0.85 similarity check.
- Honest refusal when nothing retrieved clears the bar.
- Per-question cost and token usage returned and displayed.
- Conversations persist to Wikify Ask Session/Message, so route decisions and
  citations become an evaluation set.

Pipeline fixes
- Sectioning stopped at the first heading longer than the 140-char title column,
  after the old tree was deleted — silently dropping 93.6% of a 236-page
  document. Titles are now clipped; coverage went 6.4% -> 99.8%.
- Page classification gated on `chars < 250 AND drawings > 40`, which no
  born-digital diagram page can satisfy; replaced with per-region detection
  (1/236 -> 231/236 pages routed correctly).
- Tables were being encoded as mermaid flowcharts, destroying row-to-rate
  correspondence; they now emit HTML tables, and diagrams are parsed and
  repaired (quoting node labels, `&` chains) before storage.
- Page verdicts were frozen at the baseline parse, so remediated pages still
  read "review"; they now track the adopted content, with a backfill patch.
- Remediation could adopt a near-empty candidate over a good one.
- Page edits now propagate into the sections and index built from them.

Frontend
- /ask and /rag-lab, with sources rendered before the answer, citation chips,
  provenance, and a naive-vs-routed comparison that shows what similarity search
  missed. Unranked results no longer render a meaningless full score bar.
- Mobile support across the app: shell navigation, drill-down replacements for
  desktop splits, and graph views that degrade to a list rather than an
  unreadable canvas.

Evaluation
- 12 golden questions with recall, precision and completeness, plus an HTML
  scorecard. Routed retrieval beats naive 66% -> 87-90% recall on the demo
  corpus; routing is an LLM call, so treat the figure as a range.
---
 docs/rag-explained.md                         |  313 ++++
 frontend/package.json                         |    1 +
 frontend/src/components/AgentChatPanel.vue    |   15 +-
 frontend/src/components/AppShell.vue          |   98 +-
 frontend/src/components/Explore.vue           |   37 +-
 frontend/src/components/GraphView.vue         |  176 +-
 frontend/src/components/MarkdownPreview.vue   |   33 +-
 frontend/src/components/NewImportDialog.vue   |   10 +-
 frontend/src/components/PageReview.vue        |   85 +-
 frontend/src/components/ProjectSettings.vue   |    8 +-
 frontend/src/components/SectionTree.vue       |   68 +-
 frontend/src/components/TypeChip.vue          |    6 +-
 frontend/src/components/WikiGenerate.vue      |   21 +-
 frontend/src/components/WikiPreview.vue       |   55 +-
 frontend/src/components/rag/CostMeter.vue     |   58 +
 frontend/src/components/rag/CoverageBar.vue   |   47 +
 .../src/components/rag/EvalScoreboard.vue     |   53 +
 .../src/components/rag/IndexStatusCard.vue    |  109 ++
 frontend/src/components/rag/RelevanceTag.vue  |   94 +
 frontend/src/components/rag/RouteBadge.vue    |   88 +
 frontend/src/components/rag/SourceCard.vue    |  259 +++
 frontend/src/composables/useMediaQuery.js     |   34 +
 frontend/src/composables/useRag.js            |  331 ++++
 frontend/src/pages/AskWiki.vue                |  296 +++
 frontend/src/pages/ExploreGlobal.vue          |   48 +-
 frontend/src/pages/ImportDetail.vue           |   57 +-
 frontend/src/pages/ImportGraph.vue            |    4 +-
 frontend/src/pages/ImportList.vue             |   30 +-
 frontend/src/pages/ProjectDetail.vue          |   22 +-
 frontend/src/pages/ProjectGraph.vue           |    2 +-
 frontend/src/pages/ProjectList.vue            |   20 +-
 frontend/src/pages/RagLab.vue                 |  338 ++++
 frontend/src/router.js                        |   10 +
 frontend/src/utils/actionButton.js            |    7 +
 frontend/src/utils/mermaid.css                |   32 +
 frontend/src/utils/mermaid.js                 |  103 +-
 frontend/yarn.lock                            |  666 ++++++-
 pyproject.toml                                |    2 +
 wikify/agent/llm.py                           |    9 +-
 wikify/agent/registry.py                      |    4 +-
 wikify/agent/tools/retrieve.py                |   94 +
 wikify/api/ask_history.py                     |   31 +
 wikify/api/rag.py                             |  262 +++
 wikify/engine/diagrams.py                     |  337 ++++
 wikify/engine/loader/sectionizer.py           |   64 +-
 wikify/engine/parsers/vlm.py                  |   53 +-
 wikify/engine/pdf_utils.py                    |   30 +-
 wikify/engine/regions.py                      |  398 ++++
 wikify/engine/remediate.py                    |   93 +-
 wikify/engine/reparse.py                      |   12 +-
 wikify/engine/store.py                        |   10 +-
 wikify/engine/verify/__init__.py              |    4 +-
 wikify/engine/verify/harness.py               |    7 +-
 wikify/hooks.py                               |   22 +-
 wikify/patches.txt                            |    1 +
 .../v1_0/backfill_canonical_verdict.py        |   45 +
 wikify/rag/__init__.py                        |   13 +
 wikify/rag/answer.py                          |  228 +++
 wikify/rag/chunk.py                           |  404 ++++
 wikify/rag/embed.py                           |   46 +
 wikify/rag/eval.py                            |  831 +++++++++
 wikify/rag/events.py                          |  248 +++
 wikify/rag/evidence.py                        |  537 ++++++
 wikify/rag/history.py                         |  305 +++
 wikify/rag/index.py                           |  135 ++
 wikify/rag/router.py                          |  208 +++
 wikify/rag/search.py                          |  424 +++++
 wikify/rag/store.py                           |  103 +
 wikify/rag/usage.py                           |   58 +
 wikify/seed.py                                |   37 +-
 wikify/tests/fixtures/__init__.py             |    0
 wikify/tests/fixtures/demo_corpus.py          | 1653 +++++++++++++++++
 .../tests/fixtures/icai_page_11_canonical.md  |   35 +
 wikify/tests/test_ask_history.py              |  273 +++
 wikify/tests/test_canonical_verdict.py        |  108 ++
 wikify/tests/test_diagrams.py                 |  208 +++
 wikify/tests/test_evidence.py                 |  371 ++++
 wikify/tests/test_page_propagation.py         |  242 +++
 wikify/tests/test_rag_api.py                  |  558 ++++++
 wikify/tests/test_rag_core.py                 |  636 +++++++
 wikify/tests/test_rag_eval.py                 |  305 +++
 wikify/tests/test_regions.py                  |  104 ++
 wikify/tests/test_remediate_adoption.py       |   92 +
 wikify/tests/test_sectionize.py               |   88 +-
 .../doctype/source_page/source_page.json      |    2 +-
 .../doctype/wikify_ask_message/__init__.py    |    0
 .../wikify_ask_message.json                   |  190 ++
 .../wikify_ask_message/wikify_ask_message.py  |   10 +
 .../doctype/wikify_ask_session/__init__.py    |    0
 .../wikify_ask_session.json                   |  137 ++
 .../wikify_ask_session/wikify_ask_session.py  |   17 +
 91 files changed, 13385 insertions(+), 333 deletions(-)
 create mode 100644 docs/rag-explained.md
 create mode 100644 frontend/src/components/rag/CostMeter.vue
 create mode 100644 frontend/src/components/rag/CoverageBar.vue
 create mode 100644 frontend/src/components/rag/EvalScoreboard.vue
 create mode 100644 frontend/src/components/rag/IndexStatusCard.vue
 create mode 100644 frontend/src/components/rag/RelevanceTag.vue
 create mode 100644 frontend/src/components/rag/RouteBadge.vue
 create mode 100644 frontend/src/components/rag/SourceCard.vue
 create mode 100644 frontend/src/composables/useMediaQuery.js
 create mode 100644 frontend/src/composables/useRag.js
 create mode 100644 frontend/src/pages/AskWiki.vue
 create mode 100644 frontend/src/pages/RagLab.vue
 create mode 100644 frontend/src/utils/actionButton.js
 create mode 100644 frontend/src/utils/mermaid.css
 create mode 100644 wikify/agent/tools/retrieve.py
 create mode 100644 wikify/api/ask_history.py
 create mode 100644 wikify/api/rag.py
 create mode 100644 wikify/engine/diagrams.py
 create mode 100644 wikify/engine/regions.py
 create mode 100644 wikify/patches/v1_0/backfill_canonical_verdict.py
 create mode 100644 wikify/rag/__init__.py
 create mode 100644 wikify/rag/answer.py
 create mode 100644 wikify/rag/chunk.py
 create mode 100644 wikify/rag/embed.py
 create mode 100644 wikify/rag/eval.py
 create mode 100644 wikify/rag/events.py
 create mode 100644 wikify/rag/evidence.py
 create mode 100644 wikify/rag/history.py
 create mode 100644 wikify/rag/index.py
 create mode 100644 wikify/rag/router.py
 create mode 100644 wikify/rag/search.py
 create mode 100644 wikify/rag/store.py
 create mode 100644 wikify/rag/usage.py
 create mode 100644 wikify/tests/fixtures/__init__.py
 create mode 100644 wikify/tests/fixtures/demo_corpus.py
 create mode 100644 wikify/tests/fixtures/icai_page_11_canonical.md
 create mode 100644 wikify/tests/test_ask_history.py
 create mode 100644 wikify/tests/test_canonical_verdict.py
 create mode 100644 wikify/tests/test_diagrams.py
 create mode 100644 wikify/tests/test_evidence.py
 create mode 100644 wikify/tests/test_page_propagation.py
 create mode 100644 wikify/tests/test_rag_api.py
 create mode 100644 wikify/tests/test_rag_core.py
 create mode 100644 wikify/tests/test_rag_eval.py
 create mode 100644 wikify/tests/test_regions.py
 create mode 100644 wikify/tests/test_remediate_adoption.py
 create mode 100644 wikify/wikify/doctype/wikify_ask_message/__init__.py
 create mode 100644 wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.json
 create mode 100644 wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.py
 create mode 100644 wikify/wikify/doctype/wikify_ask_session/__init__.py
 create mode 100644 wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.json
 create mode 100644 wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.py

diff --git a/docs/rag-explained.md b/docs/rag-explained.md
new file mode 100644
index 0000000..75900d3
--- /dev/null
+++ b/docs/rag-explained.md
@@ -0,0 +1,313 @@
+# RAG, explained — and how Wikify's actually works
+
+A from-scratch explanation, written against the real system on `wikify.localhost`.
+Every number here was measured, not estimated.
+
+---
+
+## 1. The problem RAG solves
+
+A language model only knows what it read during training. It has never seen your ICAI
+booklet. There are three ways to fix that:
+
+| Approach | Why it fails here |
+|---|---|
+| Retrain the model on your documents | Expensive, slow, and it still can't tell you **where** an answer came from |
+| Paste the whole document into every question | Your ICAI PDF is **528,336 characters**. Too big, too slow, and models get measurably worse at finding details in a huge pile |
+| **Retrieve only the relevant bits, then ask** | ← this is RAG |
+
+**RAG = Retrieval-Augmented Generation.** It is genuinely only two steps:
+
+1. **Retrieve** — find the handful of passages that could answer this question.
+2. **Generate** — hand *only* those passages to the model and say "answer using these,
+   and cite them."
+
+Everything else in this document is engineering to make step 1 actually work. **Step 1 is
+where RAG systems live or die.** If retrieval hands over the wrong pages, no model on
+earth can save the answer.
+
+---
+
+## 2. Three different ways to "find" — and when each fails
+
+This is the part most people skip, and it's the part that matters.
+
+### Vector search (semantic)
+
+Text is converted into a list of numbers — a **vector** — that encodes *meaning*. Similar
+meanings land close together in that space, so you find answers by measuring distance.
+
+Real example from this codebase: searching *"who works on the UI"* correctly returned a
+chunk about a **Frontend Engineer (Vue, Vite)** — sharing **not one word** with the query.
+
+- **Good at:** paraphrase, synonyms, fuzzy "tell me about…" questions.
+- **Fails at:** exact tokens. It may happily blur `194-I` and `194-J`. For tax law that is
+  not a small error.
+- **Also fails at:** completeness. It returns the top *k* most similar. It has no idea
+  whether the right answer is 3 items or 300.
+
+### Keyword search (BM25 / full-text)
+
+Classic text matching, the way search engines worked before embeddings.
+
+- **Good at:** `115BAC`, `₹2,40,000`, section numbers, proper nouns, acronyms.
+- **Fails at:** paraphrase. Asking "how much extra tax do the rich pay" won't match a
+  heading that says "SURCHARGE".
+
+### Metadata filter
+
+Not search at all — a database `WHERE` clause.
+`section_type = 'job_description'` → returns **every** matching section, guaranteed.
+
+- **Good at:** completeness. Exhaustive by construction.
+- **Fails at:** anything requiring understanding of meaning.
+
+### Why this matters — the measured proof
+
+Someone asks: *"give me all the job descriptions across all the documents."*
+
+| Method | Found | Documents covered | Recall |
+|---|---|---|---|
+| Naive top-8 vector search | 8 sections (only 6 correct) | 4 of 5 | **40%** |
+| Metadata filter | **15 sections** | **5 of 5** | **100%** |
+
+Vector search **missed 9 of 15 sections and an entire document** — and, worse, reported no
+sign that anything was missing. It looked confident.
+
+> **The core insight:** "give me all X" is an *exhaustive* question. It's a database query
+> wearing a search query's clothes. Answering it with similarity is guessing.
+> Similarity is for "tell me about Y".
+
+The `/rag-lab` page exists purely to make this visible — it runs both and highlights what
+naive retrieval missed.
+
+### Hybrid + RRF
+
+In practice you run vector **and** keyword together and fuse the two ranked lists with
+**Reciprocal Rank Fusion** — a simple rule that rewards documents ranking well in either
+list. Nobody serious ships pure vector search.
+
+⚠️ **Gotcha we hit:** RRF scores are derived from *rank position*, not confidence. An
+on-topic and a nonsense query can produce nearly identical RRF scores. So you **cannot**
+use an RRF score as a "is this relevant enough to answer?" threshold. We learned this the
+hard way and moved the refusal check onto the reranker instead.
+
+---
+
+## 3. The ingestion pipeline (document → searchable)
+
+```
+PDF  (236 pages, 528,336 chars)
+  │
+  │  1. PARSE
+  │     Each page → markdown. Text-layer pages parse cheaply; diagram-heavy
+  │     pages go to a vision model. Stored on Source Page.canonical_markdown
+  ▼
+Source Page × 236
+  │
+  │  2. SECTION
+  │     Pages → a *tree* of meaningful units: "Basic Concepts" → "Rates of
+  │     Tax" → "Surcharge". Each carries a title, page range, and type.
+  ▼
+Source Section × 296          ← 44 before we fixed the page-19 bug
+  │
+  │  3. CHUNK
+  │     Sections → ~1,200-character pieces with ~150 overlap, split on
+  │     heading/paragraph boundaries. Each piece remembers its parent.
+  ▼
+Chunk × 648
+  │
+  │  4. EMBED
+  │     Each chunk → a 256-number vector (model2vec potion-base-8M).
+  │     Runs locally. No API key. No GPU. Free.
+  ▼
+  │  5. INDEX
+  │     Vectors + text + metadata → LanceDB: an embedded database that is
+  │     just a directory on disk. No server to run.
+  ▼
+sites/wikify.localhost/private/files/wikify_lance
+```
+
+### Why chunk at all?
+
+Two reasons. Precision — a 40-page section is mostly irrelevant to any one question, and
+embedding it produces a vague "average" vector that matches nothing well. And limits —
+models have a finite context window.
+
+**But** chunks are bad to *read* (they start mid-thought). So we use **parent-document
+retrieval**: search the small chunks for precision, then hand the model the **full parent
+section** for context. Best of both.
+
+---
+
+## 4. The query pipeline (question → cited answer)
+
+```
+"what is the TDS rate on rent under section 194-I"
+  │
+  │  1. ROUTE  — a cheap LLM call
+  │     Is this exhaustive ("all X" → filter) or semantic ("about Y" → search)?
+  │     Also rewrites follow-ups: "what about the second one?" → standalone.
+  │     ➜ shown in the UI as the route badge, with its reason in plain words
+  ▼
+  │  2. RETRIEVE
+  │     Vector + keyword, fused by RRF. Metadata and permissions are applied
+  │     as a WHERE clause *inside* the query — a pre-filter, never a
+  │     post-filter (see §6).
+  ▼
+  │  3. RERANK  — a second, cheap LLM pass
+  │     Fetch ~50 candidates, score each against the question, keep the best 8.
+  │     Biggest single quality jump after hybrid search.
+  ▼
+  │  4. EXPAND
+  │     Swap each chunk for its full parent section.
+  ▼
+  │  5. SYNTHESISE
+  │     "Answer using ONLY these excerpts. Cite each claim. If they don't
+  │      contain the answer, say so."
+  ▼
+Answer + [1][2][3] + page numbers + cost
+```
+
+Real output from this exact query (previously **impossible** — page 154 wasn't indexed):
+
+```
+HTTP 200 · 17.3s · $0.0024
+  2%  — plant & machinery or equipment [3]
+  10% — land, building, furniture or fittings [3]
+  threshold > ₹2,40,000 in a F.Y. [3]
+```
+
+---
+
+## 5. Design choices that aren't standard RAG
+
+**Contextual retrieval.** Before embedding, each chunk is prefixed with
+`document title › section path`. A chunk reading *"10% for land and building"* is
+meaningless in isolation; with its breadcrumb it becomes findable. Anthropic measured
+~35% fewer retrieval failures from this technique. Most teams pay an LLM call per chunk to
+generate that context — we got it free because the section tree already existed.
+
+Crucially the prefix goes into `embed_text` (what gets embedded) and **not** `text` (what
+gets displayed). The user never sees it.
+
+**Filter-first routing.** §2's thesis, implemented.
+
+**Page-level citations.** Most RAG can cite a *document*. Ours cites `p. 154`, because
+sections carry page ranges. That's what makes an answer checkable against the source PDF.
+
+**Honest refusal.** Below a confidence bar it says "I couldn't find this in the wiki"
+rather than inventing an answer. A confident wrong answer about a tax rate is worse than
+no answer.
+
+**Permission-aware retrieval.** See §6 — it's the subtlest thing here.
+
+---
+
+## 6. Two traps worth understanding
+
+### Pre-filter vs post-filter (a real security bug we fixed)
+
+```python
+# WRONG — post-filter
+hits = search(query, limit=10)
+hits = [h for h in hits if h.project in allowed]   # may return 0 of 10
+
+# RIGHT — pre-filter
+hits = search(query, limit=10, where="project IN (...)")
+```
+
+Post-filtering searches everything and *then* removes what you can't see — so you might
+get 10 results, discard 9, and show 1. Worse, forbidden content passed through the
+process. Pre-filtering means the database never considers it.
+
+We shipped a real fail-open bug here: the "no ACL restriction" sentinel was `None`, which
+is *also* what a forgotten argument looks like. Calling `answer()` with default arguments
+leaked **15 citations** to a user with no read permission. Fix: make "search everything"
+a distinct object that you must pass deliberately, and reject `None` outright.
+
+> **Lesson:** never let "I forgot" and "I meant to allow everything" be spelled the same
+> way.
+
+### Fuzzy matching on numbers
+
+When verifying that a quoted citation really appears in the source, fuzzy matching is
+right for prose and **catastrophic** for numbers. We measured: a flipped `(+)` → `(-)`
+sign scored **0.85 similarity** and passed as verified. A wrong digit failed only by
+threshold luck.
+
+For exam prep, a citation that *renders as verified* while containing a wrong rate is
+worse than no citation — it manufactures trust. Fix: fuzzy for prose, **exact
+character-for-character** for digits, percentages, currency, signs, and statutory
+references. A digit is either right or it isn't; there is no "85% right".
+
+---
+
+## 7. Where everything lives
+
+| Concept | File |
+|---|---|
+| Text → vectors | `wikify/rag/embed.py` |
+| LanceDB connection + schema | `wikify/rag/store.py` |
+| Sections → chunks (+ contextual prefix) | `wikify/rag/chunk.py` |
+| Build / refresh the index | `wikify/rag/index.py` |
+| The four search modes + RRF + ACL | `wikify/rag/search.py` |
+| Intent routing + query rewriting | `wikify/rag/router.py` |
+| Synthesis, citations, refusal | `wikify/rag/answer.py` |
+| Citation verification | `wikify/rag/evidence.py` |
+| Whitelisted endpoints | `wikify/api/rag.py` |
+| Quality measurement | `wikify/rag/eval.py` |
+| Ask UI / proof UI | `frontend/src/pages/AskWiki.vue`, `RagLab.vue` |
+
+**Four search modes** in `search.py`:
+
+| Mode | What it does | Use for |
+|---|---|---|
+| `vector` | Pure semantic similarity | "tell me about…" |
+| `fts` | Keyword / BM25 | exact codes, section numbers |
+| `hybrid` | Both, fused by RRF | the sensible default |
+| `filter` | Metadata `WHERE`, **no top-k limit** | "give me ALL X" |
+
+---
+
+## 8. What measured quality looks like
+
+You cannot improve what you don't measure. `rag/eval.py` runs 12 golden questions with
+known-correct answers and reports:
+
+- **recall@k** — of the sources that *should* have been found, how many were?
+- **precision@k** — of what was returned, how much was actually relevant?
+- **completeness** — did we return **all** expected sources? (the one that matters for
+  "all X")
+
+Current results on the demo corpus:
+
+| | naive | routed |
+|---|---|---|
+| Mean recall | 66% | **87–90%** |
+| Completeness | 45% | **73–82%** |
+
+⚠️ Routed numbers are a **range**, not a point, because routing is an LLM call and
+therefore non-deterministic. Quote it honestly.
+
+On the real ICAI document, graded against the source PDF: **7 of 12 correct**. Failure
+modes worth knowing: a false refusal when the reranker misfired, and a fabricated category
+caused by a mind-map diagram being flattened into a bullet list — the structure was lost
+at *parse* time, long before retrieval ran.
+
+> **Lesson:** most RAG quality problems are actually **ingestion** problems. Our single
+> biggest win today wasn't a retrieval tweak — it was discovering that a 168-character
+> heading exceeded a 140-character database column, which threw an exception mid-loop and
+> silently dropped every section after page 19. That one bug hid **93.6% of the document**
+> from search. Coverage went from 6.4% to 99.8% with no re-parsing and zero LLM cost.
+
+---
+
+## 9. The one-paragraph version
+
+Slice documents into meaningful pieces. Store each piece as coordinates in "meaning space"
+alongside its metadata — page numbers, type, permissions. When a question arrives, work out
+what *kind* of question it is, fetch the right pieces (by meaning, by keyword, or by
+database filter), hand **only** those to the model, and make it cite them. The thing that
+separates a good RAG system from a demo is knowing when **not** to use the clever
+similarity search — and measuring yourself honestly enough to find out.
diff --git a/frontend/package.json b/frontend/package.json
index 82953d4..6adb2f4 100644
--- a/frontend/package.json
+++ b/frontend/package.json
@@ -13,6 +13,7 @@
     "d3-selection": "^3.0.0",
     "d3-zoom": "^3.0.0",
     "frappe-ui": "1.0.0-beta.24",
+    "mermaid": "^11.16.1",
     "pdfjs-dist": "^4.7.76",
     "socket.io-client": "^4.7.2",
     "splitpanes": "^3.1.5",
diff --git a/frontend/src/components/AgentChatPanel.vue b/frontend/src/components/AgentChatPanel.vue
index bec11dc..492c24d 100644
--- a/frontend/src/components/AgentChatPanel.vue
+++ b/frontend/src/components/AgentChatPanel.vue
@@ -38,10 +38,19 @@ function saveGeo() {
 	localStorage.setItem(GEO_KEY, JSON.stringify({ ...geo }));
 }
 
+// A geometry saved on a wide screen would otherwise stay wider than a narrow viewport the
+// window is later opened in — a fixed element that overhangs the viewport drags the whole
+// page into horizontal scroll, and its close button can land off-screen.
+function fitToViewport() {
+	Object.assign(geo, clamped(geo));
+}
+window.addEventListener("resize", fitToViewport);
+onBeforeUnmount(() => window.removeEventListener("resize", fitToViewport));
+
 const windowStyle = computed(() =>
 	isPhone.value
 		? {}
-		: { left: `${geo.x}px`, top: `${geo.y}px`, width: `${geo.w}px`, height: `${geo.h}px` }
+		: { left: `${geo.x}px`, top: `${geo.y}px`, width: `${geo.w}px`, height: `${geo.h}px` },
 );
 
 const minimized = ref(false);
@@ -136,14 +145,14 @@ async function refreshSessions() {
 
 // Model picker — populated from get_agent_models when the panel first opens.
 const modelOptions = computed(() =>
-	(models.value || []).map((m) => ({ label: shortModel(m), onClick: () => (model.value = m) }))
+	(models.value || []).map((m) => ({ label: shortModel(m), onClick: () => (model.value = m) })),
 );
 watch(
 	() => props.open,
 	(open) => {
 		if (open) chat.loadModels();
 	},
-	{ immediate: true }
+	{ immediate: true },
 );
 
 // Rename dialog.
diff --git a/frontend/src/components/AppShell.vue b/frontend/src/components/AppShell.vue
index a6107a5..0d47bd7 100644
--- a/frontend/src/components/AppShell.vue
+++ b/frontend/src/components/AppShell.vue
@@ -1,7 +1,6 @@
 <script setup>
 import {
 	BottomSheet,
-	Button,
 	DesktopShell,
 	MobileNav,
 	MobileNavItem,
@@ -9,7 +8,7 @@ import {
 	Sidebar,
 } from "frappe-ui";
 import { computed, onMounted, ref, watch } from "vue";
-import { useRoute } from "vue-router";
+import { RouterLink, useRoute } from "vue-router";
 import { useTheme } from "@/utils/useTheme";
 import { useIsMobile } from "@/composables/useIsMobile";
 import { session } from "@/data/session";
@@ -22,6 +21,8 @@ const isMobile = useIsMobile();
 
 const settingsOpen = ref(false);
 const mobileMenuOpen = ref(false);
+// The agent panel is mounted once here so it's available on every screen (slice 12).
+const agentOpen = ref(false);
 
 const menuItems = computed(() => [
 	{
@@ -51,21 +52,48 @@ const header = computed(() => ({
 const PROJECT_ROUTES = ["Projects", "ProjectDetail", "ProjectSettings", "ImportDetail"];
 const projectsActive = computed(() => PROJECT_ROUTES.includes(route.name));
 
+// One list of destinations drives the desktop sidebar AND the mobile navigation, so a
+// screen can never be reachable on one and unreachable on the other.
+const destinations = computed(() => [
+	{
+		label: "Projects",
+		icon: "lucide-folder",
+		to: { name: "Projects" },
+		isActive: projectsActive.value,
+	},
+	{
+		label: "Explore",
+		icon: "lucide-shapes",
+		to: { name: "Explore" },
+		isActive: route.name === "Explore",
+	},
+	{
+		label: "Ask",
+		icon: "lucide-message-circle-question",
+		to: { name: "AskWiki" },
+		isActive: route.name === "AskWiki",
+	},
+	{
+		label: "RAG Lab",
+		icon: "lucide-flask-conical",
+		to: { name: "RagLab" },
+		isActive: route.name === "RagLab",
+	},
+]);
+
+// The assistant is an action, not a route, so it gets its own group under the
+// destinations. It used to be a floating button pinned to the viewport corner, which
+// covered card actions ("Open in wiki") on every screen narrower than a desktop.
 const sections = computed(() => [
+	{ label: "", items: destinations.value },
 	{
 		label: "",
 		items: [
 			{
-				label: "Projects",
-				icon: "lucide-folder",
-				to: { name: "Projects" },
-				isActive: projectsActive.value,
-			},
-			{
-				label: "Explore",
-				icon: "lucide-shapes",
-				to: { name: "Explore" },
-				isActive: route.name === "Explore",
+				label: "Assistant",
+				icon: "lucide-sparkles",
+				active: agentOpen.value,
+				onClick: () => (agentOpen.value = true),
 			},
 		],
 	},
@@ -79,10 +107,6 @@ const pageScroll = computed(() => !FIXED_HEIGHT_ROUTES.includes(route.name));
 const collapsed = ref(localStorage.getItem("sidebar-collapsed") === "true");
 watch(collapsed, (v) => localStorage.setItem("sidebar-collapsed", v));
 
-// The agent panel + its floating button are mounted once here so they're available on
-// every screen (slice 12). On mobile the floating button is replaced by a MobileNav tab.
-const agentOpen = ref(false);
-
 function runMenuItem(item) {
 	mobileMenuOpen.value = false;
 	item.onClick();
@@ -112,16 +136,24 @@ onMounted(initializeTheme);
 						:to="{ name: 'Explore' }"
 						:active="route.name === 'Explore'"
 					/>
+					<MobileNavItem
+						label="Ask"
+						icon="lucide-message-circle-question"
+						:to="{ name: 'AskWiki' }"
+						:active="route.name === 'AskWiki'"
+					/>
 					<MobileNavItem
 						label="Assistant"
 						icon="lucide-sparkles"
 						:active="agentOpen"
 						@click="agentOpen = true"
 					/>
+					<!-- Five tabs is the most that stays legible at 360px, so the least-used
+					     destination (RAG Lab) lives one tap deeper, inside this sheet. -->
 					<MobileNavItem
-						label="Menu"
+						label="More"
 						icon="lucide-menu"
-						:active="mobileMenuOpen"
+						:active="mobileMenuOpen || route.name === 'RagLab'"
 						@click="mobileMenuOpen = true"
 					/>
 				</MobileNav>
@@ -151,9 +183,27 @@ onMounted(initializeTheme);
 
 		<AppSettingsDialog v-model:open="settingsOpen" />
 
-		<!-- Mobile overflow menu (settings / theme / logout). -->
+		<!-- Mobile overflow sheet: the destinations that don't fit the tab bar, then the
+		     user actions (settings / theme / logout). -->
 		<BottomSheet v-model:open="mobileMenuOpen" title="Wikify">
 			<div class="flex flex-col px-2 pb-6">
+				<RouterLink
+					v-for="destination in destinations"
+					:key="destination.label"
+					:to="destination.to"
+					class="flex items-center gap-3 rounded-md px-3 py-3 text-base active:bg-surface-gray-2"
+					:class="destination.isActive ? 'text-ink-gray-9' : 'text-ink-gray-8'"
+					@click="mobileMenuOpen = false"
+				>
+					<span
+						:class="[destination.icon, 'size-5 text-ink-gray-6']"
+						aria-hidden="true"
+					/>
+					{{ destination.label }}
+				</RouterLink>
+
+				<div class="my-2 border-t border-outline-gray-1" />
+
 				<button
 					v-for="item in menuItems"
 					:key="item.label"
@@ -166,16 +216,6 @@ onMounted(initializeTheme);
 			</div>
 		</BottomSheet>
 
-		<!-- Floating assistant button (desktop only — mobile uses the nav tab). -->
-		<Button
-			v-if="!isMobile"
-			v-show="!agentOpen"
-			variant="solid"
-			icon="lucide-sparkles"
-			class="fixed bottom-5 right-5 z-30 !size-11 !rounded-full shadow-lg"
-			tooltip="Ask the assistant"
-			@click="agentOpen = true"
-		/>
 		<AgentChatPanel v-model:open="agentOpen" />
 	</div>
 </template>
diff --git a/frontend/src/components/Explore.vue b/frontend/src/components/Explore.vue
index f87025b..bf8623c 100644
--- a/frontend/src/components/Explore.vue
+++ b/frontend/src/components/Explore.vue
@@ -3,12 +3,16 @@ import { computed, ref, watch, onMounted, onUnmounted } from "vue";
 import { Badge, Button, useCall, toast } from "frappe-ui";
 import { useSocket } from "@/socket";
 import TypeChip from "@/components/TypeChip.vue";
+import { useIsMobile } from "@/composables/useIsMobile";
+import { actionButtonProps } from "@/utils/actionButton";
 
 const props = defineProps({
 	sourceDocument: { type: String, default: null },
 	importName: { type: String, default: null },
 });
 
+const isMobile = useIsMobile();
+
 // Fetches are driven explicitly via watchers (reactive `auto` on useCall is unreliable
 // when the dep flips — the Slice 4 gotcha), so both calls are immediate:false + submit.
 const summary = useCall({
@@ -105,8 +109,8 @@ function pageRange(s) {
 <template>
 	<div class="flex h-full flex-col">
 		<!-- Chip bar + reclassify -->
-		<div class="flex items-center gap-2 border-b border-outline-gray-1 px-3 py-2">
-			<div class="flex flex-1 flex-wrap items-center gap-1.5">
+		<div class="flex items-start gap-2 border-b border-outline-gray-1 px-3 py-2">
+			<div class="flex min-w-0 flex-1 flex-wrap items-center gap-1.5">
 				<TypeChip
 					v-for="t in chips"
 					:key="t.type_name"
@@ -123,8 +127,14 @@ function pageRange(s) {
 			<Button
 				size="sm"
 				variant="subtle"
-				icon-left="lucide-tags"
-				:label="reclassifying ? 'Classifying…' : 'Reclassify'"
+				class="shrink-0"
+				v-bind="
+					actionButtonProps(
+						isMobile,
+						'lucide-tags',
+						reclassifying ? 'Classifying…' : 'Reclassify'
+					)
+				"
 				:loading="reclassifying"
 				:disabled="reclassifying"
 				@click="runReclassify"
@@ -149,14 +159,17 @@ function pageRange(s) {
 					<p class="truncate text-base text-ink-gray-8">{{ s.title }}</p>
 					<p class="truncate text-xs text-ink-gray-5">{{ s.hierarchy_path }}</p>
 				</div>
-				<Badge
-					v-if="pageRange(s)"
-					:label="pageRange(s)"
-					theme="gray"
-					variant="subtle"
-					size="sm"
-					class="w-20 shrink-0 justify-end"
-				/>
+				<!-- The badge sizes to its text inside a fixed column, so it reads as a chip
+				     rather than an 80px pill with the range shoved to one end. -->
+				<span class="flex w-20 shrink-0 justify-end">
+					<Badge
+						v-if="pageRange(s)"
+						:label="pageRange(s)"
+						theme="gray"
+						variant="subtle"
+						size="sm"
+					/>
+				</span>
 			</div>
 			<p
 				v-if="!sections.length && selectedType && !results.loading"
diff --git a/frontend/src/components/GraphView.vue b/frontend/src/components/GraphView.vue
index 4c47c7f..d66162c 100644
--- a/frontend/src/components/GraphView.vue
+++ b/frontend/src/components/GraphView.vue
@@ -10,9 +10,10 @@
  * Canvas can't consume Tailwind classes, so semantic-token colors are resolved from
  * probe elements at mount and re-resolved on theme change.
  */
-import { computed, onBeforeUnmount, onMounted, ref, watch } from "vue";
-import { Button, FormControl, useCall } from "frappe-ui";
+import { computed, nextTick, onBeforeUnmount, onMounted, ref, watch } from "vue";
+import { Badge, Button, FormControl, useCall } from "frappe-ui";
 import TypeChip from "@/components/TypeChip.vue";
+import { useIsNarrow } from "@/composables/useMediaQuery";
 import { forceCollide, forceLink, forceManyBody, forceSimulation, forceX, forceY } from "d3-force";
 import { select } from "d3-selection";
 import { zoom, zoomIdentity } from "d3-zoom";
@@ -50,6 +51,57 @@ function toggleType(name) {
 	hiddenTypes.value = next;
 }
 
+// A 236-node force graph on a 390px screen is a hairball behind a pinch-zoom fight, and
+// the hover tooltips that carry the labels never fire on touch. Narrow screens get the
+// same graph as a list instead — grouped by Section Type, carrying the one thing a plain
+// tree can't show: how many REFERENCES each section is on. The lenses still apply.
+const isNarrow = useIsNarrow();
+
+const sectionNodes = computed(() => (graph.data?.nodes || []).filter((n) => n.kind === "section"));
+
+const referenceCounts = computed(() => {
+	const counts = {};
+	for (const edge of graph.data?.edges || []) {
+		if (edge.rel !== "REFERENCES") continue;
+		counts[edge.src] = (counts[edge.src] || 0) + 1;
+		counts[edge.dst] = (counts[edge.dst] || 0) + 1;
+	}
+	return counts;
+});
+
+const listGroups = computed(() => {
+	const query = search.value.trim().toLowerCase();
+	const labels = {};
+	for (const type of meta.value.types || []) labels[type.name] = type.label || type.name;
+
+	const groups = new Map();
+	for (const node of sectionNodes.value) {
+		if (query && !node.label.toLowerCase().includes(query)) continue;
+		if (focusDoc.value && node.doc !== focusDoc.value) continue;
+		const type = node.section_type || "";
+		if (type && hiddenTypes.value.has(type)) continue;
+		if (!groups.has(type)) groups.set(type, []);
+		groups.get(type).push(node);
+	}
+	return [...groups.entries()]
+		.map(([type, sections]) => ({
+			type,
+			label: labels[type] || "Untyped",
+			color: typeColor[type],
+			sections,
+		}))
+		.sort((a, b) => b.sections.length - a.sections.length);
+});
+
+const listCount = computed(() => listGroups.value.reduce((n, g) => n + g.sections.length, 0));
+
+function pageRange(node) {
+	if (!node.page_start) return "";
+	return node.page_start === node.page_end
+		? `p${node.page_start}`
+		: `p${node.page_start}–${node.page_end}`;
+}
+
 watch([search, hiddenTypes, showHierarchy, showRefs, sizeMode, focusDoc], () => scheduleDraw());
 
 const graph = useCall({ url: props.url, method: "POST", immediate: false });
@@ -141,7 +193,11 @@ function setGraph(data) {
 	}
 
 	sim?.stop();
+	sim = null;
 	userInteracted = false;
+	// Narrow screens render the list, so there is no layout to solve — don't spend a
+	// phone's battery on a force simulation nobody sees.
+	if (isNarrow.value) return;
 	sim = forceSimulation(nodes)
 		.force(
 			"link",
@@ -389,7 +445,8 @@ function resize() {
 let resizeObserver = null;
 let themeObserver = null;
 
-onMounted(() => {
+function addCanvas() {
+	if (!canvas.value || ctx) return;
 	ctx = canvas.value.getContext("2d");
 	resolveColors();
 	resize();
@@ -408,23 +465,48 @@ onMounted(() => {
 		attributes: true,
 		attributeFilter: ["data-theme", "class"],
 	});
-});
+}
 
-onBeforeUnmount(() => {
+function removeCanvas() {
 	sim?.stop();
 	sim = null;
 	if (raf) cancelAnimationFrame(raf);
+	raf = 0;
+	ctx = null;
 	resizeObserver?.disconnect();
+	resizeObserver = null;
 	themeObserver?.disconnect();
+	themeObserver = null;
+}
+
+onMounted(() => !isNarrow.value && addCanvas());
+
+// Rotating a tablet crosses the breakpoint: rebuild the canvas and its layout, or drop
+// them for the list.
+watch(isNarrow, async (narrow) => {
+	if (narrow) {
+		removeCanvas();
+		return;
+	}
+	await nextTick();
+	addCanvas();
+	if (graph.data) setGraph(graph.data);
 });
+
+onBeforeUnmount(removeCanvas);
 </script>
 
 <template>
-	<div ref="wrapper" class="relative h-full w-full overflow-hidden bg-surface-base">
-		<canvas ref="canvas" class="block" />
+	<div
+		ref="wrapper"
+		class="relative h-full w-full overflow-hidden bg-surface-base"
+		:class="isNarrow ? 'flex flex-col' : ''"
+	>
+		<canvas v-if="!isNarrow" ref="canvas" class="block" />
 
 		<!-- Lens toolbar: filters dim, never remove, so the layout stays put. -->
 		<div
+			v-if="!isNarrow"
 			class="absolute left-3 top-3 z-10 flex max-w-[calc(100%-1.5rem)] flex-wrap items-center gap-2"
 		>
 			<FormControl
@@ -459,9 +541,85 @@ onBeforeUnmount(() => {
 			</div>
 		</div>
 
+		<!-- Narrow fallback: the same nodes and lenses, read as a list. -->
+		<template v-if="isNarrow">
+			<div class="shrink-0 space-y-2 border-b border-outline-gray-1 px-3 py-2">
+				<p class="text-xs text-ink-gray-5">
+					A force graph needs room to read, so this screen lists the same sections by
+					type with their reference counts. Open it on a wider screen for the map.
+				</p>
+				<FormControl v-model="search" type="text" size="sm" placeholder="Search sections…" />
+				<FormControl
+					v-if="showDocFilter"
+					v-model="focusDoc"
+					type="select"
+					size="sm"
+					:options="docOptions"
+				/>
+				<div v-if="meta.types.length" class="flex flex-wrap gap-1.5">
+					<TypeChip
+						v-for="t in meta.types"
+						:key="t.name"
+						:label="t.label || t.name"
+						:color="typeColor[t.name]"
+						:count="t.count"
+						:active="!hiddenTypes.has(t.name)"
+						@click="toggleType(t.name)"
+					/>
+				</div>
+			</div>
+
+			<div class="min-h-0 flex-1 overflow-y-auto">
+				<div v-for="group in listGroups" :key="group.type" class="mb-4">
+					<div
+						class="sticky top-0 flex items-center gap-2 border-b border-outline-gray-1 bg-surface-base px-4 py-2"
+					>
+						<span
+							class="size-2.5 shrink-0 rounded-full"
+							:class="group.color ? '' : 'bg-surface-gray-5'"
+							:style="group.color ? { backgroundColor: group.color } : {}"
+							aria-hidden="true"
+						/>
+						<h2 class="min-w-0 flex-1 truncate text-sm font-medium text-ink-gray-8">
+							{{ group.label }}
+						</h2>
+						<Badge
+							:label="String(group.sections.length)"
+							theme="gray"
+							variant="subtle"
+							size="sm"
+						/>
+					</div>
+					<button
+						v-for="node in group.sections"
+						:key="node.id"
+						class="flex w-full items-center gap-2 border-b border-outline-gray-1 px-4 py-2.5 text-left last:border-b-0 active:bg-surface-gray-2"
+						@click="emit('select', node)"
+					>
+						<span class="min-w-0 flex-1 truncate text-sm text-ink-gray-8">{{
+							node.label
+						}}</span>
+						<span
+							v-if="referenceCounts[node.id]"
+							class="shrink-0 text-xs text-ink-gray-5"
+							>{{ referenceCounts[node.id] }} refs</span
+						>
+						<span
+							v-if="pageRange(node)"
+							class="shrink-0 text-xs tabular-nums text-ink-gray-4"
+							>{{ pageRange(node) }}</span
+						>
+					</button>
+				</div>
+				<p v-if="!listCount && !graph.loading" class="py-10 text-center text-sm text-ink-gray-5">
+					No sections match these filters.
+				</p>
+			</div>
+		</template>
+
 		<!-- Legend doubles as the type filter (click a chip to dim that type). -->
 		<div
-			v-if="meta.types.length"
+			v-if="!isNarrow && meta.types.length"
 			class="absolute bottom-3 left-3 z-10 flex max-w-[calc(100%-1.5rem)] flex-wrap gap-1.5"
 		>
 			<TypeChip
@@ -500,7 +658,7 @@ onBeforeUnmount(() => {
 			</p>
 		</div>
 		<p
-			v-else-if="graph.data && !refCount"
+			v-else-if="!isNarrow && graph.data && !refCount"
 			class="pointer-events-none absolute bottom-3 left-1/2 -translate-x-1/2 text-xs text-ink-gray-4"
 		>
 			Showing hierarchy only — reference links appear when sections cite pages.
diff --git a/frontend/src/components/MarkdownPreview.vue b/frontend/src/components/MarkdownPreview.vue
index 4dd0e76..ef6be63 100644
--- a/frontend/src/components/MarkdownPreview.vue
+++ b/frontend/src/components/MarkdownPreview.vue
@@ -1,7 +1,7 @@
 <script setup>
 // Rendered-markdown view shared by the page Preview tab and the Tree section body.
 // Renders markdown with `marked` (prose-styled) and post-processes ```mermaid fences
-// into SVG diagrams via the wiki app's mermaid loader (see utils/mermaid).
+// into SVG diagrams (see utils/mermaid).
 import { computed, ref, watch, nextTick, onMounted } from "vue";
 import { marked } from "marked";
 import { renderMermaidIn } from "@/utils/mermaid";
@@ -15,7 +15,7 @@ const html = computed(() => marked.parse(props.content || "", { async: false }))
 
 async function renderDiagrams() {
 	await nextTick();
-	renderMermaidIn(container.value);
+	await renderMermaidIn(container.value);
 }
 
 onMounted(renderDiagrams);
@@ -23,18 +23,27 @@ watch(() => props.content, renderDiagrams);
 </script>
 
 <template>
-	<div ref="container" class="prose prose-sm dark:prose-invert max-w-none" v-html="html" />
+	<div
+		ref="container"
+		class="markdown-body prose prose-sm dark:prose-invert max-w-none"
+		v-html="html"
+	/>
 </template>
 
 <style>
-.mermaid-figure {
-	display: flex;
-	justify-content: center;
-	overflow-x: auto;
-	margin: 1rem 0;
-}
-.mermaid-figure svg {
-	max-width: 100%;
-	height: auto;
+/* Page markdown carries wide tables and unbroken identifiers. Below the split
+   breakpoint each table becomes its own horizontal scroller and long words wrap, so a
+   phone-width column never has to widen to fit them. Wide viewports are untouched. */
+@media (max-width: 1023px) {
+	.markdown-body table {
+		display: block;
+		width: max-content;
+		max-width: 100%;
+		overflow-x: auto;
+	}
+
+	.markdown-body {
+		overflow-wrap: break-word;
+	}
 }
 </style>
diff --git a/frontend/src/components/NewImportDialog.vue b/frontend/src/components/NewImportDialog.vue
index d718486..db02c4f 100644
--- a/frontend/src/components/NewImportDialog.vue
+++ b/frontend/src/components/NewImportDialog.vue
@@ -95,7 +95,7 @@ function reset() {
 						@success="onUpload"
 					>
 						<template #default="{ openFileSelector, uploading, progress }">
-							<div class="flex items-center gap-3">
+							<div class="flex min-w-0 items-center gap-3">
 								<Button
 									:loading="uploading"
 									:label="
@@ -108,9 +108,11 @@ function reset() {
 									icon-left="lucide-upload"
 									@click="openFileSelector"
 								/>
-								<span v-if="pdfName" class="truncate text-sm text-ink-gray-7">{{
-									pdfName
-								}}</span>
+								<span
+									v-if="pdfName"
+									class="min-w-0 truncate text-sm text-ink-gray-7"
+									>{{ pdfName }}</span
+								>
 							</div>
 						</template>
 					</FileUploader>
diff --git a/frontend/src/components/PageReview.vue b/frontend/src/components/PageReview.vue
index c22d463..c746dab 100644
--- a/frontend/src/components/PageReview.vue
+++ b/frontend/src/components/PageReview.vue
@@ -1,11 +1,12 @@
 <script setup>
-import { computed, onBeforeUnmount, ref, watch } from "vue";
+import { computed, ref, watch } from "vue";
 import { useRoute, useRouter } from "vue-router";
 import { Badge, Button, Dropdown, Popover, useList } from "frappe-ui";
 import { CodeEditor } from "frappe-ui/code-editor";
 import { Splitpanes, Pane } from "splitpanes";
 import "splitpanes/dist/splitpanes.css";
 import MarkdownPreview from "@/components/MarkdownPreview.vue";
+import { useIsNarrow, useMediaQuery } from "@/composables/useMediaQuery";
 import { setPage } from "@/data/agentContext";
 
 const props = defineProps({
@@ -67,6 +68,19 @@ const selected = computed(
 	() => (pages.data || []).find((p) => p.name === selectedName.value) || null
 );
 
+// Narrow screens drill down instead of splitting: the list fills the width, tapping a
+// page swaps in its detail, Back returns. A ?page= deep link opens straight on detail.
+// Swapping the Splitpanes host for a plain <div> keeps one copy of the markup rather
+// than forking a phone-only template.
+const isNarrow = useIsNarrow();
+const showDetail = ref(!!route.query.page);
+const SplitHost = computed(() => (isNarrow.value ? "div" : Splitpanes));
+const SplitPane = computed(() => (isNarrow.value ? "div" : Pane));
+function openPage(name) {
+	selectedName.value = name;
+	showDetail.value = true;
+}
+
 // Attach the selected page as the agent's default context (swaps out any section chip).
 watch(selected, (p) => {
 	if (p) setPage({ name: p.name, label: `Page ${p.page_no}` });
@@ -121,11 +135,7 @@ const filterOptions = computed(() => [
 // 23) — the Page tab only exists in the narrow fallback, where the split collapses
 // back to tabs. Viewing the whole PDF lives at the document level (ImportDetail's
 // top-level PDF tab), since it isn't page-specific.
-const wideQuery = window.matchMedia("(min-width: 1100px)");
-const isWide = ref(wideQuery.matches);
-const onWideChange = (e) => (isWide.value = e.matches);
-wideQuery.addEventListener("change", onWideChange);
-onBeforeUnmount(() => wideQuery.removeEventListener("change", onWideChange));
+const isWide = useMediaQuery("(min-width: 1100px)");
 
 // Icon-only tabs (tooltip carries the name) — same icons WikiPreview's toggle uses.
 const tabs = computed(() =>
@@ -145,14 +155,21 @@ watch(isWide, (wide) => {
 	if (wide && activeTab.value === "page") activeTab.value = "preview";
 });
 
-// User-draggable image∥preview ratio, persisted like other pane sizes.
+// User-draggable image∥preview ratio, persisted like other pane sizes. Clamped on read:
+// a stored size dragged to the edge on a desktop would otherwise strand the next visitor
+// with a few unreadable pixels of one pane.
 const SPLIT_KEY = "wikify:pageReviewSplit";
-const imgPaneSize = ref(Number(localStorage.getItem(SPLIT_KEY)) || 50);
+const MIN_SPLIT = 20;
+const imgPaneSize = ref(clampSplit(Number(localStorage.getItem(SPLIT_KEY))));
+function clampSplit(size) {
+	if (!size) return 50;
+	return Math.min(100 - MIN_SPLIT, Math.max(MIN_SPLIT, size));
+}
 function onSplitResized(event) {
 	const size = (event?.panes || event)?.[0]?.size;
 	if (size) {
-		imgPaneSize.value = size;
-		localStorage.setItem(SPLIT_KEY, String(size));
+		imgPaneSize.value = clampSplit(size);
+		localStorage.setItem(SPLIT_KEY, String(imgPaneSize.value));
 	}
 }
 
@@ -257,15 +274,25 @@ function fmtDelta(v) {
 			No pages yet — parse still running or not started.
 		</p>
 
-		<Splitpanes v-else class="h-full">
+		<component :is="SplitHost" v-else :class="isNarrow ? 'flex h-full flex-col' : 'h-full'">
 			<!-- Left: thumbnail list -->
-			<Pane :size="30" :min-size="20" class="flex flex-col border-r border-outline-gray-1">
-				<div class="flex items-center gap-1 border-b border-outline-gray-1 px-3 py-2">
+			<component
+				:is="SplitPane"
+				v-show="!isNarrow || !showDetail"
+				:size="isNarrow ? undefined : 30"
+				:min-size="isNarrow ? undefined : 20"
+				class="flex flex-col"
+				:class="isNarrow ? 'min-h-0 flex-1' : 'border-r border-outline-gray-1'"
+			>
+				<div
+					class="flex items-center gap-1 overflow-x-auto border-b border-outline-gray-1 px-3 py-2"
+				>
 					<Button
 						v-for="f in filterOptions"
 						:key="f.key"
 						:label="`${f.label} (${f.count})`"
 						size="sm"
+						class="shrink-0"
 						:variant="filter === f.key ? 'subtle' : 'ghost'"
 						@click="filter = f.key"
 					/>
@@ -287,8 +314,8 @@ function fmtDelta(v) {
 						v-for="page in visiblePages"
 						:key="page.name"
 						class="mb-1 flex w-full items-center gap-2 rounded-md p-1.5 text-left hover:bg-surface-gray-2"
-						:class="selectedName === page.name ? 'bg-surface-gray-3' : ''"
-						@click="selectedName = page.name"
+						:class="selectedName === page.name && !isNarrow ? 'bg-surface-gray-3' : ''"
+						@click="openPage(page.name)"
 					>
 						<img
 							v-if="page.image"
@@ -297,7 +324,7 @@ function fmtDelta(v) {
 							class="h-14 w-11 shrink-0 rounded border border-outline-gray-1 object-cover object-top"
 						/>
 						<div class="min-w-0 flex-1">
-							<div class="flex items-center gap-1.5">
+							<div class="flex flex-wrap items-center gap-1.5">
 								<span class="text-sm font-medium text-ink-gray-8"
 									>Page {{ page.page_no }}</span
 								>
@@ -308,7 +335,7 @@ function fmtDelta(v) {
 									size="sm"
 								/>
 							</div>
-							<div class="mt-1 flex items-center gap-1.5">
+							<div class="mt-1 flex flex-wrap items-center gap-1.5">
 								<Badge
 									:label="page.verdict || '—'"
 									:theme="verdictTheme[page.verdict] || 'gray'"
@@ -329,15 +356,29 @@ function fmtDelta(v) {
 						</div>
 					</button>
 				</div>
-			</Pane>
+			</component>
 
 			<!-- Right: detail -->
-			<Pane :size="70" class="flex flex-col">
+			<component
+				:is="SplitPane"
+				v-show="!isNarrow || showDetail"
+				:size="isNarrow ? undefined : 70"
+				class="flex flex-col"
+				:class="isNarrow ? 'min-h-0 flex-1' : ''"
+			>
 				<template v-if="selected">
 					<!-- Audit strip: verdict + audit score + cost; sub-metrics in Details -->
 					<div class="border-b border-outline-gray-1 px-4 py-3">
 						<div class="flex flex-wrap items-center gap-x-5 gap-y-2">
 							<div class="flex items-center gap-2">
+								<Button
+									v-if="isNarrow"
+									size="sm"
+									variant="ghost"
+									icon="lucide-arrow-left"
+									aria-label="Back to pages"
+									@click="showDetail = false"
+								/>
 								<span class="text-base font-medium text-ink-gray-9"
 									>Page {{ selected.page_no }}</span
 								>
@@ -384,7 +425,7 @@ function fmtDelta(v) {
 									/>
 								</template>
 								<template #body-main>
-									<div class="w-80 p-3">
+									<div class="w-72 p-3 sm:w-80">
 										<div class="flex flex-wrap gap-x-5 gap-y-1">
 											<div
 												v-for="c in scoreCells"
@@ -628,7 +669,7 @@ function fmtDelta(v) {
 				<p v-else class="py-10 text-center text-sm text-ink-gray-5">
 					Select a page to review.
 				</p>
-			</Pane>
-		</Splitpanes>
+			</component>
+		</component>
 	</div>
 </template>
diff --git a/frontend/src/components/ProjectSettings.vue b/frontend/src/components/ProjectSettings.vue
index ff1c1a4..64ab00e 100644
--- a/frontend/src/components/ProjectSettings.vue
+++ b/frontend/src/components/ProjectSettings.vue
@@ -78,17 +78,17 @@ function submit() {
 				<nav class="flex min-w-0 items-center gap-1.5 text-base">
 					<RouterLink
 						:to="{ name: 'Projects' }"
-						class="text-ink-gray-5 hover:text-ink-gray-7"
+						class="hidden text-ink-gray-5 hover:text-ink-gray-7 lg:block"
 						>Projects</RouterLink
 					>
-					<span class="text-ink-gray-4" aria-hidden="true">/</span>
+					<span class="hidden text-ink-gray-4 lg:block" aria-hidden="true">/</span>
 					<RouterLink
 						:to="{ name: 'ProjectDetail', params: { name } }"
 						class="truncate text-ink-gray-5 hover:text-ink-gray-7"
 						>{{ project.doc?.project_name || name }}</RouterLink
 					>
-					<span class="text-ink-gray-4" aria-hidden="true">/</span>
-					<span class="truncate text-ink-gray-9">Settings</span>
+					<span class="shrink-0 text-ink-gray-4" aria-hidden="true">/</span>
+					<span class="shrink-0 text-ink-gray-9">Settings</span>
 				</nav>
 			</div>
 		</PageHeader>
diff --git a/frontend/src/components/SectionTree.vue b/frontend/src/components/SectionTree.vue
index 53b2c03..a96d477 100644
--- a/frontend/src/components/SectionTree.vue
+++ b/frontend/src/components/SectionTree.vue
@@ -4,6 +4,7 @@ import { Badge, Button, Dropdown, Tree, dialog, useCall, useList, toast } from "
 import { Splitpanes, Pane } from "splitpanes";
 import "splitpanes/dist/splitpanes.css";
 import WikiPreview from "@/components/WikiPreview.vue";
+import { useIsNarrow } from "@/composables/useMediaQuery";
 import { setSection } from "@/data/agentContext";
 
 const props = defineProps({
@@ -107,8 +108,17 @@ watch(
 
 const selectedName = ref(null);
 const selected = computed(() => byName.value[selectedName.value] || null);
+
+// Narrow screens drill down instead of splitting: the tree fills the width, tapping a
+// section swaps in its preview, Back returns. Swapping the Splitpanes host for a plain
+// <div> keeps one copy of the markup rather than forking a phone-only template.
+const isNarrow = useIsNarrow();
+const showPreview = ref(!!props.initialSection);
+const SplitHost = computed(() => (isNarrow.value ? "div" : Splitpanes));
+const SplitPane = computed(() => (isNarrow.value ? "div" : Pane));
 function onSelect(name) {
 	selectedName.value = name;
+	showPreview.value = true;
 }
 
 // Attach the selected section as the agent's default context (swaps out any page chip).
@@ -235,6 +245,12 @@ function onRemove(node) {
 
 // --- Approve & build graph -----------------------------------------------------------
 const isGraphed = computed(() => props.status === "Graphed");
+// "Approve & Build Graph" pushes the toolbar past a phone's width; the shorter label
+// says the same thing next to the section count it sits beside.
+const graphLabel = computed(() => {
+	if (isGraphed.value) return "Rebuild graph";
+	return isNarrow.value ? "Build graph" : "Approve & Build Graph";
+});
 async function buildGraph() {
 	try {
 		await graph.submit({ import_name: props.importName });
@@ -258,11 +274,18 @@ async function buildGraph() {
 			No sections yet — parse the document to build its tree.
 		</p>
 
-		<Splitpanes v-else class="h-full">
+		<component :is="SplitHost" v-else :class="isNarrow ? 'flex h-full flex-col' : 'h-full'">
 			<!-- Left: the editable section tree -->
-			<Pane :size="40" :min-size="25" class="flex flex-col border-r border-outline-gray-1">
+			<component
+				:is="SplitPane"
+				v-show="!isNarrow || !showPreview"
+				:size="isNarrow ? undefined : 40"
+				:min-size="isNarrow ? undefined : 25"
+				class="flex flex-col"
+				:class="isNarrow ? 'min-h-0 flex-1' : 'border-r border-outline-gray-1'"
+			>
 				<div
-					class="flex items-center gap-2 border-b border-outline-gray-1 px-3 py-2 text-sm text-ink-gray-6"
+					class="flex flex-wrap items-center gap-2 border-b border-outline-gray-1 px-3 py-2 text-sm text-ink-gray-6"
 				>
 					<span class="font-medium text-ink-gray-8">Sections</span>
 					<Badge :label="String(sectionCount)" theme="gray" variant="subtle" size="sm" />
@@ -275,15 +298,17 @@ async function buildGraph() {
 					/>
 					<span v-if="mutating" class="text-xs text-ink-gray-4">Saving…</span>
 					<Button
-						class="ml-auto"
+						class="ml-auto shrink-0"
 						size="sm"
 						variant="solid"
-						:label="isGraphed ? 'Rebuild graph' : 'Approve & Build Graph'"
+						:label="graphLabel"
 						:loading="graph.loading"
 						@click="buildGraph"
 					/>
 				</div>
-				<div class="flex-1 overflow-auto p-2">
+				<!-- A tighter indent on narrow screens: ICAI titles run to 140 chars and every
+				     nesting step is width the title no longer gets. -->
+				<div class="flex-1 overflow-auto p-2 [--tree-indent:14px] lg:[--tree-indent:24px]">
 					<Tree
 						:nodes="tree"
 						node-key="name"
@@ -330,7 +355,7 @@ async function buildGraph() {
 									@click.stop="onSelect(node.name)"
 								>
 									<span
-										class="truncate text-sm"
+										class="min-w-0 truncate text-sm"
 										:class="
 											node.include_in_wiki
 												? 'text-ink-gray-8'
@@ -338,12 +363,16 @@ async function buildGraph() {
 										"
 										>{{ node.title }}</span
 									>
+									<!-- The type is one of several chips competing with a 140-char
+									     title; on narrow screens the title wins (Explore groups
+									     by type anyway). -->
 									<Badge
 										v-if="node.section_type"
 										:label="node.section_type"
 										theme="blue"
 										variant="subtle"
 										size="sm"
+										class="hidden lg:inline-flex"
 									/>
 									<Badge
 										v-if="node.lint_count"
@@ -362,9 +391,11 @@ async function buildGraph() {
 									>
 								</button>
 
+								<!-- Touch has no hover, so the row menu stays visible on narrow
+								     screens instead of being hover-revealed. -->
 								<Dropdown :options="rowActions(node)" placement="right">
 									<button
-										class="shrink-0 rounded p-0.5 text-ink-gray-5 opacity-0 hover:bg-surface-gray-3 group-hover:opacity-100"
+										class="shrink-0 rounded p-0.5 text-ink-gray-5 hover:bg-surface-gray-3 lg:opacity-0 lg:group-hover:opacity-100"
 										@click.stop
 									>
 										<span
@@ -377,12 +408,23 @@ async function buildGraph() {
 						</template>
 					</Tree>
 				</div>
-			</Pane>
+			</component>
 
 			<!-- Right: wiki-fidelity preview of the selected section -->
-			<Pane :size="60" class="flex flex-col">
-				<WikiPreview :section="selectedName" @navigate="onSelect" />
-			</Pane>
-		</Splitpanes>
+			<component
+				:is="SplitPane"
+				v-show="!isNarrow || showPreview"
+				:size="isNarrow ? undefined : 60"
+				class="flex flex-col"
+				:class="isNarrow ? 'min-h-0 flex-1' : ''"
+			>
+				<WikiPreview
+					:section="selectedName"
+					:show-back="isNarrow"
+					@navigate="onSelect"
+					@back="showPreview = false"
+				/>
+			</component>
+		</component>
 	</div>
 </template>
diff --git a/frontend/src/components/TypeChip.vue b/frontend/src/components/TypeChip.vue
index dbd9a8a..62c6b73 100644
--- a/frontend/src/components/TypeChip.vue
+++ b/frontend/src/components/TypeChip.vue
@@ -13,7 +13,7 @@ defineProps({
 <template>
 	<button
 		type="button"
-		class="flex items-center gap-1.5 rounded-full border px-2.5 py-1 text-sm transition-colors"
+		class="flex max-w-full min-w-0 items-center gap-1.5 rounded-full border px-2.5 py-1 text-sm transition-colors"
 		:class="
 			active
 				? 'border-outline-gray-3 bg-surface-gray-3 text-ink-gray-9'
@@ -25,7 +25,7 @@ defineProps({
 			:style="{ backgroundColor: color }"
 			aria-hidden="true"
 		/>
-		<span class="truncate">{{ label }}</span>
-		<span v-if="count != null" class="text-ink-gray-5">{{ count }}</span>
+		<span class="min-w-0 truncate">{{ label }}</span>
+		<span v-if="count != null" class="shrink-0 text-ink-gray-5">{{ count }}</span>
 	</button>
 </template>
diff --git a/frontend/src/components/WikiGenerate.vue b/frontend/src/components/WikiGenerate.vue
index baf488b..60eab29 100644
--- a/frontend/src/components/WikiGenerate.vue
+++ b/frontend/src/components/WikiGenerate.vue
@@ -150,6 +150,13 @@ function pageRange(n) {
 	return n.page_start === n.page_end ? `p${n.page_start}` : `p${n.page_start}–${n.page_end}`;
 }
 
+// Past four levels the indent costs more title than the extra nesting communicates —
+// at phone width a depth-8 row would have nothing left to show its label in.
+const MAX_INDENT_DEPTH = 4;
+function rowIndent(depth) {
+	return `${Math.min(depth, MAX_INDENT_DEPTH) * 1.25 + 0.5}rem`;
+}
+
 // Clicking a projected node opens the same wiki preview as the Tree tab.
 const previewSection = ref(null);
 const previewOpen = computed({
@@ -191,9 +198,11 @@ onUnmounted(() => {
 			</div>
 		</div>
 
-		<div v-else class="flex min-h-0 flex-1">
+		<div v-else class="flex min-h-0 flex-1 flex-col lg:flex-row">
 			<!-- Left: target + actions -->
-			<div class="flex w-80 shrink-0 flex-col gap-4 border-r border-outline-gray-1 p-4">
+			<div
+				class="flex shrink-0 flex-col gap-4 border-b border-outline-gray-1 p-4 lg:w-80 lg:border-r lg:border-b-0"
+			>
 				<div>
 					<p class="text-sm font-medium text-ink-gray-8">Generate wiki</p>
 					<p class="mt-0.5 text-xs text-ink-gray-5">
@@ -266,7 +275,7 @@ onUnmounted(() => {
 			<!-- Right: preview of what will be generated -->
 			<div class="flex min-h-0 flex-1 flex-col">
 				<div
-					class="flex items-center gap-2 border-b border-outline-gray-1 px-4 py-2 text-sm text-ink-gray-6"
+					class="flex flex-wrap items-center gap-2 border-b border-outline-gray-1 px-4 py-2 text-sm text-ink-gray-6"
 				>
 					<span class="font-medium text-ink-gray-8">Preview</span>
 					<Badge
@@ -305,7 +314,7 @@ onUnmounted(() => {
 						:key="row.name"
 						type="button"
 						class="flex w-full items-center gap-2 rounded px-2 py-1.5 text-left hover:bg-surface-gray-2"
-						:style="{ paddingLeft: `${row.depth * 1.25 + 0.5}rem` }"
+						:style="{ paddingLeft: rowIndent(row.depth) }"
 						@click="previewSection = row.name"
 					>
 						<span
@@ -313,7 +322,7 @@ onUnmounted(() => {
 							:class="row.is_group ? 'lucide-folder' : 'lucide-file-text'"
 							aria-hidden="true"
 						/>
-						<span class="truncate text-sm text-ink-gray-8">{{ row.title }}</span>
+						<span class="min-w-0 truncate text-sm text-ink-gray-8">{{ row.title }}</span>
 						<Badge
 							v-if="row.lint_count"
 							:label="`⚠ ${row.lint_count}`"
@@ -340,7 +349,7 @@ onUnmounted(() => {
 
 		<!-- Wiki preview of a projected node (same component as the Tree tab) -->
 		<Dialog v-model:open="previewOpen" size="4xl" bare>
-			<div class="h-[75vh]">
+			<div class="h-[70dvh] sm:h-[75vh]">
 				<WikiPreview :section="previewSection" @navigate="(n) => (previewSection = n)" />
 			</div>
 		</Dialog>
diff --git a/frontend/src/components/WikiPreview.vue b/frontend/src/components/WikiPreview.vue
index fed05da..8a801d2 100644
--- a/frontend/src/components/WikiPreview.vue
+++ b/frontend/src/components/WikiPreview.vue
@@ -3,17 +3,20 @@
 // MarkdownPreview. The HTML comes from the backend (`api.wiki.render_section_preview`),
 // which renders with the *same* renderer the wiki uses (markdown-it-py), so this preview
 // matches the eventual generated Wiki Document page. We only post-process ```mermaid
-// fences into SVG client-side via the shared wiki mermaid loader (same as today).
+// fences into SVG client-side via the shared mermaid util.
 import { computed, onMounted, onUnmounted, ref, watch, nextTick } from "vue";
-import { Badge, TabButtons, useCall } from "frappe-ui";
+import { Badge, Button, TabButtons, useCall } from "frappe-ui";
 import { CodeEditor } from "frappe-ui/code-editor";
 import { renderMermaidIn } from "@/utils/mermaid";
 import { useSocket } from "@/socket";
 
 const props = defineProps({
 	section: { type: String, default: null },
+	// Set by a parent that drilled down into this preview instead of showing it beside
+	// the tree, so the user has a way back to the list.
+	showBack: { type: Boolean, default: false },
 });
-const emit = defineEmits(["navigate"]);
+const emit = defineEmits(["navigate", "back"]);
 
 const preview = useCall({
 	url: "/api/v2/method/wikify.api.wiki.render_section_preview",
@@ -50,7 +53,7 @@ const lintSummary = computed(() =>
 const container = ref(null);
 async function renderDiagrams() {
 	await nextTick();
-	renderMermaidIn(container.value);
+	await renderMermaidIn(container.value);
 }
 watch([() => data.value?.html, mode], renderDiagrams);
 
@@ -76,16 +79,30 @@ function onBodyClick(e) {
 		</p>
 		<template v-else-if="data">
 			<!-- Breadcrumb + Rendered/Source toggle -->
-			<div class="flex items-center gap-2 border-b border-outline-gray-1 px-4 py-2">
+			<div
+				class="flex flex-wrap items-center gap-2 border-b border-outline-gray-1 px-4 py-2"
+			>
+				<Button
+					v-if="showBack"
+					size="sm"
+					variant="ghost"
+					icon="lucide-arrow-left"
+					aria-label="Back to sections"
+					@click="emit('back')"
+				/>
 				<nav class="flex min-w-0 flex-1 items-center gap-1 text-xs text-ink-gray-5">
+					<!-- A four-deep trail truncates to "I › 86… › P… › RAT…" at phone width,
+					     which says nothing — narrow screens keep only the current section. -->
 					<template v-for="(crumb, i) in data.breadcrumb" :key="i">
-						<span v-if="i" class="text-ink-gray-3" aria-hidden="true">›</span>
+						<span v-if="i" class="hidden text-ink-gray-3 lg:inline" aria-hidden="true"
+							>›</span
+						>
 						<span
 							class="truncate"
 							:class="
 								i === data.breadcrumb.length - 1
 									? 'font-medium text-ink-gray-7'
-									: ''
+									: 'hidden lg:inline'
 							"
 							>{{ crumb }}</span
 						>
@@ -128,7 +145,7 @@ function onBodyClick(e) {
 
 			<div class="min-h-0 flex-1 overflow-auto">
 				<!-- Rendered wiki page frame -->
-				<article v-if="mode === 'rendered'" class="mx-auto max-w-3xl px-6 py-6">
+				<article v-if="mode === 'rendered'" class="mx-auto max-w-3xl px-4 py-5 sm:px-6 sm:py-6">
 					<h1 class="mb-4 text-xl-semibold text-ink-gray-9">
 						{{ data.title }}
 					</h1>
@@ -157,21 +174,23 @@ function onBodyClick(e) {
 </template>
 
 <style>
-.wiki-preview-body .mermaid-figure {
-	display: flex;
-	justify-content: center;
-	overflow-x: auto;
-	margin: 1rem 0;
-}
-.wiki-preview-body .mermaid-figure svg {
-	max-width: 100%;
-	height: auto;
-}
 /* Approximate the wiki's table chrome so preview ≈ published page. */
 .wiki-preview-body table {
 	width: 100%;
 	border-collapse: collapse;
 }
+
+/* The ICAI pages emit rowspan/colspan tables far wider than a phone. Below the split
+   breakpoint each table becomes its own horizontal scroller so the surrounding prose
+   never widens with it; wide viewports keep the full-width table they already had. */
+@media (max-width: 1023px) {
+	.wiki-preview-body table {
+		display: block;
+		width: max-content;
+		max-width: 100%;
+		overflow-x: auto;
+	}
+}
 .wiki-preview-body th,
 .wiki-preview-body td {
 	border: 1px solid var(--outline-gray-2, #e5e7eb);
diff --git a/frontend/src/components/rag/CostMeter.vue b/frontend/src/components/rag/CostMeter.vue
new file mode 100644
index 0000000..b87785a
--- /dev/null
+++ b/frontend/src/components/rag/CostMeter.vue
@@ -0,0 +1,58 @@
+<script setup>
+// What the last answer cost, and what the session has spent so far. A running meter, not
+// a headline: quiet type, no colour, and it disappears entirely until the backend sends
+// the numbers — "$0.00" would read as free when the truth is "not measured yet".
+import { computed } from "vue";
+
+const props = defineProps({
+	cost: { type: Number, default: null },
+	promptTokens: { type: Number, default: null },
+	completionTokens: { type: Number, default: null },
+	model: { type: String, default: "" },
+	sessionCost: { type: Number, default: null },
+});
+
+// Four decimals: a single answer costs fractions of a cent, and rounding to two would
+// print $0.00 for every real charge.
+function formatCost(amount) {
+	return `$${amount.toFixed(4)}`;
+}
+
+const tokens = computed(() => (props.promptTokens || 0) + (props.completionTokens || 0));
+const hasCost = computed(() => typeof props.cost === "number" && Number.isFinite(props.cost));
+const showSessionTotal = computed(
+	() =>
+		typeof props.sessionCost === "number" &&
+		Number.isFinite(props.sessionCost) &&
+		props.sessionCost > (props.cost || 0),
+);
+</script>
+
+<template>
+	<p v-if="hasCost" class="flex flex-wrap items-center gap-x-1.5 text-xs text-ink-gray-5">
+		<span
+			class="font-mono tabular-nums"
+			:title="
+				promptTokens != null
+					? `${promptTokens} prompt + ${completionTokens} completion tokens`
+					: 'Cost of this answer'
+			"
+		>
+			{{ formatCost(cost) }}
+		</span>
+		<template v-if="tokens">
+			<span aria-hidden="true">·</span>
+			<span class="tabular-nums">{{ tokens.toLocaleString() }} tokens</span>
+		</template>
+		<template v-if="model">
+			<span aria-hidden="true">·</span>
+			<span class="truncate">{{ model }}</span>
+		</template>
+		<template v-if="showSessionTotal">
+			<span aria-hidden="true">·</span>
+			<span class="tabular-nums" title="Total spent in this session">
+				{{ formatCost(sessionCost) }} this session
+			</span>
+		</template>
+	</p>
+</template>
diff --git a/frontend/src/components/rag/CoverageBar.vue b/frontend/src/components/rag/CoverageBar.vue
new file mode 100644
index 0000000..ac4ea45
--- /dev/null
+++ b/frontend/src/components/rag/CoverageBar.vue
@@ -0,0 +1,47 @@
+<script setup>
+// A bar for the one thing a bar can honestly say here: a share of a known whole —
+// sections covered out of the sections that exist, or recall out of 1. Retrieval scores
+// are NOT proportions and must never be drawn with this.
+import { computed } from "vue";
+
+const props = defineProps({
+	value: { type: Number, default: 0 },
+	total: { type: Number, default: 1 },
+	label: { type: String, default: "coverage" },
+	tone: { type: String, default: "neutral" }, // neutral | good | warn
+});
+
+const TONES = {
+	neutral: "bg-surface-gray-8",
+	good: "bg-surface-green-7",
+	warn: "bg-surface-amber-7",
+};
+
+const percent = computed(() => {
+	const total = props.total > 0 ? props.total : 1;
+	const ratio = Math.max(0, Math.min(1, (props.value || 0) / total));
+	return Math.round(ratio * 100);
+});
+
+// A data-driven width has no static Tailwind equivalent; the bound style is confined to
+// this one dimension and every colour still comes from a semantic token.
+const fillStyle = computed(() => ({ width: `${percent.value}%` }));
+</script>
+
+<template>
+	<div class="flex items-center gap-2">
+		<div class="h-2 min-w-16 flex-1 overflow-hidden rounded-full bg-surface-gray-3">
+			<div
+				class="h-full rounded-full transition-all"
+				:class="TONES[tone] || TONES.neutral"
+				:style="fillStyle"
+				role="meter"
+				:aria-label="label"
+				:aria-valuenow="percent"
+				aria-valuemin="0"
+				aria-valuemax="100"
+			/>
+		</div>
+		<span class="shrink-0 font-mono text-xs tabular-nums text-ink-gray-6">{{ percent }}%</span>
+	</div>
+</template>
diff --git a/frontend/src/components/rag/EvalScoreboard.vue b/frontend/src/components/rag/EvalScoreboard.vue
new file mode 100644
index 0000000..c967ef6
--- /dev/null
+++ b/frontend/src/components/rag/EvalScoreboard.vue
@@ -0,0 +1,53 @@
+<script setup>
+// Recall@k per golden question. The eval endpoint is not in the frozen contract yet, so
+// this renders an explicitly-labelled placeholder until a later slice wires it up.
+import { computed } from "vue";
+import CoverageBar from "@/components/rag/CoverageBar.vue";
+
+const props = defineProps({
+	rows: { type: Array, default: () => [] },
+	loading: { type: Boolean, default: false },
+});
+
+const averageRecall = computed(() => {
+	if (!props.rows.length) return null;
+	const total = props.rows.reduce((sum, row) => sum + (row.recall || 0), 0);
+	return total / props.rows.length;
+});
+</script>
+
+<template>
+	<section class="rounded-lg border border-outline-gray-2 bg-surface-elevation-1">
+		<header class="flex flex-wrap items-center gap-2 border-b border-outline-gray-2 px-4 py-3">
+			<span class="lucide-target size-4 text-ink-gray-5" aria-hidden="true" />
+			<h2 class="text-base font-medium text-ink-gray-9">Eval scoreboard</h2>
+			<span class="text-xs text-ink-gray-5">recall@k per golden question</span>
+			<div class="flex-1" />
+			<span v-if="averageRecall != null" class="text-sm text-ink-gray-7">
+				avg {{ (averageRecall * 100).toFixed(0) }}%
+			</span>
+		</header>
+
+		<div v-if="rows.length" class="divide-y divide-outline-gray-1">
+			<div
+				v-for="row in rows"
+				:key="row.question"
+				class="flex flex-col gap-2 px-4 py-3 sm:flex-row sm:items-center sm:gap-4"
+			>
+				<p class="min-w-0 flex-1 truncate text-base text-ink-gray-8">{{ row.question }}</p>
+				<div class="w-full sm:w-48">
+					<CoverageBar :value="row.recall" :total="1" tone="good" label="recall" />
+				</div>
+			</div>
+		</div>
+
+		<div v-else class="flex flex-col items-center gap-2 px-4 py-10 text-center">
+			<span class="lucide-flask-conical size-6 text-ink-gray-4" aria-hidden="true" />
+			<p class="text-base text-ink-gray-7">Not wired up yet</p>
+			<p class="max-w-md text-sm text-ink-gray-5">
+				A later slice adds the golden-question eval endpoint; this board reads it and plots
+				recall@k per question — a true proportion, so it gets a real bar.
+			</p>
+		</div>
+	</section>
+</template>
diff --git a/frontend/src/components/rag/IndexStatusCard.vue b/frontend/src/components/rag/IndexStatusCard.vue
new file mode 100644
index 0000000..29342b4
--- /dev/null
+++ b/frontend/src/components/rag/IndexStatusCard.vue
@@ -0,0 +1,109 @@
+<script setup>
+// What the retriever actually has in it — counts, embedding dim, freshness — plus the
+// one action that fixes a stale index.
+import { computed } from "vue";
+import { Button, dayjs } from "frappe-ui";
+
+const props = defineProps({
+	status: { type: Object, default: null },
+	loading: { type: Boolean, default: false },
+	errorText: { type: String, default: "" },
+	reindexing: { type: Boolean, default: false },
+	jobName: { type: String, default: "" },
+	canReindex: { type: Boolean, default: false },
+});
+
+defineEmits(["reindex"]);
+
+const metrics = computed(() => [
+	{ label: "Chunks", value: props.status?.chunks },
+	{ label: "Sections", value: props.status?.sections },
+	{ label: "Documents", value: props.status?.documents },
+	{ label: "Embedding dim", value: props.status?.dim },
+]);
+
+const indexedAt = computed(() =>
+	props.status?.indexed_at ? dayjs(props.status.indexed_at).format("D MMM YYYY, h:mm a") : "",
+);
+</script>
+
+<template>
+	<section class="rounded-lg border border-outline-gray-2 bg-surface-elevation-1">
+		<header class="flex flex-wrap items-center gap-2 border-b border-outline-gray-2 px-4 py-3">
+			<span class="lucide-database size-4 text-ink-gray-5" aria-hidden="true" />
+			<h2 class="text-base font-medium text-ink-gray-9">Index status</h2>
+			<span
+				v-if="status?.stale"
+				class="rounded-full bg-surface-amber-2 px-2 py-0.5 text-xs font-medium text-ink-gray-9"
+			>
+				stale
+			</span>
+			<span
+				v-else-if="status"
+				class="rounded-full bg-surface-green-2 px-2 py-0.5 text-xs font-medium text-ink-gray-9"
+			>
+				fresh
+			</span>
+			<div class="flex-1" />
+			<!-- Reindexing is per-project, so with "All projects" selected there is nothing
+			     to rebuild; say that instead of showing a dead button. -->
+			<span v-if="!canReindex" class="text-xs text-ink-gray-5">
+				Pick a project to reindex
+			</span>
+			<Button
+				v-else
+				variant="subtle"
+				icon-left="lucide-refresh-cw"
+				label="Reindex"
+				:loading="reindexing"
+				tooltip="Rebuild this project’s index"
+				@click="$emit('reindex')"
+			/>
+		</header>
+
+		<div class="px-4 py-4">
+			<p v-if="errorText" class="text-sm text-ink-red-8">{{ errorText }}</p>
+
+			<!-- The skeleton mirrors the tile grid, so the card keeps its height while the
+			     index is read instead of collapsing to a line of text. -->
+			<div v-else-if="loading && !status" class="grid grid-cols-2 gap-3 sm:grid-cols-4">
+				<div
+					v-for="metric in metrics"
+					:key="metric.label"
+					class="rounded-md bg-surface-gray-1 px-3 py-2"
+				>
+					<div class="h-3 w-16 animate-pulse rounded bg-surface-gray-3" />
+					<div class="mt-1.5 h-5 w-10 animate-pulse rounded bg-surface-gray-3" />
+				</div>
+			</div>
+
+			<p v-else-if="!status" class="text-sm text-ink-gray-5">
+				No index yet — pick a project and reindex to build one.
+			</p>
+
+			<dl v-else class="grid grid-cols-2 gap-3 sm:grid-cols-4">
+				<div
+					v-for="metric in metrics"
+					:key="metric.label"
+					class="rounded-md bg-surface-gray-1 px-3 py-2"
+				>
+					<!-- Fixed label height keeps the numbers on one baseline when a label
+					     wraps to two lines ("Embedding dim" at narrow widths). -->
+					<dt class="flex h-8 items-start text-xs text-ink-gray-5">
+						{{ metric.label }}
+					</dt>
+					<dd class="text-lg font-medium tabular-nums text-ink-gray-9">
+						{{ metric.value ?? "—" }}
+					</dd>
+				</div>
+			</dl>
+
+			<p v-if="indexedAt" class="mt-3 text-xs text-ink-gray-5">
+				Last indexed {{ indexedAt }}
+			</p>
+			<p v-if="jobName" class="mt-1 text-xs text-ink-gray-5">
+				Reindex queued as <span class="font-mono break-all">{{ jobName }}</span>
+			</p>
+		</div>
+	</section>
+</template>
diff --git a/frontend/src/components/rag/RelevanceTag.vue b/frontend/src/components/rag/RelevanceTag.vue
new file mode 100644
index 0000000..ffc6cf9
--- /dev/null
+++ b/frontend/src/components/rag/RelevanceTag.vue
@@ -0,0 +1,94 @@
+<script setup>
+// How a hit earned its place, stated honestly.
+//
+// There used to be a filled "score" bar here. It lied twice: the exhaustive (`filter`)
+// leg has no ranking at all, so every hit carried the sentinel score 1.0 and every bar
+// drew full width; and the fused legs score with reciprocal-rank fusion (~0.031 for
+// rank 1, ~0.030 for rank 5), which is rank-derived, unbounded-below and not comparable
+// across queries — normalising it against the best hit paints near-full bars for
+// everything. So a bar survives only on the plain vector leg, where the number is an
+// absolute 0-1 similarity that can sit on a 0-1 axis; everywhere else the honest claim is
+// position in this list, or nothing at all.
+import { computed } from "vue";
+import CoverageBar from "@/components/rag/CoverageBar.vue";
+
+const props = defineProps({
+	rank: { type: Number, default: null },
+	total: { type: Number, default: 0 },
+	score: { type: Number, default: null },
+	basis: { type: String, default: "hybrid" }, // exhaustive | hybrid | vector | fts
+	// Set when every hit in the list shares one score, whatever the route said it was.
+	unrankedSet: { type: Boolean, default: false },
+});
+
+const BASES = {
+	exhaustive: {
+		label: "exhaustive match — unranked",
+		icon: "lucide-list-checks",
+		chip: "bg-surface-violet-2 text-ink-violet-8",
+		hint: "This query used an exhaustive metadata filter: every matching section is returned, so there is no ranking to show.",
+	},
+	hybrid: {
+		label: "fused rank",
+		icon: "lucide-shuffle",
+		chip: "bg-surface-gray-3 text-ink-gray-8",
+		hint: "Position after reciprocal-rank fusion of the keyword and meaning searches. A rank, not a confidence — not comparable between queries.",
+	},
+	vector: {
+		label: "meaning rank",
+		icon: "lucide-brain",
+		chip: "bg-surface-gray-3 text-ink-gray-8",
+		hint: "Position by embedding similarity to the question. A rank within these results, not a confidence score.",
+	},
+	fts: {
+		label: "keyword rank",
+		icon: "lucide-type",
+		chip: "bg-surface-gray-3 text-ink-gray-8",
+		hint: "Position by full-text (BM25) keyword match. A rank within these results, not a confidence score.",
+	},
+};
+
+const basis = computed(() => BASES[props.basis] || BASES.hybrid);
+const unranked = computed(
+	() => props.basis === "exhaustive" || props.unrankedSet || props.rank == null,
+);
+// Only the vector leg produces a value on a real 0-1 axis, so only it earns a bar.
+const showSimilarity = computed(
+	() => props.basis === "vector" && !unranked.value && props.score != null,
+);
+const hint = computed(() =>
+	props.score == null || unranked.value
+		? basis.value.hint
+		: `${basis.value.hint} Raw score ${props.score.toFixed(4)}.`,
+);
+</script>
+
+<template>
+	<span
+		v-if="unranked"
+		class="inline-flex items-center gap-1.5 rounded-full px-2 py-0.5 text-xs font-medium"
+		:class="BASES.exhaustive.chip"
+		:title="BASES.exhaustive.hint"
+	>
+		<span :class="[BASES.exhaustive.icon, 'size-3.5']" aria-hidden="true" />
+		{{ BASES.exhaustive.label }}
+	</span>
+
+	<span v-else class="inline-flex min-w-0 flex-1 items-center gap-2 text-xs" :title="hint">
+		<span
+			class="inline-flex shrink-0 items-center gap-1 rounded-full px-2 py-0.5 font-mono font-medium tabular-nums"
+			:class="basis.chip"
+		>
+			<span :class="[basis.icon, 'size-3.5']" aria-hidden="true" />
+			#{{ rank }}
+		</span>
+		<CoverageBar
+			v-if="showSimilarity"
+			class="min-w-16 flex-1"
+			:value="score"
+			:total="1"
+			label="similarity to the question"
+		/>
+		<span v-else class="text-ink-gray-5">of {{ total }} by {{ basis.label }}</span>
+	</span>
+</template>
diff --git a/frontend/src/components/rag/RouteBadge.vue b/frontend/src/components/rag/RouteBadge.vue
new file mode 100644
index 0000000..70201de
--- /dev/null
+++ b/frontend/src/components/rag/RouteBadge.vue
@@ -0,0 +1,88 @@
+<script setup>
+// The routing decision made visible — the product thesis, not a footnote. Shows the
+// chosen intent, any section-type filter it applied, and the router's reason in words.
+import { computed } from "vue";
+
+const props = defineProps({
+	route: { type: Object, default: null },
+	loading: { type: Boolean, default: false },
+});
+
+const INTENTS = {
+	exhaustive: {
+		label: "Exhaustive",
+		icon: "lucide-list-checks",
+		blurb: "Returns every matching section, not just the top few.",
+		chip: "bg-surface-violet-2 text-ink-violet-8",
+		accent: "text-ink-violet-8",
+	},
+	semantic: {
+		label: "Semantic",
+		icon: "lucide-brain",
+		blurb: "Ranks by meaning — closest passages win.",
+		chip: "bg-surface-blue-2 text-ink-blue-8",
+		accent: "text-ink-blue-8",
+	},
+	hybrid: {
+		label: "Hybrid",
+		icon: "lucide-shuffle",
+		blurb: "Blends keyword and meaning, fused by reciprocal rank.",
+		chip: "bg-surface-green-2 text-ink-green-8",
+		accent: "text-ink-green-8",
+	},
+};
+
+const intent = computed(() => INTENTS[props.route?.intent] || null);
+</script>
+
+<template>
+	<div
+		v-if="loading"
+		class="flex items-center gap-3 rounded-lg border border-outline-gray-2 bg-surface-gray-1 px-4 py-3"
+	>
+		<div class="h-7 w-28 animate-pulse rounded-full bg-surface-gray-3" />
+		<div class="h-3 w-56 animate-pulse rounded bg-surface-gray-3" />
+	</div>
+
+	<div
+		v-else-if="intent"
+		class="rounded-lg border border-outline-gray-2 bg-surface-gray-1 px-4 py-3"
+	>
+		<!-- Side by side only from `lg`. At tablet widths the chips row is unshrinkable, so
+		     a two-column split left the reason paragraph one word wide and spilled it out
+		     of the card. -->
+		<div class="flex flex-col gap-2 lg:flex-row lg:items-start lg:gap-4">
+			<div class="flex w-full min-w-0 flex-wrap items-center gap-2 lg:w-auto">
+				<span class="text-xs font-medium tracking-wide text-ink-gray-5 uppercase">
+					Route
+				</span>
+				<span
+					class="inline-flex items-center gap-1.5 rounded-full px-3 py-1 text-sm font-semibold"
+					:class="intent.chip"
+				>
+					<span :class="[intent.icon, 'size-4', intent.accent]" aria-hidden="true" />
+					{{ intent.label }}
+				</span>
+				<span
+					v-if="route.section_type"
+					class="inline-flex min-w-0 max-w-full basis-full items-center gap-1.5 rounded-full border border-outline-gray-2 bg-surface-elevation-1 px-2.5 py-1 text-sm text-ink-gray-7 lg:basis-auto"
+				>
+					<span
+						class="lucide-filter size-3.5 shrink-0 text-ink-gray-5"
+						aria-hidden="true"
+					/>
+					<!-- Without `min-w-0` this flex item refuses to shrink, so a long
+					     section_type pushes the chip past the card edge instead of
+					     truncating inside it. -->
+					<span class="min-w-0 truncate">{{ route.section_type }}</span>
+				</span>
+			</div>
+			<div class="min-w-0 flex-1">
+				<p class="text-base text-ink-gray-8">{{ route.reason || intent.blurb }}</p>
+				<p v-if="route.query" class="line-clamp-1 text-xs break-all text-ink-gray-5">
+					Searched as “{{ route.query }}”
+				</p>
+			</div>
+		</div>
+	</div>
+</template>
diff --git a/frontend/src/components/rag/SourceCard.vue b/frontend/src/components/rag/SourceCard.vue
new file mode 100644
index 0000000..c73a597
--- /dev/null
+++ b/frontend/src/components/rag/SourceCard.vue
@@ -0,0 +1,259 @@
+<script setup>
+// One retrieved chunk, shown as provenance: where it came from in the document, why it
+// ranked (vector vs full-text rank), and a deep link into the generated wiki.
+import { computed } from "vue";
+import RelevanceTag from "@/components/rag/RelevanceTag.vue";
+
+const props = defineProps({
+	hit: { type: Object, required: true },
+	index: { type: Number, required: true },
+	total: { type: Number, default: 0 },
+	basis: { type: String, default: "hybrid" },
+	unrankedSet: { type: Boolean, default: false },
+	highlighted: { type: Boolean, default: false },
+	missed: { type: Boolean, default: false },
+	compact: { type: Boolean, default: false },
+});
+
+// The stored chunk is markdown; the card shows a plain-text preview, so heading hashes
+// and emphasis markers are stripped rather than rendered.
+const snippet = computed(() =>
+	(props.hit.text || "")
+		.replace(/^#{1,6}\s+/gm, "")
+		.replace(/\*\*/g, "")
+		.replace(/`/g, "")
+		// Rate tables are common in this corpus and their pipes and dash rules read as
+		// line noise in a three-line preview, so cells become a readable run of values.
+		.replace(/^\s*\|?[\s:|-]{6,}\|?\s*$/gm, "")
+		.replace(/[ \t]*\|[ \t]*/g, " · ")
+		.replace(/(?:\s*·\s*){2,}/g, " · ")
+		.trim(),
+);
+
+// The backend zero-fills the provenance block on a citation it never quoted, so 0 means
+// "not resolved" here, not page zero — without this the unquoted majority of a set renders
+// as a confident "≈ p. 0 · line 0".
+function resolved(value) {
+	return value != null && value > 0;
+}
+
+function formatLineRange(start, end) {
+	if (!resolved(start)) return "";
+	if (resolved(end) && end !== start) return ` · lines ${start}-${end}`;
+	return ` · line ${start}`;
+}
+
+// Provenance a student can check against the printed page. `quote_page_no` is the page the
+// quote was actually located on, so it earns line numbers with it; an approximate page
+// could not be pinned down inside a multi-page section, so it is marked and its lines are
+// withheld rather than implying a precision that isn't there. With neither, the card falls
+// back to the section's page range and says so.
+//
+// These fields ride on the HTTP response only — the realtime `citations` event fires
+// before the answer exists, so a streamed card simply shows the range until the final
+// payload replaces it.
+const provenance = computed(() => {
+	const hit = props.hit;
+	if (resolved(hit.quote_page_no)) {
+		const approximate = hit.quote_page_approximate === true;
+		const lines = approximate
+			? ""
+			: formatLineRange(hit.quote_page_line_start, hit.quote_page_line_end);
+		return {
+			text: `${approximate ? "≈ p. " : "p. "}${hit.quote_page_no}${lines}`,
+			approximate,
+			title: approximate
+				? "Approximate: the page could not be pinned down inside a multi-page section."
+				: "Exact page and line resolved in the source document.",
+		};
+	}
+	const start = hit.page_start;
+	if (!resolved(start)) return null;
+	const end = hit.page_end;
+	return {
+		text: start === end ? `p. ${start}` : `pp. ${start}-${end}`,
+		approximate: start !== end,
+		title:
+			start === end
+				? "Page of the source section."
+				: "The section spans these pages; the exact page is not resolved.",
+	};
+});
+
+// A citation is only as good as its evidence. `unquoted` means the backend pulled no
+// quote at all, and that is shown as an absence — the one thing this card must never do is
+// let an unchecked quote read as a confirmed one, because a citation that looks confirmed
+// while misstating a rate is worse for a student than no citation.
+const QUOTE_STATUS = {
+	verified: {
+		icon: "lucide-check",
+		tone: "text-ink-gray-5",
+		text: "Quote located in the source page",
+	},
+	unverified: {
+		icon: "lucide-triangle-alert",
+		tone: "text-ink-amber-8",
+		text: "Not found in the source page — check it before relying on this",
+	},
+};
+
+const quote = computed(() => (props.hit.quote || "").trim());
+const quoteStatus = computed(() => (quote.value ? QUOTE_STATUS[props.hit.quote_status] : null));
+
+const wikiHref = computed(() => {
+	if (!props.hit.wiki_route) return "";
+	const page = resolved(props.hit.quote_page_no)
+		? props.hit.quote_page_no
+		: props.hit.page_start;
+	return resolved(page) ? `/${props.hit.wiki_route}#page-${page}` : `/${props.hit.wiki_route}`;
+});
+</script>
+
+<template>
+	<article
+		:id="`rag-source-${index}`"
+		class="flex min-w-0 gap-3 rounded-lg border p-3 transition-colors"
+		:class="[
+			highlighted
+				? 'border-outline-blue-3 bg-surface-blue-2'
+				: missed
+					? 'border-outline-amber-3 border-l-4 bg-surface-amber-2'
+					: 'border-outline-gray-2 bg-surface-elevation-1',
+		]"
+	>
+		<span
+			class="flex size-6 shrink-0 items-center justify-center rounded-md text-xs font-semibold tabular-nums"
+			:class="
+				highlighted
+					? 'bg-surface-gray-9 text-ink-gray-1'
+					: missed
+						? 'border border-outline-amber-3 bg-surface-amber-2 text-ink-gray-9'
+						: 'bg-surface-gray-3 text-ink-gray-7'
+			"
+		>
+			{{ index }}
+		</span>
+
+		<div class="min-w-0 flex-1">
+			<div class="flex flex-wrap items-center gap-x-2 gap-y-1">
+				<!-- Section titles in this corpus run long ("Health and Education Cess on
+				     income-tax and surcharge"). Clamping to two wrapped lines keeps the
+				     title readable on a phone without ever widening the card. -->
+				<h3
+					class="line-clamp-2 min-w-0 flex-1 text-base font-medium break-words text-ink-gray-9"
+				>
+					{{ hit.title || hit.document_title || hit.section }}
+				</h3>
+				<!-- The flag claims a whole line above the title on a phone; sharing one
+				     line left the title about ten characters wide. The wrapper carries the
+				     line break so the pill itself keeps its natural width. -->
+				<div v-if="missed" class="order-first basis-full sm:order-none sm:basis-auto">
+					<span
+						class="inline-flex shrink-0 items-center gap-1 rounded-full border border-outline-amber-3 px-2 py-0.5 text-xs font-semibold tracking-wide text-ink-gray-9 uppercase"
+					>
+						<span class="lucide-eye-off size-3 text-ink-amber-8" aria-hidden="true" />
+						Naive missed this
+					</span>
+				</div>
+			</div>
+
+			<p class="line-clamp-2 text-xs break-words text-ink-gray-5">
+				<span class="text-ink-gray-6">{{ hit.document_title }}</span>
+				<span v-if="hit.hierarchy_path"> › {{ hit.hierarchy_path }}</span>
+			</p>
+
+			<div class="mt-1.5 flex flex-wrap items-center gap-1.5">
+				<span
+					v-if="provenance"
+					class="rounded px-1.5 py-0.5 font-mono text-xs"
+					:class="
+						provenance.approximate
+							? 'border border-dashed border-outline-gray-3 text-ink-gray-6'
+							: 'bg-surface-gray-3 font-medium text-ink-gray-8'
+					"
+					:title="provenance.title"
+				>
+					{{ provenance.text }}
+				</span>
+				<span
+					v-if="hit.section_type"
+					class="max-w-full min-w-0 truncate rounded bg-surface-gray-2 px-1.5 py-0.5 text-xs text-ink-gray-7"
+				>
+					{{ hit.section_type }}
+				</span>
+				<span
+					v-if="hit.vector_rank != null"
+					class="rounded bg-surface-blue-2 px-1.5 py-0.5 text-xs text-ink-blue-8"
+					title="Rank from the vector (meaning) search"
+				>
+					vec #{{ hit.vector_rank }}
+				</span>
+				<span
+					v-if="hit.fts_rank != null"
+					class="rounded bg-surface-green-2 px-1.5 py-0.5 text-xs text-ink-green-8"
+					title="Rank from the full-text (keyword) search"
+				>
+					fts #{{ hit.fts_rank }}
+				</span>
+				<span
+					v-if="hit.rerank_score != null"
+					class="rounded bg-surface-violet-2 px-1.5 py-0.5 text-xs text-ink-gray-8"
+					title="Score after reranking, 0-10"
+				>
+					rerank {{ hit.rerank_score.toFixed(1) }}
+				</span>
+			</div>
+
+			<figure v-if="quote" class="mt-2">
+				<blockquote
+					class="border-l-2 py-0.5 pl-3 text-sm break-words text-ink-gray-8 italic"
+					:class="
+						hit.quote_status === 'unverified'
+							? 'border-outline-amber-3'
+							: 'border-outline-gray-3'
+					"
+				>
+					“{{ quote }}”
+				</blockquote>
+				<figcaption
+					v-if="quoteStatus"
+					class="mt-1 flex items-start gap-1 text-xs"
+					:class="quoteStatus.tone"
+				>
+					<span
+						:class="[quoteStatus.icon, 'mt-0.5 size-3.5 shrink-0']"
+						aria-hidden="true"
+					/>
+					{{ quoteStatus.text }}
+				</figcaption>
+			</figure>
+
+			<p
+				v-if="!compact && snippet"
+				class="mt-2 line-clamp-3 text-sm break-words text-ink-gray-7"
+			>
+				{{ snippet }}
+			</p>
+
+			<div class="mt-2 flex flex-wrap items-center gap-x-3 gap-y-1.5">
+				<RelevanceTag
+					:rank="index"
+					:total="total || index"
+					:score="hit.score"
+					:basis="basis"
+					:unranked-set="unrankedSet"
+				/>
+				<a
+					v-if="wikiHref"
+					:href="wikiHref"
+					target="_blank"
+					rel="noopener"
+					class="inline-flex shrink-0 items-center gap-1 text-xs text-ink-blue-8 hover:underline"
+				>
+					<span class="lucide-external-link size-3.5" aria-hidden="true" />
+					Open in wiki
+				</a>
+			</div>
+		</div>
+	</article>
+</template>
diff --git a/frontend/src/composables/useMediaQuery.js b/frontend/src/composables/useMediaQuery.js
new file mode 100644
index 0000000..7082b5f
--- /dev/null
+++ b/frontend/src/composables/useMediaQuery.js
@@ -0,0 +1,34 @@
+// Reactive `matchMedia` for layout decisions JS has to make (Splitpanes is a component,
+// not a CSS rule, so a media query alone can't collapse a split). One MediaQueryList per
+// query string is shared and reference-counted, since several screens ask for the same
+// breakpoint at once.
+import { onScopeDispose, ref } from "vue";
+
+const queries = new Map();
+
+export function useMediaQuery(query) {
+	let entry = queries.get(query);
+	if (!entry) {
+		const list = window.matchMedia(query);
+		const matches = ref(list.matches);
+		const onChange = (event) => (matches.value = event.matches);
+		list.addEventListener("change", onChange);
+		entry = { matches, list, onChange, users: 0 };
+		queries.set(query, entry);
+	}
+	entry.users += 1;
+	onScopeDispose(() => {
+		entry.users -= 1;
+		if (entry.users > 0) return;
+		entry.list.removeEventListener("change", entry.onChange);
+		queries.delete(query);
+	});
+	return entry.matches;
+}
+
+// The app-wide "a side-by-side split no longer fits" breakpoint. Below it (phones and
+// tablet portrait, where the desktop sidebar already eats ~220px) a list∥detail split
+// would leave both panes too narrow to read, so those screens drill down instead.
+export function useIsNarrow() {
+	return useMediaQuery("(max-width: 1023px)");
+}
diff --git a/frontend/src/composables/useRag.js b/frontend/src/composables/useRag.js
new file mode 100644
index 0000000..57a5f0a
--- /dev/null
+++ b/frontend/src/composables/useRag.js
@@ -0,0 +1,331 @@
+// Data layer for the RAG surfaces (/ask and /rag-lab) — thin wrappers over the
+// `wikify.api.rag.*` contract. Every call degrades gracefully: the backend may not be
+// deployed yet, so callers get `errorText` instead of an exception.
+import { computed, ref, watch } from "vue";
+import { useCall, useList } from "frappe-ui";
+import { useSocket } from "@/socket";
+
+// `ask` streams over one shared channel and tags every payload with the session it
+// belongs to (see the contract), unlike the agent loop's `wikify_agent_*:<sid>` events.
+const ANSWER_CHANNEL = "wikify_rag_answer";
+
+function newSessionId() {
+	return `rag-${Date.now()}-${Math.random().toString(36).slice(2, 8)}`;
+}
+
+// Frappe wraps server errors as "PermissionError: You are not allowed to read X" — the
+// exception class name is noise to the reader, so only the sentence survives.
+function errorMessage(error) {
+	if (!error) return "";
+	const text = error.messages?.[0] || error.message || String(error);
+	if (text === "Failed to fetch") {
+		return "Couldn’t reach the server — the request didn’t complete. Try again.";
+	}
+	return text.replace(/^[A-Za-z]*Error:\s*/, "");
+}
+
+// What produced the ordering of a result list, which decides how a hit may be labelled.
+// The exhaustive intent runs a metadata filter with no ranking at all — its hits carry a
+// constant placeholder score, so they must never be shown as if they were ranked.
+const BASIS_FOR_INTENT = { exhaustive: "exhaustive", semantic: "hybrid", hybrid: "hybrid" };
+
+export function relevanceBasis(route) {
+	return BASIS_FOR_INTENT[route?.intent] || "hybrid";
+}
+
+// A whole result set is unranked when every hit carries the same score: that is the
+// exhaustive leg stamping its sentinel, not agreement between hits. Drawn as bars those
+// identical numbers fill every bar to the brim and read as certainty no retriever
+// claimed — so the set is labelled unranked instead.
+export function isUnrankedSet(hits) {
+	const scores = (hits || []).map((hit) => hit.score).filter((score) => score != null);
+	return scores.length > 1 && scores.every((score) => score === scores[0]);
+}
+
+// The project scope selector, shared by /ask and /rag-lab so the two pages can't drift.
+// The rows are held once at module scope and the last non-empty list is kept: a reload
+// empties `data` for a beat, and with no options to match against, the select falls back
+// to its "Select option" placeholder — so the scope the user chose appears to clear itself
+// mid-answer.
+let projectList = null;
+const lastProjectRows = ref([]);
+const projectOptions = computed(() => [
+	{ label: "All projects", value: "" },
+	...lastProjectRows.value.map((row) => ({ label: row.project_name, value: row.name })),
+]);
+
+export function useProjectOptions() {
+	if (!projectList) {
+		projectList = useList({
+			doctype: "Wikify Project",
+			fields: ["name", "project_name", "is_default"],
+			orderBy: "is_default desc, project_name asc",
+			limit: 100,
+		});
+		watch(
+			() => projectList.data,
+			(rows) => {
+				if (rows?.length) lastProjectRows.value = rows;
+			},
+			{ immediate: true },
+		);
+	}
+	return projectOptions;
+}
+
+// Kept at module scope, not per-instance: the layout remounts these pages on a viewport
+// breakpoint change, and a remount must not throw away the question, the project scope or
+// the results the user is reading.
+const askState = {
+	question: ref(""),
+	project: ref(""),
+	askedQuestion: ref(""),
+	sources: ref([]),
+	answerText: ref(""),
+	route: ref(null),
+	refused: ref(false),
+	tookMs: ref(null),
+	errorText: ref(""),
+	// Spend on the last answer, and the running total for this browser session. The
+	// backend may not report cost yet, so these stay null until a payload carries them
+	// rather than defaulting to a zero that would read as "free".
+	usage: ref(null),
+	sessionCost: ref(0),
+	// True when the request never reached the server, so nothing was retrieved. The page
+	// must then show the failure alone — empty "Sources 0 / No answer" panels would claim
+	// a search happened and came back empty.
+	failed: ref(false),
+	streaming: ref(false),
+	sessionId: ref(null),
+};
+
+// The request itself is module state too, and for a stronger reason than the results:
+// `useCall` aborts its in-flight fetch whenever it is re-executed, and a component-scoped
+// call is thrown away with the component. An answer takes 10-25 s, long enough for a
+// remount (viewport breakpoint, hot reload, a nav there and back) to land mid-flight and
+// kill a request the server is still happily answering — which surfaces as a bare
+// "Failed to fetch". One app-lifetime call outlives every remount.
+const askCall = useCall({
+	url: "/api/v2/method/wikify.api.rag.ask",
+	method: "POST",
+	immediate: false,
+});
+
+const {
+	question,
+	project,
+	askedQuestion,
+	sources,
+	answerText,
+	route,
+	refused,
+	tookMs,
+	errorText,
+	failed,
+	usage,
+	sessionCost,
+	streaming,
+	sessionId,
+} = askState;
+
+function handleAnswerEvent(payload) {
+	// Strict match: realtime is per-user, not per-tab, so another tab's (or another
+	// session's) answer would otherwise stream into this one.
+	if (!payload || payload.session !== sessionId.value) return;
+	if (payload.route) route.value = payload.route;
+	if (payload.citations) sources.value = payload.citations;
+	if (payload.delta) answerText.value += payload.delta;
+	if (payload.done) {
+		addUsage(payload);
+		streaming.value = false;
+	}
+}
+
+// The `done` event and the HTTP body carry the same fields; whichever arrives first
+// records the spend, and the second is ignored so a session total never double-counts.
+function addUsage(payload) {
+	if (typeof payload?.cost !== "number" || usage.value) return;
+	usage.value = {
+		cost: payload.cost,
+		promptTokens: payload.prompt_tokens ?? null,
+		completionTokens: payload.completion_tokens ?? null,
+		model: payload.model || "",
+	};
+	sessionCost.value += payload.cost;
+}
+
+function reset() {
+	sources.value = [];
+	answerText.value = "";
+	route.value = null;
+	refused.value = false;
+	tookMs.value = null;
+	errorText.value = "";
+	failed.value = false;
+	usage.value = null;
+}
+
+async function ask() {
+	const text = question.value.trim();
+	if (!text || streaming.value) return;
+	reset();
+	askedQuestion.value = text;
+	sessionId.value = newSessionId();
+	streaming.value = true;
+	try {
+		const response = await askCall.submit({
+			question: text,
+			project: project.value || null,
+			session: sessionId.value,
+		});
+		if (askCall.error) {
+			errorText.value = errorMessage(askCall.error);
+			failed.value = !sources.value.length && !answerText.value;
+			return;
+		}
+		if (!response) return;
+		if (response.route) route.value = response.route;
+		if (response.citations?.length) sources.value = response.citations;
+		// Realtime is best-effort (no replay); the HTTP body is authoritative when
+		// no deltas arrived — e.g. socketio down, or the worker finished first.
+		if (!answerText.value) answerText.value = response.answer || "";
+		refused.value = Boolean(response.refused);
+		tookMs.value = response.took_ms ?? null;
+		addUsage(response);
+	} finally {
+		streaming.value = false;
+	}
+}
+
+// Bound once, for the same reason the call is: a remount mid-answer must not unsubscribe
+// the stream it is still receiving.
+let answerChannelBound = false;
+
+export function useRagAsk() {
+	const socket = useSocket();
+	if (socket && !answerChannelBound) {
+		socket.on(ANSWER_CHANNEL, handleAnswerEvent);
+		answerChannelBound = true;
+	}
+
+	return {
+		question,
+		project,
+		askedQuestion,
+		sources,
+		answerText,
+		route,
+		refused,
+		tookMs,
+		streaming,
+		errorText,
+		failed,
+		usage,
+		sessionCost,
+		ask,
+		reset,
+	};
+}
+
+const compareState = {
+	query: ref(""),
+	project: ref(""),
+	comparedQuery: ref(""),
+	naive: ref([]),
+	routed: ref([]),
+	missedSections: ref([]),
+	route: ref(null),
+	errorText: ref(""),
+};
+
+// Module scope for the same reason as `askCall` — a remount must not abort a comparison
+// that is still running.
+const compareCall = useCall({
+	url: "/api/v2/method/wikify.api.rag.compare",
+	method: "POST",
+	immediate: false,
+});
+
+async function compare() {
+	const text = compareState.query.value.trim();
+	if (!text || compareCall.loading) return;
+	compareState.errorText.value = "";
+	compareState.comparedQuery.value = text;
+	const response = await compareCall.submit({
+		query: text,
+		project: compareState.project.value || null,
+	});
+	if (compareCall.error) {
+		compareState.errorText.value = errorMessage(compareCall.error);
+		// Nothing ran, so the comparison is retracted rather than shown as two empty
+		// columns — an empty column reads as "the retriever found nothing".
+		compareState.comparedQuery.value = "";
+		compareState.naive.value = [];
+		compareState.routed.value = [];
+		compareState.missedSections.value = [];
+		compareState.route.value = null;
+		return;
+	}
+	compareState.naive.value = response?.naive || [];
+	compareState.routed.value = response?.routed || [];
+	compareState.missedSections.value = response?.missed_by_naive || [];
+	compareState.route.value = response?.route || null;
+}
+
+export function useRagCompare() {
+	return { ...compareState, compare, compareCall };
+}
+
+// Module scope again: revisiting the lab should show the counts it already read, not drop
+// back to a skeleton while it re-reads them.
+const indexState = {
+	status: ref(null),
+	errorText: ref(""),
+	reindexing: ref(false),
+	reindexedJob: ref(""),
+};
+
+const statusCall = useCall({
+	url: "/api/v2/method/wikify.api.rag.index_status",
+	method: "GET",
+	immediate: false,
+});
+const reindexCall = useCall({
+	url: "/api/v2/method/wikify.api.rag.reindex",
+	method: "POST",
+	immediate: false,
+});
+
+export function useIndexStatus() {
+	const { status, errorText, reindexing, reindexedJob } = indexState;
+
+	async function refresh(project) {
+		errorText.value = "";
+		const response = await statusCall.submit(project ? { project } : {});
+		if (statusCall.error) {
+			errorText.value = errorMessage(statusCall.error);
+			status.value = null;
+			return;
+		}
+		status.value = response;
+	}
+
+	async function reindex(project) {
+		if (!project || reindexing.value) return;
+		reindexing.value = true;
+		reindexedJob.value = "";
+		errorText.value = "";
+		try {
+			const response = await reindexCall.submit({ project });
+			if (reindexCall.error) {
+				errorText.value = errorMessage(reindexCall.error);
+				return;
+			}
+			reindexedJob.value = response?.job || "";
+		} finally {
+			reindexing.value = false;
+		}
+	}
+
+	return { status, errorText, reindexing, reindexedJob, refresh, reindex, statusCall };
+}
diff --git a/frontend/src/pages/AskWiki.vue b/frontend/src/pages/AskWiki.vue
new file mode 100644
index 0000000..8263d32
--- /dev/null
+++ b/frontend/src/pages/AskWiki.vue
@@ -0,0 +1,296 @@
+<script setup>
+// /ask — ask a question of the indexed wiki. Sources land before the answer (they are
+// published first), the routing decision is shown up front, and citation chips in the
+// answer jump to the source they came from.
+import { computed, nextTick, ref } from "vue";
+import { marked } from "marked";
+import { Button, FormControl, PageHeader } from "frappe-ui";
+import CostMeter from "@/components/rag/CostMeter.vue";
+import RouteBadge from "@/components/rag/RouteBadge.vue";
+import SourceCard from "@/components/rag/SourceCard.vue";
+import { isUnrankedSet, relevanceBasis, useProjectOptions, useRagAsk } from "@/composables/useRag";
+
+const projectOptions = useProjectOptions();
+
+const {
+	question,
+	project,
+	askedQuestion,
+	sources,
+	answerText,
+	route,
+	refused,
+	tookMs,
+	streaming,
+	errorText,
+	failed,
+	usage,
+	sessionCost,
+	ask,
+} = useRagAsk();
+
+const highlightedSource = ref(0);
+
+const basis = computed(() => relevanceBasis(route.value));
+const sourcesUnranked = computed(() => isUnrankedSet(sources.value));
+const documentCount = computed(
+	() => new Set(sources.value.map((hit) => hit.source_document).filter(Boolean)).size,
+);
+const hasResult = computed(() => Boolean(askedQuestion.value) && !failed.value);
+const hasStreamedContent = computed(() => Boolean(sources.value.length || answerText.value));
+const elapsed = computed(() =>
+	tookMs.value >= 1000 ? `${(tookMs.value / 1000).toFixed(1)} s` : `${tookMs.value} ms`,
+);
+
+// Citation markers `[1]` become clickable chips that scroll to their source card.
+const answerHtml = computed(() => {
+	const markdown = answerText.value || "";
+	if (!markdown) return "";
+	const rendered = marked.parse(markdown, { async: false });
+	return rendered.replace(
+		/\[(\d+)\]/g,
+		(match, number) =>
+			`<button type="button" class="rag-citation" data-citation="${number}">${number}</button>`,
+	);
+});
+
+async function scrollToSource(index) {
+	highlightedSource.value = index;
+	await nextTick();
+	const card = document.getElementById(`rag-source-${index}`);
+	card?.scrollIntoView({ behavior: "smooth", block: "center" });
+}
+
+function handleAnswerClick(event) {
+	const chip = event.target.closest?.("[data-citation]");
+	if (chip) scrollToSource(Number(chip.dataset.citation));
+}
+</script>
+
+<template>
+	<div class="mx-auto w-full max-w-6xl px-4 pb-16 sm:px-6 sm:pb-28">
+		<PageHeader>
+			<div class="flex min-w-0 items-center gap-3">
+				<h1 class="text-md text-ink-gray-9">Ask</h1>
+				<span class="hidden truncate text-sm text-ink-gray-5 lg:inline">
+					Answers grounded in your indexed documents
+				</span>
+			</div>
+			<div class="w-32 shrink-0 sm:w-44">
+				<FormControl v-model="project" type="select" :options="projectOptions" />
+			</div>
+		</PageHeader>
+
+		<!-- Question. Sticky so a reader deep in a long source list can ask the next
+		     question without scrolling back up, and so the input stays put when the
+		     on-screen keyboard shrinks the viewport. -->
+		<form class="sticky top-0 z-10 flex gap-2 bg-surface-base py-3" @submit.prevent="ask">
+			<FormControl
+				v-model="question"
+				type="text"
+				class="flex-1"
+				placeholder="What do you want to know?"
+				autocomplete="off"
+			/>
+			<Button
+				class="shrink-0"
+				variant="solid"
+				label="Ask"
+				icon-left="lucide-sparkles"
+				:loading="streaming"
+				:disabled="!question.trim()"
+				@click="ask"
+			/>
+		</form>
+
+		<!-- The request can fail after realtime already delivered the route and sources, so
+		     a dropped connection only takes over the page when nothing arrived at all. -->
+		<div
+			v-if="failed"
+			class="mt-3 flex flex-col gap-2 rounded-md border border-outline-red-2 bg-surface-red-1 px-4 py-3 sm:flex-row sm:items-center sm:justify-between"
+		>
+			<div class="min-w-0">
+				<p class="text-base font-medium text-ink-gray-9">Nothing was searched</p>
+				<p class="text-sm text-ink-gray-7">{{ errorText }}</p>
+			</div>
+			<Button
+				class="shrink-0"
+				variant="subtle"
+				label="Retry"
+				icon-left="lucide-rotate-cw"
+				:loading="streaming"
+				@click="ask"
+			/>
+		</div>
+		<p
+			v-else-if="errorText"
+			class="mt-3 rounded-md bg-surface-amber-2 px-3 py-2 text-sm text-ink-gray-8"
+		>
+			The connection dropped mid-answer — showing everything that streamed in.
+		</p>
+
+		<!-- The routing decision, up front -->
+		<div v-if="hasResult && (route || streaming)" class="mt-4">
+			<RouteBadge :route="route" :loading="streaming && !route" />
+		</div>
+
+		<!-- Nothing asked yet -->
+		<div
+			v-if="!hasResult && !failed"
+			class="mt-10 flex flex-col items-center gap-3 rounded-lg border border-dashed border-outline-gray-2 px-6 py-16 text-center"
+		>
+			<span
+				class="lucide-message-circle-question size-7 text-ink-gray-4"
+				aria-hidden="true"
+			/>
+			<p class="text-base text-ink-gray-7">Ask a question of this wiki</p>
+			<p class="max-w-md text-sm text-ink-gray-5">
+				Every answer cites the sections it came from, and shows how the question was routed
+				— exhaustive, semantic, or hybrid.
+			</p>
+		</div>
+
+		<div v-else-if="hasResult" class="mt-4 grid grid-cols-1 gap-4 lg:grid-cols-12">
+			<!-- Sources arrive first, so they lead the desktop layout. On a phone the answer
+			     goes first: burying it under fifteen source cards makes the reader scroll
+			     past the evidence to find what it is evidence for. -->
+			<section class="order-2 min-w-0 lg:order-1 lg:col-span-5">
+				<div class="mb-2 flex flex-wrap items-center gap-2">
+					<h2 class="text-base font-medium text-ink-gray-9">Sources</h2>
+					<span
+						class="rounded-full bg-surface-gray-2 px-2 py-0.5 text-xs text-ink-gray-7"
+					>
+						{{ sources.length }}
+					</span>
+					<span v-if="documentCount" class="text-xs text-ink-gray-5">
+						across {{ documentCount }}
+						{{ documentCount === 1 ? "document" : "documents" }}
+					</span>
+				</div>
+
+				<div v-if="sources.length" class="flex flex-col gap-2">
+					<SourceCard
+						v-for="(hit, position) in sources"
+						:key="hit.chunk_id || position"
+						:hit="hit"
+						:index="position + 1"
+						:total="sources.length"
+						:basis="basis"
+						:unranked-set="sourcesUnranked"
+						:highlighted="highlightedSource === position + 1"
+					/>
+				</div>
+				<div
+					v-else
+					class="rounded-lg border border-dashed border-outline-gray-2 px-4 py-10 text-center text-sm text-ink-gray-5"
+				>
+					{{ streaming ? "Retrieving sources…" : "No sources matched this question." }}
+				</div>
+			</section>
+
+			<!-- Answer -->
+			<section class="order-1 min-w-0 lg:order-2 lg:col-span-7">
+				<div class="mb-2 flex flex-wrap items-center gap-x-3 gap-y-1">
+					<h2 class="text-base font-medium text-ink-gray-9">Answer</h2>
+					<span v-if="tookMs != null" class="text-xs text-ink-gray-5">
+						{{ elapsed }}
+					</span>
+					<div class="flex-1" />
+					<CostMeter
+						:cost="usage?.cost ?? null"
+						:prompt-tokens="usage?.promptTokens ?? null"
+						:completion-tokens="usage?.completionTokens ?? null"
+						:model="usage?.model || ''"
+						:session-cost="sessionCost"
+					/>
+				</div>
+
+				<div
+					v-if="refused"
+					class="rounded-lg border border-outline-amber-1 bg-surface-amber-2 px-4 py-4"
+				>
+					<div class="flex items-center gap-2">
+						<span class="lucide-info size-4 text-ink-amber-8" aria-hidden="true" />
+						<p class="text-base font-medium text-ink-gray-9">Not in this wiki</p>
+					</div>
+					<p class="mt-1.5 text-sm text-ink-gray-7">
+						{{
+							answerText ||
+							"The indexed documents don’t cover this. Rather than guess, the answer is withheld — try rephrasing, or widen the project scope."
+						}}
+					</p>
+				</div>
+
+				<div
+					v-else-if="answerHtml"
+					class="rag-answer prose prose-sm dark:prose-invert max-w-none rounded-lg border border-outline-gray-2 bg-surface-elevation-1 px-4 py-4"
+					@click="handleAnswerClick"
+					v-html="answerHtml"
+				/>
+
+				<!-- "No answer returned" is only true when the request completed and the
+				     model said nothing. If the connection dropped, the answer was lost in
+				     transit — saying otherwise sends the reader looking for a gap in the
+				     corpus that isn't there. -->
+				<div
+					v-else
+					class="rounded-lg border border-outline-gray-2 bg-surface-elevation-1 px-4 py-10 text-center text-sm text-ink-gray-5"
+				>
+					<template v-if="streaming">Composing the answer…</template>
+					<template v-else-if="errorText">
+						<p class="text-ink-gray-7">
+							The answer was lost in transit — the sources beside it were retrieved
+							before the connection dropped.
+						</p>
+						<Button
+							class="mt-3"
+							variant="subtle"
+							label="Retry"
+							icon-left="lucide-rotate-cw"
+							@click="ask"
+						/>
+					</template>
+					<template v-else>No answer returned.</template>
+				</div>
+			</section>
+		</div>
+	</div>
+</template>
+
+<style>
+/* Citation chips are injected into rendered markdown, so they can't carry scoped
+   classes — style them globally against the same tokens the rest of the page uses. */
+.rag-citation {
+	display: inline-flex;
+	align-items: center;
+	justify-content: center;
+	min-width: 1.15rem;
+	height: 1.15rem;
+	margin: 0 0.1rem;
+	padding: 0 0.25rem;
+	border-radius: 0.25rem;
+	font-size: 0.7rem;
+	font-weight: 600;
+	line-height: 1;
+	vertical-align: baseline;
+	cursor: pointer;
+	background-color: var(--surface-blue-2);
+	color: var(--ink-blue-8);
+}
+.rag-citation:hover {
+	background-color: var(--surface-blue-3);
+}
+
+/* The answer is model-authored markdown: it can contain a rate table, a fenced block or a
+   bare URL far wider than a phone. Each of those scrolls inside itself so the page body
+   never does. */
+.rag-answer {
+	overflow-wrap: break-word;
+}
+.rag-answer pre,
+.rag-answer table {
+	display: block;
+	max-width: 100%;
+	overflow-x: auto;
+}
+</style>
diff --git a/frontend/src/pages/ExploreGlobal.vue b/frontend/src/pages/ExploreGlobal.vue
index 2ac01fc..90dc48f 100644
--- a/frontend/src/pages/ExploreGlobal.vue
+++ b/frontend/src/pages/ExploreGlobal.vue
@@ -35,7 +35,7 @@ const matchCount = computed(() => (groups.data || []).reduce((n, g) => n + g.sec
 
 const selectedType = ref(null);
 const selected = computed(
-	() => types.value.find((t) => t.type_name === selectedType.value) || null
+	() => types.value.find((t) => t.type_name === selectedType.value) || null,
 );
 
 // Re-pull the type summary whenever the project scope changes; the selected-type watcher
@@ -65,17 +65,16 @@ function pageRange(s) {
 	<div class="flex h-full flex-col">
 		<PageHeader>
 			<div class="flex min-w-0 items-center gap-3">
-				<h1 class="text-md text-ink-gray-9">Explore</h1>
-				<span class="truncate text-sm text-ink-gray-5"
+				<h1 class="shrink-0 text-md text-ink-gray-9">Explore</h1>
+				<span class="hidden truncate text-sm text-ink-gray-5 lg:block"
 					>Sections by type across documents</span
 				>
 			</div>
-			<FormControl
-				v-model="selectedProject"
-				type="select"
-				:options="projectOptions"
-				class="w-48"
-			/>
+			<!-- FormControl puts its class on an inner element, so the width lives on a
+			     wrapper — without it the select takes the whole header row. -->
+			<div class="w-36 shrink-0 sm:w-48">
+				<FormControl v-model="selectedProject" type="select" :options="projectOptions" />
+			</div>
 		</PageHeader>
 
 		<!-- Empty state: nothing classified anywhere yet -->
@@ -92,16 +91,21 @@ function pageRange(s) {
 			</p>
 		</div>
 
-		<div v-else class="flex min-h-0 flex-1">
-			<!-- Type rail -->
-			<aside class="w-60 shrink-0 overflow-auto border-r border-outline-gray-1 p-2">
-				<p class="px-2 py-1.5 text-xs font-medium tracking-wide text-ink-gray-5 uppercase">
+		<div v-else class="flex min-h-0 flex-1 flex-col lg:flex-row">
+			<!-- Type rail — a vertical rail beside the results on wide screens, a
+			     horizontally scrolling strip above them when there's no room for one. -->
+			<aside
+				class="flex shrink-0 gap-1 overflow-x-auto border-b border-outline-gray-1 p-2 lg:w-60 lg:flex-col lg:overflow-x-visible lg:overflow-y-auto lg:border-r lg:border-b-0"
+			>
+				<p
+					class="hidden px-2 py-1.5 text-xs font-medium tracking-wide text-ink-gray-5 uppercase lg:block"
+				>
 					Types
 				</p>
 				<button
 					v-for="t in types"
 					:key="t.type_name"
-					class="flex w-full items-center gap-2 rounded px-2 py-1.5 text-left text-base"
+					class="flex shrink-0 items-center gap-2 rounded px-2 py-1.5 text-left text-base lg:w-full"
 					:class="
 						t.type_name === selectedType
 							? 'bg-surface-gray-3 text-ink-gray-9'
@@ -114,7 +118,7 @@ function pageRange(s) {
 						:style="{ backgroundColor: t.color }"
 						aria-hidden="true"
 					/>
-					<span class="flex-1 truncate">{{ t.label }}</span>
+					<span class="truncate lg:flex-1">{{ t.label }}</span>
 					<span class="shrink-0 text-sm text-ink-gray-5">{{ t.count }}</span>
 				</button>
 			</aside>
@@ -123,33 +127,36 @@ function pageRange(s) {
 			<section class="min-w-0 flex-1 overflow-auto">
 				<div
 					v-if="selected"
-					class="sticky top-0 flex items-center gap-2 border-b border-outline-gray-1 bg-surface-base px-5 py-3"
+					class="sticky top-0 flex items-center gap-2 border-b border-outline-gray-1 bg-surface-base px-4 py-3 sm:px-5"
 				>
 					<span
 						class="size-3 shrink-0 rounded-full"
 						:style="{ backgroundColor: selected.color }"
 						aria-hidden="true"
 					/>
-					<h2 class="text-base font-medium text-ink-gray-9">{{ selected.label }}</h2>
+					<h2 class="min-w-0 truncate text-base font-medium text-ink-gray-9">
+						{{ selected.label }}
+					</h2>
 					<Badge :label="`${matchCount}`" theme="gray" variant="subtle" size="sm" />
 				</div>
 
-				<div class="px-5 py-3">
+				<div class="px-4 py-3 sm:px-5">
 					<div v-for="g in groups.data || []" :key="g.source_document" class="mb-6">
 						<button
-							class="mb-1.5 flex items-center gap-2 text-sm font-medium text-ink-gray-8 hover:text-ink-gray-9"
+							class="mb-1.5 flex w-full min-w-0 items-center gap-2 text-sm font-medium text-ink-gray-8 hover:text-ink-gray-9"
 							@click="openImport(g.import_name)"
 						>
 							<span
 								class="lucide-file-text size-4 text-ink-gray-5"
 								aria-hidden="true"
 							/>
-							<span class="truncate">{{ g.doc_title }}</span>
+							<span class="min-w-0 truncate">{{ g.doc_title }}</span>
 							<Badge
 								:label="`${g.sections.length}`"
 								theme="gray"
 								variant="subtle"
 								size="sm"
+								class="shrink-0"
 							/>
 						</button>
 						<div class="rounded-md border border-outline-gray-1">
@@ -170,6 +177,7 @@ function pageRange(s) {
 									theme="gray"
 									variant="subtle"
 									size="sm"
+									class="shrink-0"
 								/>
 							</div>
 						</div>
diff --git a/frontend/src/pages/ImportDetail.vue b/frontend/src/pages/ImportDetail.vue
index 584613d..1d22162 100644
--- a/frontend/src/pages/ImportDetail.vue
+++ b/frontend/src/pages/ImportDetail.vue
@@ -13,6 +13,8 @@ import {
 	useList,
 } from "frappe-ui";
 import { useSocket } from "@/socket";
+import { useIsMobile } from "@/composables/useIsMobile";
+import { actionButtonProps } from "@/utils/actionButton";
 import { statusTheme, isActive } from "@/utils/status";
 import PageReview from "@/components/PageReview.vue";
 import SectionTree from "@/components/SectionTree.vue";
@@ -27,6 +29,7 @@ const props = defineProps({
 
 const route = useRoute();
 const router = useRouter();
+const isMobile = useIsMobile();
 
 const imp = useDoc({ doctype: "Wikify Import", name: props.name });
 
@@ -62,7 +65,7 @@ watch(
 	(key) => {
 		const i = tabKeys.indexOf(key);
 		if (i >= 0 && i !== activeTab.value) activeTab.value = i;
-	}
+	},
 );
 
 // Streaming log
@@ -88,7 +91,7 @@ watch(
 		if (sd) setDocument({ name: sd, label: imp.doc?.import_title || props.name }, projectChip);
 		else if (projectChip) setProject(projectChip);
 	},
-	{ immediate: true }
+	{ immediate: true },
 );
 
 const pageReview = ref(null);
@@ -104,7 +107,7 @@ const sdStats = useList({
 });
 const sourceStats = computed(() => sdStats.data?.[0] || null);
 const docAudit = computed(
-	() => sourceStats.value?.canonical_mean ?? sourceStats.value?.mean_score
+	() => sourceStats.value?.canonical_mean ?? sourceStats.value?.mean_score,
 );
 function fmtCost(v) {
 	return v ? `$${Number(v).toFixed(4)}` : "—";
@@ -178,7 +181,7 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 <template>
 	<div>
 		<PageHeader>
-			<div class="flex min-w-0 items-center gap-3">
+			<div class="flex min-w-0 items-center gap-2 sm:gap-3">
 				<Button
 					variant="ghost"
 					icon="lucide-arrow-left"
@@ -191,7 +194,7 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 				<RouterLink
 					v-if="imp.doc?.project"
 					:to="{ name: 'ProjectDetail', params: { name: imp.doc.project } }"
-					class="shrink-0 text-base text-ink-gray-5 hover:text-ink-gray-7"
+					class="hidden shrink-0 text-base text-ink-gray-5 hover:text-ink-gray-7 lg:block"
 					>{{ imp.doc.project_name || "Project" }}
 					<span class="text-ink-gray-4" aria-hidden="true">/</span></RouterLink
 				>
@@ -203,24 +206,24 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 					:label="status"
 					:theme="statusTheme(status)"
 					variant="subtle"
+					class="shrink-0"
 				/>
 				<Progress
 					v-if="isActive(status)"
 					:value="imp.doc?.stage_progress || 0"
 					size="sm"
-					class="w-40"
+					class="w-16 shrink-0 sm:w-40"
 				/>
-				<span v-if="isActive(status)" class="truncate text-sm text-ink-gray-5">{{
+				<span v-if="isActive(status)" class="hidden truncate text-sm text-ink-gray-5 lg:block">{{
 					imp.doc?.stage_label
 				}}</span>
 			</div>
 
-			<div class="flex items-center gap-2">
+			<div class="flex shrink-0 items-center gap-2 pl-2">
 				<Button
 					v-if="imp.doc?.source_document"
 					variant="subtle"
-					label="Graph"
-					icon-left="lucide-waypoints"
+					v-bind="actionButtonProps(isMobile, 'lucide-waypoints', 'Graph')"
 					:route="{ name: 'ImportGraph', params: { name: props.name } }"
 				/>
 				<Dropdown
@@ -233,8 +236,8 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 					<Button
 						variant="solid"
 						theme="gray"
-						label="Remediate"
-						icon-right="lucide-chevron-down"
+						v-bind="actionButtonProps(isMobile, 'lucide-wand-sparkles', 'Remediate')"
+						:icon-right="isMobile ? undefined : 'lucide-chevron-down'"
 						:loading="remediate.loading"
 					/>
 				</Dropdown>
@@ -307,19 +310,19 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 								:class="levelColor[entry.level] || 'text-ink-gray-7'"
 							>
 								<span class="shrink-0 text-ink-gray-4">[{{ entry.stage }}]</span>
-								<span>{{ entry.message }}</span>
+								<span class="min-w-0 break-words">{{ entry.message }}</span>
 							</div>
 						</div>
 					</div>
 				</div>
 
 				<!-- Pages -->
-				<div v-else-if="tab.key === 'pages'" class="h-[calc(100vh-7rem)]">
+				<div v-else-if="tab.key === 'pages'" class="h-[calc(100dvh-9.5rem)] sm:h-[calc(100vh-7rem)]">
 					<PageReview ref="pageReview" :source-document="imp.doc?.source_document" />
 				</div>
 
 				<!-- Tree -->
-				<div v-else-if="tab.key === 'tree'" class="h-[calc(100vh-7rem)]">
+				<div v-else-if="tab.key === 'tree'" class="h-[calc(100dvh-9.5rem)] sm:h-[calc(100vh-7rem)]">
 					<SectionTree
 						ref="sectionTree"
 						:source-document="imp.doc?.source_document"
@@ -332,12 +335,12 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 				</div>
 
 				<!-- Explore -->
-				<div v-else-if="tab.key === 'explore'" class="h-[calc(100vh-7rem)]">
+				<div v-else-if="tab.key === 'explore'" class="h-[calc(100dvh-9.5rem)] sm:h-[calc(100vh-7rem)]">
 					<Explore :source-document="imp.doc?.source_document" :import-name="name" />
 				</div>
 
 				<!-- Wiki -->
-				<div v-else-if="tab.key === 'wiki'" class="h-[calc(100vh-7rem)]">
+				<div v-else-if="tab.key === 'wiki'" class="h-[calc(100dvh-9.5rem)] sm:h-[calc(100vh-7rem)]">
 					<WikiGenerate
 						:source-document="imp.doc?.source_document"
 						:import-name="name"
@@ -348,12 +351,28 @@ const levelColor = { info: "text-ink-gray-7", warn: "text-ink-amber-6", error: "
 				</div>
 
 				<!-- PDF (whole document) -->
-				<div v-else-if="tab.key === 'pdf'" class="h-[calc(100vh-7rem)]">
+				<div
+					v-else-if="tab.key === 'pdf'"
+					class="flex h-[calc(100dvh-9.5rem)] flex-col sm:h-[calc(100vh-7rem)]"
+				>
+					<!-- Mobile browsers routinely render an embedded PDF as a blank box, so the
+					     way out is offered up front rather than only in the <object> fallback. -->
+					<div
+						v-if="imp.doc?.pdf"
+						class="flex shrink-0 items-center justify-end border-b border-outline-gray-1 px-3 py-1.5 sm:hidden"
+					>
+						<a
+							:href="imp.doc.pdf"
+							target="_blank"
+							class="text-sm text-ink-blue-6 hover:underline"
+							>Open PDF in a new tab ↗</a
+						>
+					</div>
 					<object
 						v-if="imp.doc?.pdf"
 						:data="`${imp.doc.pdf}#view=FitH`"
 						type="application/pdf"
-						class="h-full w-full"
+						class="min-h-0 w-full flex-1"
 					>
 						<p class="p-4 text-sm text-ink-gray-5">
 							Can't embed the PDF here —
diff --git a/frontend/src/pages/ImportGraph.vue b/frontend/src/pages/ImportGraph.vue
index e2a2109..126e2c8 100644
--- a/frontend/src/pages/ImportGraph.vue
+++ b/frontend/src/pages/ImportGraph.vue
@@ -40,7 +40,7 @@ function onSelect(node) {
 				<RouterLink
 					v-if="imp.doc?.project"
 					:to="{ name: 'ProjectDetail', params: { name: imp.doc.project } }"
-					class="shrink-0 text-base text-ink-gray-5 hover:text-ink-gray-7"
+					class="hidden shrink-0 text-base text-ink-gray-5 hover:text-ink-gray-7 lg:block"
 					>{{ imp.doc.project_name || "Project" }}
 					<span class="text-ink-gray-4" aria-hidden="true">/</span></RouterLink
 				>
@@ -50,7 +50,7 @@ function onSelect(node) {
 					>{{ imp.doc?.import_title || props.name }}
 					<span class="text-ink-gray-4" aria-hidden="true">/</span></RouterLink
 				>
-				<h1 class="text-md text-ink-gray-9">Graph</h1>
+				<h1 class="shrink-0 text-md text-ink-gray-9">Graph</h1>
 			</div>
 		</PageHeader>
 
diff --git a/frontend/src/pages/ImportList.vue b/frontend/src/pages/ImportList.vue
index d1e9bd1..e3223f0 100644
--- a/frontend/src/pages/ImportList.vue
+++ b/frontend/src/pages/ImportList.vue
@@ -45,6 +45,11 @@ function fmtDate(d) {
 	if (!d) return "";
 	return new Date(d.replace(" ", "T")).toLocaleString();
 }
+
+// A bare number reads as noise once the row stacks and loses its column header.
+function pageLabel(row) {
+	return row.page_count ? `${row.page_count} pages` : "—";
+}
 </script>
 
 <template>
@@ -87,9 +92,12 @@ function fmtDate(d) {
 		</div>
 
 		<!-- List -->
+		<!-- Four fixed columns leave a phone ~80px for the title, so below sm the row
+		     wraps into stacked lines and the column header (which labels nothing once
+		     they're stacked) drops out. -->
 		<div v-else class="rounded-md border border-outline-gray-1">
 			<div
-				class="flex items-center gap-4 border-b border-outline-gray-1 px-4 py-2 text-sm text-ink-gray-5"
+				class="hidden items-center gap-4 border-b border-outline-gray-1 px-4 py-2 text-sm text-ink-gray-5 sm:flex"
 			>
 				<span class="flex-1">Title</span>
 				<span class="w-40 shrink-0">Status</span>
@@ -99,13 +107,13 @@ function fmtDate(d) {
 			<button
 				v-for="row in imports.data"
 				:key="row.name"
-				class="flex w-full items-center gap-4 border-b border-outline-gray-1 px-4 py-2.5 text-left last:border-b-0 hover:bg-surface-gray-2"
+				class="flex w-full flex-wrap items-center gap-x-4 gap-y-1.5 border-b border-outline-gray-1 px-4 py-2.5 text-left last:border-b-0 hover:bg-surface-gray-2 sm:flex-nowrap"
 				@click="openImport(row.name)"
 			>
-				<span class="flex-1 truncate text-base text-ink-gray-8">{{
+				<span class="w-full truncate text-base text-ink-gray-8 sm:w-auto sm:flex-1">{{
 					row.import_title
 				}}</span>
-				<span class="flex w-40 shrink-0 items-center gap-2">
+				<span class="flex shrink-0 items-center gap-2 sm:w-40">
 					<Badge :label="row.status" :theme="statusTheme(row.status)" variant="subtle" />
 					<Progress
 						v-if="isActive(row.status)"
@@ -114,12 +122,14 @@ function fmtDate(d) {
 						class="w-16"
 					/>
 				</span>
-				<span class="w-16 shrink-0 text-right text-sm text-ink-gray-6">{{
-					row.page_count || "—"
-				}}</span>
-				<span class="w-44 shrink-0 truncate text-right text-sm text-ink-gray-5">{{
-					fmtDate(row.modified)
-				}}</span>
+				<span class="shrink-0 text-sm text-ink-gray-6 sm:w-16 sm:text-right">
+					<span class="sm:hidden">{{ pageLabel(row) }}</span>
+					<span class="hidden sm:inline">{{ row.page_count || "—" }}</span>
+				</span>
+				<span
+					class="ml-auto shrink-0 truncate text-sm text-ink-gray-5 sm:ml-0 sm:w-44 sm:text-right"
+					>{{ fmtDate(row.modified) }}</span
+				>
 			</button>
 		</div>
 	</div>
diff --git a/frontend/src/pages/ProjectDetail.vue b/frontend/src/pages/ProjectDetail.vue
index 6796092..4151fd3 100644
--- a/frontend/src/pages/ProjectDetail.vue
+++ b/frontend/src/pages/ProjectDetail.vue
@@ -3,23 +3,27 @@ import { ref, watch } from "vue";
 import { Badge, Button, PageHeader, useDoc } from "frappe-ui";
 import ImportList from "@/pages/ImportList.vue";
 import NewImportDialog from "@/components/NewImportDialog.vue";
+import { useIsMobile } from "@/composables/useIsMobile";
+import { actionButtonProps } from "@/utils/actionButton";
 import { setProject } from "@/data/agentContext";
 
 const props = defineProps({
 	name: { type: String, required: true },
 });
 
+const isMobile = useIsMobile();
+
 const project = useDoc({ doctype: "Wikify Project", name: props.name });
 
 // Attach this project as the agent's default context.
 watch(
 	() => props.name,
 	(name) => name && setProject({ name, label: project.doc?.project_name || name }),
-	{ immediate: true }
+	{ immediate: true },
 );
 watch(
 	() => project.doc?.project_name,
-	(label) => label && setProject({ name: props.name, label })
+	(label) => label && setProject({ name: props.name, label }),
 );
 
 const showNewImport = ref(false);
@@ -33,10 +37,10 @@ const showNewImport = ref(false);
 				<nav class="flex min-w-0 items-center gap-1.5 text-base">
 					<RouterLink
 						:to="{ name: 'Projects' }"
-						class="text-ink-gray-5 hover:text-ink-gray-7"
+						class="hidden text-ink-gray-5 hover:text-ink-gray-7 sm:block"
 						>Projects</RouterLink
 					>
-					<span class="text-ink-gray-4" aria-hidden="true">/</span>
+					<span class="hidden text-ink-gray-4 sm:block" aria-hidden="true">/</span>
 					<span class="truncate text-ink-gray-9">{{
 						project.doc?.project_name || name
 					}}</span>
@@ -47,26 +51,26 @@ const showNewImport = ref(false);
 					theme="gray"
 					variant="subtle"
 					size="sm"
+					class="shrink-0"
 				/>
 			</div>
 
-			<div class="flex items-center gap-2">
+			<div class="flex shrink-0 items-center gap-2 pl-2">
 				<Button
 					variant="subtle"
-					label="Graph"
-					icon-left="lucide-waypoints"
+					v-bind="actionButtonProps(isMobile, 'lucide-waypoints', 'Graph')"
 					:route="{ name: 'ProjectGraph', params: { name } }"
 				/>
 				<Button
 					variant="ghost"
 					icon="lucide-settings"
+					aria-label="Project settings"
 					:route="{ name: 'ProjectSettings', params: { name } }"
 				/>
 				<Button
 					variant="solid"
 					theme="gray"
-					icon-left="lucide-plus"
-					label="New Document"
+					v-bind="actionButtonProps(isMobile, 'lucide-plus', 'New Document')"
 					@click="showNewImport = true"
 				/>
 			</div>
diff --git a/frontend/src/pages/ProjectGraph.vue b/frontend/src/pages/ProjectGraph.vue
index f20f8d8..20480e3 100644
--- a/frontend/src/pages/ProjectGraph.vue
+++ b/frontend/src/pages/ProjectGraph.vue
@@ -59,7 +59,7 @@ function onSelect(node) {
 					>{{ project.doc?.project_name || props.name }}
 					<span class="text-ink-gray-4" aria-hidden="true">/</span></RouterLink
 				>
-				<h1 class="text-md text-ink-gray-9">Graph</h1>
+				<h1 class="shrink-0 text-md text-ink-gray-9">Graph</h1>
 			</div>
 		</PageHeader>
 
diff --git a/frontend/src/pages/ProjectList.vue b/frontend/src/pages/ProjectList.vue
index 112f681..839eec8 100644
--- a/frontend/src/pages/ProjectList.vue
+++ b/frontend/src/pages/ProjectList.vue
@@ -12,9 +12,12 @@ import {
 	useCall,
 	useList,
 } from "frappe-ui";
+import { useIsMobile } from "@/composables/useIsMobile";
+import { actionButtonProps } from "@/utils/actionButton";
 import { clear as clearAgentContext } from "@/data/agentContext";
 
 const router = useRouter();
+const isMobile = useIsMobile();
 
 // Global landing — the agent opens with no default attachment here.
 onMounted(clearAgentContext);
@@ -32,10 +35,10 @@ const projects = useList({
 // default is never archivable, so it always shows).
 const showArchived = ref(false);
 const visibleProjects = computed(() =>
-	(projects.data || []).filter((p) => showArchived.value || p.status !== "Archived")
+	(projects.data || []).filter((p) => showArchived.value || p.status !== "Archived"),
 );
 const archivedCount = computed(
-	() => (projects.data || []).filter((p) => p.status === "Archived").length
+	() => (projects.data || []).filter((p) => p.status === "Archived").length,
 );
 
 const showNew = ref(false);
@@ -73,19 +76,24 @@ function openProject(name) {
 	<div>
 		<PageHeader>
 			<h1 class="text-md text-ink-gray-9">Projects</h1>
-			<div class="flex items-center gap-2">
+			<div class="flex shrink-0 items-center gap-2 pl-2">
 				<Button
 					v-if="archivedCount"
 					variant="ghost"
 					theme="gray"
-					:label="showArchived ? 'Hide archived' : `Show archived (${archivedCount})`"
+					v-bind="
+						actionButtonProps(
+							isMobile,
+							'lucide-archive',
+							showArchived ? 'Hide archived' : `Show archived (${archivedCount})`
+						)
+					"
 					@click="showArchived = !showArchived"
 				/>
 				<Button
 					variant="solid"
 					theme="gray"
-					icon-left="lucide-plus"
-					label="New Project"
+					v-bind="actionButtonProps(isMobile, 'lucide-plus', 'New Project')"
 					@click="showNew = true"
 				/>
 			</div>
diff --git a/frontend/src/pages/RagLab.vue b/frontend/src/pages/RagLab.vue
new file mode 100644
index 0000000..5772ac5
--- /dev/null
+++ b/frontend/src/pages/RagLab.vue
@@ -0,0 +1,338 @@
+<script setup>
+// /rag-lab — the proof surface. Same query through a naive top-k vector search and
+// through the router + hybrid retriever, side by side, so the difference is legible
+// without reading a log.
+import { computed, watch } from "vue";
+import { Button, FormControl, PageHeader } from "frappe-ui";
+import CoverageBar from "@/components/rag/CoverageBar.vue";
+import EvalScoreboard from "@/components/rag/EvalScoreboard.vue";
+import IndexStatusCard from "@/components/rag/IndexStatusCard.vue";
+import RouteBadge from "@/components/rag/RouteBadge.vue";
+import SourceCard from "@/components/rag/SourceCard.vue";
+import {
+	isUnrankedSet,
+	relevanceBasis,
+	useIndexStatus,
+	useProjectOptions,
+	useRagCompare,
+} from "@/composables/useRag";
+
+const projectOptions = useProjectOptions();
+
+const {
+	query,
+	project,
+	comparedQuery,
+	naive,
+	routed,
+	missedSections,
+	route,
+	errorText,
+	compare,
+	compareCall,
+} = useRagCompare();
+const {
+	status: indexStatusData,
+	errorText: indexErrorText,
+	reindexing,
+	reindexedJob,
+	refresh: refreshIndexStatus,
+	reindex: reindexProject,
+	statusCall,
+} = useIndexStatus();
+
+watch(project, (name) => refreshIndexStatus(name || null), { immediate: true });
+
+function sectionKey(hit) {
+	return hit.section || hit.chunk_id;
+}
+function chunkKey(hit) {
+	return hit.chunk_id || `${hit.section}-${hit.page_start}`;
+}
+
+const naiveSections = computed(() => new Set(naive.value.map(sectionKey)));
+// The server names the sections the naive leg missed; only fall back to deriving the
+// diff here if an older build doesn't send them.
+const missedKeys = computed(() =>
+	missedSections.value.length
+		? new Set(missedSections.value)
+		: new Set(
+				routed.value
+					.filter((hit) => !naiveSections.value.has(sectionKey(hit)))
+					.map(sectionKey),
+			),
+);
+const hasComparison = computed(() => Boolean(comparedQuery.value));
+
+// The naive leg is always a plain vector top-k; the routed leg follows the router, and an
+// exhaustive route has no ranking to report at all.
+const routedBasis = computed(() => relevanceBasis(route.value));
+// The two columns sit side by side, so a bar drawn from a real cosine on the left and a
+// bar drawn from a sentinel on the right would invite exactly the comparison neither
+// number supports. Each column decides for itself whether its scores mean anything.
+const naiveUnranked = computed(() => isUnrankedSet(naive.value));
+const routedUnranked = computed(() => isUnrankedSet(routed.value));
+
+function countDistinct(hits, key) {
+	return new Set(hits.map((hit) => hit[key]).filter(Boolean)).size;
+}
+const naiveSummary = computed(() => ({
+	sections: naiveSections.value.size,
+	documents: countDistinct(naive.value, "source_document"),
+}));
+const routedSummary = computed(() => ({
+	sections: new Set(routed.value.map(sectionKey)).size,
+	documents: countDistinct(routed.value, "source_document"),
+}));
+// The yardstick both columns are measured against: everything either retriever found.
+const universe = computed(() => ({
+	sections: new Set([...naiveSections.value, ...routed.value.map(sectionKey)]).size,
+	documents: countDistinct([...naive.value, ...routed.value], "source_document"),
+}));
+const missedDocuments = computed(() => {
+	const seen = new Set(naive.value.map((hit) => hit.source_document));
+	return countDistinct(
+		routed.value.filter((hit) => !seen.has(hit.source_document)),
+		"source_document",
+	);
+});
+</script>
+
+<template>
+	<div class="mx-auto w-full max-w-7xl px-4 pb-16 sm:px-6 sm:pb-28">
+		<PageHeader>
+			<div class="flex min-w-0 items-center gap-3">
+				<h1 class="text-md text-ink-gray-9">RAG Lab</h1>
+				<span class="hidden truncate text-sm text-ink-gray-5 lg:inline">
+					Naive top-k vector vs routed hybrid, on the same query
+				</span>
+			</div>
+			<div class="w-32 shrink-0 sm:w-44">
+				<FormControl v-model="project" type="select" :options="projectOptions" />
+			</div>
+		</PageHeader>
+
+		<form class="sticky top-0 z-10 flex gap-2 bg-surface-base py-3" @submit.prevent="compare">
+			<FormControl
+				v-model="query"
+				type="text"
+				class="flex-1"
+				placeholder="Try a query that keyword search alone would miss…"
+				autocomplete="off"
+			/>
+			<Button
+				variant="solid"
+				label="Compare"
+				icon-left="lucide-columns-2"
+				class="shrink-0"
+				:loading="compareCall.loading"
+				:disabled="!query.trim()"
+				@click="compare"
+			/>
+		</form>
+
+		<p
+			v-if="errorText"
+			class="mt-3 rounded-md bg-surface-red-1 px-3 py-2 text-sm text-ink-red-8"
+		>
+			{{ errorText }}
+		</p>
+
+		<div v-if="hasComparison && route" class="mt-4">
+			<RouteBadge :route="route" />
+		</div>
+
+		<div
+			v-if="!hasComparison"
+			class="mt-6 flex flex-col items-center gap-3 rounded-lg border border-dashed border-outline-gray-2 px-6 py-14 text-center"
+		>
+			<span class="lucide-flask-conical size-7 text-ink-gray-4" aria-hidden="true" />
+			<p class="text-base text-ink-gray-7">Run a query to see both retrievers</p>
+			<p class="max-w-lg text-sm text-ink-gray-5">
+				The left column is a plain top-k vector search. The right column is the routed
+				hybrid — anything it surfaced that the naive search never saw is flagged.
+			</p>
+		</div>
+
+		<template v-else>
+			<p v-if="compareCall.loading" class="mt-4 text-sm text-ink-gray-5">
+				Running both retrievers over “{{ comparedQuery }}”…
+			</p>
+			<!-- The recall gap, as loud as it deserves to be — the whole argument of the POC. -->
+			<div
+				v-else-if="missedKeys.size"
+				class="mt-4 flex flex-col gap-3 rounded-lg border border-outline-amber-3 border-l-4 bg-surface-amber-2 px-4 py-4 sm:flex-row sm:items-center sm:gap-5"
+			>
+				<div class="flex items-center gap-3">
+					<span
+						class="lucide-eye-off size-6 shrink-0 text-ink-amber-8"
+						aria-hidden="true"
+					/>
+					<span class="text-4xl font-semibold tabular-nums text-ink-gray-9">
+						{{ missedKeys.size }}
+					</span>
+				</div>
+				<div class="min-w-0">
+					<p class="text-lg font-medium text-ink-gray-9">
+						sections naive search never saw
+						<template v-if="missedDocuments">
+							— across {{ missedDocuments }}
+							{{ missedDocuments === 1 ? "document" : "documents" }} it never opened
+						</template>
+					</p>
+					<p class="text-sm text-ink-gray-7">
+						Same query, same corpus: “{{ comparedQuery }}”. Naive found
+						{{ naiveSummary.sections }} of {{ universe.sections }}; the routed leg
+						found {{ routedSummary.sections }}. Every missed section is flagged below.
+					</p>
+				</div>
+			</div>
+			<p v-else class="mt-4 text-sm text-ink-gray-6">
+				Both retrievers returned the same sections for “{{ comparedQuery }}”.
+			</p>
+
+			<!-- On a phone the two columns stack, so they can never be read against each
+			     other. This scoreboard carries the comparison instead: both bars on one
+			     screen, on the same scale, immediately above the stacked lists. -->
+			<div
+				v-if="!compareCall.loading"
+				class="mt-3 flex flex-col gap-2 rounded-lg border border-outline-gray-2 bg-surface-elevation-1 px-3 py-3 lg:hidden"
+			>
+				<p class="text-xs font-medium tracking-wide text-ink-gray-5 uppercase">
+					Sections found, of {{ universe.sections }}
+				</p>
+				<div class="flex items-center gap-2">
+					<span class="w-14 shrink-0 text-xs text-ink-gray-7">Naive</span>
+					<CoverageBar
+						class="min-w-0 flex-1"
+						:value="naiveSummary.sections"
+						:total="universe.sections"
+						tone="warn"
+						label="sections found by naive search"
+					/>
+					<span
+						class="w-12 shrink-0 text-right font-mono text-xs tabular-nums text-ink-gray-8"
+					>
+						{{ naiveSummary.sections }}/{{ universe.sections }}
+					</span>
+				</div>
+				<div class="flex items-center gap-2">
+					<span class="w-14 shrink-0 text-xs text-ink-gray-7">Routed</span>
+					<CoverageBar
+						class="min-w-0 flex-1"
+						:value="routedSummary.sections"
+						:total="universe.sections"
+						tone="good"
+						label="sections found by routed retrieval"
+					/>
+					<span
+						class="w-12 shrink-0 text-right font-mono text-xs tabular-nums text-ink-gray-8"
+					>
+						{{ routedSummary.sections }}/{{ universe.sections }}
+					</span>
+				</div>
+			</div>
+
+			<div class="mt-3 grid grid-cols-1 gap-4 lg:grid-cols-2">
+				<section class="min-w-0">
+					<header
+						class="mb-2 flex flex-col gap-1.5 rounded-md bg-surface-gray-2 px-3 py-2"
+					>
+						<div class="flex flex-wrap items-center gap-2">
+							<span class="lucide-brain size-4 text-ink-gray-6" aria-hidden="true" />
+							<h2 class="text-base font-medium text-ink-gray-8">
+								Naive top-k vector
+							</h2>
+							<span class="text-xs text-ink-gray-5">
+								{{ naiveSummary.sections }} of {{ universe.sections }} sections ·
+								{{ naiveSummary.documents }} of {{ universe.documents }} documents
+							</span>
+						</div>
+						<CoverageBar
+							class="hidden lg:flex"
+							:value="naiveSummary.sections"
+							:total="universe.sections"
+							tone="warn"
+							label="sections found by naive search"
+						/>
+					</header>
+					<div v-if="naive.length" class="flex flex-col gap-2">
+						<SourceCard
+							v-for="(hit, position) in naive"
+							:key="chunkKey(hit)"
+							:hit="hit"
+							:index="position + 1"
+							:total="naive.length"
+							basis="vector"
+							:unranked-set="naiveUnranked"
+							compact
+						/>
+					</div>
+					<p
+						v-else
+						class="rounded-lg border border-dashed border-outline-gray-2 px-4 py-10 text-center text-sm text-ink-gray-5"
+					>
+						{{ compareCall.loading ? "Searching…" : "No hits." }}
+					</p>
+				</section>
+
+				<section class="min-w-0">
+					<header
+						class="mb-2 flex flex-col gap-1.5 rounded-md bg-surface-green-2 px-3 py-2"
+					>
+						<div class="flex flex-wrap items-center gap-2">
+							<span
+								class="lucide-shuffle size-4 text-ink-green-8"
+								aria-hidden="true"
+							/>
+							<h2 class="text-base font-medium text-ink-gray-9">Routed retrieval</h2>
+							<span class="text-xs text-ink-gray-7">
+								{{ routedSummary.sections }} of {{ universe.sections }} sections ·
+								{{ routedSummary.documents }} of {{ universe.documents }} documents
+							</span>
+						</div>
+						<CoverageBar
+							class="hidden lg:flex"
+							:value="routedSummary.sections"
+							:total="universe.sections"
+							tone="good"
+							label="sections found by routed retrieval"
+						/>
+					</header>
+					<div v-if="routed.length" class="flex flex-col gap-2">
+						<SourceCard
+							v-for="(hit, position) in routed"
+							:key="chunkKey(hit)"
+							:hit="hit"
+							:index="position + 1"
+							:total="routed.length"
+							:basis="routedBasis"
+							:unranked-set="routedUnranked"
+							:missed="missedKeys.has(sectionKey(hit))"
+							compact
+						/>
+					</div>
+					<p
+						v-else
+						class="rounded-lg border border-dashed border-outline-gray-2 px-4 py-10 text-center text-sm text-ink-gray-5"
+					>
+						{{ compareCall.loading ? "Searching…" : "No hits." }}
+					</p>
+				</section>
+			</div>
+		</template>
+
+		<div class="mt-6 grid min-w-0 grid-cols-1 gap-4 xl:grid-cols-2">
+			<IndexStatusCard
+				:status="indexStatusData"
+				:loading="statusCall.loading"
+				:error-text="indexErrorText"
+				:reindexing="reindexing"
+				:job-name="reindexedJob"
+				:can-reindex="Boolean(project)"
+				@reindex="reindexProject(project)"
+			/>
+			<EvalScoreboard />
+		</div>
+	</div>
+</template>
diff --git a/frontend/src/router.js b/frontend/src/router.js
index aaa33b2..5a078c4 100644
--- a/frontend/src/router.js
+++ b/frontend/src/router.js
@@ -38,6 +38,16 @@ const routes = [
 		component: () => import("@/pages/ImportDetail.vue"),
 		props: true,
 	},
+	{
+		path: "/ask",
+		name: "AskWiki",
+		component: () => import("@/pages/AskWiki.vue"),
+	},
+	{
+		path: "/rag-lab",
+		name: "RagLab",
+		component: () => import("@/pages/RagLab.vue"),
+	},
 	{
 		path: "/explore",
 		name: "Explore",
diff --git a/frontend/src/utils/actionButton.js b/frontend/src/utils/actionButton.js
new file mode 100644
index 0000000..63e53a8
--- /dev/null
+++ b/frontend/src/utils/actionButton.js
@@ -0,0 +1,7 @@
+// A labelled header action shrinks to an icon-only button on phones — header rows run
+// out of width long before the label stops being useful, and PageHeader never wraps.
+// The tooltip/aria-label keeps the action named for pointer and screen-reader users.
+export function actionButtonProps(compact, icon, label) {
+	if (compact) return { icon, tooltip: label, "aria-label": label };
+	return { iconLeft: icon, label };
+}
diff --git a/frontend/src/utils/mermaid.css b/frontend/src/utils/mermaid.css
new file mode 100644
index 0000000..2225b95
--- /dev/null
+++ b/frontend/src/utils/mermaid.css
@@ -0,0 +1,32 @@
+/* Presentation for the blocks `renderMermaidIn` swaps into a preview. Lives with the
+   util so MarkdownPreview and WikiPreview share one definition instead of two copies. */
+
+.mermaid-figure {
+	display: flex;
+	justify-content: center;
+	overflow-x: auto;
+	margin: 1rem 0;
+}
+
+.mermaid-figure svg {
+	max-width: 100%;
+	height: auto;
+}
+
+/* Failure state: the chip explains it, the source stays readable underneath. */
+.mermaid-figure-failed {
+	flex-direction: column;
+	align-items: flex-start;
+	gap: 0.5rem;
+}
+
+.mermaid-figure-failed .mermaid-error-chip {
+	margin: 0;
+}
+
+.mermaid-figure-failed .mermaid-error-source {
+	width: 100%;
+	margin: 0;
+	overflow-x: auto;
+	white-space: pre;
+}
diff --git a/frontend/src/utils/mermaid.js b/frontend/src/utils/mermaid.js
index 4927445..80fa156 100644
--- a/frontend/src/utils/mermaid.js
+++ b/frontend/src/utils/mermaid.js
@@ -1,41 +1,51 @@
-// Mermaid rendering for markdown previews — reuses the Frappe Wiki app's vendored
-// mermaid loader (wiki is an installed app and serves it at /assets/wiki/js/...). That
-// keeps mermaid out of our bundle (~3 MB) and makes the preview theme identical to the
-// published wiki, which reads its config from the live Frappe UI design tokens.
+// Mermaid rendering for markdown previews.
 //
-// The loader asset exposes:
-//   window.wikiGetMermaid({ assetUrl })  → Promise<mermaid> (loaded + initialized)
-//   window.wikiMermaidThemeConfig()      → theme config from current design tokens
-// If the asset is unavailable (wiki not installed), rendering degrades gracefully:
-// mermaid code fences are left as plain code blocks.
+// Mermaid is a real frontend dependency loaded through a dynamic `import()`, so Vite
+// code-splits it into its own chunk (~3 MB) that is fetched only when a preview
+// actually contains a diagram. The Frappe Wiki app is NOT a dependency here: it ships
+// mermaid only as a hashed chunk inside its own SPA bundle, so `/assets/wiki/js/...`
+// 404s and every diagram used to degrade silently to a plain code block. If some other
+// app on the site happens to have published `window.wikiGetMermaid`, we take it as a
+// free fast path, but nothing depends on it.
 
-const LOADER_URL = "/assets/wiki/js/mermaid-loader.js";
-const ASSET_URL = "/assets/wiki/js/vendor/mermaid/mermaid.min.js";
+import "./mermaid.css";
 
-let loaderPromise = null;
+let mermaidPromise = null;
 let counter = 0;
 
-function loadWikiMermaidLoader() {
-	if (window.wikiGetMermaid) return Promise.resolve();
-	if (!loaderPromise) {
-		loaderPromise = new Promise((resolve, reject) => {
-			const script = document.createElement("script");
-			script.src = LOADER_URL;
-			script.onload = () => resolve();
-			script.onerror = () => reject(new Error("Unable to load the wiki mermaid loader"));
-			document.head.appendChild(script);
-		});
+async function getMermaid() {
+	if (!mermaidPromise) {
+		mermaidPromise = window.wikiGetMermaid
+			? Promise.resolve(window.wikiGetMermaid())
+			: import("mermaid").then((module) => module.default);
 	}
-	return loaderPromise;
+	return mermaidPromise;
 }
 
-async function getMermaid() {
-	await loadWikiMermaidLoader();
-	return window.wikiGetMermaid({ assetUrl: ASSET_URL });
+function getThemeConfig() {
+	if (window.wikiMermaidThemeConfig) return window.wikiMermaidThemeConfig();
+	const isDark = document.documentElement.getAttribute("data-theme") === "dark";
+	return { theme: isDark ? "dark" : "default" };
 }
 
-function themeConfig() {
-	return window.wikiMermaidThemeConfig ? window.wikiMermaidThemeConfig() : {};
+function formatFailure(source, message) {
+	const figure = document.createElement("div");
+	figure.className = "mermaid-figure mermaid-figure-failed";
+	figure.setAttribute("data-mermaid-done", "");
+
+	const chip = document.createElement("p");
+	chip.className =
+		"mermaid-error-chip inline-flex items-center gap-1 rounded border border-outline-red-2 bg-surface-red-1 px-2 py-1 text-xs text-ink-red-4";
+	// Mermaid parse errors carry a multi-line ASCII caret diagram; the first line names
+	// the problem and the source below already shows where it is.
+	chip.textContent = `Diagram failed to render — ${String(message).split("\n")[0]}`;
+
+	const code = document.createElement("pre");
+	code.className = "mermaid-error-source";
+	code.textContent = source;
+
+	figure.append(chip, code);
+	return figure;
 }
 
 /**
@@ -43,39 +53,46 @@ function themeConfig() {
  * browser `marked` output (`<pre><code class="language-mermaid">`, used by
  * MarkdownPreview) and the wiki renderer's server-side output (`<pre class="mermaid">`,
  * used by WikiPreview) so the same util covers both previews.
- * Idempotent per element; failures leave the original block in place.
+ * Idempotent per element; a block that cannot render shows its source plus an error chip.
  */
 export async function renderMermaidIn(root) {
 	if (!root) return;
 	const blocks = [...root.querySelectorAll("code.language-mermaid, pre.mermaid")].filter(
-		(el) => !el.closest("[data-mermaid-done]")
+		(element) => !element.closest("[data-mermaid-done]")
 	);
 	if (!blocks.length) return;
+	// Claim the blocks before the first await so a re-entrant call (content watcher firing
+	// mid-load) cannot pick up the same ones.
+	const targets = blocks.map((element) => {
+		const host = element.closest("pre") || element;
+		host.setAttribute("data-mermaid-done", "");
+		return { host, source: element.textContent || "" };
+	});
 
 	let mermaid;
 	try {
 		mermaid = await getMermaid();
-		mermaid.initialize({ startOnLoad: false, securityLevel: "strict", ...themeConfig() });
-	} catch {
-		return; // mermaid asset unavailable — leave the code blocks untouched
+		mermaid.initialize({ startOnLoad: false, securityLevel: "strict", ...getThemeConfig() });
+	} catch (error) {
+		for (const { host, source } of targets) {
+			host.replaceWith(formatFailure(source, error.message || "mermaid failed to load"));
+		}
+		return;
 	}
 
-	for (const el of blocks) {
-		const host = el.closest("pre") || el;
-		host.setAttribute("data-mermaid-done", "");
+	for (const { host, source } of targets) {
+		counter += 1;
 		try {
-			counter += 1;
-			const { svg } = await mermaid.render(
-				`wikify-mermaid-${counter}`,
-				el.textContent || ""
-			);
+			const { svg } = await mermaid.render(`wikify-mermaid-${counter}`, source);
 			const figure = document.createElement("div");
 			figure.className = "mermaid-figure";
 			figure.setAttribute("data-mermaid-done", "");
 			figure.innerHTML = svg;
 			host.replaceWith(figure);
-		} catch {
-			// Invalid diagram source — keep the raw fence (marked as done so we don't retry).
+		} catch (error) {
+			host.replaceWith(formatFailure(source, error.message || "invalid mermaid syntax"));
+			// A failed render leaves mermaid's offscreen sandbox element behind.
+			document.getElementById(`dwikify-mermaid-${counter}`)?.remove();
 		}
 	}
 }
diff --git a/frontend/yarn.lock b/frontend/yarn.lock
index 9e403c8..1689fcf 100644
--- a/frontend/yarn.lock
+++ b/frontend/yarn.lock
@@ -40,6 +40,16 @@
     "@babel/helper-string-parser" "^7.29.7"
     "@babel/helper-validator-identifier" "^7.29.7"
 
+"@braintree/sanitize-url@^7.1.2":
+  version "7.1.2"
+  resolved "https://registry.yarnpkg.com/@braintree/sanitize-url/-/sanitize-url-7.1.2.tgz#ca2035b0fefe956a8676ff0c69af73e605fcd81f"
+  integrity sha512-jigsZK+sMF/cuiB7sERuo9V7N9jx+dhmHHnQyDSVdpZwVutaBu7WvNYqMDLSgFgfB30n452TP3vjDAvFC973mA==
+
+"@chevrotain/types@~11.1.2":
+  version "11.1.2"
+  resolved "https://registry.yarnpkg.com/@chevrotain/types/-/types-11.1.2.tgz#e83a1a2704f0c5e49e7592b214031a0f4a34d7e5"
+  integrity sha512-U+HFai5+zmJCkK86QsaJtoITlboZHBqrVketcO2ROv865xfCMSFpELQoz1GkX5GzME8pTa+3kbKrZHQtI0gdbw==
+
 "@codemirror/autocomplete@^6.0.0", "@codemirror/autocomplete@^6.3.2", "@codemirror/autocomplete@^6.7.1":
   version "6.20.3"
   resolved "https://registry.yarnpkg.com/@codemirror/autocomplete/-/autocomplete-6.20.3.tgz#696b740312c6a962e14567b49a3661b5924bc5ae"
@@ -460,6 +470,13 @@
   resolved "https://registry.yarnpkg.com/@marijn/find-cluster-break/-/find-cluster-break-1.0.2.tgz#775374306116d51c0c500b8c4face0f9a04752d8"
   integrity sha512-l0h88YhZFyKdXIFNfSWpyjStDjGHwZ/U7iobcK1cQQD8sejsONdQtTVU+1wVN1PBw40PiiHB1vA5S7VTfQiP9g==
 
+"@mermaid-js/parser@^1.2.0":
+  version "1.2.0"
+  resolved "https://registry.yarnpkg.com/@mermaid-js/parser/-/parser-1.2.0.tgz#266d728c54d2d4034d270f8b31d790e26296a5fa"
+  integrity sha512-oYPyv8A4As1yH5Bx+04iQEQxXuIQDe0GKCNSRgao6z8AM9jixXIfP0vsppRLvGf+nKIOb9/LdpWA4YuJiVvESA==
+  dependencies:
+    "@chevrotain/types" "~11.1.2"
+
 "@napi-rs/canvas-android-arm64@0.1.100":
   version "0.1.100"
   resolved "https://registry.yarnpkg.com/@napi-rs/canvas-android-arm64/-/canvas-android-arm64-0.1.100.tgz#b7c68b91d57702a5fc523fa82de72ea741e6766e"
@@ -967,11 +984,226 @@
   dependencies:
     tslib "^2.4.0"
 
+"@types/d3-array@*":
+  version "3.2.2"
+  resolved "https://registry.yarnpkg.com/@types/d3-array/-/d3-array-3.2.2.tgz#e02151464d02d4a1b44646d0fcdb93faf88fde8c"
+  integrity sha512-hOLWVbm7uRza0BYXpIIW5pxfrKe0W+D5lrFiAEYR+pb6w3N2SwSMaJbXdUfSEv+dT4MfHBLtn5js0LAWaO6otw==
+
+"@types/d3-axis@*":
+  version "3.0.6"
+  resolved "https://registry.yarnpkg.com/@types/d3-axis/-/d3-axis-3.0.6.tgz#e760e5765b8188b1defa32bc8bb6062f81e4c795"
+  integrity sha512-pYeijfZuBd87T0hGn0FO1vQ/cgLk6E1ALJjfkC0oJ8cbwkZl3TpgS8bVBLZN+2jjGgg38epgxb2zmoGtSfvgMw==
+  dependencies:
+    "@types/d3-selection" "*"
+
+"@types/d3-brush@*":
+  version "3.0.6"
+  resolved "https://registry.yarnpkg.com/@types/d3-brush/-/d3-brush-3.0.6.tgz#c2f4362b045d472e1b186cdbec329ba52bdaee6c"
+  integrity sha512-nH60IZNNxEcrh6L1ZSMNA28rj27ut/2ZmI3r96Zd+1jrZD++zD3LsMIjWlvg4AYrHn/Pqz4CF3veCxGjtbqt7A==
+  dependencies:
+    "@types/d3-selection" "*"
+
+"@types/d3-chord@*":
+  version "3.0.6"
+  resolved "https://registry.yarnpkg.com/@types/d3-chord/-/d3-chord-3.0.6.tgz#1706ca40cf7ea59a0add8f4456efff8f8775793d"
+  integrity sha512-LFYWWd8nwfwEmTZG9PfQxd17HbNPksHBiJHaKuY1XeqscXacsS2tyoo6OdRsjf+NQYeB6XrNL3a25E3gH69lcg==
+
+"@types/d3-color@*":
+  version "3.1.3"
+  resolved "https://registry.yarnpkg.com/@types/d3-color/-/d3-color-3.1.3.tgz#368c961a18de721da8200e80bf3943fb53136af2"
+  integrity sha512-iO90scth9WAbmgv7ogoq57O9YpKmFBbmoEoCHDB2xMBY0+/KVrqAaCDyCE16dUspeOvIxFFRI+0sEtqDqy2b4A==
+
+"@types/d3-contour@*":
+  version "3.0.6"
+  resolved "https://registry.yarnpkg.com/@types/d3-contour/-/d3-contour-3.0.6.tgz#9ada3fa9c4d00e3a5093fed0356c7ab929604231"
+  integrity sha512-BjzLgXGnCWjUSYGfH1cpdo41/hgdWETu4YxpezoztawmqsvCeep+8QGfiY6YbDvfgHz/DkjeIkkZVJavB4a3rg==
+  dependencies:
+    "@types/d3-array" "*"
+    "@types/geojson" "*"
+
+"@types/d3-delaunay@*":
+  version "6.0.4"
+  resolved "https://registry.yarnpkg.com/@types/d3-delaunay/-/d3-delaunay-6.0.4.tgz#185c1a80cc807fdda2a3fe960f7c11c4a27952e1"
+  integrity sha512-ZMaSKu4THYCU6sV64Lhg6qjf1orxBthaC161plr5KuPHo3CNm8DTHiLw/5Eq2b6TsNP0W0iJrUOFscY6Q450Hw==
+
+"@types/d3-dispatch@*":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@types/d3-dispatch/-/d3-dispatch-3.0.7.tgz#ef004d8a128046cfce434d17182f834e44ef95b2"
+  integrity sha512-5o9OIAdKkhN1QItV2oqaE5KMIiXAvDWBDPrD85e58Qlz1c1kI/J0NcqbEG88CoTwJrYe7ntUCVfeUl2UJKbWgA==
+
+"@types/d3-drag@*":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@types/d3-drag/-/d3-drag-3.0.7.tgz#b13aba8b2442b4068c9a9e6d1d82f8bcea77fc02"
+  integrity sha512-HE3jVKlzU9AaMazNufooRJ5ZpWmLIoc90A37WU2JMmeq28w1FQqCZswHZ3xR+SuxYftzHq6WU6KJHvqxKzTxxQ==
+  dependencies:
+    "@types/d3-selection" "*"
+
+"@types/d3-dsv@*":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@types/d3-dsv/-/d3-dsv-3.0.7.tgz#0a351f996dc99b37f4fa58b492c2d1c04e3dac17"
+  integrity sha512-n6QBF9/+XASqcKK6waudgL0pf/S5XHPPI8APyMLLUHd8NqouBGLsU8MgtO7NINGtPBtk9Kko/W4ea0oAspwh9g==
+
+"@types/d3-ease@*":
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/@types/d3-ease/-/d3-ease-3.0.2.tgz#e28db1bfbfa617076f7770dd1d9a48eaa3b6c51b"
+  integrity sha512-NcV1JjO5oDzoK26oMzbILE6HW7uVXOHLQvHshBUW4UMdZGfiY6v5BeQwh9a9tCzv+CeefZQHJt5SRgK154RtiA==
+
+"@types/d3-fetch@*":
+  version "3.0.7"
+  resolved "https://registry.yarnpkg.com/@types/d3-fetch/-/d3-fetch-3.0.7.tgz#c04a2b4f23181aa376f30af0283dbc7b3b569980"
+  integrity sha512-fTAfNmxSb9SOWNB9IoG5c8Hg6R+AzUHDRlsXsDZsNp6sxAEOP0tkP3gKkNSO/qmHPoBFTxNrjDprVHDQDvo5aA==
+  dependencies:
+    "@types/d3-dsv" "*"
+
+"@types/d3-force@*":
+  version "3.0.10"
+  resolved "https://registry.yarnpkg.com/@types/d3-force/-/d3-force-3.0.10.tgz#6dc8fc6e1f35704f3b057090beeeb7ac674bff1a"
+  integrity sha512-ZYeSaCF3p73RdOKcjj+swRlZfnYpK1EbaDiYICEEp5Q6sUiqFaFQ9qgoshp5CzIyyb/yD09kD9o2zEltCexlgw==
+
+"@types/d3-format@*":
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/@types/d3-format/-/d3-format-3.0.4.tgz#b1e4465644ddb3fdf3a263febb240a6cd616de90"
+  integrity sha512-fALi2aI6shfg7vM5KiR1wNJnZ7r6UuggVqtDA+xiEdPZQwy/trcQaHnwShLuLdta2rTymCNpxYTiMZX/e09F4g==
+
+"@types/d3-geo@*":
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/@types/d3-geo/-/d3-geo-3.1.1.tgz#9e283af179601c549581600b3fec25941911329d"
+  integrity sha512-65Emv9fQiQQqphLlRkuQ5ypPsOmWPhtBGCMv61JDPEPMvsx+gzhGf74yw1a78xFKPj6zw4AgQICJoQv0vK9M2w==
+  dependencies:
+    "@types/geojson" "*"
+
+"@types/d3-hierarchy@*":
+  version "3.1.7"
+  resolved "https://registry.yarnpkg.com/@types/d3-hierarchy/-/d3-hierarchy-3.1.7.tgz#6023fb3b2d463229f2d680f9ac4b47466f71f17b"
+  integrity sha512-tJFtNoYBtRtkNysX1Xq4sxtjK8YgoWUNpIiUee0/jHGRwqvzYxkq0hGVbbOGSz+JgFxxRu4K8nb3YpG3CMARtg==
+
+"@types/d3-interpolate@*":
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/@types/d3-interpolate/-/d3-interpolate-3.0.4.tgz#412b90e84870285f2ff8a846c6eb60344f12a41c"
+  integrity sha512-mgLPETlrpVV1YRJIglr4Ez47g7Yxjl1lj7YKsiMCb27VJH9W8NVM6Bb9d8kkpG/uAQS5AmbA48q2IAolKKo1MA==
+  dependencies:
+    "@types/d3-color" "*"
+
+"@types/d3-path@*":
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/@types/d3-path/-/d3-path-3.1.1.tgz#f632b380c3aca1dba8e34aa049bcd6a4af23df8a"
+  integrity sha512-VMZBYyQvbGmWyWVea0EHs/BwLgxc+MKi1zLDCONksozI4YJMcTt8ZEuIR4Sb1MMTE8MMW49v0IwI5+b7RmfWlg==
+
+"@types/d3-polygon@*":
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/@types/d3-polygon/-/d3-polygon-3.0.2.tgz#dfae54a6d35d19e76ac9565bcb32a8e54693189c"
+  integrity sha512-ZuWOtMaHCkN9xoeEMr1ubW2nGWsp4nIql+OPQRstu4ypeZ+zk3YKqQT0CXVe/PYqrKpZAi+J9mTs05TKwjXSRA==
+
+"@types/d3-quadtree@*":
+  version "3.0.6"
+  resolved "https://registry.yarnpkg.com/@types/d3-quadtree/-/d3-quadtree-3.0.6.tgz#d4740b0fe35b1c58b66e1488f4e7ed02952f570f"
+  integrity sha512-oUzyO1/Zm6rsxKRHA1vH0NEDG58HrT5icx/azi9MF1TWdtttWl0UIUsjEQBBh+SIkrpd21ZjEv7ptxWys1ncsg==
+
+"@types/d3-random@*":
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/@types/d3-random/-/d3-random-3.0.4.tgz#6bd3683b8332fc0f01e7059b7636bc5c7ede7337"
+  integrity sha512-UHYId5WTCx4L4YNel7NU00XUXXgvgpgZOvp10PuvsQENjMDXhh2RyFc0KBjO7B45ne4Ha1yVH7ii0vnzKkuzWA==
+
+"@types/d3-scale-chromatic@*":
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/@types/d3-scale-chromatic/-/d3-scale-chromatic-3.1.0.tgz#dc6d4f9a98376f18ea50bad6c39537f1b5463c39"
+  integrity sha512-iWMJgwkK7yTRmWqRB5plb1kadXyQ5Sj8V/zYlFGMUBbIPKQScw+Dku9cAAMgJG+z5GYDoMjWGLVOvjghDEFnKQ==
+
+"@types/d3-scale@*":
+  version "4.0.9"
+  resolved "https://registry.yarnpkg.com/@types/d3-scale/-/d3-scale-4.0.9.tgz#57a2f707242e6fe1de81ad7bfcccaaf606179afb"
+  integrity sha512-dLmtwB8zkAeO/juAMfnV+sItKjlsw2lKdZVVy6LRr0cBmegxSABiLEpGVmSJJ8O08i4+sGR6qQtb6WtuwJdvVw==
+  dependencies:
+    "@types/d3-time" "*"
+
+"@types/d3-selection@*":
+  version "3.0.11"
+  resolved "https://registry.yarnpkg.com/@types/d3-selection/-/d3-selection-3.0.11.tgz#bd7a45fc0a8c3167a631675e61bc2ca2b058d4a3"
+  integrity sha512-bhAXu23DJWsrI45xafYpkQ4NtcKMwWnAC/vKrd2l+nxMFuvOT3XMYTIj2opv8vq8AO5Yh7Qac/nSeP/3zjTK0w==
+
+"@types/d3-shape@*":
+  version "3.1.8"
+  resolved "https://registry.yarnpkg.com/@types/d3-shape/-/d3-shape-3.1.8.tgz#d1516cc508753be06852cd06758e3bb54a22b0e3"
+  integrity sha512-lae0iWfcDeR7qt7rA88BNiqdvPS5pFVPpo5OfjElwNaT2yyekbM0C9vK+yqBqEmHr6lDkRnYNoTBYlAgJa7a4w==
+  dependencies:
+    "@types/d3-path" "*"
+
+"@types/d3-time-format@*":
+  version "4.0.3"
+  resolved "https://registry.yarnpkg.com/@types/d3-time-format/-/d3-time-format-4.0.3.tgz#d6bc1e6b6a7db69cccfbbdd4c34b70632d9e9db2"
+  integrity sha512-5xg9rC+wWL8kdDj153qZcsJ0FWiFt0J5RB6LYUNZjwSnesfblqrI/bJ1wBdJ8OQfncgbJG5+2F+qfqnqyzYxyg==
+
+"@types/d3-time@*":
+  version "3.0.4"
+  resolved "https://registry.yarnpkg.com/@types/d3-time/-/d3-time-3.0.4.tgz#8472feecd639691450dd8000eb33edd444e1323f"
+  integrity sha512-yuzZug1nkAAaBlBBikKZTgzCeA+k1uy4ZFwWANOfKw5z5LRhV0gNA7gNkKm7HoK+HRN0wX3EkxGk0fpbWhmB7g==
+
+"@types/d3-timer@*":
+  version "3.0.2"
+  resolved "https://registry.yarnpkg.com/@types/d3-timer/-/d3-timer-3.0.2.tgz#70bbda77dc23aa727413e22e214afa3f0e852f70"
+  integrity sha512-Ps3T8E8dZDam6fUyNiMkekK3XUsaUEik+idO9/YjPtfj2qruF8tFBXS7XhtE4iIXBLxhmLjP3SXpLhVf21I9Lw==
+
+"@types/d3-transition@*":
+  version "3.0.9"
+  resolved "https://registry.yarnpkg.com/@types/d3-transition/-/d3-transition-3.0.9.tgz#1136bc57e9ddb3c390dccc9b5ff3b7d2b8d94706"
+  integrity sha512-uZS5shfxzO3rGlu0cC3bjmMFKsXv+SmZZcgp0KD22ts4uGXp5EVYGzu/0YdwZeKmddhcAccYtREJKkPfXkZuCg==
+  dependencies:
+    "@types/d3-selection" "*"
+
+"@types/d3-zoom@*":
+  version "3.0.8"
+  resolved "https://registry.yarnpkg.com/@types/d3-zoom/-/d3-zoom-3.0.8.tgz#dccb32d1c56b1e1c6e0f1180d994896f038bc40b"
+  integrity sha512-iqMC4/YlFCSlO8+2Ii1GGGliCAY4XdeG748w5vQUbevlbDu0zSjH/+jojorQVBK/se0j6DUFNPBGSqD3YWYnDw==
+  dependencies:
+    "@types/d3-interpolate" "*"
+    "@types/d3-selection" "*"
+
+"@types/d3@^7.4.3":
+  version "7.4.3"
+  resolved "https://registry.yarnpkg.com/@types/d3/-/d3-7.4.3.tgz#d4550a85d08f4978faf0a4c36b848c61eaac07e2"
+  integrity sha512-lZXZ9ckh5R8uiFVt8ogUNf+pIrK4EsWrx2Np75WvF/eTpJ0FMHNhjXk8CKEx/+gpHbNQyJWehbFaTvqmHWB3ww==
+  dependencies:
+    "@types/d3-array" "*"
+    "@types/d3-axis" "*"
+    "@types/d3-brush" "*"
+    "@types/d3-chord" "*"
+    "@types/d3-color" "*"
+    "@types/d3-contour" "*"
+    "@types/d3-delaunay" "*"
+    "@types/d3-dispatch" "*"
+    "@types/d3-drag" "*"
+    "@types/d3-dsv" "*"
+    "@types/d3-ease" "*"
+    "@types/d3-fetch" "*"
+    "@types/d3-force" "*"
+    "@types/d3-format" "*"
+    "@types/d3-geo" "*"
+    "@types/d3-hierarchy" "*"
+    "@types/d3-interpolate" "*"
+    "@types/d3-path" "*"
+    "@types/d3-polygon" "*"
+    "@types/d3-quadtree" "*"
+    "@types/d3-random" "*"
+    "@types/d3-scale" "*"
+    "@types/d3-scale-chromatic" "*"
+    "@types/d3-selection" "*"
+    "@types/d3-shape" "*"
+    "@types/d3-time" "*"
+    "@types/d3-time-format" "*"
+    "@types/d3-timer" "*"
+    "@types/d3-transition" "*"
+    "@types/d3-zoom" "*"
+
 "@types/estree@^1.0.0":
   version "1.0.9"
   resolved "https://registry.yarnpkg.com/@types/estree/-/estree-1.0.9.tgz#cf3f0e876d7bee15a93ab925b82bf570a3904a24"
   integrity sha512-GhdPgy1el4/ImP05X05Uw4cw2/M93BCUmnEvWZNStlCzEKME4Fkk+YpoA5OiHNQmoS7Cafb8Xa3Pya8m1Qrzeg==
 
+"@types/geojson@*":
+  version "7946.0.16"
+  resolved "https://registry.yarnpkg.com/@types/geojson/-/geojson-7946.0.16.tgz#8ebe53d69efada7044454e3305c19017d97ced2a"
+  integrity sha512-6C8nqWur3j98U6+lXDfTUWIfgvZU+EumvpHKcYjujKH7woYyLj2sUmff0tRhrqM7BohUw7Pz3ZB1jj2gW9Fvmg==
+
 "@types/hast@^3.0.0":
   version "3.0.4"
   resolved "https://registry.yarnpkg.com/@types/hast/-/hast-3.0.4.tgz#1d6b39993b82cea6ad783945b0508c25903e15aa"
@@ -999,6 +1231,14 @@
   resolved "https://registry.yarnpkg.com/@types/web-bluetooth/-/web-bluetooth-0.0.21.tgz#525433c784aed9b457aaa0ee3d92aeb71f346b63"
   integrity sha512-oIQLCGWtcFZy2JW77j9k8nHzAOpqMHLQejDA48XXMWH6tjCQHz5RCFz1bzsmROyL6PUm+LLnUiI4BCn221inxA==
 
+"@upsetjs/venn.js@^2.0.0":
+  version "2.0.0"
+  resolved "https://registry.yarnpkg.com/@upsetjs/venn.js/-/venn.js-2.0.0.tgz#3be192038cdda927aa4f8b22ab51af82abf47f34"
+  integrity sha512-WbBhLrooyePuQ1VZxrJjtLvTc4NVfpOyKx0sKqioq9bX1C1m7Jgykkn8gLrtwumBioXIqam8DLxp88Adbue6Hw==
+  optionalDependencies:
+    d3-selection "^3.0.0"
+    d3-transition "^3.0.1"
+
 "@vexip-ui/hooks@^2.8.0":
   version "2.9.4"
   resolved "https://registry.yarnpkg.com/@vexip-ui/hooks/-/hooks-2.9.4.tgz#fbae097129ab5d14832bd403c220d54d6c939a30"
@@ -1336,11 +1576,21 @@ color-name@~1.1.4:
   resolved "https://registry.yarnpkg.com/color-name/-/color-name-1.1.4.tgz#c2a09a87acbde69543de6f63fa3995c826c536a2"
   integrity sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA==
 
+commander@7:
+  version "7.2.0"
+  resolved "https://registry.yarnpkg.com/commander/-/commander-7.2.0.tgz#a36cb57d0b501ce108e4d20559a150a391d97ab7"
+  integrity sha512-QrWXB+ZQSVPmIWIhtEO9H+gwHaMGYiF5ChvoJ+K9ZGHG/sVsa6yiesAD1GC/x46sET00Xlwo1u49RVVVzvcSkw==
+
 commander@^4.0.0:
   version "4.1.1"
   resolved "https://registry.yarnpkg.com/commander/-/commander-4.1.1.tgz#9fd602bd936294e9e9ef46a3f4d6964044b18068"
   integrity sha512-NOKm8xhkzAjzFx8B2v5OAHT+u5pRQc2UCa2Vq9jYL/31o2wi9mxBA7LIFs3sV5VSC49z6pEhfbMULvShKj26WA==
 
+commander@^8.3.0:
+  version "8.3.0"
+  resolved "https://registry.yarnpkg.com/commander/-/commander-8.3.0.tgz#4837ea1b2da67b9c616a67afbb0fafee567bca66"
+  integrity sha512-OkTL9umf+He2DZkUq8f8J9of7yL6RJKI24dVITBmNfZBmri9zYZQrKkuXiKhyfPSu8tUhnVBB1iKXevvnlR4Ww==
+
 confbox@^0.1.8:
   version "0.1.8"
   resolved "https://registry.yarnpkg.com/confbox/-/confbox-0.1.8.tgz#820d73d3b3c82d9bd910652c5d4d599ef8ff8b06"
@@ -1356,6 +1606,20 @@ core-js@^3.1.3:
   resolved "https://registry.yarnpkg.com/core-js/-/core-js-3.49.0.tgz#8b4d520ac034311fa21aa616f017ada0e0dbbddd"
   integrity sha512-es1U2+YTtzpwkxVLwAFdSpaIMyQaq0PBgm3YD1W3Qpsn1NAmO3KSgZfu+oGSWVu6NvLHoHCV/aYcsE5wiB7ALg==
 
+cose-base@^1.0.0:
+  version "1.0.3"
+  resolved "https://registry.yarnpkg.com/cose-base/-/cose-base-1.0.3.tgz#650334b41b869578a543358b80cda7e0abe0a60a"
+  integrity sha512-s9whTXInMSgAp/NVXVNuVxVKzGH2qck3aQlVHxDCdAEPgtMKwc4Wq6/QKhgdEdgbLSi9rBTAcPoRa6JpiG4ksg==
+  dependencies:
+    layout-base "^1.0.0"
+
+cose-base@^2.2.0:
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/cose-base/-/cose-base-2.2.0.tgz#1c395c35b6e10bb83f9769ca8b817d614add5c01"
+  integrity sha512-AzlgcsCbUMymkADOJtQm3wO9S3ltPfYOFD5033keQn9NJzIbtnZj+UdBJe7DYml/8TdbtHJW3j58SOnKhWY/5g==
+  dependencies:
+    layout-base "^2.0.0"
+
 crelt@^1.0.5, crelt@^1.0.6:
   version "1.0.6"
   resolved "https://registry.yarnpkg.com/crelt/-/crelt-1.0.6.tgz#7cc898ea74e190fb6ef9dae57f8f81cf7302df72"
@@ -1371,17 +1635,87 @@ csstype@^3.2.3:
   resolved "https://registry.yarnpkg.com/csstype/-/csstype-3.2.3.tgz#ec48c0f3e993e50648c86da559e2610995cf989a"
   integrity sha512-z1HGKcYy2xA8AGQfwrn0PAy+PB7X/GSj3UVJW9qKyn43xWa+gl5nXmU4qqLMRzWVLFC8KusUX8T/0kCiOYpAIQ==
 
-"d3-color@1 - 3":
+cytoscape-cose-bilkent@^4.1.0:
+  version "4.1.0"
+  resolved "https://registry.yarnpkg.com/cytoscape-cose-bilkent/-/cytoscape-cose-bilkent-4.1.0.tgz#762fa121df9930ffeb51a495d87917c570ac209b"
+  integrity sha512-wgQlVIUJF13Quxiv5e1gstZ08rnZj2XaLHGoFMYXz7SkNfCDOOteKBE6SYRfA9WxxI/iBc3ajfDoc6hb/MRAHQ==
+  dependencies:
+    cose-base "^1.0.0"
+
+cytoscape-fcose@^2.2.0:
+  version "2.2.0"
+  resolved "https://registry.yarnpkg.com/cytoscape-fcose/-/cytoscape-fcose-2.2.0.tgz#e4d6f6490df4fab58ae9cea9e5c3ab8d7472f471"
+  integrity sha512-ki1/VuRIHFCzxWNrsshHYPs6L7TvLu3DL+TyIGEsRcvVERmxokbf5Gdk7mFxZnTdiGtnA4cfSmjZJMviqSuZrQ==
+  dependencies:
+    cose-base "^2.2.0"
+
+cytoscape@^3.33.3:
+  version "3.34.0"
+  resolved "https://registry.yarnpkg.com/cytoscape/-/cytoscape-3.34.0.tgz#5fbe2eb1cf76b070a8ecd5647c35f65aa097c9c6"
+  integrity sha512-62rNSrioXw93uliKFBwjukeQyeWwH2PqDrTac31r2P6464u3AUvTk0xS4LVvT251g7IgkFunrI48ZEZGjywSOg==
+
+"d3-array@1 - 2":
+  version "2.12.1"
+  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-2.12.1.tgz#e20b41aafcdffdf5d50928004ececf815a465e81"
+  integrity sha512-B0ErZK/66mHtEsR1TkPEEkwdy+WDesimkM5gpZr5Dsg54BiTA5RXtYW5qTLIAcekaS9xfZrzBLF/OAkB3Qn1YQ==
+  dependencies:
+    internmap "^1.0.0"
+
+"d3-array@2 - 3", "d3-array@2.10.0 - 3", "d3-array@2.5.0 - 3", d3-array@3, d3-array@^3.2.0:
+  version "3.2.4"
+  resolved "https://registry.yarnpkg.com/d3-array/-/d3-array-3.2.4.tgz#15fec33b237f97ac5d7c986dc77da273a8ed0bb5"
+  integrity sha512-tdQAmyA18i4J7wprpYq8ClcxZy3SC31QMeByyCFyRt7BVHdREQZ5lpzoe5mFEYZUWe+oq8HBvk9JjpibyEV4Jg==
+  dependencies:
+    internmap "1 - 2"
+
+d3-axis@3:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/d3-axis/-/d3-axis-3.0.0.tgz#c42a4a13e8131d637b745fc2973824cfeaf93322"
+  integrity sha512-IH5tgjV4jE/GhHkRV0HiVYPDtvfjHQlQfJHs0usq7M30XcSBvOotpmH1IgkcXsO/5gEQZD43B//fc7SRT5S+xw==
+
+d3-brush@3:
+  version "3.0.0"
+  resolved "https://registry.yarnpkg.com/d3-brush/-/d3-brush-3.0.0.tgz#6f767c4ed8dcb79de7ede3e1c0f89e63ef64d31c"
+  integrity sha512-ALnjWlVYkXsVIGlOsuWH1+3udkYFI48Ljihfnh8FZPF2QS9o+PzGLBslO0PjzVoHLZ2KCVgAM8NVkXPJB2aNnQ==
+  dependencies:
+    d3-dispatch "1 - 3"
+    d3-drag "2 - 3"
+    d3-interpolate "1 - 3"
+    d3-selection "3"
+    d3-transition "3"
+
+d3-chord@3:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/d3-chord/-/d3-chord-3.0.1.tgz#d156d61f485fce8327e6abf339cb41d8cbba6966"
+  integrity sha512-VE5S6TNa+j8msksl7HwjxMHDM2yNK3XCkusIlpX5kwauBfXuyLAtNg9jCp/iHH61tgI4sb6R/EIMWCqEIdjT/g==
+  dependencies:
+    d3-path "1 - 3"
+
+"d3-color@1 - 3", d3-color@3:
   version "3.1.0"
   resolved "https://registry.yarnpkg.com/d3-color/-/d3-color-3.1.0.tgz#395b2833dfac71507f12ac2f7af23bf819de24e2"
   integrity sha512-zg/chbXyeBtMQ1LbD/WSoW2DpC3I0mpmPdW+ynRTj/x2DAWYrIY7qeZIHidozwV24m4iavr15lNwIwLxRmOxhA==
 
-"d3-dispatch@1 - 3":
+d3-contour@4:
+  version "4.0.2"
+  resolved "https://registry.yarnpkg.com/d3-contour/-/d3-contour-4.0.2.tgz#bb92063bc8c5663acb2422f99c73cbb6c6ae3bcc"
+  integrity sha512-4EzFTRIikzs47RGmdxbeUvLWtGedDUNkTcmzoeyg4sP/dvCexO47AaQL7VKy/gul85TOxw+IBgA8US2xwbToNA==
+  dependencies:
+    d3-array "^3.2.0"
+
+d3-delaunay@6:
+  version "6.0.4"
+  resolved "https://registry.yarnpkg.com/d3-delaunay/-/d3-delaunay-6.0.4.tgz#98169038733a0a5babbeda55054f795bb9e4a58b"
+  integrity sha512-mdjtIZ1XLAM8bm/hx3WwjfHt6Sggek7qH043O8KEjDXN40xi3vx/6pYSVTwLjEgiXQTbvaouWKynLBiUZ6SK6A==
+  dependencies:
+    delaunator "5"
+
+"d3-dispatch@1 - 3", d3-dispatch@3:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/d3-dispatch/-/d3-dispatch-3.0.1.tgz#5fc75284e9c2375c36c839411a0cf550cbfc4d5e"
   integrity sha512-rzUyPU/S7rwUflMyLc1ETDeBj0NRuHKKAcvukozwhshr6g6c5d8zh4c2gQjY2bZ0dXeGLWc1PF174P2tVvKhfg==
 
-"d3-drag@2 - 3", d3-drag@^3.0.0:
+"d3-drag@2 - 3", d3-drag@3, d3-drag@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/d3-drag/-/d3-drag-3.0.0.tgz#994aae9cd23c719f53b5e10e3a0a6108c69607ba"
   integrity sha512-pWbUJLdETVA8lQNJecMxoXfH6x+mO2UQo8rSmZ+QqxcbyA3hfeprFgIT//HW2nlHChWeIIMwS2Fq+gEARkhTkg==
@@ -1389,12 +1723,28 @@ csstype@^3.2.3:
     d3-dispatch "1 - 3"
     d3-selection "3"
 
-"d3-ease@1 - 3":
+"d3-dsv@1 - 3", d3-dsv@3:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/d3-dsv/-/d3-dsv-3.0.1.tgz#c63af978f4d6a0d084a52a673922be2160789b73"
+  integrity sha512-UG6OvdI5afDIFP9w4G0mNq50dSOsXHJaRE8arAS5o9ApWnIElp8GZw1Dun8vP8OyHOZ/QJUKUJwxiiCCnUwm+Q==
+  dependencies:
+    commander "7"
+    iconv-lite "0.6"
+    rw "1"
+
+"d3-ease@1 - 3", d3-ease@3:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/d3-ease/-/d3-ease-3.0.1.tgz#9658ac38a2140d59d346160f1f6c30fda0bd12f4"
   integrity sha512-wR/XK3D3XcLIZwpbvQwQ5fK+8Ykds1ip7A2Txe0yxncXSdq1L9skcG7blcedkOX+ZcgxGAmLX1FrRGbADwzi0w==
 
-d3-force@^3.0.0:
+d3-fetch@3:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/d3-fetch/-/d3-fetch-3.0.1.tgz#83141bff9856a0edb5e38de89cdcfe63d0a60a22"
+  integrity sha512-kpkQIM20n3oLVBKGg6oHrUchHM3xODkTzjMoj7aWQFq5QEM+R6E4WkzT5+tojDY7yjez8KgCBRoj4aEr99Fdqw==
+  dependencies:
+    d3-dsv "1 - 3"
+
+d3-force@3, d3-force@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/d3-force/-/d3-force-3.0.0.tgz#3e2ba1a61e70888fe3d9194e30d6d14eece155c4"
   integrity sha512-zxV/SsA+U4yte8051P4ECydjD/S+qeYtnaIyAs9tgHCqfguma/aAQDjo85A9Z6EKhBirHRJHXIgJUlffT4wdLg==
@@ -1403,29 +1753,121 @@ d3-force@^3.0.0:
     d3-quadtree "1 - 3"
     d3-timer "1 - 3"
 
-"d3-interpolate@1 - 3":
+"d3-format@1 - 3", d3-format@3:
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/d3-format/-/d3-format-3.1.2.tgz#01fdb46b58beb1f55b10b42ad70b6e344d5eb2ae"
+  integrity sha512-AJDdYOdnyRDV5b6ArilzCPPwc1ejkHcoyFarqlPqT7zRYjhavcT3uSrqcMvsgh2CgoPbK3RCwyHaVyxYcP2Arg==
+
+d3-geo@3:
+  version "3.1.1"
+  resolved "https://registry.yarnpkg.com/d3-geo/-/d3-geo-3.1.1.tgz#6027cf51246f9b2ebd64f99e01dc7c3364033a4d"
+  integrity sha512-637ln3gXKXOwhalDzinUgY83KzNWZRKbYubaG+fGVuc/dxO64RRljtCTnf5ecMyE1RIdtqpkVcq0IbtU2S8j2Q==
+  dependencies:
+    d3-array "2.5.0 - 3"
+
+d3-hierarchy@3:
+  version "3.1.2"
+  resolved "https://registry.yarnpkg.com/d3-hierarchy/-/d3-hierarchy-3.1.2.tgz#b01cd42c1eed3d46db77a5966cf726f8c09160c6"
+  integrity sha512-FX/9frcub54beBdugHjDCdikxThEqjnR93Qt7PvQTOHxyiNCAlvMrHhclk3cD5VeAaq9fxmfRp+CnWw9rEMBuA==
+
+"d3-interpolate@1 - 3", "d3-interpolate@1.2.0 - 3", d3-interpolate@3:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/d3-interpolate/-/d3-interpolate-3.0.1.tgz#3c47aa5b32c5b3dfb56ef3fd4342078a632b400d"
   integrity sha512-3bYs1rOD33uo8aqJfKP3JWPAibgw8Zm2+L9vBKEHJ2Rg+viTR7o5Mmv5mZcieN+FRYaAOWX5SJATX6k1PWz72g==
   dependencies:
     d3-color "1 - 3"
 
-"d3-quadtree@1 - 3":
+d3-path@1:
+  version "1.0.9"
+  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-1.0.9.tgz#48c050bb1fe8c262493a8caf5524e3e9591701cf"
+  integrity sha512-VLaYcn81dtHVTjEHd8B+pbe9yHWpXKZUC87PzoFmsFrJqgFwDe/qxfp5MlfsfM1V5E/iVt0MmEbWQ7FVIXh/bg==
+
+"d3-path@1 - 3", d3-path@3, d3-path@^3.1.0:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/d3-path/-/d3-path-3.1.0.tgz#22df939032fb5a71ae8b1800d61ddb7851c42526"
+  integrity sha512-p3KP5HCf/bvjBSSKuXid6Zqijx7wIfNW+J/maPs+iwR35at5JCbLUT0LzF1cnjbCHWhqzQTIN2Jpe8pRebIEFQ==
+
+d3-polygon@3:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/d3-polygon/-/d3-polygon-3.0.1.tgz#0b45d3dd1c48a29c8e057e6135693ec80bf16398"
+  integrity sha512-3vbA7vXYwfe1SYhED++fPUQlWSYTTGmFmQiany/gdbiWgU/iEyQzyymwL9SkJjFFuCS4902BSzewVGsHHmHtXg==
+
+"d3-quadtree@1 - 3", d3-quadtree@3:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/d3-quadtree/-/d3-quadtree-3.0.1.tgz#6dca3e8be2b393c9a9d514dabbd80a92deef1a4f"
   integrity sha512-04xDrxQTDTCFwP5H6hRhsRcb9xxv2RzkcsygFzmkSIOJy3PeRJP7sNk3VRIbKXcog561P9oU0/rVH6vDROAgUw==
 
+d3-random@3:
+  version "3.0.1"
+  resolved "https://registry.yarnpkg.com/d3-random/-/d3-random-3.0.1.tgz#d4926378d333d9c0bfd1e6fa0194d30aebaa20f4"
+  integrity sha512-FXMe9GfxTxqd5D6jFsQ+DJ8BJS4E/fT5mqqdjovykEB2oFbTMDVdg1MGFxfQW+FBOGoB++k8swBrgwSHT1cUXQ==
+
+d3-sankey@^0.12.3:
+  version "0.12.3"
+  resolved "https://registry.yarnpkg.com/d3-sankey/-/d3-sankey-0.12.3.tgz#b3c268627bd72e5d80336e8de6acbfec9d15d01d"
+  integrity sha512-nQhsBRmM19Ax5xEIPLMY9ZmJ/cDvd1BG3UVvt5h3WRxKg5zGRbvnteTyWAbzeSvlh3tW7ZEmq4VwR5mB3tutmQ==
+  dependencies:
+    d3-array "1 - 2"
+    d3-shape "^1.2.0"
+
+d3-scale-chromatic@3:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/d3-scale-chromatic/-/d3-scale-chromatic-3.1.0.tgz#34c39da298b23c20e02f1a4b239bd0f22e7f1314"
+  integrity sha512-A3s5PWiZ9YCXFye1o246KoscMWqf8BsD9eRiJ3He7C9OBaxKhAd5TFCdEx/7VbKtxxTsu//1mMJFrEt572cEyQ==
+  dependencies:
+    d3-color "1 - 3"
+    d3-interpolate "1 - 3"
+
+d3-scale@4:
+  version "4.0.2"
+  resolved "https://registry.yarnpkg.com/d3-scale/-/d3-scale-4.0.2.tgz#82b38e8e8ff7080764f8dcec77bd4be393689396"
+  integrity sha512-GZW464g1SH7ag3Y7hXjf8RoUuAFIqklOAq3MRl4OaWabTFJY9PN/E1YklhXLh+OQ3fM9yS2nOkCoS+WLZ6kvxQ==
+  dependencies:
+    d3-array "2.10.0 - 3"
+    d3-format "1 - 3"
+    d3-interpolate "1.2.0 - 3"
+    d3-time "2.1.1 - 3"
+    d3-time-format "2 - 4"
+
 "d3-selection@2 - 3", d3-selection@3, d3-selection@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/d3-selection/-/d3-selection-3.0.0.tgz#c25338207efa72cc5b9bd1458a1a41901f1e1b31"
   integrity sha512-fmTRWbNMmsmWq6xJV8D19U/gw/bwrHfNXxrIN+HfZgnzqTHp9jOmKMhsTUjXOJnZOdZY9Q28y4yebKzqDKlxlQ==
 
-"d3-timer@1 - 3":
+d3-shape@3:
+  version "3.2.0"
+  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-3.2.0.tgz#a1a839cbd9ba45f28674c69d7f855bcf91dfc6a5"
+  integrity sha512-SaLBuwGm3MOViRq2ABk3eLoxwZELpH6zhl3FbAoJ7Vm1gofKx6El1Ib5z23NUEhF9AsGl7y+dzLe5Cw2AArGTA==
+  dependencies:
+    d3-path "^3.1.0"
+
+d3-shape@^1.2.0:
+  version "1.3.7"
+  resolved "https://registry.yarnpkg.com/d3-shape/-/d3-shape-1.3.7.tgz#df63801be07bc986bc54f63789b4fe502992b5d7"
+  integrity sha512-EUkvKjqPFUAZyOlhY5gzCxCeI0Aep04LwIRpsZ/mLFelJiUfnK56jo5JMDSE7yyP2kLSb6LtF+S5chMk7uqPqw==
+  dependencies:
+    d3-path "1"
+
+"d3-time-format@2 - 4", d3-time-format@4:
+  version "4.1.0"
+  resolved "https://registry.yarnpkg.com/d3-time-format/-/d3-time-format-4.1.0.tgz#7ab5257a5041d11ecb4fe70a5c7d16a195bb408a"
+  integrity sha512-dJxPBlzC7NugB2PDLwo9Q8JiTR3M3e4/XANkreKSUxF8vvXKqm1Yfq4Q5dl8budlunRVlUUaDUgFt7eA8D6NLg==
+  dependencies:
+    d3-time "1 - 3"
+
+"d3-time@1 - 3", "d3-time@2.1.1 - 3", d3-time@3:
+  version "3.1.0"
+  resolved "https://registry.yarnpkg.com/d3-time/-/d3-time-3.1.0.tgz#9310db56e992e3c0175e1ef385e545e48a9bb5c7"
+  integrity sha512-VqKjzBLejbSMT4IgbmVgDjpkYrNWUYJnbCGo874u7MMKIWsILRX+OpX/gTk8MqjpT1A/c6HY2dCA77ZN0lkQ2Q==
+  dependencies:
+    d3-array "2 - 3"
+
+"d3-timer@1 - 3", d3-timer@3:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/d3-timer/-/d3-timer-3.0.1.tgz#6284d2a2708285b1abb7e201eda4380af35e63b0"
   integrity sha512-ndfJ/JxxMd3nw31uyKoY2naivF+r29V+Lc0svZxe1JvvIRmi8hUsrMvdOwgS1o6uBHmiz91geQ0ylPP0aj1VUA==
 
-"d3-transition@2 - 3":
+"d3-transition@2 - 3", d3-transition@3, d3-transition@^3.0.1:
   version "3.0.1"
   resolved "https://registry.yarnpkg.com/d3-transition/-/d3-transition-3.0.1.tgz#6869fdde1448868077fdd5989200cb61b2a1645f"
   integrity sha512-ApKvfjsSR6tg06xrL434C0WydLr7JewBB3V+/39RMHsaXTOG0zmt/OAXeng5M5LBm0ojmxJrpomQVZ1aPvBL4w==
@@ -1436,7 +1878,7 @@ d3-force@^3.0.0:
     d3-interpolate "1 - 3"
     d3-timer "1 - 3"
 
-d3-zoom@^3.0.0:
+d3-zoom@3, d3-zoom@^3.0.0:
   version "3.0.0"
   resolved "https://registry.yarnpkg.com/d3-zoom/-/d3-zoom-3.0.0.tgz#d13f4165c73217ffeaa54295cd6969b3e7aee8f3"
   integrity sha512-b8AmV3kfQaqWAuacbPuNbL6vahnOJflOhexLzMMNLga62+/nh0JzvJ0aO/5a5MVgUFGS7Hu1P9P03o3fJkDCyw==
@@ -1447,7 +1889,51 @@ d3-zoom@^3.0.0:
     d3-selection "2 - 3"
     d3-transition "2 - 3"
 
-dayjs@^1.11.13:
+d3@^7.9.0:
+  version "7.9.0"
+  resolved "https://registry.yarnpkg.com/d3/-/d3-7.9.0.tgz#579e7acb3d749caf8860bd1741ae8d371070cd5d"
+  integrity sha512-e1U46jVP+w7Iut8Jt8ri1YsPOvFpg46k+K8TpCb0P+zjCkjkPnV7WzfDJzMHy1LnA+wj5pLT1wjO901gLXeEhA==
+  dependencies:
+    d3-array "3"
+    d3-axis "3"
+    d3-brush "3"
+    d3-chord "3"
+    d3-color "3"
+    d3-contour "4"
+    d3-delaunay "6"
+    d3-dispatch "3"
+    d3-drag "3"
+    d3-dsv "3"
+    d3-ease "3"
+    d3-fetch "3"
+    d3-force "3"
+    d3-format "3"
+    d3-geo "3"
+    d3-hierarchy "3"
+    d3-interpolate "3"
+    d3-path "3"
+    d3-polygon "3"
+    d3-quadtree "3"
+    d3-random "3"
+    d3-scale "4"
+    d3-scale-chromatic "3"
+    d3-selection "3"
+    d3-shape "3"
+    d3-time "3"
+    d3-time-format "4"
+    d3-timer "3"
+    d3-transition "3"
+    d3-zoom "3"
+
+dagre-d3-es@7.0.14:
+  version "7.0.14"
+  resolved "https://registry.yarnpkg.com/dagre-d3-es/-/dagre-d3-es-7.0.14.tgz#1272276e26457cf3b97dac569f8f0531ec33c377"
+  integrity sha512-P4rFMVq9ESWqmOgK+dlXvOtLwYg0i7u0HBGJER0LZDJT2VHIPAMZ/riPxqJceWMStH5+E61QxFra9kIS3AqdMg==
+  dependencies:
+    d3 "^7.9.0"
+    lodash-es "^4.17.21"
+
+dayjs@^1.11.13, dayjs@^1.11.20:
   version "1.11.21"
   resolved "https://registry.yarnpkg.com/dayjs/-/dayjs-1.11.21.tgz#57f87562e62de76f3c704bd2b8d522fc33068eb2"
   integrity sha512-98IT+HOahAisibz/yjKbzuOBwYcjJ7BCLPzARyHiyEBmRz4fatF+KPJszEHXsGYjUG234aH/cOjW1wwTbKUZlA==
@@ -1471,6 +1957,13 @@ defu@^6.1.5:
   resolved "https://registry.yarnpkg.com/defu/-/defu-6.1.7.tgz#72543567c8e9f97ff13ce402b6dbe09ac5ae4d23"
   integrity sha512-7z22QmUWiQ/2d0KkdYmANbRUVABpZ9SNYyH5vx6PZ+nE5bcC0l7uFvEfHlyld/HcGBFTL536ClDt3DEcSlEJAQ==
 
+delaunator@5:
+  version "5.1.0"
+  resolved "https://registry.yarnpkg.com/delaunator/-/delaunator-5.1.0.tgz#d13271fbf3aff6753f9ea6e235557f20901046ea"
+  integrity sha512-AGrQ4QSgssa1NGmWmLPqN5NY2KajF5MqxetNEO+o0n3ZwZZeTmt7bBnvzHWrmkZFxGgr4HdyFgelzgi06otLuQ==
+  dependencies:
+    robust-predicates "^3.0.2"
+
 dequal@^2.0.0:
   version "2.0.3"
   resolved "https://registry.yarnpkg.com/dequal/-/dequal-2.0.3.tgz#2644214f1997d39ed0ee0ece72335490a7ac67be"
@@ -1498,6 +1991,13 @@ dlv@^1.1.3:
   resolved "https://registry.yarnpkg.com/dlv/-/dlv-1.1.3.tgz#5c198a8a11453596e751494d49874bc7732f2e79"
   integrity sha512-+HlytyjlPKnIG8XuRG8WvmBP8xs8P71y+SKKS6ZXWoEgLuePxtDoUEiH7WkdePWrQ5JBpE6aoVqfZfJUQkjXwA==
 
+dompurify@^3.3.3:
+  version "3.4.13"
+  resolved "https://registry.yarnpkg.com/dompurify/-/dompurify-3.4.13.tgz#fc28949d59f92d62e28a3a764bcbeee35897a1be"
+  integrity sha512-2vmYIoqjze2d+kakP8S/nS5shfsl587kzwEjcGlTdiksUVgFHnFCsLYDVj/JNqJVOQZGSYBTmuycv0PodwmnMQ==
+  optionalDependencies:
+    "@types/trusted-types" "^2.0.7"
+
 dompurify@^3.4.0:
   version "3.4.11"
   resolved "https://registry.yarnpkg.com/dompurify/-/dompurify-3.4.11.tgz#29c8ba496475f279ef4015784068452fb14a0680"
@@ -1544,6 +2044,11 @@ es-errors@^1.3.0:
   resolved "https://registry.yarnpkg.com/es-errors/-/es-errors-1.3.0.tgz#05f75a25dab98e4fb1dcd5e1472c0546d5057c8f"
   integrity sha512-Zf5H2Kxt2xjTvbJvP2ZWLEICxA6j+hAmMzIlypy4xcBg1vKVnx89Wy0GbS+kf5cwCVFFzdCFh2XSCFNULS6csw==
 
+es-toolkit@^1.45.1:
+  version "1.50.0"
+  resolved "https://registry.yarnpkg.com/es-toolkit/-/es-toolkit-1.50.0.tgz#2665c21d0ab76286f478ece3a235463097aeabd3"
+  integrity sha512-OyZKhUVvEep9ITEiwHn8GKnMRQIVqoSIX7WnRbkWgJkllCujilqP2rD0u979tkl8wqyc8ICwlc1UBVv/Sl1G6w==
+
 escalade@^3.2.0:
   version "3.2.0"
   resolved "https://registry.yarnpkg.com/escalade/-/escalade-3.2.0.tgz#011a3f69856ba189dffa7dc8fcce99d2a87903e5"
@@ -1735,6 +2240,11 @@ grid-layout-plus@^1.1.0:
     "@vexip-ui/utils" "^2.16.1"
     interactjs "^1.10.27"
 
+hachure-fill@^0.5.2:
+  version "0.5.2"
+  resolved "https://registry.yarnpkg.com/hachure-fill/-/hachure-fill-0.5.2.tgz#d19bc4cc8750a5962b47fb1300557a85fcf934cc"
+  integrity sha512-3GKBOn+m2LX9iq+JC1064cSFprJY4jL1jCXTcpnfER5HYE2l/4EfWSGzkPa/ZDBmYI0ZOEj5VHV/eKnPGkHuOg==
+
 has-flag@^4.0.0:
   version "4.0.0"
   resolved "https://registry.yarnpkg.com/has-flag/-/has-flag-4.0.0.tgz#944771fd9c81c81265c4d6941860da06bb59479b"
@@ -1752,6 +2262,13 @@ highlight.js@^11.11.1, highlight.js@~11.11.0:
   resolved "https://registry.yarnpkg.com/highlight.js/-/highlight.js-11.11.1.tgz#fca06fa0e5aeecf6c4d437239135fabc15213585"
   integrity sha512-Xwwo44whKBVCYoliBQwaPvtd/2tYFkRQtXDWj1nackaV2JPXx3L0+Jvd8/qCJ2p+ML0/XVkJ2q+Mr+UVdpJK5w==
 
+iconv-lite@0.6:
+  version "0.6.3"
+  resolved "https://registry.yarnpkg.com/iconv-lite/-/iconv-lite-0.6.3.tgz#a52f80bf38da1952eb5c681790719871a1a72501"
+  integrity sha512-4fCk79wshMdzMp2rH06qWrJE4iolqLhCUH+OiuIgU++RB0+94NlDL81atO7GX55uUKueo0txHNtvEyI6D7WdMw==
+  dependencies:
+    safer-buffer ">= 2.1.2 < 3.0.0"
+
 idb-keyval@^6.2.0:
   version "6.2.5"
   resolved "https://registry.yarnpkg.com/idb-keyval/-/idb-keyval-6.2.5.tgz#fce562b07de93507de5ebf89dac9fd8795a9dcea"
@@ -1779,6 +2296,16 @@ interactjs@^1.10.27:
   dependencies:
     "@interactjs/types" "1.10.27"
 
+"internmap@1 - 2":
+  version "2.0.3"
+  resolved "https://registry.yarnpkg.com/internmap/-/internmap-2.0.3.tgz#6685f23755e43c524e251d29cbc97248e3061009"
+  integrity sha512-5Hh7Y1wQbvY5ooGgPbDaL5iYLAPzMTUrjMulskHLH6wnv/A+1q5rgEaiuqEjB+oxGXIVZs1FF+R/KPN3ZSQYYg==
+
+internmap@^1.0.0:
+  version "1.0.1"
+  resolved "https://registry.yarnpkg.com/internmap/-/internmap-1.0.1.tgz#0017cc8a3b99605f0302f2b198d272e015e5df95"
+  integrity sha512-lDB5YccMydFBtasVtxnZ3MRBHuaoE8GKsppq+EchKL2U4nK/DmEpPHNH8MZe5HkMtpSiTSOZwfN0tzYjO/lJEw==
+
 is-binary-path@~2.1.0:
   version "2.1.0"
   resolved "https://registry.yarnpkg.com/is-binary-path/-/is-binary-path-2.1.0.tgz#ea1f7f3b80f064236e83470f86c09c254fb45b09"
@@ -1830,6 +2357,28 @@ js-tokens@^9.0.1:
   resolved "https://registry.yarnpkg.com/js-tokens/-/js-tokens-9.0.1.tgz#2ec43964658435296f6761b34e10671c2d9527f4"
   integrity sha512-mxa9E9ITFOt0ban3j6L5MpjwegGz6lBQmM1IJkWeBZGcMxto50+eWdjC/52xDbS2vy0k7vIMK0Fe2wfL9OQSpQ==
 
+katex@^0.16.45:
+  version "0.16.47"
+  resolved "https://registry.yarnpkg.com/katex/-/katex-0.16.47.tgz#0a13a42c2deb4f74e61f162d440b9165a548030f"
+  integrity sha512-Eeo8Ys1doU1z+x8AZsPpQu+p/QcZBI5PeOo7QGQdy2x2m0MU/hYagBbGOmXwr5KVbEfVuWv9LpnQWeehogurjg==
+  dependencies:
+    commander "^8.3.0"
+
+khroma@^2.1.0:
+  version "2.1.0"
+  resolved "https://registry.yarnpkg.com/khroma/-/khroma-2.1.0.tgz#45f2ce94ce231a437cf5b63c2e886e6eb42bbbb1"
+  integrity sha512-Ls993zuzfayK269Svk9hzpeGUKob/sIgZzyHYdjQoAdQetRKpOLj+k/QQQ/6Qi0Yz65mlROrfd+Ev+1+7dz9Kw==
+
+layout-base@^1.0.0:
+  version "1.0.2"
+  resolved "https://registry.yarnpkg.com/layout-base/-/layout-base-1.0.2.tgz#1291e296883c322a9dd4c5dd82063721b53e26e2"
+  integrity sha512-8h2oVEZNktL4BH2JCOI90iD1yXwL6iNW7KcCKT2QZgQJR2vbqDsldCTPRU9NifTCqHZci57XvQQ15YTu+sTYPg==
+
+layout-base@^2.0.0:
+  version "2.0.1"
+  resolved "https://registry.yarnpkg.com/layout-base/-/layout-base-2.0.1.tgz#d0337913586c90f9c2c075292069f5c2da5dd285"
+  integrity sha512-dp3s92+uNI1hWIpPGH3jK2kxE2lMjdXdr+DH8ynZHpd6PUlH6x6cbuXnoMmiNumznqaNO31xu9e79F0uuZ0JFg==
+
 lightningcss-android-arm64@1.32.0:
   version "1.32.0"
   resolved "https://registry.yarnpkg.com/lightningcss-android-arm64/-/lightningcss-android-arm64-1.32.0.tgz#f033885116dfefd9c6f54787523e3514b61e1968"
@@ -1928,6 +2477,11 @@ local-pkg@^1.1.1, local-pkg@^1.1.2, local-pkg@^1.2.0:
     pkg-types "^2.3.0"
     quansync "^0.2.11"
 
+lodash-es@^4.17.21:
+  version "4.18.1"
+  resolved "https://registry.yarnpkg.com/lodash-es/-/lodash-es-4.18.1.tgz#b962eeb80d9d983a900bf342961fb7418ca10b1d"
+  integrity sha512-J8xewKD/Gk22OZbhpOVSwcs60zhd95ESDwezOFuA3/099925PdHJ7OFHNTGtajL3AlZkykD32HykiMo+BIBI8A==
+
 log-symbols@^4.1.0:
   version "4.1.0"
   resolved "https://registry.yarnpkg.com/log-symbols/-/log-symbols-4.1.0.tgz#3fbdbb95b4683ac9fc785111e792e558d4abd503"
@@ -1967,6 +2521,11 @@ marked@^15.0.12:
   resolved "https://registry.yarnpkg.com/marked/-/marked-15.0.12.tgz#30722c7346e12d0a2d0207ab9b0c4f0102d86c4e"
   integrity sha512-8dD6FusOQSrpv9Z1rdNMdlSgQOIP880DHqnohobOmYLElGEqAL/JvxvuxZO16r4HtjTlfPRDC1hbvxC9dPN2nA==
 
+marked@^16.3.0:
+  version "16.4.2"
+  resolved "https://registry.yarnpkg.com/marked/-/marked-16.4.2.tgz#4959a64be6c486f0db7467ead7ce288de54290a3"
+  integrity sha512-TI3V8YYWvkVf3KJe1dRkpnjs68JUPyEa5vjKrp1XEEJUAOaQc+Qj+L1qWbPd0SJuAdQkFU0h73sXXqwDYxsiDA==
+
 marked@^17.0.1:
   version "17.0.6"
   resolved "https://registry.yarnpkg.com/marked/-/marked-17.0.6.tgz#2a97586a272d3be5880f198e020b74ad27cf86ba"
@@ -1977,6 +2536,33 @@ merge2@^1.3.0:
   resolved "https://registry.yarnpkg.com/merge2/-/merge2-1.4.1.tgz#4368892f885e907455a6fd7dc55c0c9d404990ae"
   integrity sha512-8q7VEgMJW4J8tcfVPy8g09NcQwZdbwFEqhe/WZkoIzjn/3TGDwtOCYtXGxA3O8tPzpczCCDgv+P2P5y00ZJOOg==
 
+mermaid@^11.16.1:
+  version "11.16.1"
+  resolved "https://registry.yarnpkg.com/mermaid/-/mermaid-11.16.1.tgz#57ae2342f6c45b967113b04c9258430bdd057ee8"
+  integrity sha512-TQsq6u22fAn3rek5VOubrhKPo1g5hwC3FXUN9hiyupTckcYiGuuKGkNQrKYwGJkXUxZdojwRG46gsSCFZMDp4g==
+  dependencies:
+    "@braintree/sanitize-url" "^7.1.2"
+    "@iconify/utils" "^3.0.2"
+    "@mermaid-js/parser" "^1.2.0"
+    "@types/d3" "^7.4.3"
+    "@upsetjs/venn.js" "^2.0.0"
+    cytoscape "^3.33.3"
+    cytoscape-cose-bilkent "^4.1.0"
+    cytoscape-fcose "^2.2.0"
+    d3 "^7.9.0"
+    d3-sankey "^0.12.3"
+    dagre-d3-es "7.0.14"
+    dayjs "^1.11.20"
+    dompurify "^3.3.3"
+    es-toolkit "^1.45.1"
+    katex "^0.16.45"
+    khroma "^2.1.0"
+    marked "^16.3.0"
+    roughjs "^4.6.6"
+    stylis "^4.3.6"
+    ts-dedent "^2.2.0"
+    uuid "^11.1.0 || ^12 || ^13 || ^14.0.0"
+
 micromatch@^4.0.8:
   version "4.0.8"
   resolved "https://registry.yarnpkg.com/micromatch/-/micromatch-4.0.8.tgz#d66fa18f3a47076789320b9b1af32bd86d9fa202"
@@ -2086,6 +2672,11 @@ package-manager-detector@^1.3.0:
   resolved "https://registry.yarnpkg.com/package-manager-detector/-/package-manager-detector-1.6.0.tgz#70d0cf0aa02c877eeaf66c4d984ede0be9130734"
   integrity sha512-61A5ThoTiDG/C8s8UMZwSorAGwMJ0ERVGj2OjoW5pAalsNOg15+iQiPzrLJ4jhZ1HJzmC2PIHT2oEiH3R5fzNA==
 
+path-data-parser@0.1.0, path-data-parser@^0.1.0:
+  version "0.1.0"
+  resolved "https://registry.yarnpkg.com/path-data-parser/-/path-data-parser-0.1.0.tgz#8f5ba5cc70fc7becb3dcefaea08e2659aba60b8c"
+  integrity sha512-NOnmBpt5Y2RWbuv0LMzsayp3lVylAHLPUTut412ZA3l+C4uw4ZVkQbjShYCQ8TCpUMdPapr4YjUqLYD6v68j+w==
+
 path-parse@^1.0.7:
   version "1.0.7"
   resolved "https://registry.yarnpkg.com/path-parse/-/path-parse-1.0.7.tgz#fbc114b60ca42b30d9daf5858e4bd68bbedb6735"
@@ -2146,6 +2737,19 @@ pkg-types@^2.1.0, pkg-types@^2.3.0:
     exsolve "^1.0.8"
     pathe "^2.0.3"
 
+points-on-curve@0.2.0, points-on-curve@^0.2.0:
+  version "0.2.0"
+  resolved "https://registry.yarnpkg.com/points-on-curve/-/points-on-curve-0.2.0.tgz#7dbb98c43791859434284761330fa893cb81b4d1"
+  integrity sha512-0mYKnYYe9ZcqMCWhUjItv/oHjvgEsfKvnUTg8sAtnHr3GVy7rGkXCb6d5cSyqrWqL4k81b9CPg3urd+T7aop3A==
+
+points-on-path@^0.2.1:
+  version "0.2.1"
+  resolved "https://registry.yarnpkg.com/points-on-path/-/points-on-path-0.2.1.tgz#553202b5424c53bed37135b318858eacff85dd52"
+  integrity sha512-25ClnWWuw7JbWZcgqY/gJ4FQWadKxGWk+3kR/7kD0tCaDtPPMj7oHu2ToLaVhfpnHrZzYby2w6tUA0eOIuUg8g==
+  dependencies:
+    path-data-parser "0.1.0"
+    points-on-curve "0.2.0"
+
 postcss-import@^15.1.0:
   version "15.1.0"
   resolved "https://registry.yarnpkg.com/postcss-import/-/postcss-import-15.1.0.tgz#41c64ed8cc0e23735a9698b3249ffdbf704adc70"
@@ -2401,6 +3005,11 @@ reusify@^1.0.4:
   resolved "https://registry.yarnpkg.com/reusify/-/reusify-1.1.0.tgz#0fe13b9522e1473f51b558ee796e08f11f9b489f"
   integrity sha512-g6QUff04oZpHs0eG5p83rFLhHeV00ug/Yf9nZM6fLeUrPguBTkTQOdpAWWspMh55TZfVQDPaN3NQJfbVRAxdIw==
 
+robust-predicates@^3.0.2:
+  version "3.0.3"
+  resolved "https://registry.yarnpkg.com/robust-predicates/-/robust-predicates-3.0.3.tgz#1099061b3349e2c5abec6c2ab0acd440d24d4062"
+  integrity sha512-NS3levdsRIUOmiJ8FZWCP7LG3QpJyrs/TE0Zpf1yvZu8cAJJ6QMW92H1c7kWpdIHo8RvmLxN/o2JXTKHp74lUA==
+
 rolldown@1.0.3:
   version "1.0.3"
   resolved "https://registry.yarnpkg.com/rolldown/-/rolldown-1.0.3.tgz#db88a3008fb0e28230a00423727ce75ba32121ac"
@@ -2430,6 +3039,16 @@ rope-sequence@^1.3.0:
   resolved "https://registry.yarnpkg.com/rope-sequence/-/rope-sequence-1.3.4.tgz#df85711aaecd32f1e756f76e43a415171235d425"
   integrity sha512-UT5EDe2cu2E/6O4igUr5PSFs23nvvukicWHx6GnOPlHAiiYbzNuCRQCuiUdHJQcqKalLKlrYJnjY0ySGsXNQXQ==
 
+roughjs@^4.6.6:
+  version "4.6.6"
+  resolved "https://registry.yarnpkg.com/roughjs/-/roughjs-4.6.6.tgz#1059f49a5e0c80dee541a005b20cc322b222158b"
+  integrity sha512-ZUz/69+SYpFN/g/lUlo2FXcIjRkSu3nDarreVdGGndHEBJ6cXPdKguS8JGxwj5HA5xIbVKSmLgr5b3AWxtRfvQ==
+  dependencies:
+    hachure-fill "^0.5.2"
+    path-data-parser "^0.1.0"
+    points-on-curve "^0.2.0"
+    points-on-path "^0.2.1"
+
 run-parallel@^1.1.9:
   version "1.2.0"
   resolved "https://registry.yarnpkg.com/run-parallel/-/run-parallel-1.2.0.tgz#66d1368da7bdf921eb9d95bd1a9229e7f21a43ee"
@@ -2437,11 +3056,21 @@ run-parallel@^1.1.9:
   dependencies:
     queue-microtask "^1.2.2"
 
+rw@1:
+  version "1.3.3"
+  resolved "https://registry.yarnpkg.com/rw/-/rw-1.3.3.tgz#3f862dfa91ab766b14885ef4d01124bfda074fb4"
+  integrity sha512-PdhdWy89SiZogBLaw42zdeqtRJ//zFd2PgQavcICDUgJT5oW10QCRKbJ6bg4r0/UY2M6BWd5tkxuGFRvCkgfHQ==
+
 safe-buffer@~5.2.0:
   version "5.2.1"
   resolved "https://registry.yarnpkg.com/safe-buffer/-/safe-buffer-5.2.1.tgz#1eaf9fa9bdb1fdd4ec75f58f9cdb4e6b7827eec6"
   integrity sha512-rp3So07KcdmmKbGvgaNxQSJr7bGVSVk5S9Eq1F+ppbRo70+YeaDxkw5Dd8NPN+GD6bjnYm2VuPuCXmpuYvmCXQ==
 
+"safer-buffer@>= 2.1.2 < 3.0.0":
+  version "2.1.2"
+  resolved "https://registry.yarnpkg.com/safer-buffer/-/safer-buffer-2.1.2.tgz#44fa161b0187b9549dd84bb91802f9bd8385cd6a"
+  integrity sha512-YZo3K82SD7Riyi0E1EQPojLz7kpepnSQI9IyPbHHg1XXXevb5dJI7tpyN2ADxGcQbHG7vcyRHk0cbwqcQriUtg==
+
 scule@^1.3.0:
   version "1.3.0"
   resolved "https://registry.yarnpkg.com/scule/-/scule-1.3.0.tgz#6efbd22fd0bb801bdcc585c89266a7d2daa8fbd3"
@@ -2511,6 +3140,11 @@ style-mod@^4.0.0, style-mod@^4.1.0:
   resolved "https://registry.yarnpkg.com/style-mod/-/style-mod-4.1.3.tgz#6e9012255bb799bdac37e288f7671b5d71bf9f73"
   integrity sha512-i/n8VsZydrugj3Iuzll8+x/00GH2vnYsk1eomD8QiRrSAeW6ItbCQDtfXCeJHd0iwiNagqjQkvpvREEPtW3IoQ==
 
+stylis@^4.3.6:
+  version "4.4.0"
+  resolved "https://registry.yarnpkg.com/stylis/-/stylis-4.4.0.tgz#c5846c9345f4bfc51bd0cbd7ca35a0744f485a5d"
+  integrity sha512-5Z9ZpRzfuH6l/UAvCPAPUo3665Nk2wLaZU3x+TLHKVzIz33+sbJqbtrYoC3KD4/uVOr2Zp+L0LySezP9OHV9yA==
+
 sucrase@^3.35.0:
   version "3.35.1"
   resolved "https://registry.yarnpkg.com/sucrase/-/sucrase-3.35.1.tgz#4619ea50393fe8bd0ae5071c26abd9b2e346bfe1"
@@ -2605,6 +3239,11 @@ to-regex-range@^5.0.1:
   dependencies:
     is-number "^7.0.0"
 
+ts-dedent@^2.2.0:
+  version "2.3.0"
+  resolved "https://registry.yarnpkg.com/ts-dedent/-/ts-dedent-2.3.0.tgz#8fac36c7902b541c154ac13a27ac467997af11f8"
+  integrity sha512-JfJeIHke7y2egdGGgRAvpCwYFUsHlM2gPcrVOxFkznt/4uzQ7HFmvE63iFHVLBJNDuyDOQgijDK/tXH/f6Msjg==
+
 ts-interface-checker@^0.1.9:
   version "0.1.13"
   resolved "https://registry.yarnpkg.com/ts-interface-checker/-/ts-interface-checker-0.1.13.tgz#784fd3d679722bc103b1b4b8030bcddb5db2a699"
@@ -2736,6 +3375,11 @@ util-deprecate@^1.0.1, util-deprecate@^1.0.2:
   resolved "https://registry.yarnpkg.com/util-deprecate/-/util-deprecate-1.0.2.tgz#450d4dc9fa70de732762fbd2d4a28981419a0ccf"
   integrity sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==
 
+"uuid@^11.1.0 || ^12 || ^13 || ^14.0.0":
+  version "14.0.1"
+  resolved "https://registry.yarnpkg.com/uuid/-/uuid-14.0.1.tgz#8a5975b3e038902bfd169a10b5202f5ec0cf3faf"
+  integrity sha512-6ZxzVpzDXDa3bJWaHilVayA+BH/1zmxCJoVgvmqJnid/gPoKHxUrS/aC/T6LGQtNHT+XHG9fXPJB4d+IrU30Ew==
+
 vite@^8.0.1:
   version "8.0.16"
   resolved "https://registry.yarnpkg.com/vite/-/vite-8.0.16.tgz#ae073866c06563d6634a90169a496e11bd84f1a6"
diff --git a/pyproject.toml b/pyproject.toml
index 09944ff..06c4252 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -12,6 +12,8 @@ dependencies = [
     "pymupdf>=1.24",        # fitz: render pages to PNG + ground-truth text
     "pymupdf4llm>=0.0.17",  # baseline page -> markdown parse
     "litellm>=1.55",        # agent LLM client (streaming tool-calling via OpenRouter)
+    "lancedb>=0.36",        # embedded vector + FTS store for retrieval (no server)
+    "model2vec>=0.7",       # static sentence embeddings (numpy-only; no torch)
 ]
 
 [build-system]
diff --git a/wikify/agent/llm.py b/wikify/agent/llm.py
index e59c1ad..cec106e 100644
--- a/wikify/agent/llm.py
+++ b/wikify/agent/llm.py
@@ -55,11 +55,15 @@ def _openrouter_model(model: str) -> str:
 	return model if model.startswith("openrouter/") else f"openrouter/{model}"
 
 
-def complete_with_tools(model: str, messages: list, tools: list, *, stream: bool = True):
+def complete_with_tools(
+	model: str, messages: list, tools: list, *, stream: bool = True, include_usage: bool = False
+):
 	"""Stream a tool-calling completion. `tools` is a list of `registry.Tool`.
 
 	Returns the litellm streaming response (iterate chunks) when `stream`, else the
-	full response object.
+	full response object. `include_usage` asks the provider for a final, choice-less chunk
+	carrying the token + cost totals — off by default because a caller that iterates
+	chunks without guarding `chunk.choices` would trip over it.
 	"""
 	key = settings.openrouter_key()
 	if not key:
@@ -82,6 +86,7 @@ def complete_with_tools(model: str, messages: list, tools: list, *, stream: bool
 		messages=messages,
 		tools=tool_schemas,
 		stream=stream,
+		stream_options={"include_usage": True} if stream and include_usage else None,
 		api_key=key,
 		num_retries=2,
 	)
diff --git a/wikify/agent/registry.py b/wikify/agent/registry.py
index 4c3616b..faf2049 100644
--- a/wikify/agent/registry.py
+++ b/wikify/agent/registry.py
@@ -32,10 +32,10 @@ class Tool:
 
 def build_default_registry() -> dict[str, Tool]:
 	"""All available tools, keyed by name (read + write/reparse/pipeline/converse)."""
-	from wikify.agent.tools import content, converse, pipeline, read, reparse, taxonomy, tree
+	from wikify.agent.tools import content, converse, pipeline, read, reparse, retrieve, taxonomy, tree
 
 	registry: dict[str, Tool] = {}
-	for module in (read, tree, taxonomy, content, reparse, pipeline, converse):
+	for module in (read, retrieve, tree, taxonomy, content, reparse, pipeline, converse):
 		for tool in module.TOOLS:
 			registry[tool.name] = tool
 	return registry
diff --git a/wikify/agent/tools/retrieve.py b/wikify/agent/tools/retrieve.py
new file mode 100644
index 0000000..7d93d40
--- /dev/null
+++ b/wikify/agent/tools/retrieve.py
@@ -0,0 +1,94 @@
+"""Retrieval tool — gives the agent loop genuine multi-hop search.
+
+`semantic_search` returns ranked excerpts *with their section ids*, so the model can chain
+search → `read_section` → search again without any loop changes. The handler calls the
+whitelisted `api.rag.search`, which is where routing and the permission pre-filter already
+live; the tool re-implements nothing.
+
+Routing is off here on purpose: the model is doing the routing, so it passes
+`section_type` / `mode` itself and we don't pay for a second classifier call per hop.
+"""
+
+from __future__ import annotations
+
+from frappe import _
+
+from wikify.agent.context import Ctx
+from wikify.agent.registry import Tool
+from wikify.rag.chunk import CONTEXT_SEPARATOR
+
+# Excerpts are trimmed hard — the model pulls the full body with read_section when a hit
+# looks worth reading.
+EXCERPT_LIMIT = 700
+
+
+def format_hits(hits: list[dict], mode: str) -> str:
+	lines = [f"{len(hits)} result(s) ({mode}):"]
+	for position, hit in enumerate(hits, start=1):
+		pages = f"p.{hit['page_start']}"
+		if hit.get("page_end") and hit["page_end"] != hit["page_start"]:
+			pages = f"p.{hit['page_start']}-{hit['page_end']}"
+		type_label = f" ({hit['section_type']})" if hit.get("section_type") else ""
+		crumb = f"{hit['document_title']}{CONTEXT_SEPARATOR}{hit['hierarchy_path'] or hit['title']}"
+		lines.append(
+			f"\n[{position}] {crumb}{type_label} [{pages}] <{hit['section']}> score={hit['score']:.4f}"
+		)
+		text = hit.get("text") or ""
+		lines.append(text if len(text) <= EXCERPT_LIMIT else text[:EXCERPT_LIMIT] + "…")
+	return "\n".join(lines)
+
+
+def semantic_search(ctx: Ctx, args: dict) -> str:
+	from wikify.api.rag import search
+
+	query = (args.get("query") or "").strip()
+	if not query:
+		return _("Provide a `query` to search for.")
+	result = search(
+		query,
+		project=args.get("project") or ctx.project,
+		source_document=ctx.default_document(args.get("source_document")),
+		section_type=args.get("section_type"),
+		limit=args.get("limit") or 8,
+		mode=args.get("mode") or "hybrid",
+		use_router=False,
+	)
+	hits = result["hits"]
+	if not hits:
+		return _("Nothing in the index matches that. Try different wording, or drop the filters.")
+	return format_hits(hits, result["mode"])
+
+
+TOOLS = [
+	Tool(
+		name="semantic_search",
+		side="server",
+		description=(
+			"Search the indexed wiki content by meaning and by keyword, returning ranked "
+			"excerpts with their section ids — follow up with read_section to read a hit in "
+			"full, then search again. Use mode='filter' with a section_type when the user "
+			"wants EVERY item of a kind (it returns all matches, not a top-k); use "
+			"mode='hybrid' for open questions. Defaults to the attached project/document."
+		),
+		parameters={
+			"type": "object",
+			"properties": {
+				"query": {"type": "string", "description": "What to search for."},
+				"section_type": {
+					"type": "string",
+					"description": "Restrict to one Section Type (see list_section_types).",
+				},
+				"mode": {
+					"type": "string",
+					"enum": ["hybrid", "vector", "fts", "filter"],
+					"description": "hybrid (default), vector, fts (keyword), or filter (all matches).",
+				},
+				"limit": {"type": "integer", "description": "Max results, default 8."},
+				"project": {"type": "string", "description": "Optional Wikify Project scope."},
+				"source_document": {"type": "string", "description": "Optional single-document scope."},
+			},
+			"required": ["query"],
+		},
+		handler=semantic_search,
+	),
+]
diff --git a/wikify/api/ask_history.py b/wikify/api/ask_history.py
new file mode 100644
index 0000000..9414692
--- /dev/null
+++ b/wikify/api/ask_history.py
@@ -0,0 +1,31 @@
+"""Whitelisted read API for Ask conversations — the sidebar and the transcript view.
+
+Kept out of `api/rag.py` on purpose: that module is the retrieval surface, this one only
+reads back what `rag/history.py` wrote. Scoping is the DocType's `if_owner` permission,
+applied by `get_list`/`check_permission` inside `rag.history`, so a user sees their own
+conversations and a System Manager sees every one — no filter is re-implemented here.
+"""
+
+from __future__ import annotations
+
+import frappe
+from frappe.utils.data import cint
+
+from wikify.rag import history
+
+
+@frappe.whitelist()
+def list_sessions(project: str | None = None, limit: int = history.SESSION_PAGE_LENGTH) -> list[dict]:
+	"""Conversations the current user may read, newest first."""
+	return history.list_sessions(project=project, limit=cint(limit) or history.SESSION_PAGE_LENGTH)
+
+
+@frappe.whitelist()
+def get_session(name: str | int) -> dict:
+	"""One conversation with its turns, each answer carrying its route, citations and cost."""
+	return history.get_session(name)
+
+
+@frappe.whitelist(methods=["POST"])
+def delete_session(name: str | int) -> None:
+	history.delete_session(name)
diff --git a/wikify/api/rag.py b/wikify/api/rag.py
new file mode 100644
index 0000000..788c9ed
--- /dev/null
+++ b/wikify/api/rag.py
@@ -0,0 +1,262 @@
+"""Whitelisted APIs for retrieval + grounded answering (POC-2 phase 1).
+
+Five methods: `search` (the three legs), `ask` (grounded, streamed, cited), `index_status`,
+`reindex`, and `compare` — the demo punchline that runs the same query as naive top-k
+vector search *and* as the routed hybrid, so the interface can show what naive retrieval
+missed.
+
+Permissions: every read path resolves the projects the user may read and hands them to
+`search()` as a pre-filter, so the ACL is applied inside the store's `where` clause rather
+than by trimming rows after the fact — a post-hoc trim would let an unreadable project's
+chunks consume the top-k budget and silently starve the answer.
+"""
+
+from __future__ import annotations
+
+import time
+
+import frappe
+from frappe import _
+from frappe.utils.data import cint, sbool
+
+from wikify.rag import answer as rag_answer
+from wikify.rag import embed
+from wikify.rag import history as rag_history
+from wikify.rag import index as rag_index
+from wikify.rag import search as rag_search
+from wikify.rag.router import route as route_question
+
+STREAM_EVENT = "wikify_rag_answer"
+
+# The naive leg of `compare`: plain vector top-k with no filter and no routing — exactly
+# what a textbook RAG pipeline does, which is the baseline the thesis argues against.
+NAIVE_LIMIT = 8
+
+
+def readable_projects() -> list[str]:
+	"""Wikify Projects the current user may read (`get_list` applies permissions).
+
+	A user with no read permission at all gets `[]`, not an exception: an unscoped search
+	must come back empty for them, and `search()` reads an empty ACL list as "no rows".
+	"""
+	if not frappe.has_permission("Wikify Project", ptype="read"):
+		return []
+	return frappe.get_list("Wikify Project", pluck="name", limit_page_length=0)
+
+
+def assert_readable(project: str | None, source_document: str | None = None) -> None:
+	"""Guard an explicit scope: both a project and a document resolve to a project check."""
+	if source_document and not project:
+		project = frappe.db.get_value("Source Document", source_document, "project")
+	if not project:
+		return
+	if not frappe.has_permission("Wikify Project", doc=project):
+		frappe.throw(_("You are not allowed to read {0}.").format(project), frappe.PermissionError)
+
+
+def documents_in_projects(projects: list[str]) -> list[str]:
+	return frappe.get_all("Source Document", filters={"project": ["in", projects]}, pluck="name")
+
+
+@frappe.whitelist()
+def search(
+	query: str,
+	project: str | None = None,
+	source_document: str | None = None,
+	section_type: str | None = None,
+	limit: int = 8,
+	mode: str = "hybrid",
+	rerank: bool = False,
+	use_router: bool = True,
+) -> dict:
+	"""Retrieve chunks for a query.
+
+	The router runs by default and supplies the `section_type` filter when the caller did
+	not name one; an explicit `mode` is always honoured. Pass `use_router=False` for a raw,
+	unrouted leg (what `compare`'s naive side needs) — `route` then comes back as null.
+	"""
+	query = (query or "").strip()
+	if not query:
+		frappe.throw(_("Enter something to search for."))
+	assert_readable(project, source_document)
+
+	started = time.monotonic()
+	decided = route_question(query, project) if sbool(use_router) else None
+	if decided:
+		section_type = section_type or decided.section_type
+		query = decided.query
+
+	hits = rag_search.search(
+		query,
+		project=project,
+		source_document=source_document,
+		section_type=section_type,
+		limit=cint(limit) or 8,
+		mode=mode,
+		rerank=sbool(rerank),
+		allowed_projects=readable_projects(),
+	)
+	return {
+		"hits": [hit.as_dict() for hit in hits],
+		"route": decided.as_dict() if decided else None,
+		"took_ms": int((time.monotonic() - started) * 1000),
+		"mode": mode,
+	}
+
+
+@frappe.whitelist()
+def ask(
+	question: str,
+	project: str | None = None,
+	session: str | None = None,
+	rerank: bool = True,
+) -> dict:
+	"""Answer a question from the wiki, with citations, streaming as it goes.
+
+	Realtime on `wikify_rag_answer` mirrors the agent loop's ordering: the route lands
+	first (the interface shows *why* this retrieval strategy), then the sources, then the
+	answer deltas, then `done` — which carries what the turn cost, so the price can be
+	shown the moment the answer finishes. The same payload is also returned, so a caller
+	that isn't listening still gets the whole answer.
+	"""
+	question = (question or "").strip()
+	if not question:
+		frappe.throw(_("Ask a question."))
+	assert_readable(project)
+
+	user = frappe.session.user
+	started = time.monotonic()
+	rag_history.start_turn()
+
+	def publish(payload: dict) -> None:
+		frappe.publish_realtime(STREAM_EVENT, {"session": session, **payload}, user=user)
+
+	result = rag_answer.answer(
+		question,
+		project=project,
+		history=session_history(session),
+		rerank=sbool(rerank),
+		allowed_projects=readable_projects(),
+		on_route=lambda route: publish({"route": route}),
+		on_citations=lambda citations: publish({"citations": citations}),
+		on_delta=lambda delta: publish({"delta": delta}),
+	)
+	result["took_ms"] = int((time.monotonic() - started) * 1000)
+	publish(
+		{
+			"done": True,
+			"refused": result["refused"],
+			"took_ms": result["took_ms"],
+			"cost": result["cost"],
+			"prompt_tokens": result["prompt_tokens"],
+			"completion_tokens": result["completion_tokens"],
+			"model": result["model"],
+		}
+	)
+	# The log is the product's own eval set: route decisions and citations are what let us
+	# ask later which questions were routed wrong. A logging failure must never cost the
+	# user an answer they already have.
+	try:
+		result["session"] = rag_history.record_turn(
+			session,
+			question,
+			result,
+			project=project,
+			took_ms=result["took_ms"],
+			model=result["model"],
+		)
+	except Exception:
+		frappe.log_error(title="Wikify: could not record the ask turn")
+	return result
+
+
+def session_history(session: str | None) -> list[dict]:
+	"""Prior turns of an Ask conversation, so the router can rewrite follow-up questions.
+
+	Delegates to `rag.history`, which owns the Ask session doctypes and drops an unreadable
+	session rather than replaying someone else's conversation into the router prompt.
+	"""
+	return rag_history.recent_turns(session)
+
+
+@frappe.whitelist()
+def index_status(project: str | None = None) -> dict:
+	"""Index counts plus whether sections have changed since the index was written.
+
+	Unscoped, this reports the union of the projects the user may read rather than the
+	whole site — the card must not tell someone how much content they cannot see.
+	"""
+	assert_readable(project)
+	projects = [project] if project else readable_projects()
+	# ponytail: one index scan per readable project, replace with a single grouped scan
+	# once a site carries more than a handful of projects.
+	totals = {"chunks": 0, "sections": 0, "documents": 0, "indexed_at": None, "dim": embed.EMBED_DIM}
+	for name in projects:
+		stats = rag_index.index_stats(name)
+		for key in ("chunks", "sections", "documents"):
+			totals[key] += stats[key]
+		if stats["indexed_at"] and (not totals["indexed_at"] or stats["indexed_at"] > totals["indexed_at"]):
+			totals["indexed_at"] = stats["indexed_at"]
+	totals["stale"] = is_stale(project, totals["indexed_at"])
+	return totals
+
+
+def is_stale(project: str | None, indexed_at) -> bool:
+	"""True when any in-scope section was edited after the index was last written."""
+	if not indexed_at:
+		return True
+	filters: dict = {"modified": [">", indexed_at]}
+	projects = [project] if project else readable_projects()
+	if not projects:
+		return False
+	filters["source_document"] = ["in", documents_in_projects(projects)]
+	return bool(frappe.get_all("Source Section", filters=filters, limit=1, pluck="name"))
+
+
+@frappe.whitelist()
+def reindex(project: str) -> dict:
+	"""Rebuild a project's index in the background (embedding the corpus is slow)."""
+	if not frappe.has_permission("Wikify Project", ptype="write", doc=project):
+		frappe.throw(_("You are not allowed to reindex {0}.").format(project), frappe.PermissionError)
+	job = frappe.enqueue(
+		"wikify.rag.index.rebuild_project",
+		queue="long",
+		timeout=3600,
+		project=project,
+	)
+	frappe.local.response["http_status_code"] = 202
+	return {"job": job.id}
+
+
+@frappe.whitelist()
+def compare(query: str, project: str | None = None) -> dict:
+	"""The demo punchline: naive top-k vector search beside the routed strategy.
+
+	Same query, same corpus, same ACL. The naive leg is what a textbook RAG pipeline
+	returns; the routed leg follows the router's decision — for an "all X" question that
+	is an exhaustive metadata filter, which is where the recall gap shows up.
+	"""
+	query = (query or "").strip()
+	if not query:
+		frappe.throw(_("Enter a query to compare."))
+	assert_readable(project)
+
+	allowed = readable_projects()
+	naive = rag_search.search(
+		query,
+		project=project,
+		limit=NAIVE_LIMIT,
+		mode="vector",
+		allowed_projects=allowed,
+	)
+	decided = route_question(query, project)
+	routed = rag_answer.retrieve(decided, project, False, allowed_projects=allowed)
+	found_by_naive = {hit.section for hit in naive}
+	return {
+		"naive": [hit.as_dict() for hit in naive],
+		"routed": [hit.as_dict() for hit in routed],
+		"route": decided.as_dict(),
+		# The headline number: the sections the routed leg found that naive top-k never saw.
+		# Precomputed here so the interface can highlight them without re-deriving the diff.
+		"missed_by_naive": [hit.section for hit in routed if hit.section not in found_by_naive],
+	}
diff --git a/wikify/engine/diagrams.py b/wikify/engine/diagrams.py
new file mode 100644
index 0000000..c66ed77
--- /dev/null
+++ b/wikify/engine/diagrams.py
@@ -0,0 +1,337 @@
+"""Server-side mermaid gate: nothing unverified reaches the database.
+
+Two failures are caught here, both measured on the ICAI referencer:
+
+1. **Broken syntax.** A diagram that does not parse renders as raw text (or as nothing) for the
+   reader. Storing it is storing a defect; we would rather store the page crop.
+2. **A table drawn as a flowchart.** This is the dangerous one. `flowchart TD` is a tree grammar
+   and a rate table is a 2-D grid, so the VLM flattens the grid into two parallel branches — the
+   income slabs under one parent, the surcharge rates under another — with nothing binding a slab
+   to its rate. A student reading it gets the rate wrong, and confidently wrong beats missing on
+   every exam-prep metric. Any flowchart carrying the signature of a flattened grid is rejected
+   whatever its syntax.
+
+Before either verdict, one **lossless** repair is attempted: quoting node labels. ICAI prints
+every statutory reference in square brackets, so the VLM writes `A[CAPITAL ASSET<br>[Section
+2(14)]]` — correct content that mermaid cannot parse, because the inner `]` closes the node
+early. `A["CAPITAL ASSET<br>[Section 2(14)]"]` is valid and reads identically. Repair only ever
+adds quotes; it never drops or rewrites a character, and the result still has to pass the full
+gate before it is stored. What is *not* repaired is meaning: a guess about which rate belongs to
+which slab would be the same failure with better syntax, so a flattened grid is still rejected.
+
+The page keeps whatever HTML/pipe table and prose the model also produced; when nothing tabular
+survives, the page crop goes in instead, so the reader always has the original to read.
+
+Pure text in, text out — no LLM, no I/O, no frappe imports.
+"""
+
+from __future__ import annotations
+
+import re
+from itertools import pairwise
+
+# We only ever ask for flowcharts. Any other diagram type is out of contract, so it has not been
+# validated and must not be stored.
+_HEADER_RE = re.compile(r"^\s*(?:flowchart|graph)\s+(?:TD|TB|BT|LR|RL)\s*$", re.IGNORECASE)
+_MERMAID_BLOCK_RE = re.compile(
+	r"^[ \t]*```[ \t]*mermaid[ \t]*\n(.*?)^[ \t]*```[ \t]*$", re.MULTILINE | re.DOTALL
+)
+
+_NODE_ID = r"[A-Za-z][A-Za-z0-9_]*"
+# A quoted label may hold anything but a quote — including the brackets that make the unquoted
+# form unparseable — so the quoted alternatives are tried first.
+_QUOTED_LABEL = r'(?:\[\s*"[^"]*"\s*\]|\(\s*"[^"]*"\s*\)|\{\s*"[^"]*"\s*\})'
+_LABEL = rf"(?:{_QUOTED_LABEL}|\[[^\[\]]*\]|\([^()]*\)|\{{[^{{}}]*\}}|>[^<>]*\])"
+_ARROW_RE = re.compile(r"\s*(?:--+>|--+|-\.-+>|-\.-+|==+>|==+)\s*")
+_PIECE_RE = re.compile(rf"^({_NODE_ID})\s*({_LABEL})?$")
+_EDGE_LABEL_RE = re.compile(r"^\|[^|]*\|\s*")
+
+# Directives we tolerate but do not interpret — they carry no content, so they cannot corrupt data.
+_DIRECTIVES = ("subgraph", "end", "classdef", "class ", "style", "click", "linkstyle", "%%")
+
+# Node-shape delimiters, opener -> closer. `>` (the flag shape) is deliberately absent: it also
+# ends an arrow, so treating it as an opener would swallow the rest of the statement.
+_SHAPE_DELIMITERS = {"[": "]", "(": ")", "{": "}"}
+
+# A leaf whose whole label is a bare rate or amount is a table cell that lost its row.
+_VALUE_LEAF_RE = re.compile(
+	r"^(?:not\s+exceeding\s+|upto\s+|up\s+to\s+|max\.?\s+)?"
+	r"(?:₹|rs\.?)?\s*[\d,]+(?:\.\d+)?\s*(?:%|per\s*cent|lakhs?|crores?)?$",
+	re.IGNORECASE,
+)
+MIN_VALUE_LEAVES = 3
+MIN_PARALLEL_GROUP = 3
+
+
+def mermaid_blocks(markdown: str) -> list[str]:
+	"""The source of every ```mermaid fenced block, in document order."""
+	return [match.group(1) for match in _MERMAID_BLOCK_RE.finditer(markdown or "")]
+
+
+def split_outside_labels(line: str, separator: str = ";") -> list[str]:
+	"""Split a statement on `separator` only where it sits outside every label and quote.
+
+	A label may legitimately carry one — `A["Rs 1,00,000; see proviso"]`, `B["Profit & Loss"]`,
+	and the `#quot;` entity we escape with ends in one — and splitting there would tear a valid
+	node in half.
+	"""
+	parts: list[str] = []
+	depth = 0
+	quoted = False
+	start = 0
+	for index, char in enumerate(line):
+		if char == '"':
+			quoted = not quoted
+		elif quoted:
+			continue
+		elif char in _SHAPE_DELIMITERS:
+			depth += 1
+		elif char in _SHAPE_DELIMITERS.values():
+			depth = max(0, depth - 1)
+		elif char == separator and not depth:
+			parts.append(line[start:index])
+			start = index + 1
+	parts.append(line[start:])
+	return parts
+
+
+def statements(source: str) -> list[str]:
+	"""Content lines of a diagram body, semicolons split, directives dropped."""
+	lines: list[str] = []
+	for raw in (source or "").splitlines()[1:]:
+		for part in split_outside_labels(raw):
+			line = part.strip()
+			if line and not line.lower().startswith(_DIRECTIVES):
+				lines.append(line)
+	return lines
+
+
+def quoted_label(label: str) -> str:
+	"""The label as a mermaid string literal, with any quote it carries escaped as `#quot;`.
+
+	Left untouched when it is already a clean quoted string, or when it is a compound node
+	shape (`[(cylinder)]`, `([stadium])`) whose own delimiters would be captured as text.
+	"""
+	text = label.strip()
+	if len(text) >= 2 and text[0] == '"' and text[-1] == '"' and '"' not in text[1:-1]:
+		return label
+	if len(text) >= 2 and text[0] in _SHAPE_DELIMITERS and text[-1] == _SHAPE_DELIMITERS[text[0]]:
+		return label
+	return '"' + label.replace('"', "#quot;") + '"'
+
+
+def quote_labels_in_statement(line: str) -> str:
+	"""One statement with every node label quoted. Nesting is matched per shape family, so the
+	inner brackets of `A[CAPITAL ASSET<br>[Section 2(14)]]` end up inside the label, not ending
+	it. An unterminated label is left exactly as written — that is a real defect, not a quoting
+	one, and the gate must still see it."""
+	if line.lstrip().startswith("%%"):
+		return line
+	repaired: list[str] = []
+	index = 0
+	while index < len(line):
+		char = line[index]
+		follows_node_id = index == 0 or line[index - 1].isalnum() or line[index - 1] == "_"
+		if char not in _SHAPE_DELIMITERS or not follows_node_id:
+			repaired.append(char)
+			index += 1
+			continue
+		closer = _SHAPE_DELIMITERS[char]
+		run = 0
+		while index + run < len(line) and line[index + run] == char:
+			run += 1
+		depth = run
+		scan = index + run
+		while scan < len(line) and depth:
+			if line[scan] == char:
+				depth += 1
+			elif line[scan] == closer:
+				depth -= 1
+			scan += 1
+		if depth:
+			repaired.append(line[index:])
+			break
+		repaired.append(char * run + quoted_label(line[index + run : scan - run]) + closer * run)
+		index = scan
+	return "".join(repaired)
+
+
+def quote_node_labels(source: str) -> str:
+	"""The diagram body with every node label wrapped in double quotes. Lossless — it only ever
+	adds quotes, so a repaired block still has to clear `diagram_errors` before it is stored."""
+	lines = (source or "").splitlines(keepends=True)
+	return "".join(lines[:1] + [quote_labels_in_statement(line) for line in lines[1:]])
+
+
+def unquote(label: str) -> str:
+	"""Drop a *balanced* pair of wrapping quotes. A lone quote is left in place — it is the
+	unterminated label that stops mermaid parsing, and stripping it would hide the defect."""
+	if len(label) >= 2 and label[0] == '"' and label[-1] == '"':
+		return label[1:-1].strip()
+	return label
+
+
+def parse_flowchart(source: str) -> tuple[dict[str, str], list[tuple[str, str]], list[str]]:
+	"""(node labels, edges, unparseable statements) for a flowchart body."""
+	labels: dict[str, str] = {}
+	edges: list[tuple[str, str]] = []
+	broken: list[str] = []
+	for line in statements(source):
+		# `A & B --> C` is mermaid's shorthand for every node on the left joining every node on
+		# the right, so a statement is a chain of *groups*, not of single nodes.
+		groups: list[list[str]] = []
+		for piece in _ARROW_RE.split(line):
+			nodes: list[str] = []
+			for item in split_outside_labels(piece, "&"):
+				match = _PIECE_RE.match(_EDGE_LABEL_RE.sub("", item).strip())
+				if not match:
+					broken.append(line)
+					nodes = []
+					break
+				node_id, label = match.group(1), match.group(2)
+				labels.setdefault(node_id, unquote(label[1:-1].strip()) if label else "")
+				nodes.append(node_id)
+			if not nodes:
+				groups = []
+				break
+			groups.append(nodes)
+		for left, right in pairwise(groups):
+			edges.extend((source_node, target) for source_node in left for target in right)
+	return labels, edges, broken
+
+
+def syntax_errors(source: str) -> list[str]:
+	"""Everything that would stop this block parsing or rendering. Empty means it is safe."""
+	body = (source or "").strip()
+	if not body:
+		return ["empty diagram"]
+	header = body.splitlines()[0]
+	if not _HEADER_RE.match(header):
+		return [f"unsupported diagram type: {header.strip()[:40]!r}"]
+
+	errors: list[str] = []
+	labels, edges, broken = parse_flowchart(body)
+	if broken:
+		errors.append(f"unparseable statement: {broken[0][:60]!r}")
+	for node_id, label in labels.items():
+		if label.count('"') % 2:
+			errors.append(f"unbalanced quotes in node {node_id}")
+		if "\\n" in label:
+			errors.append(f"literal \\n in node {node_id} — mermaid needs <br>")
+	if not edges:
+		errors.append("no edges — a flowchart with no connections carries no structure")
+	elif len(components(labels, edges)) > 1:
+		errors.append(f"disconnected: {len(components(labels, edges))} unlinked fragments")
+	return errors
+
+
+def components(labels: dict[str, str], edges: list[tuple[str, str]]) -> list[set[str]]:
+	"""Connected components of the (undirected) node graph."""
+	neighbours: dict[str, set[str]] = {node: set() for node in labels}
+	for source_node, target in edges:
+		neighbours.setdefault(source_node, set()).add(target)
+		neighbours.setdefault(target, set()).add(source_node)
+	seen: set[str] = set()
+	found: list[set[str]] = []
+	for node in neighbours:
+		if node in seen:
+			continue
+		group = {node}
+		frontier = [node]
+		while frontier:
+			current = frontier.pop()
+			for neighbour in neighbours[current] - group:
+				group.add(neighbour)
+				frontier.append(neighbour)
+		seen |= group
+		found.append(group)
+	return found
+
+
+def tabular_signals(source: str) -> list[str]:
+	"""Signs this "flowchart" is really a grid whose row↔value binding has been lost."""
+	labels, edges, _ = parse_flowchart(source)
+	if not edges:
+		return []
+	children: dict[str, list[str]] = {}
+	has_outgoing = {source_node for source_node, _ in edges}
+	for source_node, target in edges:
+		children.setdefault(source_node, []).append(target)
+
+	signals: list[str] = []
+	value_leaves = [
+		node
+		for node, label in labels.items()
+		if node not in has_outgoing and label and _VALUE_LEAF_RE.match(label.strip())
+	]
+	if len(value_leaves) >= MIN_VALUE_LEAVES:
+		signals.append(
+			f"{len(value_leaves)} nodes are bare values ({', '.join(sorted(value_leaves)[:4])}) — "
+			"these are table cells, not process steps"
+		)
+
+	# Two parents each fanning out to leaves, with nothing crossing between the fans: the
+	# left-hand column and the right-hand column of a grid, drawn side by side and unbound.
+	edge_set = set(edges)
+	leaf_fans = [
+		(parent, set(kids))
+		for parent, kids in children.items()
+		if len(kids) >= MIN_PARALLEL_GROUP and all(kid not in has_outgoing for kid in kids)
+	]
+	for index, (parent, kids) in enumerate(leaf_fans):
+		for other_parent, other_kids in leaf_fans[index + 1 :]:
+			crossing = any((a, b) in edge_set or (b, a) in edge_set for a in kids for b in other_kids)
+			if not crossing:
+				signals.append(
+					f"parallel leaf groups under {parent} and {other_parent} with no edge "
+					"binding them — a grid flattened into two dangling columns"
+				)
+				return signals
+	return signals
+
+
+def diagram_errors(source: str) -> list[str]:
+	"""Every reason this mermaid block must not be stored."""
+	return syntax_errors(source) + tabular_signals(source)
+
+
+def has_table(markdown: str) -> bool:
+	"""True when the markdown still carries a grid the reader can use (HTML or pipe)."""
+	text = markdown or ""
+	if re.search(r"<table[\s>]", text, re.IGNORECASE):
+		return True
+	return sum(1 for line in text.splitlines() if line.strip().startswith("|")) >= 2
+
+
+def remove_unverified_diagrams(markdown: str, fallback_image_url: str = "") -> tuple[str, list[str]]:
+	"""Repair what can be repaired, drop every mermaid block that still fails the gate; return
+	(markdown, notes).
+
+	When the rejection leaves the page with no grid at all, the page/region crop is embedded so
+	the content is still readable — a picture of the truth beats a well-formed lie.
+	"""
+	text = markdown or ""
+	notes: list[str] = []
+	rejected = 0
+
+	def replace(match: re.Match) -> str:
+		nonlocal rejected
+		source = match.group(1)
+		errors = diagram_errors(source)
+		if not errors:
+			return match.group(0)
+		repaired = quote_node_labels(source)
+		if repaired != source and not diagram_errors(repaired):
+			notes.append("mermaid repaired: node labels quoted")
+			return match.group(0).replace(source, repaired, 1)
+		rejected += 1
+		notes.append(f"mermaid rejected: {errors[0]}")
+		return ""
+
+	cleaned = _MERMAID_BLOCK_RE.sub(replace, text)
+	if rejected:
+		cleaned = re.sub(r"\n{3,}", "\n\n", cleaned).strip()
+		if fallback_image_url and not has_table(cleaned):
+			cleaned = f"{cleaned}\n\n![Source region]({fallback_image_url})".strip()
+			notes.append("embedded the source crop in place of the rejected diagram")
+	return cleaned, notes
diff --git a/wikify/engine/loader/sectionizer.py b/wikify/engine/loader/sectionizer.py
index 8f5de41..80c2393 100644
--- a/wikify/engine/loader/sectionizer.py
+++ b/wikify/engine/loader/sectionizer.py
@@ -11,10 +11,24 @@
 from __future__ import annotations
 
 import re
+from collections import defaultdict
 from dataclasses import dataclass
 
 from wikify.engine.loader.toc import correct_level
 
+# A title lands in `Source Section.title`, a Data column capped at `frappe.db.VARCHAR_LEN`.
+# An over-length title used to raise CharacterLengthExceededError mid-`replace_sections`,
+# aborting the rebuild and silently dropping every section after the offending heading —
+# on the 236-page ICAI referencer that cost pages 20-236 (93.6% of the document). The
+# loader stays frappe-free, so the limit is mirrored here and pinned by a test.
+MAX_TITLE_LENGTH = 140
+
+# A heading that reappears at the top of many pages is the chapter's running page header,
+# not a new section each time. Only the first occurrence opens a section; the repeats are
+# page furniture and are dropped so the body flows on into the open section.
+RUNNING_HEADER_MIN_PAGES = 3
+TOP_OF_PAGE_LINES = 3
+
 _HEADING_RE = re.compile(r"^(#{1,6})\s+(.*\S)\s*$")
 # Numbered headings ("1.", "1.1", "1.1.1") encode their own level — recover it.
 # Allow an optional trailing dot so top-level "1." is caught, not just "1.1".
@@ -28,7 +42,12 @@ def _clean_title(raw: str) -> str:
 	# wrapping bold/italic markers (`*` and `_`) so the tree title is plain text. This
 	# also helps level inference — `_**2.1 Foo**_` → `2.1 Foo` now matches the numbering
 	# regex — and stops a fully-bold numbered chapter being mis-demoted as a list item.
-	return raw.strip().strip("*_").strip()
+	title = raw.strip().strip("*_").strip()
+	if len(title) <= MAX_TITLE_LENGTH:
+		return title
+	clipped = title[: MAX_TITLE_LENGTH - 1]
+	head, _, _ = clipped.rpartition(" ")
+	return f"{(head or clipped).rstrip()}…"
 
 
 def _infer_level(title: str, fallback: int) -> int:
@@ -49,6 +68,37 @@ def _looks_like_list_item(title: str) -> bool:
 	return "*" in title or bool(_DOUBLE_NUM.match(title))
 
 
+def running_header_titles(pages: list[tuple[int, str]]) -> set[str]:
+	"""Cleaned heading titles that are really the chapter's running page header.
+
+	A section heading is written once; a running header is re-stamped at the top of every
+	page of its chapter, so the parser emits it as a fresh heading per page. Detected
+	structurally — same title heading pages, most of them within the first few non-blank
+	lines of the page — so no document-specific strings are needed.
+
+	# ponytail: repetition + top-of-page placement is the whole signal; a genuine heading
+	# that opens three or more short pages of its own (a glossary of one-term-per-page,
+	# say) would be misread as furniture — require the occurrences to carry no body of
+	# their own if such a corpus turns up.
+	"""
+	occurrences: dict[str, list[int]] = defaultdict(list)
+	pages_seen: dict[str, set[int]] = defaultdict(set)
+	for page_no, md in pages:
+		for position, line in enumerate(line for line in md.splitlines() if line.strip()):
+			match = _HEADING_RE.match(line)
+			if not match:
+				continue
+			title = _clean_title(match.group(2))
+			occurrences[title].append(position)
+			pages_seen[title].add(page_no)
+	return {
+		title
+		for title, positions in occurrences.items()
+		if len(pages_seen[title]) >= RUNNING_HEADER_MIN_PAGES
+		and sum(1 for position in positions if position < TOP_OF_PAGE_LINES) * 2 >= len(positions)
+	}
+
+
 @dataclass
 class Section:
 	title: str
@@ -68,6 +118,8 @@ def sectionize(pages: list[tuple[int, str]], level_map: dict[str, int] | None =
 	current: Section | None = None
 	buf: list[str] = []
 	max_chapter = 0  # highest accepted top-level chapter number (sequence validation)
+	running_headers = running_header_titles(pages)
+	opened_headers: set[str] = set()
 
 	def flush():
 		if current is not None:
@@ -78,9 +130,17 @@ def flush():
 		for line in md.splitlines():
 			m = _HEADING_RE.match(line)
 			if m:
+				title = _clean_title(m.group(2))
+				if title in running_headers:
+					# First stamp opens the chapter; every later one is page furniture and
+					# is dropped, so the page's body continues the section already open.
+					if title in opened_headers:
+						if current is not None:
+							current.page_end = page_no
+						continue
+					opened_headers.add(title)
 				flush()
 				buf = []
-				title = _clean_title(m.group(2))
 				# Embedded ToC wins; else numbering; else the parser's '#' depth.
 				level = correct_level(title, _infer_level(title, len(m.group(1))), level_map)
 				# Heading validation: a numbered top-level heading that is out of
diff --git a/wikify/engine/parsers/vlm.py b/wikify/engine/parsers/vlm.py
index 9038c2c..5a01a4b 100644
--- a/wikify/engine/parsers/vlm.py
+++ b/wikify/engine/parsers/vlm.py
@@ -15,26 +15,51 @@
 from wikify.engine.loader.context import context_block, instruction_block
 
 _PROMPT = (
-	"Convert this PDF page into clean, faithful GitHub-flavored Markdown. "
-	"Preserve headings with correct levels, lists, and tables (use Markdown table "
-	"syntax). Transcribe text exactly as it appears — do NOT summarize, add, or "
-	"invent content.\n"
-	"If the page contains a flowchart, decision tree, or diagram with boxes and "
-	"arrows, represent it as a Mermaid diagram inside a ```mermaid fenced block. "
-	"Use `flowchart TD`. Give each node a short id (A, B, C...) and ALWAYS wrap the "
-	'node text in double quotes so special characters are safe, e.g. A["Vaginal '
-	'delivery >500 mL"]. Use <br> for line breaks inside a label (never \\n). Arrows '
-	"are -->. Capture every box and connection. You may follow the diagram with the "
-	"same content as a nested list. For non-diagram pages do not emit mermaid.\n"
+	"Convert this PDF page into clean, faithful GitHub-flavored Markdown, preserving heading "
+	"levels, lists and reading order.\n"
+	"\n"
+	"ROUTE EACH BLOCK BY ITS SHAPE — this is the most important rule:\n"
+	"1. TABULAR DATA (anything laid out as rows x columns: rate tables, slab tables, "
+	"comparison grids, matrices) becomes an HTML <table>. Use <th> for header cells and "
+	"rowspan / colspan for merged headers. Each source row is ONE <tr>, so every label stays "
+	"in the same row as its own value. NEVER represent tabular data as a mermaid diagram: a "
+	"flowchart is a tree and a table is a grid, so the row-to-value binding is destroyed and "
+	"the reader cannot tell which value belongs to which row. Use a Markdown pipe table only "
+	"for a genuinely flat grid with no merged cells.\n"
+	"2. PROCESS FLOWS and DECISION TREES (boxes joined by arrows) become a ```mermaid fenced "
+	"block using `flowchart TD`. Short node ids (A, B, C...). EVERY node label is wrapped in "
+	'double quotes, with no exceptions — write A["Turnover > Rs 10 crore"], never '
+	"A[Turnover > Rs 10 crore]. This matters most when the label contains brackets, which "
+	'statutory references always do: A["CAPITAL ASSET<br>[Section 2(14)]"] is correct and '
+	"A[CAPITAL ASSET<br>[Section 2(14)]] is broken. Inside a label, write a literal double "
+	"quote as #quot; and use <br> for line breaks (never \\n). Arrows are -->. Every node must "
+	"be joined to the diagram by an edge; if you cannot say what connects to what, it is not a "
+	"flow — emit a table or a list instead.\n"
+	"3. Everything else is prose: headings, paragraphs, lists.\n"
+	"\n"
+	"Transcribe text EXACTLY as printed. Copy rupee amounts, percentages, thresholds, dates "
+	'and statutory references (e.g. "u/s 115BAC", "section 44AD", "First Proviso") character '
+	"for character. Never round, re-word, convert or summarise a number, and never invent "
+	"content.\n"
 	"Output only the Markdown — no commentary, and no code fences except ```mermaid."
 )
 
 
 def parse_page_image(
-	image_data_url: str, model: str | None = None, project_context: str = "", instruction: str = ""
+	image_data_url: str,
+	model: str | None = None,
+	project_context: str = "",
+	instruction: str = "",
+	shape_hint: str = "",
 ) -> str:
-	"""Markdown for a single page, read from its rendered image (data URL)."""
-	preamble = context_block(project_context) + instruction_block(instruction)
+	"""Markdown for a single page, read from its rendered image (data URL).
+
+	`shape_hint` is `regions.shape_hint` for the page — a coarse "this page holds a grid /
+	a flow" line from the deterministic layout pass. It does not tell the model where the
+	cells are (it still reads the image); it stops the model reaching for a flowchart when
+	what it is looking at is a table.
+	"""
+	preamble = context_block(project_context) + instruction_block(instruction) + shape_hint
 	resp = llm.chat_completion(
 		model or settings.get("vlm_model"),
 		[
diff --git a/wikify/engine/pdf_utils.py b/wikify/engine/pdf_utils.py
index 82bef34..9a407b1 100644
--- a/wikify/engine/pdf_utils.py
+++ b/wikify/engine/pdf_utils.py
@@ -12,7 +12,7 @@
 
 import fitz  # PyMuPDF
 
-from wikify.engine import config
+from wikify.engine import config, regions
 
 
 def png_to_data_url(png_bytes: bytes) -> str:
@@ -26,20 +26,22 @@ def png_to_data_url(png_bytes: bytes) -> str:
 
 
 def classify_page(
-	page, min_chars: int = config.VISUAL_MIN_CHARS, min_drawings: int = config.VISUAL_MIN_DRAWINGS
+	page,
+	min_chars: int = config.VISUAL_MIN_CHARS,
+	min_drawings: int = config.VISUAL_MIN_DRAWINGS,
+	page_regions: list | None = None,
 ) -> str:
-	"""Heuristic page type. Visual = diagram/flowchart/image-dominant, where the
-	extractable text is too sparse to use as ground truth. Thresholds default to the
-	`engine.config` constants; the live pipeline passes the `Wikify Settings` values."""
-	nchars = len(page.get_text("text").strip())
-	n_images = len(page.get_images())
-	try:
-		n_drawings = len(page.get_drawings())
-	except Exception:
-		n_drawings = 0
-	if nchars < min_chars and (n_images > 0 or n_drawings >= min_drawings):
-		return "visual"
-	return "text"
+	"""Page type: `visual` | `mixed` | `text`.
+
+	The original rule was `chars < min_chars AND drawings >= min_drawings`, written for
+	scanned pages. On a born-digital diagram-heavy manual every page has both a full text
+	layer and heavy vector art, so the conjunction never fired and 235 of 236 ICAI pages were
+	filed `text` — the visual path was unreachable. `regions.classify_page` answers it from the
+	page's shape regions instead, and adds `mixed`: a trustworthy text layer *plus* substantial
+	table/diagram ink, which needs a vision model to recover structure but must go on being
+	scored against its text layer. Callers pass `page_regions` when they already have them.
+	"""
+	return regions.classify_page(page, min_chars, min_drawings, page_regions)
 
 
 def render_png(page, dpi: int = config.RENDER_DPI) -> bytes:
diff --git a/wikify/engine/regions.py b/wikify/engine/regions.py
new file mode 100644
index 0000000..b089c69
--- /dev/null
+++ b/wikify/engine/regions.py
@@ -0,0 +1,398 @@
+"""Per-region shape classification for a PDF page: `table` | `flow` | `figure` | `prose`.
+
+The pipeline used to ask one question per page — "is this a diagram?" — and answer it with
+`chars < 250 AND drawings > 40`. On a born-digital, diagram-heavy manual (the ICAI exam
+referencers) every page carries both a full text layer *and* heavy vector art, so the
+conjunction never fires: 235 of 236 pages came back `text` and the visual path was dead code.
+
+Worse, once a page did reach the VLM nothing routed content by *shape*, so 2-D rate tables were
+re-encoded as `flowchart TD` — a grammar that cannot express a grid, which silently destroys the
+row-to-value correspondence a student is revising from.
+
+This module answers the finer question: which *regions* of the page are table, flow, figure and
+prose. The page-level kind is derived from them (a page with a reliable text layer AND
+substantial table/diagram ink is `mixed`, not `text`), the region list is fed to the VLM as a
+shape hint.
+
+No LLM, no I/O — pure PyMuPDF geometry. Boxes are plain `(x0, y0, x1, y1)` tuples throughout:
+this runs on every page of every parse, and building `fitz.Rect` objects for the tens of
+thousands of intersection tests a drawing-heavy page needs costs more than the geometry does.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass
+
+TABLE = "table"
+FLOW = "flow"
+FIGURE = "figure"
+PROSE = "prose"
+
+# Running header/footer bands. Anything wholly inside them is page furniture, not content.
+HEADER_BAND = 0.09
+FOOTER_BAND = 0.94
+
+# A drawing smaller than this (points²) is a bullet, tick or glyph — not art and not a rule.
+MIN_DRAWING_AREA = 300.0
+
+# Rule geometry — thin, long ink that is part of a diagram's structure rather than its area.
+MAX_RULE_THICKNESS = 3.0
+MIN_RULE_LENGTH = 30.0
+
+# Grids are found from their CELLS, not their ruling lines: these documents draw tables as
+# colour-filled cells whose borders are hairlines the line-based readers miss, and
+# `page.find_tables()` costs 1-7s on the drawing-heavy pages here (and segfaulted on a
+# full-document pass) to answer a question we only need a yes/no to.
+MIN_CELL_WIDTH = 15.0
+MIN_CELL_HEIGHT = 8.0
+
+# Cells of one column share their left and right edge to within a rounding wobble.
+CELL_EDGE_TOLERANCE = 2.0
+
+# A grid needs a stack deep enough to be rows and neighbouring stacks to be columns; below
+# this it is a boxed callout or a flowchart node.
+MIN_GRID_ROWS = 3
+MIN_GRID_COLUMNS = 2
+
+# Neighbouring columns of one table touch; a wider gutter means two separate tables.
+MAX_COLUMN_GAP = 12.0
+MIN_COLUMN_OVERLAP = 0.5
+
+# Nested column variants (a cell fill and its inner text fill) are the same column.
+COLUMN_MERGE_TOLERANCE = 8.0
+
+# Vector art is clustered on an occupancy grid rather than by pairwise rect merging: O(drawings)
+# instead of O(drawings²), which matters on the pages here that carry 900 drawings.
+GRID_COLUMNS = 48
+GRID_ROWS = 64
+
+# A region below this share of the page is an inline glyph, logo or bullet cluster.
+MIN_REGION_AREA = 0.03
+
+# Vector art needs labels inside it to be a flow chart; wordless art is a figure.
+MIN_FLOW_LABELS = 3
+
+# Only a *substantial* region forces a page off the plain-text path. A lone 2x2 grid does not
+# need a vision model; a 4-row table, a wide grid, or a page-tenth of diagram does.
+SUBSTANTIAL_TABLE_ROWS = 4
+SUBSTANTIAL_TABLE_COLUMNS = 3
+SUBSTANTIAL_GRAPHIC_AREA = 0.05
+
+
+@dataclass(frozen=True)
+class Region:
+	"""One shape-classified area of a page, in PDF points."""
+
+	shape: str
+	bbox: tuple[float, float, float, float]
+	area_fraction: float
+	rows: int = 0
+	columns: int = 0
+	labels: int = 0
+
+	@property
+	def substantial(self) -> bool:
+		if self.shape == TABLE:
+			return self.rows >= SUBSTANTIAL_TABLE_ROWS or self.columns >= SUBSTANTIAL_TABLE_COLUMNS
+		if self.shape in (FLOW, FIGURE):
+			return self.area_fraction >= SUBSTANTIAL_GRAPHIC_AREA
+		return False
+
+
+def area(box: tuple) -> float:
+	return max(0.0, box[2] - box[0]) * max(0.0, box[3] - box[1])
+
+
+def area_fraction(box: tuple, page_box: tuple) -> float:
+	page_area = area(page_box)
+	return area(box) / page_area if page_area else 0.0
+
+
+def is_furniture(box: tuple, page_box: tuple) -> bool:
+	"""Wholly inside the running header or footer band."""
+	height = (page_box[3] - page_box[1]) or 1.0
+	return (box[3] - page_box[1]) / height <= HEADER_BAND or (box[1] - page_box[1]) / height >= FOOTER_BAND
+
+
+def overlap_fraction(box: tuple, others: list[tuple]) -> float:
+	"""Largest share of `box` covered by any one of `others`."""
+	own = area(box)
+	if not own:
+		return 0.0
+	best = 0.0
+	for other in others:
+		width = min(box[2], other[2]) - max(box[0], other[0])
+		height = min(box[3], other[3]) - max(box[1], other[1])
+		if width > 0 and height > 0:
+			best = max(best, width * height / own)
+	return best
+
+
+def is_rule(box: tuple) -> bool:
+	"""A thin, long line — a table rule, an underline or a connector."""
+	width, height = box[2] - box[0], box[3] - box[1]
+	return (height <= MAX_RULE_THICKNESS and width >= MIN_RULE_LENGTH) or (
+		width <= MAX_RULE_THICKNESS and height >= MIN_RULE_LENGTH
+	)
+
+
+def graphic_boxes(page, blocks: list) -> list[tuple]:
+	"""Ink worth clustering: ruling lines, substantial vector art, and raster blocks."""
+	boxes = []
+	try:
+		drawings = page.get_drawings()
+	except Exception:
+		drawings = []
+	for drawing in drawings:
+		box = tuple(float(value) for value in drawing["rect"])
+		if is_rule(box) or area(box) >= MIN_DRAWING_AREA:
+			boxes.append(box)
+	boxes.extend(tuple(float(value) for value in block[:4]) for block in blocks if block[6] == 1)
+	return boxes
+
+
+def text_boxes(blocks: list) -> list[tuple]:
+	"""Boxes of the page's non-empty text blocks."""
+	return [
+		tuple(float(value) for value in block[:4])
+		for block in blocks
+		if block[6] == 0 and (block[4] or "").strip()
+	]
+
+
+def cell_columns(boxes: list[tuple]) -> list[tuple]:
+	"""Vertical stacks of same-width cells — one entry per column, as (x0, y0, x1, y1, cells)."""
+	stacks: dict[tuple[int, int], list[tuple]] = {}
+	for box in boxes:
+		width, height = box[2] - box[0], box[3] - box[1]
+		if width < MIN_CELL_WIDTH or height < MIN_CELL_HEIGHT or width * height < MIN_DRAWING_AREA:
+			continue
+		key = (round(box[0] / CELL_EDGE_TOLERANCE), round(box[2] / CELL_EDGE_TOLERANCE))
+		stacks.setdefault(key, []).append(box)
+	columns = []
+	for cells in stacks.values():
+		if len(cells) < MIN_GRID_ROWS:
+			continue
+		columns.append(
+			(
+				min(cell[0] for cell in cells),
+				min(cell[1] for cell in cells),
+				max(cell[2] for cell in cells),
+				max(cell[3] for cell in cells),
+				len(cells),
+			)
+		)
+	return sorted(columns)
+
+
+def vertical_overlap(column: tuple, other: tuple) -> float:
+	"""Share of the shorter column's height that the two columns have in common."""
+	shortest = min(column[3] - column[1], other[3] - other[1])
+	if shortest <= 0:
+		return 0.0
+	return max(0.0, min(column[3], other[3]) - max(column[1], other[1])) / shortest
+
+
+def table_regions(page_box: tuple, boxes: list[tuple]) -> list[Region]:
+	"""Grids, found by chaining adjacent, vertically-aligned columns of cells."""
+	chains: list[list[tuple]] = []
+	for column in cell_columns(boxes):
+		joined = next(
+			(
+				chain
+				for chain in chains
+				if column[0] <= max(member[2] for member in chain) + MAX_COLUMN_GAP
+				and vertical_overlap(column, span_of(chain)) >= MIN_COLUMN_OVERLAP
+			),
+			None,
+		)
+		if joined is None:
+			chains.append([column])
+		else:
+			joined.append(column)
+
+	regions = []
+	for chain in chains:
+		# Nested variants (a cell fill and the inner fill behind its text) are one column.
+		starts = {round(column[0] / COLUMN_MERGE_TOLERANCE) for column in chain}
+		if len(starts) < MIN_GRID_COLUMNS:
+			continue
+		box = span_of(chain)
+		fraction = area_fraction(box, page_box)
+		if fraction < MIN_REGION_AREA:
+			continue
+		rows = max(column[4] for column in chain)
+		regions.append(Region(TABLE, box, fraction, rows=rows, columns=len(starts)))
+	return regions
+
+
+def span_of(chain: list[tuple]) -> tuple[float, float, float, float]:
+	return (
+		min(column[0] for column in chain),
+		min(column[1] for column in chain),
+		max(column[2] for column in chain),
+		max(column[3] for column in chain),
+	)
+
+
+def grid_cells(box: tuple, page_box: tuple) -> set[tuple[int, int]]:
+	"""The occupancy-grid cells a box covers."""
+	width = (page_box[2] - page_box[0]) or 1.0
+	height = (page_box[3] - page_box[1]) or 1.0
+	col0 = max(0, min(GRID_COLUMNS - 1, int((box[0] - page_box[0]) / width * GRID_COLUMNS)))
+	col1 = max(0, min(GRID_COLUMNS - 1, int((box[2] - page_box[0]) / width * GRID_COLUMNS)))
+	row0 = max(0, min(GRID_ROWS - 1, int((box[1] - page_box[1]) / height * GRID_ROWS)))
+	row1 = max(0, min(GRID_ROWS - 1, int((box[3] - page_box[1]) / height * GRID_ROWS)))
+	return {(column, row) for column in range(col0, col1 + 1) for row in range(row0, row1 + 1)}
+
+
+def connected_components(cells: set[tuple[int, int]]) -> list[set[tuple[int, int]]]:
+	"""4-neighbour flood fill over occupied grid cells."""
+	remaining = set(cells)
+	components = []
+	while remaining:
+		seed = remaining.pop()
+		component = {seed}
+		frontier = [seed]
+		while frontier:
+			column, row = frontier.pop()
+			for neighbour in ((column + 1, row), (column - 1, row), (column, row + 1), (column, row - 1)):
+				if neighbour in remaining:
+					remaining.remove(neighbour)
+					component.add(neighbour)
+					frontier.append(neighbour)
+		components.append(component)
+	return components
+
+
+def component_box(component: set[tuple[int, int]], page_box: tuple) -> tuple[float, float, float, float]:
+	width = (page_box[2] - page_box[0]) / GRID_COLUMNS
+	height = (page_box[3] - page_box[1]) / GRID_ROWS
+	columns = [cell[0] for cell in component]
+	rows = [cell[1] for cell in component]
+	return (
+		page_box[0] + min(columns) * width,
+		page_box[1] + min(rows) * height,
+		page_box[0] + (max(columns) + 1) * width,
+		page_box[1] + (max(rows) + 1) * height,
+	)
+
+
+def label_count(labels: list[tuple], region_box: tuple) -> int:
+	"""Text blocks sitting inside a region — the boxes' labels, for flow vs figure."""
+	return sum(1 for box in labels if overlap_fraction(box, [region_box]) > 0.6)
+
+
+def graphic_regions(page, blocks: list, occupied: list[tuple]) -> list[Region]:
+	"""Clusters of non-grid ink, each classified `flow` or `figure`.
+
+	Labelled boxes wired together are a flow — mermaid's actual job. Wordless ink is a figure,
+	where a crop of the original is the only faithful representation we can offer.
+	"""
+	page_box = tuple(float(value) for value in page.rect)
+	labels = text_boxes(blocks)
+	boxes = [
+		box
+		for box in graphic_boxes(page, blocks)
+		if not is_furniture(box, page_box) and overlap_fraction(box, occupied) <= 0.5
+	]
+
+	cells: set[tuple[int, int]] = set()
+	for box in boxes:
+		cells |= grid_cells(box, page_box)
+
+	regions = []
+	for component in connected_components(cells):
+		region_box = component_box(component, page_box)
+		fraction = area_fraction(region_box, page_box)
+		# A component can still engulf a grid even when each of its own boxes dodged one:
+		# connectors and cell fills chain through the table's area. Announcing that span as a
+		# second `flow` region is exactly the hint that talks the model into a flowchart.
+		if fraction < MIN_REGION_AREA or overlap_fraction(region_box, occupied) > 0.5:
+			continue
+		inside = label_count(labels, region_box)
+		shape = FLOW if inside >= MIN_FLOW_LABELS else FIGURE
+		regions.append(Region(shape, region_box, fraction, labels=inside))
+	return regions
+
+
+def prose_region(page, blocks: list, occupied: list[tuple]) -> Region | None:
+	"""The union of text blocks outside every graphic region, if any survive."""
+	page_box = tuple(float(value) for value in page.rect)
+	box = None
+	for candidate in text_boxes(blocks):
+		if is_furniture(candidate, page_box) or overlap_fraction(candidate, occupied) > 0.5:
+			continue
+		box = (
+			candidate
+			if box is None
+			else (
+				min(box[0], candidate[0]),
+				min(box[1], candidate[1]),
+				max(box[2], candidate[2]),
+				max(box[3], candidate[3]),
+			)
+		)
+	if box is None:
+		return None
+	return Region(PROSE, box, area_fraction(box, page_box))
+
+
+def find_regions(page) -> list[Region]:
+	"""Shape-classified regions of one page: grids first, then the remaining ink, then prose."""
+	blocks = page.get_text("blocks")
+	page_box = tuple(float(value) for value in page.rect)
+	boxes = [box for box in graphic_boxes(page, blocks) if not is_furniture(box, page_box)]
+	regions = table_regions(page_box, boxes)
+	regions.extend(graphic_regions(page, blocks, [region.bbox for region in regions]))
+	prose = prose_region(page, blocks, [region.bbox for region in regions])
+	if prose:
+		regions.append(prose)
+	return regions
+
+
+def classify_page(page, min_chars: int, min_drawings: int, regions: list[Region] | None = None) -> str:
+	"""Page kind from its regions: `visual` | `mixed` | `text`.
+
+	`visual` keeps its old meaning — no usable text layer, so the rendered image is the only
+	ground truth. `mixed` is the case the old AND-gate could not express: a trustworthy text
+	layer *and* substantial table/diagram ink, which still needs a vision model to recover the
+	structure but must go on being scored against its text layer.
+	"""
+	if regions is None:
+		regions = find_regions(page)
+	prose_chars = len(page.get_text("text").strip())
+	graphics = [region for region in regions if region.shape in (TABLE, FLOW, FIGURE)]
+	if prose_chars < min_chars and (graphics or page.get_images()):
+		return "visual"
+	if any(region.substantial for region in graphics):
+		return "mixed"
+	try:
+		if len(page.get_drawings()) >= min_drawings:
+			return "mixed"
+	except Exception:
+		pass
+	return "text"
+
+
+def shape_hint(regions: list[Region]) -> str:
+	"""One line per non-prose shape, for the VLM prompt — what to expect and how much of it.
+
+	Deliberately coarse: it tells the model a grid is present, never where its cells are. The
+	model still reads the image; the hint only stops it inventing a flowchart for a grid.
+	"""
+	counts: dict[str, int] = {}
+	widest = 0
+	for region in regions:
+		if region.shape == PROSE:
+			continue
+		counts[region.shape] = counts.get(region.shape, 0) + 1
+		if region.shape == TABLE:
+			widest = max(widest, region.columns)
+	if not counts:
+		return ""
+	parts = [f"{count} {shape} region(s)" for shape, count in sorted(counts.items())]
+	line = "Layout analysis of this page found: " + ", ".join(parts) + "."
+	if widest >= MIN_GRID_COLUMNS:
+		line += f" The widest ruled grid has ~{widest + 1} columns — it is a TABLE, never a flowchart."
+	return line + "\n"
diff --git a/wikify/engine/remediate.py b/wikify/engine/remediate.py
index bf29a54..2e2f4d5 100644
--- a/wikify/engine/remediate.py
+++ b/wikify/engine/remediate.py
@@ -8,6 +8,10 @@
     small drop is the intended furniture removal);
   - vlm is adopt-eligible when it beats the baseline composite;
   - vlm wins over an eligible cleanup only when it also scores at least as high.
+Under all of that sit two floors (`pick_winner`): nothing dramatically below the best read
+of the page is adopted, and a near-empty candidate never displaces one carrying content.
+Every candidate — baseline included — clears the same diagram gate before it is scored, so
+a block rejected on one route cannot ride in on another.
 Always-run ≠ always-adopt. The adopted (or baseline) markdown becomes each page's
 **canonical** markdown; cross-page tables are then stitched, the doc's canonical mean is
 recomputed, and per-page LLM spend lands on `Source Page.llm_cost` /
@@ -22,11 +26,12 @@
 
 from __future__ import annotations
 
+import re
 from collections.abc import Callable
 
 import fitz  # PyMuPDF
 
-from wikify.engine import llm, pdf_utils, settings, store
+from wikify.engine import diagrams, llm, pdf_utils, regions, settings, store
 from wikify.engine.loader.cleanup_llm import clean_markdown
 from wikify.engine.loader.table_stitch import stitch_cross_page_tables
 from wikify.engine.parsers import vlm
@@ -34,17 +39,50 @@
 from wikify.engine.verify import deterministic as det
 from wikify.engine.verify import score_page
 
+# Two floors under adoption. A page had `# TIE TIT Molo` — ten characters of OCR noise — made
+# canonical at composite 0.058, because cleanup's own eligibility test only asks whether recall
+# regressed, and recall against an equally empty baseline had not. Neither floor replaces the
+# per-method rules; both sit under them, so a candidate can only ever become canonical if it is
+# also a competitive read of the page.
+ADOPTION_COMPOSITE_RATIO = 0.9
+MIN_CANONICAL_CHARS = 40
+_IMAGE_EMBED_RE = re.compile(r"!\[[^\]]*\]\([^)]*\)")
+_MARKUP_RE = re.compile(r"[\s#*_>|`~\-]+")
 
-def _pick_winner(candidates: list[tuple]) -> tuple | None:
+
+def content_chars(markdown: str) -> int:
+	"""How much the reader actually gets: markdown furniture and image embeds removed, so a
+	crop-and-nothing-else page does not read as content."""
+	return len(_MARKUP_RE.sub("", _IMAGE_EMBED_RE.sub("", markdown or "")))
+
+
+def with_page_crop(markdown: str, page_image: str | None) -> str:
+	"""A near-empty page keeps its crop. An image the reader can read beats a blank page."""
+	if not page_image or "![" in markdown or content_chars(markdown) >= MIN_CANONICAL_CHARS:
+		return markdown
+	return f"{markdown}\n\n![Source page]({page_image})".strip()
+
+
+def pick_winner(candidates: list[tuple], baseline_composite: float, baseline_markdown: str) -> tuple | None:
 	"""Best adopt-eligible candidate: vlm when it also matches/beats cleanup's composite
 	(cleanup's composite is depressed by intended furniture removal, so a tie goes to
 	vlm), else the eligible cleanup, else None (keep baseline)."""
-	eligible = [c for c in candidates if c[3]]
-	vlm_c = next((c for c in eligible if c[0] == "vlm"), None)
-	cleanup_c = next((c for c in eligible if c[0] == "cleanup"), None)
-	if vlm_c and (not cleanup_c or vlm_c[2].composite >= cleanup_c[2].composite):
-		return vlm_c
-	return cleanup_c
+	best_composite = max([baseline_composite or 0.0, *(c[2].composite for c in candidates)])
+	best_chars = max([content_chars(baseline_markdown), *(content_chars(c[1]) for c in candidates)])
+	eligible = [
+		c
+		for c in candidates
+		if c[3]
+		and c[2].composite >= best_composite * ADOPTION_COMPOSITE_RATIO
+		and (content_chars(c[1]) >= MIN_CANONICAL_CHARS or content_chars(c[1]) >= best_chars)
+	]
+	vlm_candidate = next((c for c in eligible if c[0] == "vlm"), None)
+	cleanup_candidate = next((c for c in eligible if c[0] == "cleanup"), None)
+	if vlm_candidate and (
+		not cleanup_candidate or vlm_candidate[2].composite >= cleanup_candidate[2].composite
+	):
+		return vlm_candidate
+	return cleanup_candidate
 
 
 def remediate_pdf(
@@ -96,12 +134,20 @@ def remediate_pdf(
 			page = doc[p["page_no"] - 1]
 			gt = page.get_text("text")
 			kind = p["kind"]
-			base_md = p["baseline_markdown"] or ""
+			page_image = store.get_page_image(p["name"]) or ""
 			data_url = pdf_utils.png_to_data_url(pdf_utils.render_png(page, dpi=dpi))
 
 			use_judge = judge_all or kind == "visual"
 			img = data_url if use_judge else None
 			llm.reset_metrics()
+			# Every candidate — baseline included — clears the same diagram gate before it is
+			# scored, so a block that would be rejected on one route cannot survive on another.
+			candidates: list[tuple] = []  # (method, markdown, PageScore, adopt_eligible)
+			errors: list[str] = []
+			base_md, base_notes = diagrams.remove_unverified_diagrams(
+				p["baseline_markdown"] or "", page_image
+			)
+			errors.extend(base_notes)
 			base_ps = score_page(
 				p["page_no"], base_md, gt, image_data_url=img, use_judge=use_judge, page_kind=kind
 			)
@@ -109,12 +155,18 @@ def remediate_pdf(
 			# Every page gets the vlm pass; text pages also get the cheap cleanup variant.
 			# A single page's model call failing (rate limit, billing, transient) must not
 			# abort the whole pass — note it, try the other candidate / keep baseline, move on.
-			candidates: list[tuple] = []  # (method, markdown, PageScore, adopt_eligible)
-			errors: list[str] = []
 			try:
 				vlm_md = vlm.parse_page_image(
-					data_url, project_context=project_context, instruction=instruction
+					data_url,
+					project_context=project_context,
+					instruction=instruction,
+					shape_hint=regions.shape_hint(regions.find_regions(page)),
 				)
+				# Nothing unverified is stored: a diagram that will not parse after repair, or one
+				# carrying the signature of a table flattened into dangling branches, is dropped in
+				# favour of the page image before it can be scored, adopted or read.
+				vlm_md, diagram_notes = diagrams.remove_unverified_diagrams(vlm_md, page_image)
+				errors.extend(diagram_notes)
 				vlm_ps = score_page(
 					p["page_no"], vlm_md, gt, image_data_url=img, use_judge=use_judge, page_kind=kind
 				)
@@ -123,9 +175,11 @@ def remediate_pdf(
 				errors.append(f"vlm failed: {e}")
 			if kind != "visual":
 				try:
-					clean_md = clean_markdown(
-						base_md, project_context=project_context, instruction=instruction
+					clean_md, clean_notes = diagrams.remove_unverified_diagrams(
+						clean_markdown(base_md, project_context=project_context, instruction=instruction),
+						page_image,
 					)
+					errors.extend(clean_notes)
 					clean_ps = score_page(
 						p["page_no"], clean_md, gt, image_data_url=img, use_judge=use_judge, page_kind=kind
 					)
@@ -138,7 +192,13 @@ def remediate_pdf(
 				except Exception as e:
 					errors.append(f"cleanup failed: {e}")
 
-			winner = _pick_winner(candidates)
+			winner = pick_winner(candidates, base_ps.composite, base_md)
+			best_composite = max([base_ps.composite, *(c[2].composite for c in candidates)])
+			if best_composite < float(settings.get("escalate_threshold")):
+				errors.append(
+					f"every candidate scored below the escalate threshold (best {best_composite}) — "
+					"kept the best available read; this page needs a human"
+				)
 			# Record the adopted candidate; when nothing is adopted, record the vlm attempt
 			# (the expensive audit trail) so the review UI shows what was tried and why not.
 			record = (
@@ -160,6 +220,9 @@ def remediate_pdf(
 				canon_md[p["page_no"]] = record[1]
 				canon_comp[p["page_no"]] = new_composite
 				canon_src[p["page_no"]] = method
+			else:
+				canon_md[p["page_no"]] = base_md
+			canon_md[p["page_no"]] = with_page_crop(canon_md[p["page_no"]], page_image)
 
 			doc_cost += store.add_page_cost(p["name"], llm.get_metrics())
 
diff --git a/wikify/engine/reparse.py b/wikify/engine/reparse.py
index 671d4d7..bd48d91 100644
--- a/wikify/engine/reparse.py
+++ b/wikify/engine/reparse.py
@@ -19,7 +19,7 @@
 
 import fitz  # PyMuPDF
 
-from wikify.engine import llm, pdf_utils, settings, store
+from wikify.engine import diagrams, llm, pdf_utils, regions, remediate, settings, store
 from wikify.engine.loader.cleanup_llm import clean_markdown
 from wikify.engine.parsers import vlm
 from wikify.engine.verify import score_page
@@ -60,6 +60,7 @@ def reparse_page(
 	with fitz.open(str(pdf_path)) as doc:
 		fpage = doc[page_no - 1]
 		gt = fpage.get_text("text")
+		shape_hint = regions.shape_hint(regions.find_regions(fpage))
 		data_url = pdf_utils.png_to_data_url(pdf_utils.render_png(fpage, dpi=dpi))
 
 	kind = page["kind"]
@@ -70,12 +71,17 @@ def reparse_page(
 
 	llm.reset_metrics()
 	new_md = (
-		vlm.parse_page_image(data_url, project_context=project_context, instruction=instruction)
+		vlm.parse_page_image(
+			data_url, project_context=project_context, instruction=instruction, shape_hint=shape_hint
+		)
 		if method == "vlm"
 		else clean_markdown(base_md, project_context=project_context, instruction=instruction)
 	)
+	page_image = store.get_page_image(page["name"]) or ""
+	new_md, diagram_notes = diagrams.remove_unverified_diagrams(new_md, page_image)
+	new_md = remediate.with_page_crop(new_md, page_image)
 	new_ps = score_page(page_no, new_md, gt, image_data_url=img, use_judge=use_judge, page_kind=kind)
-	notes = "; ".join(new_ps.notes) if new_ps.notes else None
+	notes = "; ".join([*new_ps.notes, *diagram_notes]) or None
 
 	# Explicit user request → adopt the re-parse as canonical (record the score too).
 	store.set_remediation(page["name"], method, new_md, new_ps, adopted=True, notes=notes)
diff --git a/wikify/engine/store.py b/wikify/engine/store.py
index 72d154c..1440671 100644
--- a/wikify/engine/store.py
+++ b/wikify/engine/store.py
@@ -11,6 +11,8 @@
 import frappe
 from frappe.utils.file_manager import save_file
 
+from wikify.engine.verify.harness import get_verdict
+
 
 def create_document(
 	title: str,
@@ -158,10 +160,16 @@ def set_remediation(
 
 
 def set_canonical(page_name: str, markdown: str, composite: float | None, source: str) -> None:
-	"""Write a page's canonical (best-per-page) markdown + its composite + provenance."""
+	"""Write a page's canonical (best-per-page) markdown + its composite + provenance.
+
+	The verdict follows the canonical composite, not the baseline one: the badge has to
+	describe the content the user is actually reading, or a remediated page reads
+	"review 0.99". `composite` (baseline) stays on the row for audit.
+	"""
 	values = {"canonical_markdown": markdown, "canonical_source": source}
 	if composite is not None:
 		values["canonical_composite"] = composite
+		values["verdict"] = get_verdict(composite)
 	frappe.db.set_value("Source Page", page_name, values)
 
 
diff --git a/wikify/engine/verify/__init__.py b/wikify/engine/verify/__init__.py
index 31630fb..13fa0b3 100644
--- a/wikify/engine/verify/__init__.py
+++ b/wikify/engine/verify/__init__.py
@@ -8,6 +8,6 @@
 
 from __future__ import annotations
 
-from wikify.engine.verify.harness import PageScore, score_page
+from wikify.engine.verify.harness import PageScore, get_verdict, score_page
 
-__all__ = ["PageScore", "score_page"]
+__all__ = ["PageScore", "get_verdict", "score_page"]
diff --git a/wikify/engine/verify/harness.py b/wikify/engine/verify/harness.py
index 32c4e61..e92e47a 100644
--- a/wikify/engine/verify/harness.py
+++ b/wikify/engine/verify/harness.py
@@ -39,7 +39,10 @@ def _composite(terms: dict, weights: dict) -> float:
 	return round(sum(weights[k] * active[k] for k in active) / total_w, 3)
 
 
-def _verdict(composite: float) -> str:
+def get_verdict(composite: float) -> str:
+	"""The pass/escalate/review badge for a composite score. Public because the verdict
+	must follow whichever composite the user is actually reading — `store.set_canonical`
+	re-derives it from the canonical composite once a remediation is adopted."""
 	if composite >= float(settings.get("pass_threshold")):
 		return "pass"
 	if composite >= float(settings.get("escalate_threshold")):
@@ -116,7 +119,7 @@ def score_page(
 		table_score=None if tscore is None else round(tscore, 3),
 		judge_score=judge_score,
 		composite=composite,
-		verdict=_verdict(composite),
+		verdict=get_verdict(composite),
 		kind=page_kind,
 		notes=notes,
 	)
diff --git a/wikify/hooks.py b/wikify/hooks.py
index 6ba29b9..33622ea 100644
--- a/wikify/hooks.py
+++ b/wikify/hooks.py
@@ -146,13 +146,21 @@
 # ---------------
 # Hook on document methods and events
 
-# doc_events = {
-# 	"*": {
-# 		"on_update": "method",
-# 		"on_cancel": "method",
-# 		"on_trash": "method"
-# 	}
-# }
+# The content chain `Source Page.canonical_markdown → Source Section.markdown → chunks →
+# index` copies at every link, so a fix that stops at one link leaves the rest serving the
+# old text. Page edits propagate into the covering sections; section edits make the
+# project's index stale. Both handlers coalesce into one queued job so a bulk pass writing
+# hundreds of rows doesn't storm the long queue — see `wikify/rag/events.py`.
+doc_events = {
+	"Source Page": {
+		"on_update": "wikify.rag.events.queue_page_propagation",
+	},
+	"Source Section": {
+		"after_insert": "wikify.rag.events.queue_reindex",
+		"on_update": "wikify.rag.events.queue_reindex",
+		"on_trash": "wikify.rag.events.queue_reindex",
+	},
+}
 
 # Scheduled Tasks
 # ---------------
diff --git a/wikify/patches.txt b/wikify/patches.txt
index 3f0a9e3..aea984f 100644
--- a/wikify/patches.txt
+++ b/wikify/patches.txt
@@ -9,3 +9,4 @@ wikify.patches.v0_2.seed_uncategorized_and_backfill
 wikify.patches.v0_4.merge_duplicate_section_types
 wikify.patches.v0_6.backfill_section_lint
 wikify.patches.v0_5.backfill_section_references
+wikify.patches.v1_0.backfill_canonical_verdict
diff --git a/wikify/patches/v1_0/backfill_canonical_verdict.py b/wikify/patches/v1_0/backfill_canonical_verdict.py
new file mode 100644
index 0000000..d70ac89
--- /dev/null
+++ b/wikify/patches/v1_0/backfill_canonical_verdict.py
@@ -0,0 +1,45 @@
+"""Re-derive `Source Page.verdict` from the canonical composite on already-parsed docs.
+
+Before the `store.set_canonical` fix, the verdict was frozen at the BASELINE parse, so a
+page whose remediated content scored 0.99 still wore a `review` badge next to its 0.99
+audit score. Pure recompute from fields already on the row — no re-parse, no LLM spend.
+
+Idempotent and console-runnable: `wikify.patches.v1_0.backfill_canonical_verdict.execute()`.
+"""
+
+from __future__ import annotations
+
+import frappe
+
+from wikify.engine.verify import get_verdict
+
+CHUNK_SIZE = 500
+
+
+def execute() -> dict[str, int]:
+	"""Returns the number of pages moved to each verdict (empty when nothing was stale)."""
+	# canonical_composite is a Frappe Float (NOT NULL, default 0), so "never remediated"
+	# reads as 0.0 — filter those out rather than badging them `review` off a phantom score.
+	pages = frappe.get_all(
+		"Source Page",
+		filters={"canonical_composite": [">", 0]},
+		fields=["name", "verdict", "canonical_composite"],
+	)
+
+	stale: dict[str, list[str]] = {}
+	for page in pages:
+		verdict = get_verdict(page["canonical_composite"])
+		if verdict != page["verdict"]:
+			stale.setdefault(verdict, []).append(page["name"])
+
+	for verdict, names in stale.items():
+		for start in range(0, len(names), CHUNK_SIZE):
+			frappe.db.set_value(
+				"Source Page",
+				{"name": ["in", names[start : start + CHUNK_SIZE]]},
+				"verdict",
+				verdict,
+				update_modified=False,
+			)
+
+	return {verdict: len(names) for verdict, names in stale.items()}
diff --git a/wikify/rag/__init__.py b/wikify/rag/__init__.py
new file mode 100644
index 0000000..da0660c
--- /dev/null
+++ b/wikify/rag/__init__.py
@@ -0,0 +1,13 @@
+"""Retrieval core for the RAG POC — chunking, embedding, LanceDB storage and search.
+
+Layout mirrors the frozen contract in `specs/poc-rag-CONTRACT.md`:
+
+- `embed`  — model2vec static embeddings (256-dim, numpy-only, no API key).
+- `store`  — the LanceDB connection + the single `chunks` table (explicit pyarrow schema).
+- `chunk`  — Source Section → Chunk, with contextual-retrieval prefixes on `embed_text`.
+- `index`  — build / refresh / drop index rows for a project or a single section.
+- `search` — vector | fts | hybrid (native RRF) | filter (exhaustive), then parent expansion.
+
+Nothing here is imported at app boot: `lancedb` and `model2vec` are heavy, so submodules
+are imported by the API/job layers on demand.
+"""
diff --git a/wikify/rag/answer.py b/wikify/rag/answer.py
new file mode 100644
index 0000000..8559482
--- /dev/null
+++ b/wikify/rag/answer.py
@@ -0,0 +1,228 @@
+"""Grounded synthesis — route, retrieve, cite, answer (or honestly refuse).
+
+The answer is written from a numbered context built out of retrieved chunks, and every
+claim must carry the `[n]` marker of the chunk it came from. Two guardrails keep it
+honest:
+
+- **Refusal.** When nothing retrieved clears the score floor we return `refused=True` and
+  say so, instead of letting the model improvise from its own memory.
+- **Citation sanitising.** Markers pointing past the end of the citation list are stripped
+  from the answer, so a rendered `[7]` always resolves to a real source card.
+"""
+
+from __future__ import annotations
+
+import re
+from collections.abc import Callable
+
+from wikify.agent import llm
+from wikify.engine import settings
+from wikify.rag import evidence, usage
+from wikify.rag import search as rag_search
+from wikify.rag.chunk import CONTEXT_SEPARATOR
+from wikify.rag.router import Route, route
+
+# Which retrieval leg each intent takes. "filter" returns every match (completeness);
+# "hybrid" is RRF-fused vector + full-text.
+MODE_FOR_INTENT = {"exhaustive": "filter", "semantic": "hybrid", "hybrid": "hybrid"}
+
+TOP_K = 8
+# An exhaustive answer is allowed to be long, but a runaway type (e.g. "other") must not
+# push a thousand chunks into the prompt.
+EXHAUSTIVE_LIMIT = 60
+
+# RRF fusion scores are RANK-based: a top-ranked irrelevant chunk scores exactly what a
+# top-ranked perfect one does (measured: 0.0328 for both an on-topic and a nonsense query
+# on the demo corpus). So hybrid/fts scores carry no absolute quality signal and can only
+# refuse on emptiness. The rerank score — the cheap model's 0-10 relevance judgement — is
+# the real guardrail, which is why `answer()` reranks by default.
+MIN_RERANK_SCORE = 3.0
+# Raw vector distance does carry signal (measured: 0.51 on-topic vs 0.33 for nonsense).
+MIN_VECTOR_SCORE = 0.36
+# ponytail: both floors are calibrated against the demo corpus with potion-base-8M; recheck
+# them from the eval harness (recall@k per golden question) if the embedder or corpus changes.
+
+REFUSAL = (
+	"I couldn't find this in the wiki. Nothing in the indexed documents is close enough to "
+	"your question for me to answer it without guessing."
+)
+
+SYSTEM_PROMPT = """You answer questions strictly from the numbered wiki excerpts given to \
+you. Rules:
+
+- Use ONLY the excerpts. If they do not cover part of the question, say that part is not \
+in the wiki.
+- Cite every factual sentence with the marker of the excerpt it came from: [1], [3]. Put \
+the marker at the end of the sentence. Never cite a number that is not in the list.
+- Answer in markdown. Lead with the answer, keep it tight, use a bullet list when the \
+question asks for several things.
+- When the excerpts are an exhaustive list of a kind of section, list every one of them \
+and say how many there are.
+- Never invent document titles, page numbers, or excerpt numbers.
+- Support each factual sentence with a short VERBATIM quote copied character-for-character \
+from the excerpt, in double quotes, immediately before the marker: "...exact words..." [3]. \
+Quote 5-40 words. Never paraphrase inside quotation marks."""
+
+CITATION_MARKER = re.compile(r"\[(\d+)\]")
+
+
+def retrieve(
+	decided: Route,
+	project: str | None,
+	rerank: bool,
+	allowed_projects=rag_search.ACL_REQUIRED,
+) -> list:
+	"""Run the leg the route chose. Exhaustive returns ALL matches, not a top-k.
+
+	`allowed_projects` carries the caller's ACL decision straight through to `search()`,
+	which is where an omitted one throws.
+	"""
+	mode = MODE_FOR_INTENT[decided.intent]
+	return rag_search.search(
+		decided.query,
+		project=project,
+		section_type=decided.section_type,
+		limit=EXHAUSTIVE_LIMIT if mode == "filter" else TOP_K,
+		mode=mode,
+		rerank=rerank,
+		allowed_projects=allowed_projects,
+	)
+
+
+def below_floor(hits: list, mode: str) -> bool:
+	"""True when nothing retrieved is good enough to answer from.
+
+	A `filter` leg is an exact metadata match — it either matched or it didn't — so only
+	emptiness refuses there, and the same holds for the rank-scored fusion legs.
+	"""
+	if not hits:
+		return True
+	# Only a real ranking may refuse. `search.rerank_hits` leaves every score unset when the
+	# reranker answers but ranks nothing, so a silent rerank failure degrades to the fusion
+	# order here rather than turning into "the wiki doesn't cover this".
+	reranked = [hit.rerank_score for hit in hits if hit.rerank_score is not None]
+	if reranked:
+		return max(reranked) < MIN_RERANK_SCORE
+	if mode == "vector":
+		return max(hit.score for hit in hits) < MIN_VECTOR_SCORE
+	return False
+
+
+def format_context(hits: list) -> str:
+	"""The numbered excerpt block the model cites against — `[n]` is the hit's position."""
+	blocks = []
+	for position, hit in enumerate(hits, start=1):
+		pages = f"p.{hit.page_start}"
+		if hit.page_end and hit.page_end != hit.page_start:
+			pages = f"p.{hit.page_start}-{hit.page_end}"
+		crumb = f"{hit.document_title}{CONTEXT_SEPARATOR}{hit.hierarchy_path or hit.title}"
+		header = f"[{position}] {crumb} ({pages})"
+		if hit.section_type:
+			header += f" — type: {hit.section_type}"
+		blocks.append(f"{header}\n{hit.text}")
+	return "\n\n---\n\n".join(blocks)
+
+
+def drop_unknown_citations(text: str, citation_count: int) -> str:
+	"""Remove `[n]` markers with no matching source card — a citation must resolve."""
+
+	def keep(match: re.Match) -> str:
+		number = int(match.group(1))
+		return match.group(0) if 1 <= number <= citation_count else ""
+
+	return CITATION_MARKER.sub(keep, text)
+
+
+def generate(question: str, context: str, model: str, on_delta: Callable | None) -> str:
+	"""Stream the synthesis, forwarding each delta to `on_delta` as it arrives."""
+	messages = [
+		{"role": "system", "content": SYSTEM_PROMPT},
+		{"role": "user", "content": f"Excerpts:\n\n{context}\n\n---\n\nQuestion: {question}"},
+	]
+	streamed = on_delta is not None
+	response = llm.complete_with_tools(model, messages, [], stream=streamed, include_usage=streamed)
+	if not streamed:
+		usage.add(getattr(response, "usage", None))
+		return response.choices[0].message.content or ""
+
+	text = ""
+	# Kept and folded in once at the end: a provider that repeats a running total on every
+	# chunk would otherwise be billed once per token.
+	streamed_usage = None
+	for chunk in response:
+		streamed_usage = getattr(chunk, "usage", None) or streamed_usage
+		delta = chunk.choices[0].delta if chunk.choices else None
+		piece = getattr(delta, "content", None) if delta else None
+		if piece:
+			text += piece
+			on_delta(piece)
+	usage.add(streamed_usage)
+	return text
+
+
+def answer(
+	question: str,
+	*,
+	project: str | None = None,
+	history: list | None = None,
+	rerank: bool = True,
+	allowed_projects=rag_search.ACL_REQUIRED,
+	on_route: Callable | None = None,
+	on_citations: Callable | None = None,
+	on_delta: Callable | None = None,
+) -> dict:
+	"""Route → retrieve → cite → synthesise.
+
+	Returns the answer, its citations, the route, and what the turn cost: `model` plus the
+	`cost` / `prompt_tokens` / `completion_tokens` of every completion it made — routing,
+	reranking and synthesis together, not synthesis alone.
+
+
+	The three callbacks let a caller stream the same work in the order the UI needs it —
+	route, then sources, then answer tokens — without re-running any of it. Omit them for
+	a plain blocking answer. `allowed_projects` is the ACL pre-filter; it is handed to
+	`search()` so it lands in the store's `where`, never as a post-hoc trim of the hits.
+	It has no permissive default — a caller that omits it throws rather than answering from
+	every project on the site.
+	"""
+	# Checked before routing so a missing ACL costs no LLM call and publishes no callback.
+	rag_search.assert_acl_decision(allowed_projects)
+	model = llm.resolve_model(project=project)
+
+	with usage.collect() as spend:
+		decided = route(question, project, history)
+		if on_route:
+			on_route(decided.as_dict())
+
+		hits = retrieve(decided, project, rerank, allowed_projects)
+		mode = MODE_FOR_INTENT[decided.intent]
+		refused = below_floor(hits, mode) or not settings.openrouter_key()
+
+		citations = [] if refused else [hit.as_dict() for hit in hits]
+		if on_citations:
+			on_citations(citations)
+
+		if refused:
+			text = REFUSAL if below_floor(hits, mode) else f"{REFUSAL} (No language model is configured.)"
+			if on_delta:
+				on_delta(text)
+			citations = []
+		else:
+			text = drop_unknown_citations(
+				generate(decided.query, format_context(hits), model, on_delta), len(hits)
+			)
+			# The quote each citation rests on is checked against the source before it is
+			# shown — a citation rendered as confirmed while misstating a figure is worse
+			# than none. `on_citations` has already fired, so only the returned dict
+			# carries the verified set.
+			citations = evidence.attach_verified_quotes(text, citations)
+
+		return {
+			"answer": text,
+			"citations": citations,
+			"route": decided.as_dict(),
+			"refused": refused,
+			"model": model,
+			# A refusal still costs the routing call, so spend is reported on both paths.
+			**spend,
+		}
diff --git a/wikify/rag/chunk.py b/wikify/rag/chunk.py
new file mode 100644
index 0000000..6a54c16
--- /dev/null
+++ b/wikify/rag/chunk.py
@@ -0,0 +1,404 @@
+"""Source Section → Chunk. Structure-aware splitting + contextual-retrieval prefixes.
+
+Two ideas carry the retrieval quality here:
+
+1. **Parent-document retrieval.** A chunk is only an addressing device — every chunk keeps
+   its `section` (the Source Section name), and search expands hits back up to the whole
+   section before showing them. So chunks can be small enough to embed well without the
+   reader ever seeing a truncated fragment.
+2. **Contextual retrieval.** `embed_text` is prefixed with the document title and the
+   section's hierarchy path, so a chunk that says only "Your task." still embeds near
+   "beat the clock". The stored `text` stays clean — the prefix is retrieval scaffolding,
+   not content, and must never reach the reader.
+
+Every chunk also carries its own **provenance**: the single page it came from (matched back
+against `Source Page.canonical_markdown`, not inherited from the section's page range), and
+its line span both inside the section and inside that page. A section can run across a dozen
+pages, so "see pages 10-12" is not a claim a student can check; "page 11, lines 14-18" is.
+When the page match is ambiguous the chunk keeps the range start and is flagged
+`page_approximate` rather than asserting a precision it does not have.
+"""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+
+import frappe
+
+from wikify.rag import evidence
+
+CONTEXT_SEPARATOR = " \u203a "  # " > " re-typeset for the breadcrumb prefix
+
+CHUNK_TARGET_CHARS = 1200
+CHUNK_OVERLAP_CHARS = 150
+
+# Sections are read in slices so a project with thousands of them can't build an
+# `IN (...)` list the database refuses (and so memory stays bounded).
+BATCH_SIZE = 500
+
+SECTION_FIELDS = [
+	"name",
+	"source_document",
+	"title",
+	"section_type",
+	"hierarchy_path",
+	"page_start",
+	"page_end",
+	"markdown",
+	"wiki_document",
+]
+
+
+@dataclass
+class Chunk:
+	id: str
+	section: str
+	source_document: str
+	project: str
+	text: str
+	embed_text: str
+	section_type: str | None
+	hierarchy_path: str
+	page_start: int
+	page_end: int
+	wiki_route: str | None
+	# Provenance. `page_no` is the page this chunk's text was actually matched back to;
+	# `page_approximate` says it is the range start because the match was ambiguous. The
+	# `line_*` pair indexes the parent section's markdown, `page_line_*` the resolved page's.
+	page_no: int = 0
+	page_approximate: bool = True
+	line_start: int = 0
+	line_end: int = 0
+	page_line_start: int = 0
+	page_line_end: int = 0
+	# A breadcrumb section whose body lives in its children: it is indexed so exhaustive
+	# filter mode can still return it, and kept out of the similarity legs (see `search`).
+	title_only: bool = False
+
+
+def get_rows_by_name(doctype: str, names: list[str], fields: list[str]) -> list[dict]:
+	"""`get_all` over a name list, sliced into batches — one query per batch, no N+1."""
+	unique = [name for name in dict.fromkeys(names) if name]
+	rows: list[dict] = []
+	for start in range(0, len(unique), BATCH_SIZE):
+		batch = unique[start : start + BATCH_SIZE]
+		rows.extend(frappe.get_all(doctype, filters={"name": ["in", batch]}, fields=fields))
+	return rows
+
+
+def is_heading(block: str) -> bool:
+	return block.lstrip().startswith("#")
+
+
+def markdown_blocks(markdown: str) -> list[str]:
+	"""Split markdown into atomic blocks: a heading always starts one, blank lines end one."""
+	blocks: list[str] = []
+	current: list[str] = []
+	for line in markdown.splitlines():
+		if is_heading(line) and current:
+			blocks.append("\n".join(current))
+			current = [line]
+			continue
+		if not line.strip():
+			if current:
+				blocks.append("\n".join(current))
+				current = []
+			continue
+		current.append(line)
+	if current:
+		blocks.append("\n".join(current))
+	return [block.strip() for block in blocks if block.strip()]
+
+
+def split_on_characters(text: str, target: int) -> list[str]:
+	"""Last-resort cut for a run of text with no whitespace to break on.
+
+	A base64 blob or a 50k-character OCR run has no word boundary, so whitespace splitting
+	returns it whole and it blows past the embedder's context window as a single chunk.
+	"""
+	if len(text) <= target:
+		return [text]
+	return [text[start : start + target] for start in range(0, len(text), target)]
+
+
+def split_long_block(block: str, target: int) -> list[str]:
+	"""Break one oversized block on whitespace — a word is never cut in half."""
+	if len(block) <= target:
+		return [block]
+	parts: list[str] = []
+	current = ""
+	for token in re.split(r"(\s+)", block):
+		if current and len(current) + len(token) > target:
+			parts.append(current.strip())
+			current = token.strip()
+			continue
+		current += token
+	if current.strip():
+		parts.append(current.strip())
+	return [piece for part in parts for piece in split_on_characters(part, target)]
+
+
+def overlap_tail(text: str, chars: int) -> str:
+	"""The last `chars` of `text`, snapped forward to the next word boundary."""
+	if len(text) <= chars:
+		return text
+	tail = text[-chars:]
+	space = tail.find(" ")
+	return tail[space + 1 :] if space != -1 else tail
+
+
+def split_pieces(markdown: str, target: int = CHUNK_TARGET_CHARS) -> list[str]:
+	"""Split a section body into embeddable pieces on structural boundaries, no overlap.
+
+	Blocks (headings / paragraphs) are packed greedily up to `target`; only a single block
+	that is itself oversized is split mid-block, and then only on whitespace.
+
+	A heading never ends up alone: it travels with the body underneath it, even when that
+	pushes the piece past `target` by the heading line. A chunk that reads "## Benefits" and
+	nothing else embeds as noise and cites as a dead end.
+
+	Overlap is deliberately a separate step: a piece here is still a contiguous run of the
+	section, which is what lets `evidence.locate_quote` pin it to a line span. Once the
+	previous piece's tail is glued on, the text no longer maps to one place in the source.
+	"""
+	markdown = (markdown or "").strip()
+	if not markdown:
+		return []
+	if len(markdown) <= target:
+		return [markdown]
+
+	pieces: list[str] = []
+	current: list[str] = []
+	for block in markdown_blocks(markdown):
+		for part in split_long_block(block, target):
+			if current and len("\n\n".join(current)) + 2 + len(part) > target:
+				carried: list[str] = []
+				while current and is_heading(current[-1]):
+					carried.insert(0, current.pop())
+				if current:
+					pieces.append("\n\n".join(current))
+				elif len("\n\n".join(carried)) >= target:
+					# Nothing but headings pending, and they already fill a piece on their own
+					# (a table of contents, or a deep heading stack). Emit them so the buffer
+					# can't grow without bound — carrying them forward forever would eventually
+					# produce one piece the size of the section, and dropping them would delete
+					# them from `text`, the FTS column, so a section stops matching its own title.
+					# ponytail: the emitted piece is headings-only and embeds poorly; give it the
+					# first body block underneath it if a real corpus starts hitting this branch.
+					pieces.append("\n\n".join(carried))
+					carried = []
+				current = carried
+			current.append(part)
+	if current:
+		pieces.append("\n\n".join(current))
+	return pieces
+
+
+def add_overlap(pieces: list[str], overlap: int = CHUNK_OVERLAP_CHARS) -> list[str]:
+	"""Give each piece after the first the previous piece's tail, so a fact straddling a
+	boundary is still retrievable from either side."""
+	if overlap <= 0:
+		return pieces
+	return [
+		piece if position == 0 else f"{overlap_tail(pieces[position - 1], overlap)}\n\n{piece}"
+		for position, piece in enumerate(pieces)
+	]
+
+
+def split_markdown(
+	markdown: str, target: int = CHUNK_TARGET_CHARS, overlap: int = CHUNK_OVERLAP_CHARS
+) -> list[str]:
+	"""The stored chunk texts for a section body: structural pieces, then overlap."""
+	return add_overlap(split_pieces(markdown, target), overlap)
+
+
+def contextual_prefix(document_title: str, hierarchy_path: str) -> str:
+	"""The retrieval breadcrumb: document title, then the section's ancestor titles.
+
+	Written with the single right-pointing angle quote (the separator the UI and the answer
+	citations also use); the source keeps it as an escape so ruff's ambiguous-unicode rule
+	stays on for everything else.
+	"""
+	path = CONTEXT_SEPARATOR.join(part.strip() for part in (hierarchy_path or "").split(">") if part.strip())
+	crumbs = [crumb for crumb in (document_title, path) if crumb]
+	return CONTEXT_SEPARATOR.join(crumbs)
+
+
+def get_pages_by_document(document_names: list[str]) -> dict[str, list[dict]]:
+	"""Every parsed page of the given documents, grouped — one query per batch, no N+1."""
+	pages: dict[str, list[dict]] = {}
+	for row in get_rows_by_name("Source Document", document_names, ["name"]):
+		pages.setdefault(row["name"], [])
+	names = list(pages)
+	for start in range(0, len(names), BATCH_SIZE):
+		batch = frappe.get_all(
+			"Source Page",
+			filters={"source_document": ["in", names[start : start + BATCH_SIZE]]},
+			fields=["source_document", "page_no", "canonical_markdown"],
+			order_by="page_no asc",
+		)
+		for row in batch:
+			pages[row["source_document"]].append(row)
+	return pages
+
+
+def section_page_index(row: dict, pages: list[dict]) -> list[tuple[int, set]]:
+	"""Shingle only the pages inside this section's declared range — once per section."""
+	start = int(row.get("page_start") or 0)
+	end = int(row.get("page_end") or start)
+	if not start:
+		return []
+	wanted = range(start, max(start, end) + 1)
+	return evidence.build_page_index([page for page in pages if page["page_no"] in wanted])
+
+
+def resolve_provenance(piece: str, markdown: str, row: dict, page_index, pages: list[dict]) -> dict:
+	"""Where one piece sits: section lines, then the page it came from and its lines there."""
+	in_section = evidence.locate_quote(piece, markdown)
+	resolved = evidence.resolve_page(piece, page_index, int(row.get("page_start") or 0))
+	provenance = {
+		"page_no": resolved["page_no"],
+		"page_approximate": resolved["page_approximate"],
+		"line_start": in_section["line_start"],
+		"line_end": in_section["line_end"],
+		"page_line_start": 0,
+		"page_line_end": 0,
+	}
+	if resolved["page_approximate"]:
+		return provenance
+	for page in pages:
+		if page["page_no"] != resolved["page_no"]:
+			continue
+		on_page = evidence.locate_quote(piece, page)
+		if on_page["found"]:
+			provenance["page_line_start"] = on_page["line_start"]
+			provenance["page_line_end"] = on_page["line_end"]
+	return provenance
+
+
+def build_chunks(
+	rows: list[dict],
+	documents: dict[str, dict],
+	wiki_routes: dict[str, str],
+	pages_by_document: dict[str, list[dict]] | None = None,
+) -> list[Chunk]:
+	"""Turn pre-fetched Source Section rows into Chunks (no per-row queries).
+
+	`pages_by_document` supplies the parsed page text used to pin each chunk to a single
+	page; omit it and every chunk falls back to its section's page range, flagged
+	approximate. It is a defaulted argument so the callers that only want text keep working.
+	"""
+	pages_by_document = pages_by_document or {}
+	chunks: list[Chunk] = []
+	for row in rows:
+		document = documents.get(row["source_document"]) or {}
+		document_title = document.get("title") or row["source_document"]
+		prefix = contextual_prefix(document_title, row.get("hierarchy_path") or "")
+		route = wiki_routes.get(row.get("wiki_document") or "")
+		markdown = row.get("markdown") or ""
+		pieces = split_pieces(markdown)
+		# A section with an empty body is a breadcrumb node whose text lives in its children.
+		# It still gets a row, because `mode="filter"` promises every matching section and a
+		# row-less section is invisible to it — carrying its title as the text so it stays
+		# readable in a citation. `title_only` then keeps it out of the similarity legs: it
+		# carries no answer but matches on title alone, so it outranks real content (measured:
+		# an empty 76-character section ranked #1 in vector, hybrid AND FTS at once).
+		title_only = not pieces
+		if title_only:
+			pieces = [row.get("title") or row["name"]]
+		pages = pages_by_document.get(row["source_document"]) or []
+		page_index = [] if title_only else section_page_index(row, pages)
+		for ordinal, (piece, text) in enumerate(zip(pieces, add_overlap(pieces), strict=True)):
+			chunks.append(
+				Chunk(
+					id=f"{row['name']}::{ordinal}",
+					section=row["name"],
+					source_document=row["source_document"],
+					project=document.get("project") or "",
+					text=text,
+					embed_text=f"{prefix}\n\n{text}" if prefix else text,
+					section_type=row.get("section_type") or None,
+					hierarchy_path=row.get("hierarchy_path") or "",
+					page_start=row.get("page_start") or 0,
+					page_end=row.get("page_end") or 0,
+					wiki_route=route or None,
+					title_only=title_only,
+					# Resolved from the piece WITHOUT its overlap tail: the tail belongs to the
+					# previous piece, so including it would drag the reported span backwards.
+					**resolve_provenance(piece, markdown, row, page_index, pages),
+				)
+			)
+	return chunks
+
+
+def resolve_context(rows: list[dict]) -> tuple[dict[str, dict], dict[str, str]]:
+	"""(documents by name, wiki route by Wiki Document name) for a batch of section rows."""
+	documents = {
+		document["name"]: document
+		for document in get_rows_by_name(
+			"Source Document",
+			[row["source_document"] for row in rows],
+			["name", "title", "project"],
+		)
+	}
+	wiki_routes = {
+		page["name"]: page["route"]
+		for page in get_rows_by_name(
+			"Wiki Document",
+			[row.get("wiki_document") for row in rows],
+			["name", "route"],
+		)
+	}
+	return documents, wiki_routes
+
+
+def chunks_for_section(section_name: str) -> list[Chunk]:
+	rows = frappe.get_all("Source Section", filters={"name": section_name}, fields=SECTION_FIELDS)
+	if not rows:
+		return []
+	documents, wiki_routes = resolve_context(rows)
+	return build_chunks(rows, documents, wiki_routes, get_pages_by_document([rows[0]["source_document"]]))
+
+
+def chunks_for_project(project: str) -> list[Chunk]:
+	"""Every chunk for every section of every document in a project (tree order)."""
+	document_names = frappe.get_all("Source Document", filters={"project": project}, pluck="name")
+	if not document_names:
+		return []
+	rows = frappe.get_all(
+		"Source Section",
+		filters={"source_document": ["in", document_names]},
+		fields=SECTION_FIELDS,
+		order_by="source_document asc, lft asc",
+	)
+	if not rows:
+		return []
+	documents, wiki_routes = resolve_context(rows)
+	return build_chunks(rows, documents, wiki_routes, get_pages_by_document(document_names))
+
+
+def chunk_row(item: Chunk, vector: list[float]) -> dict:
+	"""One Chunk as a LanceDB row. Colocated with the dataclass so a field added to `Chunk`
+	and to `store.chunk_schema()` cannot be forgotten on the write path."""
+	return {
+		"id": item.id,
+		"section": item.section,
+		"source_document": item.source_document,
+		"project": item.project,
+		"text": item.text,
+		"embed_text": item.embed_text,
+		"section_type": item.section_type,
+		"hierarchy_path": item.hierarchy_path,
+		"page_start": item.page_start,
+		"page_end": item.page_end,
+		"page_no": item.page_no,
+		"page_approximate": item.page_approximate,
+		"line_start": item.line_start,
+		"line_end": item.line_end,
+		"page_line_start": item.page_line_start,
+		"page_line_end": item.page_line_end,
+		"title_only": item.title_only,
+		"wiki_route": item.wiki_route,
+		"vector": vector,
+	}
diff --git a/wikify/rag/embed.py b/wikify/rag/embed.py
new file mode 100644
index 0000000..4760e6e
--- /dev/null
+++ b/wikify/rag/embed.py
@@ -0,0 +1,46 @@
+"""Static embeddings via model2vec (`minishlab/potion-base-8M`, 256-dim).
+
+Static means a lookup table, not a transformer: numpy-only, no torch, no API key, and
+fast enough to embed a whole project inline. Loading the model still costs ~1s, so it is
+cached module-level — loading it per call dominated everything else in the POC.
+"""
+
+from __future__ import annotations
+
+import threading
+
+MODEL_NAME = "minishlab/potion-base-8M"
+EMBED_DIM: int = 256
+
+_model = None
+_model_lock = threading.Lock()
+
+
+def get_model():
+	"""The cached `StaticModel`, loaded on first use.
+
+	Double-checked locking so two gunicorn threads racing on the first search don't both
+	pay the load (and don't both hit the HF cache concurrently).
+	"""
+	global _model
+
+	if _model is None:
+		with _model_lock:
+			if _model is None:
+				from model2vec import StaticModel
+
+				_model = StaticModel.from_pretrained(MODEL_NAME)
+	return _model
+
+
+def embed(texts: list[str]) -> list[list[float]]:
+	"""Embed a batch of texts. One `encode` call — model2vec batches internally."""
+	texts = list(texts or [])
+	if not texts:
+		return []
+	vectors = get_model().encode(texts)
+	return [[float(value) for value in vector] for vector in vectors]
+
+
+def embed_one(text: str) -> list[float]:
+	return embed([text])[0]
diff --git a/wikify/rag/eval.py b/wikify/rag/eval.py
new file mode 100644
index 0000000..c138ef6
--- /dev/null
+++ b/wikify/rag/eval.py
@@ -0,0 +1,831 @@
+"""Retrieval evaluation — the POC-2 thesis measured, not asserted.
+
+Twelve golden questions over the Demo Corpus (`specs/poc-rag-DEMO-DATA.md`) are run down
+two legs and scored side by side:
+
+- **naive**  — plain top-k vector search on the raw question, no router, no filter. This is
+  what a textbook RAG pipeline does, and it is the baseline the POC argues against.
+- **routed** — `rag.router.route()` picks an intent, and an exhaustive intent takes the
+  `mode="filter"` leg that returns **every** matching section instead of a top-k guess.
+
+Three numbers per question. `recall@k` and `precision@k` are the usual ones;
+**completeness** is the one that carries the thesis — did the leg return *all* of the
+expected sources, or only some of them? A pipeline that finds six of fifteen job
+descriptions is 40% recall and 0% complete, and for "give me all the job descriptions"
+only the second number describes what the user actually got.
+
+**Why the golden questions are transcribed here rather than parsed out of the markdown.**
+The expected-source lists in `poc-rag-DEMO-DATA.md` live in free prose ("**Job Description
+— Community Midwife** (St Aubyn); ... (Meridian)"), not in a machine-readable table, so a
+parser would be a regex over English that rots silently the first time someone rewords a
+paragraph. Instead the ground truth is transcribed below and `resolve_expected()` checks
+every title against the real Source Sections at run time — a transcription typo or a
+renamed section is a loud failure, not a quiet zero.
+
+Usage (from the bench root):
+
+    bench --site wikify.localhost execute wikify.rag.eval.main
+    bench --site wikify.localhost execute wikify.rag.eval.main --kwargs "{'k': 15}"
+
+`main()` prints the scorecard as text and writes the standalone HTML scorecard to
+`docs/implementation/media/rag-eval.html`.
+"""
+
+from __future__ import annotations
+
+import html as html_escape
+import os
+import time
+from datetime import datetime
+
+import frappe
+
+from wikify.rag import answer as rag_answer
+from wikify.rag import search as rag_search
+from wikify.rag.router import route as route_question
+
+DEMO_PROJECT_NAME = "Demo Corpus"
+DEFAULT_K = 8
+MODES = ("naive", "routed")
+SCORECARD_PATH = ("docs", "implementation", "media", "rag-eval.html")
+
+# The routed leg mirrors production exactly: same intent → mode map and same exhaustive
+# ceiling `answer()` uses, so the eval cannot flatter a strategy the app does not run.
+MODE_FOR_INTENT = rag_answer.MODE_FOR_INTENT
+EXHAUSTIVE_LIMIT = rag_answer.EXHAUSTIVE_LIMIT
+
+# The harness runs headless against one explicitly named project, so it opts out of the ACL
+# pre-filter on purpose rather than pretending to be a logged-in user. Imported directly, not
+# via `getattr` with a fallback: a fallback would silently reinstate the unscoped default this
+# sentinel exists to prevent.
+ALL_PROJECTS = rag_search.ALL_PROJECTS
+
+JD = "staff_roles_and_responsibilities"
+PAY = "administrative_policies"
+TRAINING = "training_and_audits"
+OVERVIEW = "other"
+
+NORTHFIELD_JDS = [
+	"Job Description — Ward Sister / Charge Nurse",
+	"Job Description — Staff Nurse (Band 5)",
+	"Job Description — Healthcare Assistant",
+]
+RIVERSIDE_JDS = [
+	"Job Description — Practice Manager",
+	"Job Description — General Practitioner (Salaried)",
+	"Job Description — Medical Receptionist",
+]
+ST_AUBYN_JDS = [
+	"Job Description — Consultant Obstetrician",
+	"Job Description — Community Midwife",
+	"Job Description — Maternity Support Worker",
+]
+LAKESIDE_JDS = [
+	"Job Description — Theatre Scrub Practitioner",
+	"Job Description — Operating Department Practitioner (Anaesthetics)",
+	"Job Description — Theatre Support Worker",
+]
+MERIDIAN_JDS = [
+	"Job Description — Registered Care Manager",
+	"Job Description — Community Care Worker",
+	"Job Description — Care Coordinator (Scheduling)",
+]
+
+# `expected` is the ground-truth source set (Source Section titles, unique across the demo
+# corpus). `required` is the subset the spec bolds — the source a semantic answer is wrong
+# without. `exhaustive` marks the lane where recall is scored against the COMPLETE set
+# rather than as recall@k.
+GOLDEN_QUESTIONS = [
+	{
+		"id": "G1",
+		"kind": "exhaustive",
+		"question": "Give me all the job descriptions across all the PDFs.",
+		"expected_intent": "exhaustive",
+		"expected_section_type": JD,
+		"exhaustive": True,
+		"expected": [*NORTHFIELD_JDS, *RIVERSIDE_JDS, *ST_AUBYN_JDS, *LAKESIDE_JDS, *MERIDIAN_JDS],
+		"required": [],
+	},
+	{
+		"id": "G2",
+		"kind": "exhaustive",
+		"question": "List every pay, benefits and leave policy in the corpus.",
+		"expected_intent": "exhaustive",
+		"expected_section_type": PAY,
+		"exhaustive": True,
+		"expected": [
+			"Pay Bands, Benefits and Leave Entitlements",
+			"Salary Scales, Pension and Employee Benefits",
+			"Rostering, On-Call Payments and Maternity Benefits",
+			"Theatre Staff Pay, Overtime and Benefits Policy",
+			"Reward, Mileage and Employee Benefits",
+		],
+		"required": [],
+	},
+	{
+		"id": "G3",
+		"kind": "exhaustive",
+		"question": "What training and competency requirements does every organisation set for new staff?",
+		"expected_intent": "exhaustive",
+		"expected_section_type": TRAINING,
+		"exhaustive": True,
+		"expected": [
+			"Recruitment, Qualifications and Mandatory Training",
+			"Induction, Competency Framework and Annual Appraisal",
+			"Midwifery Preceptorship and Skills Drills Programme",
+			"Surgical Competency Sign-Off and Audit Cycle",
+			"Care Certificate, Shadowing and Refresher Training Requirements",
+		],
+		"required": [],
+	},
+	{
+		"id": "G4",
+		"kind": "exhaustive",
+		"question": "Which organisations does this corpus cover, and what does each one do?",
+		"expected_intent": "exhaustive",
+		"expected_section_type": OVERVIEW,
+		"exhaustive": True,
+		"expected": [
+			"Hospital Overview and Mission",
+			"Who We Are: Riverside Community Clinic",
+			"St Aubyn Maternity Unit at a Glance",
+			"Lakeside Surgical Centre: Purpose and Services",
+			"Meridian Home Care: Company Overview",
+		],
+		"required": [],
+	},
+	{
+		"id": "G5",
+		"kind": "semantic",
+		"question": "Who is responsible for making sure the roster is published in advance, and how far ahead?",
+		"expected_intent": "semantic",
+		"expected_section_type": None,
+		"exhaustive": False,
+		"expected": [
+			"Job Description — Ward Sister / Charge Nurse",
+			"Rostering, On-Call Payments and Maternity Benefits",
+			"Job Description — Care Coordinator (Scheduling)",
+		],
+		"required": ["Job Description — Ward Sister / Charge Nurse"],
+	},
+	{
+		"id": "G6",
+		"kind": "semantic",
+		"question": "What happens if the swab count doesn't match at the end of an operation?",
+		"expected_intent": "semantic",
+		"expected_section_type": None,
+		"exhaustive": False,
+		"expected": [
+			"WHO Surgical Safety Checklist and Count Procedure",
+			"Job Description — Theatre Scrub Practitioner",
+		],
+		"required": ["WHO Surgical Safety Checklist and Count Procedure"],
+	},
+	{
+		"id": "G7",
+		"kind": "semantic",
+		"question": "A woman is bleeding heavily after giving birth — what should the team do?",
+		"expected_intent": "semantic",
+		"expected_section_type": None,
+		"exhaustive": False,
+		"expected": ["Obstetric Haemorrhage: Emergency Response"],
+		"required": ["Obstetric Haemorrhage: Emergency Response"],
+	},
+	{
+		"id": "G8",
+		"kind": "semantic",
+		"question": "How do staff who drive between visits get their travel costs back?",
+		"expected_intent": "semantic",
+		"expected_section_type": None,
+		"exhaustive": False,
+		"expected": [
+			"Reward, Mileage and Employee Benefits",
+			"Job Description — Community Care Worker",
+		],
+		"required": ["Reward, Mileage and Employee Benefits"],
+	},
+	{
+		"id": "G9",
+		"kind": "semantic",
+		"question": "What has to happen before someone is allowed to work unsupervised?",
+		"expected_intent": "semantic",
+		"expected_section_type": None,
+		"exhaustive": False,
+		"expected": [
+			"Recruitment, Qualifications and Mandatory Training",
+			"Induction, Competency Framework and Annual Appraisal",
+			"Care Certificate, Shadowing and Refresher Training Requirements",
+			"Surgical Competency Sign-Off and Audit Cycle",
+		],
+		"required": ["Recruitment, Qualifications and Mandatory Training"],
+	},
+	{
+		"id": "G10",
+		"kind": "hybrid",
+		"question": (
+			"Compare how the hospital, the maternity unit and the home care provider pay for out-of-hours work."
+		),
+		"expected_intent": "hybrid",
+		"expected_section_type": PAY,
+		"exhaustive": False,
+		"expected": [
+			"Pay Bands, Benefits and Leave Entitlements",
+			"Rostering, On-Call Payments and Maternity Benefits",
+			"Reward, Mileage and Employee Benefits",
+		],
+		"required": [
+			"Pay Bands, Benefits and Leave Entitlements",
+			"Rostering, On-Call Payments and Maternity Benefits",
+			"Reward, Mileage and Employee Benefits",
+		],
+	},
+	{
+		"id": "G11",
+		"kind": "hybrid",
+		"question": "Which roles require a driving licence?",
+		"expected_intent": "hybrid",
+		"expected_section_type": JD,
+		"exhaustive": False,
+		"expected": [
+			"Job Description — Community Midwife",
+			"Job Description — Registered Care Manager",
+			"Job Description — Community Care Worker",
+		],
+		"required": [
+			"Job Description — Community Midwife",
+			"Job Description — Registered Care Manager",
+			"Job Description — Community Care Worker",
+		],
+	},
+	{
+		"id": "G12",
+		"kind": "refusal",
+		"question": "What is the cyber security incident response plan for these organisations?",
+		"expected_intent": "semantic",
+		"expected_section_type": None,
+		"exhaustive": False,
+		"expected": [],
+		"required": [],
+		# Scored on `refused == True`, not on citations — so this is the one question that
+		# needs the synthesis step, not just retrieval.
+		"expect_refusal": True,
+	},
+]
+
+
+# --- ground truth resolution ----------------------------------------------------------
+
+
+def demo_project() -> str:
+	"""The Demo Corpus project name. Autonames are not stable across sites, so look it up."""
+	project = frappe.db.get_value("Wikify Project", {"project_name": DEMO_PROJECT_NAME}, "name")
+	if not project:
+		frappe.throw(
+			f"No Wikify Project named '{DEMO_PROJECT_NAME}'. Seed it first: "
+			"bench --site <site> execute wikify.tests.fixtures.demo_corpus.seed_demo_corpus"
+		)
+	return project
+
+
+def corpus_sections(project: str) -> list[dict]:
+	"""Every leaf Source Section in the project, with the fields the scorecard renders."""
+	documents = frappe.get_all("Source Document", filters={"project": project}, fields=["name", "title"])
+	title_of = {document["name"]: document["title"] for document in documents}
+	if not title_of:
+		return []
+	sections = frappe.get_all(
+		"Source Section",
+		filters={"source_document": ["in", list(title_of)], "is_group": 0},
+		fields=["name", "title", "section_type", "source_document", "page_start", "page_end"],
+		order_by="source_document asc, lft asc",
+	)
+	for section in sections:
+		section["document_title"] = title_of.get(section["source_document"], section["source_document"])
+	return sections
+
+
+def resolve_expected(question: dict, sections: list[dict]) -> list[dict]:
+	"""Map the transcribed expected titles onto real Source Sections.
+
+	An unresolvable or ambiguous title means the ground truth has drifted from the corpus —
+	that must fail loudly here rather than quietly score as a miss later on.
+	"""
+	by_title: dict[str, list[dict]] = {}
+	for section in sections:
+		by_title.setdefault(section["title"], []).append(section)
+
+	resolved = []
+	for title in question["expected"]:
+		matches = by_title.get(title) or []
+		if len(matches) != 1:
+			frappe.throw(
+				f"Golden question {question['id']}: expected source {title!r} matched "
+				f"{len(matches)} sections in the corpus (need exactly 1)."
+			)
+		resolved.append(matches[0])
+	return resolved
+
+
+def section_summary(section: dict) -> dict:
+	return {
+		"section": section["name"],
+		"title": section["title"],
+		"document": section["document_title"],
+		"section_type": section.get("section_type"),
+	}
+
+
+def hit_summary(hit) -> dict:
+	return {
+		"section": hit.section,
+		"title": hit.title,
+		"document": hit.document_title,
+		"section_type": hit.section_type,
+		"score": round(float(hit.score), 6),
+		"vector_rank": hit.vector_rank,
+		"fts_rank": hit.fts_rank,
+	}
+
+
+# --- retrieval legs -------------------------------------------------------------------
+
+
+def naive_hits(question_text: str, project: str, k: int) -> list:
+	"""The baseline: raw question, plain vector top-k, no router and no metadata filter."""
+	return rag_search.search(
+		question_text, project=project, limit=k, mode="vector", allowed_projects=ALL_PROJECTS
+	)
+
+
+def routed_hits(decided, project: str, k: int) -> list:
+	"""The routed leg — the same intent → mode mapping `rag.answer.retrieve()` uses."""
+	mode = MODE_FOR_INTENT.get(decided.intent, "hybrid")
+	return rag_search.search(
+		decided.query,
+		project=project,
+		section_type=decided.section_type,
+		limit=EXHAUSTIVE_LIMIT if mode == "filter" else k,
+		mode=mode,
+		allowed_projects=ALL_PROJECTS,
+	)
+
+
+# --- scoring --------------------------------------------------------------------------
+
+
+def score_leg(expected: list[dict], required: list[str], hits: list) -> dict:
+	"""recall@k, precision@k, and completeness for one retrieval leg of one question."""
+	expected_names = {section["name"] for section in expected}
+	returned = [hit_summary(hit) for hit in hits]
+	returned_names = {row["section"] for row in returned}
+	found = expected_names & returned_names
+	missed = [section_summary(section) for section in expected if section["name"] not in returned_names]
+	required_names = {section["name"] for section in expected if section["title"] in set(required)}
+
+	return {
+		"returned": returned,
+		"returned_count": len(returned),
+		"documents_covered": len({row["document"] for row in returned}),
+		"correct_count": len(found),
+		"recall": round(len(found) / len(expected_names), 4) if expected_names else None,
+		"precision": round(len(found) / len(returned), 4) if returned else None,
+		# The metric that carries the thesis: did we return ALL of them, or only some?
+		"complete": bool(expected_names) and expected_names <= returned_names,
+		"required_hit": required_names <= returned_names if required_names else None,
+		"missed": missed,
+	}
+
+
+def mean(values: list[float]) -> float | None:
+	numbers = [value for value in values if value is not None]
+	return round(sum(numbers) / len(numbers), 4) if numbers else None
+
+
+def aggregate(rows: list[dict], mode: str) -> dict:
+	"""Aggregate one mode across the scored (non-refusal) questions."""
+	scored = [row for row in rows if row["modes"].get(mode) and row["expected_sources"]]
+	exhaustive = [row for row in scored if row["exhaustive"]]
+	legs = [row["modes"][mode] for row in scored]
+	exhaustive_legs = [row["modes"][mode] for row in exhaustive]
+	complete = [leg for leg in legs if leg["complete"]]
+	return {
+		"questions": len(scored),
+		"recall": mean([leg["recall"] for leg in legs]),
+		"precision": mean([leg["precision"] for leg in legs]),
+		"completeness": round(len(complete) / len(legs), 4) if legs else None,
+		"complete_count": len(complete),
+		"exhaustive_recall": mean([leg["recall"] for leg in exhaustive_legs]),
+		"exhaustive_complete_count": len([leg for leg in exhaustive_legs if leg["complete"]]),
+		"exhaustive_questions": len(exhaustive_legs),
+		"expected_total": sum(len(row["expected_sources"]) for row in scored),
+		"found_total": sum(leg["correct_count"] for leg in legs),
+		"missed_total": sum(len(leg["missed"]) for leg in legs),
+	}
+
+
+# --- the harness ----------------------------------------------------------------------
+
+
+def run_eval(
+	project: str | None = None,
+	k: int = DEFAULT_K,
+	modes: tuple = MODES,
+	question_ids: list | None = None,
+	check_refusals: bool = True,
+) -> dict:
+	"""Run every golden question down each mode and score it. JSON-serializable throughout.
+
+	`check_refusals` runs the full `answer()` synthesis for the refusal question only — it
+	is the one question that cannot be scored from retrieval alone, and it is also the only
+	one that costs a generation call.
+	"""
+	project = project or demo_project()
+	sections = corpus_sections(project)
+	if not sections:
+		frappe.throw(f"Project {project} has no Source Sections to evaluate against.")
+
+	selected = [q for q in GOLDEN_QUESTIONS if not question_ids or q["id"] in set(question_ids)]
+	started = time.monotonic()
+	rows = []
+	for question in selected:
+		expected = resolve_expected(question, sections)
+		decided = route_question(question["question"], project) if "routed" in modes else None
+		row = {
+			"id": question["id"],
+			"kind": question["kind"],
+			"question": question["question"],
+			"exhaustive": question["exhaustive"],
+			"expected_intent": question["expected_intent"],
+			"expected_section_type": question["expected_section_type"],
+			"expected_sources": [section_summary(section) for section in expected],
+			"required_sources": question["required"],
+			"route": decided.as_dict() if decided else None,
+			"intent_match": (decided.intent == question["expected_intent"]) if decided else None,
+			"section_type_match": (
+				(decided.section_type == question["expected_section_type"]) if decided else None
+			),
+			"modes": {},
+			"refused": None,
+			"expect_refusal": bool(question.get("expect_refusal")),
+		}
+		if "naive" in modes:
+			row["modes"]["naive"] = score_leg(
+				expected, question["required"], naive_hits(question["question"], project, k)
+			)
+		if "routed" in modes:
+			row["modes"]["routed"] = score_leg(
+				expected, question["required"], routed_hits(decided, project, k)
+			)
+		if check_refusals and question.get("expect_refusal"):
+			synthesised = rag_answer.answer(
+				question["question"], project=project, allowed_projects=ALL_PROJECTS
+			)
+			row["refused"] = bool(synthesised["refused"])
+		rows.append(row)
+
+	return {
+		"project": project,
+		"k": k,
+		"modes": list(modes),
+		"generated_at": datetime.now().isoformat(timespec="seconds"),
+		"took_ms": int((time.monotonic() - started) * 1000),
+		"corpus": {
+			"documents": len({section["source_document"] for section in sections}),
+			"sections": len(sections),
+		},
+		"questions": rows,
+		"aggregate": {mode: aggregate(rows, mode) for mode in modes},
+	}
+
+
+def compare_query(query: str, project: str | None = None, k: int = DEFAULT_K) -> dict:
+	"""One query, both legs, plus the diff — the structure `/rag-lab` renders.
+
+	The sibling of `wikify.api.rag.compare`; this one is the harness-side, permission-free
+	version used by the eval and the scorecard, and it adds the document-coverage counts
+	the visual comparison leans on.
+	"""
+	query = (query or "").strip()
+	if not query:
+		frappe.throw("Enter a query to compare.")
+	project = project or demo_project()
+
+	naive = naive_hits(query, project, k)
+	decided = route_question(query, project)
+	routed = routed_hits(decided, project, k)
+	found_by_naive = {hit.section for hit in naive}
+	total_documents = len(frappe.get_all("Source Document", filters={"project": project}, pluck="name"))
+
+	return {
+		"query": query,
+		"project": project,
+		"k": k,
+		"route": decided.as_dict(),
+		"naive": {
+			"mode": "vector",
+			"hits": [hit_summary(hit) for hit in naive],
+			"count": len(naive),
+			"documents_covered": len({hit.document_title for hit in naive}),
+		},
+		"routed": {
+			"mode": MODE_FOR_INTENT.get(decided.intent, "hybrid"),
+			"hits": [hit_summary(hit) for hit in routed],
+			"count": len(routed),
+			"documents_covered": len({hit.document_title for hit in routed}),
+		},
+		"documents_in_project": total_documents,
+		"missed_by_naive": [hit_summary(hit) for hit in routed if hit.section not in found_by_naive],
+	}
+
+
+# --- HTML scorecard -------------------------------------------------------------------
+
+SCORECARD_CSS = """
+:root { color-scheme: light; }
+* { box-sizing: border-box; }
+body { margin: 0; padding: 40px 32px 72px; font: 15px/1.55 -apple-system, BlinkMacSystemFont,
+	"Segoe UI", Roboto, Helvetica, Arial, sans-serif; color: #1b1f24; background: #f6f7f9; }
+main { max-width: 1280px; margin: 0 auto; }
+h1 { font-size: 28px; margin: 0 0 6px; letter-spacing: -0.01em; }
+h2 { font-size: 17px; margin: 40px 0 12px; }
+.sub { color: #6b7280; margin: 0 0 28px; font-size: 14px; }
+/* Three across so the six cards land as a clean naive/routed grid rather than 5 + 1 orphan. */
+.cards { display: grid; grid-template-columns: repeat(3, minmax(0, 1fr)); gap: 14px; }
+@media (max-width: 760px) { .cards { grid-template-columns: repeat(2, minmax(0, 1fr)); } }
+.card { background: #fff; border: 1px solid #e3e6ea; border-radius: 12px; padding: 16px 18px; }
+.card .label { font-size: 12px; text-transform: uppercase; letter-spacing: .06em; color: #6b7280; }
+.card .value { font-size: 30px; font-weight: 650; margin-top: 6px; letter-spacing: -0.02em; }
+.card .value.naive { color: #b45309; }
+.card .value.routed { color: #15803d; }
+.card .foot { font-size: 12.5px; color: #6b7280; margin-top: 4px; }
+table { width: 100%; border-collapse: collapse; background: #fff; border: 1px solid #e3e6ea;
+	border-radius: 12px; overflow: hidden; table-layout: fixed; }
+/* Fixed columns keep the two legs the same width, so the naive/routed bars are visually
+   comparable even when one side carries a long list of missed sources. */
+col.c-id { width: 9%; } col.c-q { width: 24%; }
+col.c-leg { width: 29%; } col.c-delta { width: 9%; }
+th { text-align: left; font-size: 12px; text-transform: uppercase; letter-spacing: .05em;
+	color: #6b7280; padding: 11px 14px; background: #fafbfc; border-bottom: 1px solid #e3e6ea; }
+td { padding: 13px 14px; border-bottom: 1px solid #eef0f3; vertical-align: top; font-size: 14px; }
+tr:last-child td { border-bottom: 0; }
+.qid { font-weight: 650; }
+.q { color: #374151; }
+.tag { display: inline-block; font-size: 11px; padding: 2px 7px; border-radius: 999px;
+	background: #eef2ff; color: #4338ca; margin-right: 5px; max-width: 100%;
+	overflow-wrap: anywhere; }
+.badges { margin: 7px 0 0; }
+/* The kind tag is one short word and must never hyphenate; only the long route tag wraps. */
+.qid .tag { white-space: nowrap; margin-top: 5px; }
+.tag.exh { background: #ecfdf5; color: #047857; }
+.tag.warn { background: #fef3c7; color: #92400e; }
+.tag.bad { background: #fee2e2; color: #b91c1c; }
+.bar { position: relative; height: 9px; border-radius: 999px; background: #e9edf1; overflow: hidden;
+	margin: 4px 0 3px; min-width: 120px; }
+.bar span { position: absolute; inset: 0 auto 0 0; border-radius: 999px; }
+.bar.naive span { background: #f59e0b; }
+.bar.routed span { background: #22c55e; }
+.metric { font-variant-numeric: tabular-nums; font-size: 13px; color: #374151; }
+.metric b { font-weight: 650; }
+.missed { margin: 6px 0 0; padding: 8px 10px; border-left: 3px solid #dc2626; background: #fef2f2;
+	border-radius: 0 6px 6px 0; }
+.missed .head { font-size: 11.5px; text-transform: uppercase; letter-spacing: .05em; color: #b91c1c;
+	font-weight: 650; }
+.missed ul { margin: 4px 0 0; padding-left: 16px; color: #7f1d1d; font-size: 12.5px;
+	columns: 2; column-gap: 14px; }
+.missed li { break-inside: avoid; margin-bottom: 2px; }
+.none { color: #15803d; font-size: 12.5px; }
+.reason { font-size: 12.5px; color: #6b7280; margin-top: 6px; font-style: italic; }
+.legend { font-size: 13px; color: #6b7280; margin-top: 14px; }
+.win { color: #15803d; font-weight: 650; }
+.lose { color: #b91c1c; font-weight: 650; }
+.flat { color: #6b7280; }
+"""
+
+
+def escape(value) -> str:
+	return html_escape.escape("" if value is None else str(value))
+
+
+def percent(value) -> str:
+	return "—" if value is None else f"{value * 100:.0f}%"
+
+
+def bar(value, mode: str) -> str:
+	width = 0 if value is None else max(0.0, min(1.0, float(value))) * 100
+	return f'<div class="bar {mode}"><span style="width:{width:.1f}%"></span></div>'
+
+
+def leg_cell(leg: dict | None, mode: str, scored: bool = True) -> str:
+	"""One naive/routed cell: the recall bar, the raw counts, and the misses in red.
+
+	`scored` is false for the refusal question, which has no expected sources — an empty
+	`missed` list there means "nothing was ever expected", not "we found everything", and
+	printing a green "complete" would be the harness flattering itself.
+	"""
+	if not leg:
+		return '<td class="metric">—</td>'
+	if not scored:
+		return (
+			f'<td><div class="metric">{leg["returned_count"]} returned · '
+			f"{leg['documents_covered']} docs</div>"
+			'<div class="reason">not scored on sources — this question scores on refusal</div></td>'
+		)
+	pieces = [
+		bar(leg["recall"], mode),
+		f'<div class="metric">recall <b>{percent(leg["recall"])}</b> · '
+		f"precision <b>{percent(leg['precision'])}</b> · "
+		f"{leg['correct_count']}/{leg['correct_count'] + len(leg['missed'])} sources · "
+		f"{leg['returned_count']} returned · {leg['documents_covered']} docs</div>",
+	]
+	if leg["missed"]:
+		items = "".join(
+			f"<li>{escape(row['title'])} <em>({escape(row['document'])})</em></li>" for row in leg["missed"]
+		)
+		pieces.append(
+			f'<div class="missed"><div class="head">missed {len(leg["missed"])}</div><ul>{items}</ul></div>'
+		)
+	else:
+		pieces.append('<div class="none">complete — nothing missed</div>')
+	return f"<td>{''.join(pieces)}</td>"
+
+
+def verdict_cell(row: dict) -> str:
+	"""Did routed actually beat naive on this question? Reported honestly, including ties."""
+	naive, routed = row["modes"].get("naive"), row["modes"].get("routed")
+	if row["expect_refusal"]:
+		if row["refused"] is None:
+			return '<td class="flat">not checked</td>'
+		return '<td class="win">refused ✓</td>' if row["refused"] else '<td class="lose">answered ✗</td>'
+	if not naive or not routed:
+		return '<td class="flat">—</td>'
+	if routed["recall"] > naive["recall"]:
+		return f'<td class="win">+{(routed["recall"] - naive["recall"]) * 100:.0f} pts</td>'
+	if routed["recall"] < naive["recall"]:
+		return f'<td class="lose">-{(naive["recall"] - routed["recall"]) * 100:.0f} pts</td>'
+	return '<td class="flat">tie</td>'
+
+
+def question_row(row: dict) -> str:
+	"""One question. The route badge lives in the question cell, not the narrow id cell —
+	`staff_roles_and_responsibilities` is wider than the id column will ever be."""
+	route = row.get("route") or {}
+	kind = f'<span class="tag{" exh" if row["exhaustive"] else ""}">{escape(row["kind"])}</span>'
+	badge = ""
+	if route:
+		matched = row["intent_match"] and row["section_type_match"]
+		badge = (
+			f'<span class="tag{"" if matched else " warn"}">→ {escape(route.get("intent"))}'
+			+ (f" · {escape(route.get('section_type'))}" if route.get("section_type") else "")
+			+ "</span>"
+		)
+	reason = f'<div class="reason">{escape(route.get("reason"))}</div>' if route.get("reason") else ""
+	scored = bool(row["expected_sources"])
+	return (
+		"<tr>"
+		f'<td class="qid">{escape(row["id"])}<div>{kind}</div></td>'
+		f'<td class="q">{escape(row["question"])}<div class="badges">{badge}</div>{reason}'
+		f'<div class="metric">expected sources: <b>{len(row["expected_sources"])}</b></div></td>'
+		f"{leg_cell(row['modes'].get('naive'), 'naive', scored)}"
+		f"{leg_cell(row['modes'].get('routed'), 'routed', scored)}"
+		f"{verdict_cell(row)}"
+		"</tr>"
+	)
+
+
+def summary_cards(results: dict) -> str:
+	naive = results["aggregate"].get("naive") or {}
+	routed = results["aggregate"].get("routed") or {}
+	cards = [
+		(
+			"Naive mean recall",
+			percent(naive.get("recall")),
+			f"{naive.get('found_total', 0)}/{naive.get('expected_total', 0)} expected sources found",
+			"naive",
+		),
+		(
+			"Routed mean recall",
+			percent(routed.get("recall")),
+			f"{routed.get('found_total', 0)}/{routed.get('expected_total', 0)} expected sources found",
+			"routed",
+		),
+		(
+			"Naive completeness",
+			percent(naive.get("completeness")),
+			f"{naive.get('complete_count', 0)}/{naive.get('questions', 0)} questions fully answered",
+			"naive",
+		),
+		(
+			"Routed completeness",
+			percent(routed.get("completeness")),
+			f"{routed.get('complete_count', 0)}/{routed.get('questions', 0)} questions fully answered",
+			"routed",
+		),
+		(
+			"Exhaustive lane (naive)",
+			percent(naive.get("exhaustive_recall")),
+			f"{naive.get('exhaustive_complete_count', 0)}/{naive.get('exhaustive_questions', 0)} complete",
+			"naive",
+		),
+		(
+			"Exhaustive lane (routed)",
+			percent(routed.get("exhaustive_recall")),
+			f"{routed.get('exhaustive_complete_count', 0)}/{routed.get('exhaustive_questions', 0)} complete",
+			"routed",
+		),
+	]
+	return "".join(
+		f'<div class="card"><div class="label">{escape(label)}</div>'
+		f'<div class="value {tone}">{value}</div><div class="foot">{escape(foot)}</div></div>'
+		for label, value, foot, tone in cards
+	)
+
+
+def render_scorecard(results: dict) -> str:
+	"""The whole scorecard as one self-contained HTML document — no CDN, no assets."""
+	rows = "".join(question_row(row) for row in results["questions"])
+	corpus = results["corpus"]
+	return f"""<!doctype html>
+<html lang="en"><head><meta charset="utf-8">
+<meta name="viewport" content="width=device-width, initial-scale=1">
+<title>Wikify RAG — retrieval scorecard</title>
+<style>{SCORECARD_CSS}</style></head>
+<body><main>
+<h1>Retrieval scorecard — naive top-k vs routed</h1>
+<p class="sub">Project <b>{escape(results["project"])}</b> · {corpus["documents"]} documents ·
+{corpus["sections"]} content sections · k={results["k"]} ·
+generated {escape(results["generated_at"])} · {results["took_ms"]} ms</p>
+<div class="cards">{summary_cards(results)}</div>
+<h2>Per-question</h2>
+<table>
+<colgroup><col class="c-id"><col class="c-q"><col class="c-leg"><col class="c-leg"><col class="c-delta"></colgroup>
+<thead><tr><th>#</th><th>Question</th><th>Naive top-k (vector)</th><th>Routed</th><th>Δ</th></tr></thead>
+<tbody>{rows}</tbody>
+</table>
+<p class="legend"><b>Completeness</b> is the metric that carries the thesis: it is true only when a
+leg returned <em>every</em> expected source. A leg can score high recall and still be 0% complete —
+which is exactly what "give me all the job descriptions" looks like on a naive top-k pipeline.
+Sources shown in red were expected and never returned.</p>
+</main></body></html>
+"""
+
+
+def scorecard_path() -> str:
+	app_root = os.path.abspath(os.path.join(frappe.get_app_path("wikify"), ".."))
+	return os.path.join(app_root, *SCORECARD_PATH)
+
+
+def write_scorecard(results: dict, path: str | None = None) -> str:
+	path = path or scorecard_path()
+	os.makedirs(os.path.dirname(path), exist_ok=True)
+	with open(path, "w", encoding="utf-8") as scorecard:
+		scorecard.write(render_scorecard(results))
+	return path
+
+
+# --- headless entry point -------------------------------------------------------------
+
+
+def format_summary(results: dict) -> str:
+	"""The terminal view — the same numbers the scorecard shows, pasteable into a report."""
+	lines = [
+		f"Project {results['project']} · k={results['k']} · {results['took_ms']} ms",
+		"",
+		f"{'#':4} {'kind':11} {'exp':>4} {'naive recall':>13} {'routed recall':>14} {'naive':>6} {'routed':>7}",
+	]
+	for row in results["questions"]:
+		naive = row["modes"].get("naive") or {}
+		routed = row["modes"].get("routed") or {}
+		# The refusal question has no expected sources, so "partial" would be a lie there —
+		# it is scored on `refused`, printed on the next line.
+		scored = bool(row["expected_sources"])
+		lines.append(
+			f"{row['id']:4} {row['kind']:11} {len(row['expected_sources']):>4} "
+			f"{percent(naive.get('recall')):>13} {percent(routed.get('recall')):>14} "
+			f"{(('complete' if naive.get('complete') else 'partial') if scored else '—'):>6} "
+			f"{(('complete' if routed.get('complete') else 'partial') if scored else '—'):>7}"
+		)
+		if row["expect_refusal"]:
+			lines.append(f"{'':4} refusal check: refused={row['refused']}")
+		for missed in (routed.get("missed") or [])[:5]:
+			lines.append(f"{'':4} routed MISSED: {missed['title']} ({missed['document']})")
+
+	lines.append("")
+	for mode in results["modes"]:
+		totals = results["aggregate"][mode]
+		lines.append(
+			f"{mode:7} mean recall {percent(totals['recall'])} · mean precision "
+			f"{percent(totals['precision'])} · completeness {percent(totals['completeness'])} "
+			f"({totals['complete_count']}/{totals['questions']}) · exhaustive recall "
+			f"{percent(totals['exhaustive_recall'])} · found {totals['found_total']}/"
+			f"{totals['expected_total']} sources"
+		)
+	return "\n".join(lines)
+
+
+def main(project: str | None = None, k: int = DEFAULT_K, path: str | None = None) -> dict:
+	"""`bench --site <site> execute wikify.rag.eval.main` — run everything, write the scorecard."""
+	results = run_eval(project=project, k=int(k))
+	written = write_scorecard(results, path)
+	print(format_summary(results))
+	print(f"\nscorecard: {written}")
+	return {"aggregate": results["aggregate"], "scorecard": written, "questions": len(results["questions"])}
diff --git a/wikify/rag/events.py b/wikify/rag/events.py
new file mode 100644
index 0000000..9761a2b
--- /dev/null
+++ b/wikify/rag/events.py
@@ -0,0 +1,248 @@
+"""Keep sections and the retrieval index in step with edits upstream of them.
+
+The content chain is `Source Page.canonical_markdown` → `Source Section.markdown` →
+chunks → index, and every link is a **copy**, so a fix that stops at one link leaves the
+ones after it serving the old text. Two handlers close it:
+
+- `queue_reindex` (`Source Section`) — a section edit makes the project's index stale.
+- `queue_page_propagation` (`Source Page`) — a page fix makes every section built from
+  that page stale, and therefore the index too. Without it a re-remediated page is
+  correct in Page Review while a corrupted copy of the same table keeps being retrieved,
+  until somebody re-sections the whole document by hand.
+
+Both coalesce, because a bulk pass writes hundreds of rows in one transaction and one job
+per row would bury the long queue: the first change marks a pending key and enqueues one
+job, further changes while that job is queued enqueue nothing. Each job clears its marker
+*before* it starts work, so a change landing mid-run queues the next pass and is never
+lost (the failure mode is one redundant pass, never stale content).
+"""
+
+from __future__ import annotations
+
+import frappe
+from frappe.utils.data import cint
+
+# Safety net only — the job clears its own marker; this stops a crashed worker from
+# wedging a project's index permanently.
+PENDING_TTL_SECONDS = 1800
+
+# Redis hash accumulating the pages whose canonical markdown changed since the last
+# propagation pass, keyed `<source_document>:<page_no>`. A set, not a counter, so a page
+# saved ten times costs one rebuild.
+DIRTY_PAGES_HASH = "wikify_rag_dirty_pages"
+
+# Past this many sections in one pass, one project rebuild beats N `upsert_section` calls —
+# each of those re-creates the whole FTS index, so the per-section path stops paying off.
+PROJECT_REBUILD_FANOUT = 10
+
+# ponytail: coalescing rebuilds the WHOLE project, so a one-word title fix re-embeds every
+# chunk in it; switch to per-section `upsert_section` (keyed by a per-section marker) once
+# a project's rebuild stops fitting comfortably in the long queue's timeout.
+
+
+def pending_key(project: str) -> str:
+	return f"wikify_rag_reindex_pending:{project}"
+
+
+def page_propagation_key(source_document: str) -> str:
+	return f"wikify_rag_page_propagation_pending:{source_document}"
+
+
+def pass_already_queued(key: str) -> bool:
+	"""Is a job holding this marker right now?
+
+	`cache.get_value` answers from `frappe.local.cache` once it has read a key, so a writer
+	that set the marker keeps reading its own stale "1" long after the worker cleared it —
+	and then coalesces every later change into a job that already finished. `exists` is the
+	only read here that goes to redis, which is where the worker actually clears it.
+	"""
+	return bool(frappe.cache().exists(key))
+
+
+def indexing_suspended() -> bool:
+	"""Bulk/system contexts index once at the end, not row by row."""
+	flags = frappe.flags
+	return bool(
+		flags.get("wikify_skip_reindex")
+		or flags.in_install
+		or flags.in_migrate
+		or flags.in_patch
+		or flags.in_import
+		or flags.in_test
+	)
+
+
+def queue_reindex(doc, method: str | None = None) -> None:
+	"""`Source Section` doc_event handler — coalesce a project rebuild onto the long queue."""
+	if indexing_suspended():
+		return
+	project = frappe.db.get_value("Source Document", doc.source_document, "project")
+	# A document outside any project has nothing to scope an index to; it becomes
+	# searchable when it is assigned to one (which reindexes then).
+	if not project:
+		return
+	queue_project_rebuild(project)
+
+
+def queue_project_rebuild(project: str) -> None:
+	"""Mark the project pending and enqueue one rebuild; a no-op while one is already queued."""
+	key = pending_key(project)
+	if pass_already_queued(key):
+		return
+	frappe.cache().set_value(key, "1", expires_in_sec=PENDING_TTL_SECONDS)
+	frappe.enqueue(
+		"wikify.rag.events.rebuild_pending_project",
+		queue="long",
+		timeout=3600,
+		enqueue_after_commit=True,
+		project=project,
+	)
+
+
+def rebuild_pending_project(project: str) -> None:
+	"""Clear the pending marker, then rebuild — edits during the run queue the next pass."""
+	from wikify.rag.index import rebuild_project
+
+	frappe.cache().delete_value(pending_key(project))
+	rebuild_project(project)
+	frappe.db.commit()
+
+
+# --- page → section → index propagation ------------------------------------------------
+
+
+def queue_page_propagation(doc, method: str | None = None) -> None:
+	"""`Source Page` doc_event handler — coalesce a section rebuild onto the long queue."""
+	if indexing_suspended():
+		return
+	# A page created just now was never copied into a section, so nothing downstream of it
+	# can be stale; `has_value_changed` reads any insert as a change, hence the explicit skip.
+	if doc.get_doc_before_save() is None:
+		return
+	if not doc.has_value_changed("canonical_markdown"):
+		return
+
+	mark_pages_dirty(doc.source_document, [cint(doc.page_no)])
+	queue_page_propagation_pass(doc.source_document, after_commit=True)
+
+
+def mark_pages_dirty(source_document: str, pages: list[int]) -> None:
+	"""One hash field per page, so two writers marking different pages can't clobber each
+	other the way a read-modify-write of a per-document list would."""
+	cache = frappe.cache()
+	for page_no in pages:
+		cache.hset(DIRTY_PAGES_HASH, f"{source_document}:{page_no}", 1)
+
+
+def dirty_page_fields(source_document: str) -> list[str]:
+	"""The hash fields waiting to be propagated for one document (peek, no claim)."""
+	prefix = f"{source_document}:"
+	fields = [
+		field.decode() if isinstance(field, bytes) else field
+		for field in frappe.cache().hgetall(DIRTY_PAGES_HASH)
+	]
+	return [field for field in fields if field.startswith(prefix)]
+
+
+def take_dirty_pages(source_document: str) -> list[int]:
+	"""Claim (read and clear) the page numbers accumulated for one document."""
+	claimed = dirty_page_fields(source_document)
+	if claimed:
+		frappe.cache().hdel(DIRTY_PAGES_HASH, claimed)
+	return sorted({cint(field.split(":")[-1]) for field in claimed})
+
+
+def queue_page_propagation_pass(source_document: str, after_commit: bool = False) -> None:
+	"""Mark the document pending and enqueue one pass; a no-op while one is already queued.
+
+	`after_commit` for the doc_event path only: the pass reads the page back from the
+	database, so it must not start before the write that triggered it is committed.
+	"""
+	key = page_propagation_key(source_document)
+	if pass_already_queued(key):
+		return
+	frappe.cache().set_value(key, "1", expires_in_sec=PENDING_TTL_SECONDS)
+	frappe.enqueue(
+		"wikify.rag.events.propagate_dirty_pages",
+		queue="long",
+		timeout=3600,
+		enqueue_after_commit=after_commit,
+		source_document=source_document,
+	)
+
+
+def requeue_page_propagation(source_document: str, pages: list[int]) -> None:
+	"""Hand a half-finished propagation pass back to the queue (mirrors `index.requeue_rebuild`).
+
+	The markdown rebuild and the re-index are separate commits, so a worker dying between
+	them leaves a section holding the new text while the index still serves chunks of the
+	old — a mismatch nothing else would ever notice. Re-arming the claimed pages together
+	with the marker keeps the invariant "marker set means a pass is queued".
+	# ponytail: no backoff and no attempt cap, mirroring `index.requeue_rebuild`; add a
+	# retry counter if a propagation ever fails for a non-transient reason.
+	"""
+	mark_pages_dirty(source_document, pages)
+	queue_page_propagation_pass(source_document)
+
+
+def propagate_dirty_pages(source_document: str) -> None:
+	"""Claim the pages changed since the last pass and propagate them (the queued entry point)."""
+	frappe.cache().delete_value(page_propagation_key(source_document))
+	pages = take_dirty_pages(source_document)
+	if not pages:
+		return
+	try:
+		propagate_pages(source_document, pages)
+	finally:
+		# A writer that marked pages dirty while this pass was already queued deliberately
+		# enqueued nothing — it relies on this pass to pick them up. If it lost the race
+		# against the claim above, its pages sit in the hash with no job behind them and go
+		# stale forever (observed on a 30-page bulk write). Re-arm instead of stranding them.
+		if dirty_page_fields(source_document):
+			queue_page_propagation_pass(source_document)
+
+
+def propagate_pages(source_document: str, pages: list[int]) -> None:
+	"""Rebuild the sections covering `pages`, then push them into the index."""
+	from wikify.engine.sectionize import rebuild_section_markdown, sections_covering_page
+
+	# ponytail: one covering-section query per dirty page — fine for the handful an edit or a
+	# page-scoped re-parse touches; batch into a single range query if one pass ever carries
+	# a whole document's worth of pages.
+	section_names = []
+	for page_no in pages:
+		for section in sections_covering_page(source_document, page_no):
+			if section.name not in section_names:
+				section_names.append(section.name)
+	if not section_names:
+		return
+
+	project = frappe.db.get_value("Source Document", source_document, "project")
+	try:
+		for section_name in section_names:
+			rebuild_section_markdown(section_name)
+		reindex_sections(project, section_names)
+	except Exception:
+		requeue_page_propagation(source_document, pages)
+		raise
+	frappe.db.commit()
+
+
+def reindex_sections(project: str | None, section_names: list[str]) -> None:
+	"""Push rebuilt sections into the retrieval index.
+
+	`rebuild_section_markdown` writes through `frappe.db.set_value`, which fires no
+	doc_event — the `Source Section` reindex hook never sees it, so the index has to be
+	told here explicitly.
+	"""
+	from wikify.rag import index
+
+	# A document outside any project has nothing to scope an index to; it becomes
+	# searchable when it is assigned to one (which reindexes then).
+	if not project:
+		return
+	if len(section_names) > PROJECT_REBUILD_FANOUT:
+		queue_project_rebuild(project)
+		return
+	for section_name in section_names:
+		index.upsert_section(section_name)
diff --git a/wikify/rag/evidence.py b/wikify/rag/evidence.py
new file mode 100644
index 0000000..b94791f
--- /dev/null
+++ b/wikify/rag/evidence.py
@@ -0,0 +1,537 @@
+"""Span-level grounding — the seam that turns a citation into something checkable.
+
+A citation that names a page *range* is not verifiable: a student who is told "surcharge is
+25%, see pages 10-12" still has to read three pages to find out whether the answer is true.
+Worse, a model can invent a quote and the range will happily "support" it. This module
+closes both holes with the same machinery:
+
+- `locate_quote` finds a near-verbatim quote inside a source and returns its exact line and
+  character span. Matching is done on a normalised projection of both texts (whitespace,
+  unicode dashes, smart quotes, the rupee sign, markdown emphasis, digit group separators),
+  with offsets mapped back to the ORIGINAL text — so the line number we report is the line
+  number in the document, not in some cleaned-up copy.
+- `resolve_page` picks the single page a piece of text actually came from, by token-shingle
+  overlap against each candidate page's own markdown. When two pages are too close to call
+  it says so instead of guessing.
+- `verify_citations` runs every quote the model produced against the source it was
+  attributed to. A quote that cannot be located is reported `unverified`. The UI must never
+  render an unverified citation as confirmed — that is the whole point of the module.
+"""
+
+from __future__ import annotations
+
+import re
+import unicodedata
+from collections import Counter
+from difflib import SequenceMatcher
+
+import frappe
+
+# Token n-gram width for page attribution. Five words is long enough that a shingle is
+# effectively unique to one page of prose, short enough that a chunk of 1200 characters
+# still produces a couple of hundred of them.
+SHINGLE_SIZE = 5
+
+# A page has to carry at least this fraction of the text's shingles to be named, and it has
+# to beat the runner-up by at least this margin. Text that straddles a page break scores
+# high on the page it mostly came from and clears both bars; text that is boilerplate
+# repeated across pages clears neither and falls back to the section's page range.
+MIN_PAGE_MATCH_SCORE = 0.30
+PAGE_TIE_MARGIN = 0.10
+
+# Token-sequence similarity a fuzzy quote match must reach. A model that re-types a
+# sentence drops an article or shortens a dash; one that invents a sentence does not land
+# anywhere near this.
+MIN_QUOTE_SCORE = 0.82
+QUOTE_WINDOW_SLACK = 6
+# ponytail: a quote anchored on a token that occurs thousands of times scans that many
+# windows; capped rather than solved. Build a suffix automaton if quotes ever get long
+# enough that the rarest token is still common.
+MAX_ANCHOR_CANDIDATES = 200
+
+# Written as escapes so ruff's ambiguous-unicode rule can stay on for the rest of the app —
+# these are exactly the characters that rule exists to flag, and here they are the subject.
+DASH_CHARS = "\u2010\u2011\u2012\u2013\u2014\u2015\u2212"
+SINGLE_QUOTE_CHARS = "\u2018\u2019\u201a\u201b\u2032"
+DOUBLE_QUOTE_CHARS = "\u201c\u201d\u201e\u201f\u2033"
+# Markdown emphasis is formatting, not content: a model quoting a **bold** rate types it
+# plain, and the quote must still resolve.
+DROPPED_CHARS = frozenset("*_`#")
+# The rupee sign folds to "rs" so that "Rs. 2 crore" and "₹2 crore" share tokens.
+EXPANDED_CHARS = {"\u20b9": "rs"}
+
+TOKEN_PATTERN = re.compile(r"[0-9a-z]+")
+# The load-bearing part of a quote — the part fuzzy similarity must never be allowed to vote
+# on. Prose may wobble (a model re-typing a sentence drops an article); a figure may not,
+# because the figure is the thing the student came to check.
+#
+# Fuzzy matching is length-dependent, which makes it exactly the wrong tool here: one wrong
+# digit costs a fixed ~0.06 of the token ratio, so it sinks a short quote below the floor and
+# sails through on a long one. Measured on ICAI p.6: "@4%" retyped as "@6%" scored 0.80
+# against a 0.82 floor — rejected by luck, not by design — and "(+) surcharge" retyped as
+# "(-) surcharge" scored 0.85 and verified outright. A citation that renders as VERIFIED with
+# the wrong statutory rate in it is worse than no citation: it manufactures confidence.
+#
+# So these are compared character for character, in order, with no threshold and no tolerance:
+# any token carrying a digit (rates, "87a", "115bac", the "2"/"31" of "section 2(31)"),
+# percent signs, accounting signs ("(+)", "(-)", "-5"), the "/" of "u/s", and comparison
+# operators.
+# ponytail: a sign is only load-bearing next to a digit or inside parentheses, so a lone "-"
+# used as a dash stays free punctuation ("crore - 25%" must still match "crore | 25%");
+# tighten it if a corpus ever writes a signed figure with a space after the sign.
+# Escaped for the same reason as DASH_CHARS above: these are exactly what RUF001 flags.
+OPERATOR_CHARS = "%/=<>\u2264\u2265\u00d7\u00f7\u2260"
+FIGURE_PATTERN = re.compile(
+	f"[0-9a-z]*[0-9][0-9a-z]*|\\([+-]\\)|(?<![0-9a-z])[+-](?=[0-9])|[{OPERATOR_CHARS}]"
+)
+CITATION_MARKER = re.compile(r"\[(\d+)\]")
+# A quoted span the model attributed to a source: "...text..." optionally followed by a
+# parenthetical, then the marker. Bounded so a stray quote character cannot swallow the
+# rest of the answer.
+QUOTED_SPAN = re.compile(
+	'["\u201c]([^"\u201c\u201d\n]{15,400})["\u201d]\\s*(?:\\([^)\n]{0,60}\\))?\\s*\\[(\\d+)\\]'
+)
+
+PAGE_FIELDS = ["name", "source_document", "page_no", "canonical_markdown"]
+BATCH_SIZE = 500
+
+NOT_LOCATED = {
+	"found": False,
+	"line_start": 0,
+	"line_end": 0,
+	"char_start": 0,
+	"char_end": 0,
+	"score": 0.0,
+	"matched_text": "",
+	"reason": "quote not found in the cited source",
+}
+FIGURES_DIFFER = "quote misstates a figure in the cited source"
+
+
+def normalise(text: str) -> tuple[str, list[int]]:
+	"""Fold `text` for matching and return it with a per-character map back to the original.
+
+	The map is what makes the result verifiable: every offset we ever report is an index
+	into the caller's own string, so the line we print is the line they can go and read.
+	"""
+	normalised: list[str] = []
+	offsets: list[int] = []
+	source = text or ""
+	pending_space = False
+	for index, char in enumerate(source):
+		if char.isspace():
+			pending_space = bool(normalised)
+			continue
+		if char in DROPPED_CHARS:
+			continue
+		if (
+			char == ","
+			and normalised
+			and normalised[-1].isdigit()
+			and index + 1 < len(source)
+			and source[index + 1].isdigit()
+		):
+			continue
+		if char in DASH_CHARS:
+			mapped = "-"
+		elif char in SINGLE_QUOTE_CHARS:
+			mapped = "'"
+		elif char in DOUBLE_QUOTE_CHARS:
+			mapped = '"'
+		elif char in EXPANDED_CHARS:
+			mapped = EXPANDED_CHARS[char]
+		elif ord(char) > 127:
+			mapped = unicodedata.normalize("NFKC", char)
+		else:
+			mapped = char
+		mapped = mapped.lower()
+		if not mapped:
+			continue
+		if pending_space:
+			normalised.append(" ")
+			offsets.append(index)
+			pending_space = False
+		normalised.extend(mapped)
+		offsets.extend([index] * len(mapped))
+		# An expansion is a word in its own right: without the break, "₹2" tokenises as the
+		# single token "rs2" and stops matching the "Rs. 2" the same figure is written as
+		# elsewhere in the same document.
+		pending_space = char in EXPANDED_CHARS
+	return "".join(normalised), offsets
+
+
+def tokens_with_offsets(normalised: str, offsets: list[int]) -> list[tuple[str, int, int]]:
+	"""(token, original start, original end-exclusive) for every word in a normalised text."""
+	return [
+		(match.group(0), offsets[match.start()], offsets[match.end() - 1] + 1)
+		for match in TOKEN_PATTERN.finditer(normalised)
+	]
+
+
+def get_tokens(text: str) -> list[str]:
+	normalised, offsets = normalise(text)
+	return [token for token, _, _ in tokens_with_offsets(normalised, offsets)]
+
+
+def get_figures(text: str) -> list[str]:
+	"""The load-bearing symbols of `text`, in order — rates, statutory references, signs.
+
+	Compared for exact equality, so this is deliberately computed on the normalised
+	projection: "Rs. 1,00,000" and "₹1,00,000" must agree (both fold to the figure "100000"),
+	while 4% and 6% must not.
+	"""
+	normalised, _ = normalise(text)
+	return FIGURE_PATTERN.findall(normalised)
+
+
+def figure_window(quote: str, source: str, char_start: int, char_end: int) -> str:
+	"""The matched span, widened by as much punctuation as the quote carries at its own edges.
+
+	A span runs from its first matched token to its last, so a figure written as trailing
+	punctuation falls outside it: "25%" matches at "25" and the "%" is left behind, and a
+	quote opening "(+) surcharge" starts at "surcharge". Comparing figures across that edge
+	would fail every honest quote. Widening by the quote's own lead/tail keeps the two sides
+	measuring the same neighbourhood.
+	# ponytail: the widening is as long as the quote's edge punctuation, so a quote that opens
+	# with a long punctuation run could pull a neighbouring figure into the window; tighten to
+	# a fixed few characters if a corpus ever produces one.
+	"""
+	quote_tokens = tokens_with_offsets(*normalise(quote))
+	if not quote_tokens:
+		return source[char_start:char_end]
+	lead = quote_tokens[0][1]
+	tail = len(quote) - quote_tokens[-1][2]
+	return source[max(0, char_start - lead) : min(len(source), char_end + tail)]
+
+
+def figures_agree(quote: str, source: str, char_start: int, char_end: int) -> bool:
+	"""Whether every figure in the quote survives, in order, in the span it matched.
+
+	This is the gate fuzzy similarity does not get a vote on. A quote is accepted only when
+	the prose is close enough AND the figures are identical — never on prose alone.
+	"""
+	return get_figures(quote) == get_figures(figure_window(quote, source, char_start, char_end))
+
+
+def get_shingles(tokens: list[str], size: int = SHINGLE_SIZE) -> set[tuple[str, ...]]:
+	"""Overlapping token n-grams. A run shorter than `size` contributes itself, whole."""
+	if not tokens:
+		return set()
+	if len(tokens) < size:
+		return {tuple(tokens)}
+	return {tuple(tokens[start : start + size]) for start in range(len(tokens) - size + 1)}
+
+
+def get_source_text(chunk_or_section) -> str:
+	"""The text to search: a plain string, or the body of a Chunk / Hit / Source row."""
+	if isinstance(chunk_or_section, str):
+		return chunk_or_section
+	if isinstance(chunk_or_section, dict):
+		reader = chunk_or_section.get
+	else:
+
+		def reader(key, default=None):
+			return getattr(chunk_or_section, key, default)
+
+	for key in ("markdown", "canonical_markdown", "text"):
+		value = reader(key, None)
+		if value:
+			return value
+	return ""
+
+
+def format_span(source: str, char_start: int, char_end: int, score: float) -> dict:
+	"""A located span, with 1-based line numbers counted in the ORIGINAL source."""
+	return {
+		"found": True,
+		"reason": None,
+		"line_start": source.count("\n", 0, char_start) + 1,
+		"line_end": source.count("\n", 0, max(char_start, char_end - 1)) + 1,
+		"char_start": char_start,
+		"char_end": char_end,
+		"score": round(float(score), 4),
+		"matched_text": source[char_start:char_end],
+	}
+
+
+def best_token_window(
+	quote_tokens: list[str], source_tokens: list[tuple[str, int, int]]
+) -> tuple[int, int, float] | None:
+	"""Closest run of `source_tokens` to `quote_tokens` — (first token, last token, ratio).
+
+	Anchored on the *rarest* quote token so the number of windows we score stays small even
+	when the quote opens with "the".
+	"""
+	if not quote_tokens or not source_tokens:
+		return None
+	words = [token for token, _, _ in source_tokens]
+	counts = Counter(words)
+	anchor = min(
+		range(len(quote_tokens)), key=lambda index: counts.get(quote_tokens[index]) or len(words) + 1
+	)
+	positions = [index for index, word in enumerate(words) if word == quote_tokens[anchor]]
+	if not positions:
+		return None
+
+	width = len(quote_tokens) + QUOTE_WINDOW_SLACK
+	best: tuple[int, int, float] | None = None
+	for position in positions[:MAX_ANCHOR_CANDIDATES]:
+		start = max(0, position - anchor - QUOTE_WINDOW_SLACK // 2)
+		matcher = SequenceMatcher(None, quote_tokens, words[start : start + width], autojunk=False)
+		score = matcher.ratio()
+		blocks = [block for block in matcher.get_matching_blocks() if block.size]
+		if not blocks or (best is not None and score <= best[2]):
+			continue
+		best = (start + blocks[0].b, start + blocks[-1].b + blocks[-1].size - 1, score)
+	return best
+
+
+def locate_quote(quote: str, chunk_or_section) -> dict:
+	"""Where `quote` sits inside its source: `found` plus exact line and character span.
+
+	Exact (normalised) containment wins outright; otherwise the closest token window is
+	accepted only above `MIN_QUOTE_SCORE`. A miss still reports the best score it saw, so a
+	near-threshold failure is debuggable rather than a bare False.
+	"""
+	source = get_source_text(chunk_or_section)
+	normalised_quote, quote_offsets = normalise(quote or "")
+	normalised_source, source_offsets = normalise(source)
+	if not normalised_quote or not normalised_source:
+		return dict(NOT_LOCATED)
+
+	position = normalised_source.find(normalised_quote)
+	if position != -1:
+		return format_span(
+			source,
+			source_offsets[position],
+			source_offsets[position + len(normalised_quote) - 1] + 1,
+			1.0,
+		)
+
+	quote_tokens = [token for token, _, _ in tokens_with_offsets(normalised_quote, quote_offsets)]
+	source_tokens = tokens_with_offsets(normalised_source, source_offsets)
+	best = best_token_window(quote_tokens, source_tokens)
+	if best is None:
+		return dict(NOT_LOCATED)
+	first, last, score = best
+	if score < MIN_QUOTE_SCORE:
+		return {**NOT_LOCATED, "score": round(float(score), 4)}
+
+	span = format_span(source, source_tokens[first][1], source_tokens[last][2], score)
+	# Both gates, or nothing. Prose similarity got the quote to the right sentence; it does
+	# not get to certify the numbers in it.
+	if not figures_agree(quote, source, span["char_start"], span["char_end"]):
+		return {**NOT_LOCATED, "score": span["score"], "reason": FIGURES_DIFFER}
+	return span
+
+
+def build_page_index(pages: list[dict]) -> list[tuple[int, set[tuple[str, ...]], str]]:
+	"""Tokenise each page once — (page number, shingles, token run) — so a whole section's
+	chunks are scored against it without re-tokenising the page per chunk."""
+	index = []
+	for page in pages:
+		tokens = get_tokens(page.get("canonical_markdown") or "")
+		index.append((page["page_no"], get_shingles(tokens), f" {' '.join(tokens)} "))
+	return index
+
+
+def page_scores(tokens: list[str], page_index: list[tuple[int, set, str]]) -> list[tuple[float, int]]:
+	"""How much of `tokens` each page carries, as (score, page number).
+
+	Long text is scored by shingle overlap. Text too short to shingle — a one-line quote out
+	of a rate table — is scored by whether its token run occurs on the page at all, which
+	answers on a page that has it and abstains (via the tie rule) when several pages do.
+	"""
+	if len(tokens) >= SHINGLE_SIZE:
+		wanted = get_shingles(tokens)
+		return [(len(wanted & shingles) / len(wanted), page_no) for page_no, shingles, _ in page_index]
+	needle = f" {' '.join(tokens)} "
+	return [(1.0 if needle in run else 0.0, page_no) for page_no, _, run in page_index]
+
+
+def resolve_page(text: str, page_index: list[tuple[int, set, str]], fallback_page: int = 0) -> dict:
+	"""The one page `text` came from, or the fallback marked approximate.
+
+	Approximate is not a failure mode to hide — a citation that says "somewhere in pages
+	10-12" is honest, and a citation that says "page 11" when it does not know is not.
+	"""
+	tokens = get_tokens(text)
+	if not tokens or not page_index:
+		return {"page_no": fallback_page, "page_approximate": True, "page_score": 0.0}
+
+	scored = sorted(page_scores(tokens, page_index), key=lambda entry: (-entry[0], entry[1]))
+	best_score, best_page = scored[0]
+	runner_up = scored[1][0] if len(scored) > 1 else 0.0
+	if best_score < MIN_PAGE_MATCH_SCORE or best_score - runner_up < PAGE_TIE_MARGIN:
+		return {"page_no": fallback_page, "page_approximate": True, "page_score": round(best_score, 4)}
+	return {"page_no": best_page, "page_approximate": False, "page_score": round(best_score, 4)}
+
+
+def get_pages_by_document(document_names: list[str], page_numbers: list[int]) -> dict[str, list[dict]]:
+	"""Source Pages for the cited documents, one query per batch — never per citation."""
+	documents = [name for name in dict.fromkeys(document_names) if name]
+	numbers = sorted({number for number in page_numbers if number})
+	if not documents or not numbers:
+		return {}
+
+	rows: list[dict] = []
+	for start in range(0, len(documents), BATCH_SIZE):
+		rows.extend(
+			frappe.get_all(
+				"Source Page",
+				filters={
+					"source_document": ["in", documents[start : start + BATCH_SIZE]],
+					"page_no": ["in", numbers],
+				},
+				fields=PAGE_FIELDS,
+				order_by="page_no asc",
+			)
+		)
+	grouped: dict[str, list[dict]] = {}
+	for row in rows:
+		grouped.setdefault(row["source_document"], []).append(row)
+	return grouped
+
+
+def page_range(citation: dict) -> list[int]:
+	start = int(citation.get("page_start") or 0)
+	end = int(citation.get("page_end") or start)
+	if not start:
+		return []
+	return list(range(start, max(start, end) + 1))
+
+
+def check_quote(quote: str, citation: dict, pages_by_document: dict[str, list[dict]]) -> dict:
+	"""Locate `quote` in the cited source and resolve it down to a page and a page line."""
+	located = locate_quote(quote, citation)
+	result = {
+		"status": "verified" if located["found"] else "unverified",
+		"reason": located.get("reason"),
+		"line_start": located["line_start"],
+		"line_end": located["line_end"],
+		"char_start": located["char_start"],
+		"char_end": located["char_end"],
+		"score": located["score"],
+		"page_no": int(citation.get("page_start") or 0),
+		"page_approximate": True,
+		"page_score": 0.0,
+		"page_line_start": 0,
+		"page_line_end": 0,
+	}
+	if not located["found"]:
+		return result
+
+	wanted = page_range(citation)
+	pages = [
+		page
+		for page in pages_by_document.get(citation.get("source_document") or "", [])
+		if page["page_no"] in wanted
+	]
+	result.update(resolve_page(located["matched_text"], build_page_index(pages), result["page_no"]))
+	if result["page_approximate"]:
+		return result
+
+	for page in pages:
+		if page["page_no"] != result["page_no"]:
+			continue
+		on_page = locate_quote(quote, page)
+		if on_page["found"]:
+			result["page_line_start"] = on_page["line_start"]
+			result["page_line_end"] = on_page["line_end"]
+	return result
+
+
+def verify_citations(answer_markdown: str, citations: list[dict]) -> dict:
+	"""Check every quote the model produced against the source it attributed it to.
+
+	Two populations are checked and reported separately: the `quote` a citation carries, and
+	any quoted span written inline in the answer immediately before a `[n]` marker. A marker
+	pointing past the end of the citation list is itself an unverified quote — a fabricated
+	source is the same failure as a fabricated sentence.
+	"""
+	citations = citations or []
+	pages_by_document = get_pages_by_document(
+		[citation.get("source_document") for citation in citations],
+		[number for citation in citations for number in page_range(citation)],
+	)
+	cited = {int(number) for number in CITATION_MARKER.findall(answer_markdown or "")}
+
+	checked: list[dict] = []
+	for position, citation in enumerate(citations, start=1):
+		quote = (citation.get("quote") or "").strip()
+		row = {
+			"index": position,
+			"section": citation.get("section"),
+			"source_document": citation.get("source_document"),
+			"cited": position in cited,
+			"quote": quote or None,
+			"status": "unquoted",
+			"reason": None if quote else "citation carries no quote",
+		}
+		if quote:
+			row.update(check_quote(quote, citation, pages_by_document))
+		checked.append(row)
+
+	inline: list[dict] = []
+	for match in QUOTED_SPAN.finditer(answer_markdown or ""):
+		quote, number = match.group(1).strip(), int(match.group(2))
+		if not 1 <= number <= len(citations):
+			inline.append(
+				{
+					"index": number,
+					"quote": quote,
+					"status": "unverified",
+					"reason": "citation marker has no source",
+				}
+			)
+			continue
+		inline.append(
+			{"index": number, "quote": quote, **check_quote(quote, citations[number - 1], pages_by_document)}
+		)
+
+	statuses = [row["status"] for row in checked + inline]
+	return {
+		"citations": checked,
+		"quotes": inline,
+		"total": len(statuses),
+		"verified": statuses.count("verified"),
+		"unverified": statuses.count("unverified"),
+		"unquoted": statuses.count("unquoted"),
+		"grounded": "unverified" not in statuses,
+	}
+
+
+def attach_verified_quotes(answer_markdown: str, citations: list[dict]) -> list[dict]:
+	"""Citations with their supporting quote located, verified, and pinned to a page + line.
+
+	The single call the synthesis path needs. Every returned citation carries `quote`,
+	`quote_status` (`verified` / `unverified` / `unquoted`) and the resolved `page_no` /
+	`page_approximate` / line span. A citation whose quote could not be located keeps the
+	quote AND the `unverified` status — it is the UI's job to show it as unconfirmed, and it
+	cannot do that if we drop the evidence of the miss.
+	"""
+	report = verify_citations(answer_markdown, citations)
+	best_inline: dict[int, dict] = {}
+	for row in report["quotes"]:
+		current = best_inline.get(row["index"])
+		if current is None or (row["status"] == "verified" and current["status"] != "verified"):
+			best_inline[row["index"]] = row
+
+	attached = []
+	for row in report["citations"]:
+		citation = dict(citations[row["index"] - 1])
+		chosen = row if row["status"] == "verified" else best_inline.get(row["index"], row)
+		citation.update(
+			{
+				"quote": chosen.get("quote"),
+				"quote_status": chosen.get("status"),
+				"quote_line_start": chosen.get("line_start") or 0,
+				"quote_line_end": chosen.get("line_end") or 0,
+				"quote_page_no": chosen.get("page_no") or 0,
+				"quote_page_approximate": bool(chosen.get("page_approximate", True)),
+				"quote_page_line_start": chosen.get("page_line_start") or 0,
+				"quote_page_line_end": chosen.get("page_line_end") or 0,
+			}
+		)
+		attached.append(citation)
+	return attached
diff --git a/wikify/rag/history.py b/wikify/rag/history.py
new file mode 100644
index 0000000..ecac78a
--- /dev/null
+++ b/wikify/rag/history.py
@@ -0,0 +1,305 @@
+"""Conversation persistence + per-ask cost for the Ask surface.
+
+Facade over `Wikify Ask Session` + `Wikify Ask Message`, mirroring `agent/session.py`:
+turns are standalone rows rather than a child table, so a turn is appended without
+rewriting the parent.
+
+The log is deliberately more than display state. Every answer row keeps the route the
+question took *and* the citations the answer stood on, so the accumulated conversations
+double as a free eval set — "which questions did we route wrong", "which sources do
+answers actually rest on" — which is why `route_intent` is indexed and why a `turn`
+integer pairs a question row with its answer row instead of leaving that to creation order.
+
+Cost is summed across all three legs of one ask:
+
+- **routing** and **rerank** go through `engine.llm.chat_completion`, which already records
+  cost + tokens into its own metrics buffer — read here by watermark rather than by
+  `reset_metrics()`, because a parse job sharing the process would lose its buffer.
+- **synthesis** goes through litellm (`agent/llm.complete_with_tools`), which reports usage
+  only through a success callback; `add_litellm_metrics_callback` registers one.
+"""
+
+from __future__ import annotations
+
+import json
+import threading
+import time
+
+import frappe
+from frappe import _
+from frappe.utils.data import cint, flt
+
+from wikify.agent import llm as agent_llm
+from wikify.engine import llm as engine_llm
+from wikify.engine.store import cost_of
+
+# How many prior turns the router replays to rewrite a follow-up question.
+HISTORY_TURNS = 10
+
+# Newest-first page of conversations for the sidebar.
+SESSION_PAGE_LENGTH = 50
+
+# litellm hands a streamed completion to its success callback from a thread pool
+# (`executor.submit(self.logging_obj.success_handler, ...)` at end-of-stream), so the
+# synthesis usage can land a few milliseconds after `answer()` has already returned.
+# The answer is fully streamed to the user by then, so waiting briefly for it costs the
+# reader nothing and is the difference between logging the whole ask and logging only
+# its two cheap legs.
+SYNTHESIS_GRACE_SECONDS = 0.3
+SYNTHESIS_POLL_SECONDS = 0.02
+
+_metrics_lock = threading.Lock()
+_litellm_metrics: list[dict] = []
+_callback_registered = False
+
+# Watermarks into both buffers, per thread, taken by `start_turn()`.
+_watermarks: dict[int, tuple[int, int]] = {}
+
+
+# --- cost capture -------------------------------------------------------------------
+
+
+def add_litellm_metrics_callback() -> None:
+	"""Register the litellm success callback once, so synthesis reports cost + tokens."""
+	global _callback_registered
+	if _callback_registered:
+		return
+
+	import litellm
+
+	litellm.success_callback = [*litellm.success_callback, record_litellm_metrics]
+	_callback_registered = True
+
+
+def record_litellm_metrics(kwargs, response, start_time, end_time) -> None:
+	"""litellm success callback — mirrors an `engine.llm` metrics entry.
+
+	Never raise: litellm swallows callback errors, but a broken callback would still cost
+	every agent completion a traceback in the logs.
+	"""
+	try:
+		usage = getattr(response, "usage", None)
+		with _metrics_lock:
+			_litellm_metrics.append(
+				{
+					"label": "synthesis",
+					"model": kwargs.get("model"),
+					"cost": kwargs.get("response_cost"),
+					"prompt_tokens": getattr(usage, "prompt_tokens", None),
+					"completion_tokens": getattr(usage, "completion_tokens", None),
+				}
+			)
+	except Exception:
+		frappe.log_error(title="Wikify ask cost callback failed")
+
+
+def start_turn() -> None:
+	"""Mark where this ask's LLM calls begin. Call immediately before `answer()`."""
+	add_litellm_metrics_callback()
+	with _metrics_lock:
+		# ponytail: both buffers are process-wide, so a second ask running concurrently in
+		# the same worker can inflate this turn's cost; give engine.llm a per-turn buffer
+		# if per-ask cost ever has to be billable rather than indicative.
+		_watermarks[threading.get_ident()] = (len(engine_llm.get_metrics()), len(_litellm_metrics))
+
+
+def turn_metrics(*, expect_synthesis: bool = True) -> list[dict]:
+	"""Every LLM call recorded since `start_turn()` — routing, rerank and synthesis."""
+	engine_mark, litellm_mark = _watermarks.pop(threading.get_ident(), (0, 0))
+	if expect_synthesis:
+		wait_for_synthesis_metrics(litellm_mark)
+	with _metrics_lock:
+		synthesis = _litellm_metrics[litellm_mark:]
+		del _litellm_metrics[litellm_mark:]
+	return engine_llm.get_metrics()[engine_mark:] + synthesis
+
+
+def wait_for_synthesis_metrics(litellm_mark: int) -> None:
+	deadline = time.monotonic() + SYNTHESIS_GRACE_SECONDS
+	while time.monotonic() < deadline:
+		with _metrics_lock:
+			if len(_litellm_metrics) > litellm_mark:
+				return
+		time.sleep(SYNTHESIS_POLL_SECONDS)
+
+
+def tokens_of(metrics: list[dict]) -> tuple[int, int]:
+	"""Prompt and completion tokens across a metrics buffer."""
+	prompt = sum(cint(entry.get("prompt_tokens")) for entry in metrics)
+	completion = sum(cint(entry.get("completion_tokens")) for entry in metrics)
+	return prompt, completion
+
+
+# --- conversations ------------------------------------------------------------------
+
+
+def start_session(project: str | None = None, title: str | None = None) -> str:
+	"""Open a conversation owned by the current user and return its name."""
+	if project and not frappe.has_permission("Wikify Project", doc=project):
+		frappe.throw(_("You are not allowed to read {0}.").format(project), frappe.PermissionError)
+
+	session = frappe.new_doc("Wikify Ask Session")
+	session.project = project
+	session.title = summarize(title) if title else None
+	session.insert()
+	return session.name
+
+
+def summarize(question: str) -> str:
+	"""A conversation title: the first line of the first question, trimmed."""
+	first_line = (question or "").strip().splitlines()
+	return first_line[0][:120] if first_line else ""
+
+
+def record_turn(
+	session: str | None,
+	question: str,
+	result: dict,
+	*,
+	project: str | None = None,
+	took_ms: int | None = None,
+	model: str | None = None,
+	llm_metrics: list[dict] | None = None,
+) -> str:
+	"""Persist one ask — the question, the answer, its route, citations and cost.
+
+	`result` is the dict `rag.answer.answer()` returns. Opens a conversation when `session`
+	is empty — or names one that no longer exists, the same tolerance
+	`agent.session.get_or_create` has — and returns the conversation name either way, so
+	the caller can hand it back to the interface as one line at the end of `ask()`.
+	"""
+	if not session or not frappe.db.exists("Wikify Ask Session", session):
+		session = start_session(project=project, title=question)
+	conversation = frappe.get_doc("Wikify Ask Session", session)
+	conversation.check_permission("write")
+
+	route = result.get("route") or {}
+	refused = bool(result.get("refused"))
+	section_type = route.get("section_type")
+	# A type deleted between routing and logging must not cost the user a turn that was
+	# already answered and streamed, so an unknown one is dropped rather than raised —
+	# the router degrades on an unknown type the same way.
+	if section_type and not frappe.db.exists("Section Type", section_type):
+		section_type = None
+	metrics = llm_metrics if llm_metrics is not None else turn_metrics(expect_synthesis=not refused)
+	prompt_tokens, completion_tokens = tokens_of(metrics)
+	cost = flt(cost_of(metrics), 6)
+	turn = cint(frappe.db.count("Wikify Ask Message", {"session": session, "role": "question"})) + 1
+
+	append_message(session, "question", question, turn=turn)
+	append_message(
+		session,
+		"answer",
+		result.get("answer") or "",
+		turn=turn,
+		route_intent=route.get("intent"),
+		route_section_type=section_type,
+		route_reason=route.get("reason"),
+		citations=result.get("citations") or [],
+		refused=refused,
+		took_ms=cint(took_ms or result.get("took_ms")),
+		model=model or agent_llm.resolve_model(project=conversation.project),
+		prompt_tokens=prompt_tokens,
+		completion_tokens=completion_tokens,
+		cost=cost,
+	)
+
+	conversation.title = conversation.title or summarize(question)
+	conversation.message_count = cint(conversation.message_count) + 2
+	conversation.total_tokens = cint(conversation.total_tokens) + prompt_tokens + completion_tokens
+	conversation.total_cost = flt(flt(conversation.total_cost) + cost, 6)
+	conversation.save()
+	return session
+
+
+def append_message(session: str, role: str, content: str, **values) -> str:
+	"""Insert one `Wikify Ask Message` row and return its name."""
+	if values.get("citations") is not None:
+		values["citations"] = json.dumps(values["citations"])
+	message = frappe.get_doc(
+		{"doctype": "Wikify Ask Message", "session": session, "role": role, "content": content, **values}
+	)
+	message.insert()
+	return message.name
+
+
+def list_sessions(project: str | None = None, limit: int = SESSION_PAGE_LENGTH) -> list[dict]:
+	"""Conversations the current user may read, newest first.
+
+	`get_list` (not `get_all`) so the DocType's `if_owner` permission does the scoping —
+	a user sees their own, a System Manager sees every one.
+	"""
+	filters = {"project": project} if project else None
+	return frappe.get_list(
+		"Wikify Ask Session",
+		filters=filters,
+		fields=[
+			"name",
+			"title",
+			"user",
+			"project",
+			"started_at",
+			"message_count",
+			"total_tokens",
+			"total_cost",
+			"modified",
+		],
+		order_by="modified desc",
+		limit_page_length=cint(limit),
+	)
+
+
+def get_session(name: str | int) -> dict:
+	"""One conversation with its turns in order."""
+	conversation = frappe.get_doc("Wikify Ask Session", name)
+	conversation.check_permission("read")
+	# Read permission on the turns is derived from the parent, which was just checked.
+	messages = frappe.get_all(
+		"Wikify Ask Message",
+		filters={"session": conversation.name},
+		fields=[
+			"name",
+			"role",
+			"turn",
+			"content",
+			"route_intent",
+			"route_section_type",
+			"route_reason",
+			"citations",
+			"refused",
+			"took_ms",
+			"model",
+			"prompt_tokens",
+			"completion_tokens",
+			"cost",
+			"creation",
+		],
+		order_by="creation asc",
+	)
+	for message in messages:
+		message["citations"] = frappe.parse_json(message["citations"]) or []
+	return {**conversation.as_dict(), "messages": messages}
+
+
+def recent_turns(session: str | int | None) -> list[dict]:
+	"""Prior turns as router-shaped messages, so a follow-up question can be rewritten.
+
+	A forged session id must not feed someone else's conversation into the router prompt,
+	so an unreadable one replays as no history rather than throwing a user's follow-up away.
+	"""
+	if not session or not frappe.has_permission("Wikify Ask Session", doc=session):
+		return []
+	messages = frappe.get_all(
+		"Wikify Ask Message",
+		filters={"session": session},
+		fields=["role", "content"],
+		order_by="creation desc",
+		limit=HISTORY_TURNS * 2,
+	)
+	roles = {"question": "user", "answer": "assistant"}
+	return [{"role": roles[row.role], "content": row.content or ""} for row in reversed(messages)]
+
+
+def delete_session(name: str | int) -> None:
+	"""Delete a conversation and, via its `on_trash`, every turn in it."""
+	frappe.delete_doc("Wikify Ask Session", name)
diff --git a/wikify/rag/index.py b/wikify/rag/index.py
new file mode 100644
index 0000000..9410f05
--- /dev/null
+++ b/wikify/rag/index.py
@@ -0,0 +1,135 @@
+"""Building and refreshing the LanceDB `chunks` table.
+
+Every write is delete-then-add scoped by `project` or `section`, which makes rebuilds
+idempotent and disposes of orphans (sections deleted in the tree review, or documents
+moved to another project) without a separate reconciliation pass.
+"""
+
+from __future__ import annotations
+
+import time
+
+import frappe
+
+from wikify.rag import chunk, embed, store
+
+FTS_COLUMN = "text"
+
+
+def chunk_rows(chunks: list[chunk.Chunk]) -> list[dict]:
+	"""Chunks → LanceDB rows, embedding the whole batch in one `encode` call.
+
+	The row shape lives next to the dataclass in `chunk.chunk_row`: LanceDB accepts a row
+	that omits a declared column and stores it null rather than raising, so a field added to
+	the schema but forgotten here reads back empty on every chunk instead of failing loudly.
+	"""
+	vectors = embed.embed([item.embed_text for item in chunks])
+	return [chunk.chunk_row(item, vector) for item, vector in zip(chunks, vectors, strict=True)]
+
+
+def refresh_fts_index(table) -> None:
+	"""(Re)create the full-text index over `text`.
+
+	Rebuilt after every write because an FTS index only covers the rows present when it was
+	built; searching then falls back to a flat scan for the rest, which scores differently.
+	# ponytail: full FTS rebuild per write is fine at POC corpus size, move to an
+	# incremental `optimize()` once a project exceeds ~100k chunks.
+	# ponytail: two rebuilds racing overwrite each other's index, so the loser's rows stay
+	# FTS-invisible until the next write; serialise on a per-table lock once more than one
+	# project can be rebuilt at a time.
+	"""
+	from lancedb.index import FTS
+
+	if not table.count_rows():
+		return
+	table.create_index(FTS_COLUMN, config=FTS())
+
+
+def requeue_rebuild(project: str) -> None:
+	"""Hand a half-finished rebuild back to the coalescing queue.
+
+	The delete and the add are separate LanceDB commits, so a worker that dies between them
+	leaves the project with zero rows — silently unsearchable, with no pending marker and
+	nothing queued, until somebody happens to edit a section. Re-arming the marker together
+	with the job keeps `events.py`'s invariant (marker set means a rebuild is queued).
+	# ponytail: no backoff and no attempt cap, so a project that fails deterministically
+	# re-queues forever; add a retry counter if a rebuild ever fails for a non-transient reason.
+	"""
+	from wikify.rag import events
+
+	frappe.cache().set_value(events.pending_key(project), "1", expires_in_sec=events.PENDING_TTL_SECONDS)
+	frappe.enqueue("wikify.rag.events.rebuild_pending_project", queue="long", timeout=3600, project=project)
+
+
+def rebuild_project(project: str) -> dict:
+	"""Re-index a whole project from scratch. Idempotent; orphaned rows are dropped."""
+	started = time.monotonic()
+	chunks = chunk.chunks_for_project(project)
+	table = store.chunks_table(create=True)
+	# Embed before deleting anything: embedding is the slow step and the one most likely to
+	# die, and doing it first keeps the window where the project has no rows to two adjacent
+	# LanceDB commits instead of spanning the whole encode.
+	rows = chunk_rows(chunks) if chunks else []
+	try:
+		table.delete(f"project = {store.sql_literal(project)}")
+		if rows:
+			table.add(rows)
+		refresh_fts_index(table)
+	except Exception:
+		requeue_rebuild(project)
+		raise
+	return {
+		"chunks": len(chunks),
+		"sections": len({item.section for item in chunks}),
+		"seconds": round(time.monotonic() - started, 3),
+	}
+
+
+def upsert_section(section_name: str) -> int:
+	"""Re-index one section (after an edit/split/merge). Returns the chunk count written."""
+	chunks = chunk.chunks_for_section(section_name)
+	table = store.chunks_table(create=True)
+	rows = chunk_rows(chunks) if chunks else []
+	table.delete(f"section = {store.sql_literal(section_name)}")
+	if rows:
+		table.add(rows)
+	refresh_fts_index(table)
+	return len(rows)
+
+
+def drop_section(section_name: str) -> None:
+	table = store.chunks_table()
+	if table is None:
+		return
+	table.delete(f"section = {store.sql_literal(section_name)}")
+
+
+def indexed_at(table) -> str | None:
+	"""Timestamp of the table's latest version — 'when was this index last written'."""
+	versions = table.list_versions()
+	if not versions:
+		return None
+	timestamp = versions[-1].get("timestamp")
+	return timestamp.isoformat() if timestamp else None
+
+
+def index_stats(project: str | None = None) -> dict:
+	"""Counts for the index-status card. `project=None` covers the whole site."""
+	table = store.chunks_table()
+	empty = {"chunks": 0, "sections": 0, "documents": 0, "indexed_at": None, "dim": embed.EMBED_DIM}
+	if table is None:
+		return empty
+
+	query = table.search().select(["section", "source_document"]).limit(None)
+	if project:
+		query = query.where(f"project = {store.sql_literal(project)}")
+	rows = query.to_list()
+	if not rows:
+		return empty
+	return {
+		"chunks": len(rows),
+		"sections": len({row["section"] for row in rows}),
+		"documents": len({row["source_document"] for row in rows}),
+		"indexed_at": indexed_at(table),
+		"dim": embed.EMBED_DIM,
+	}
diff --git a/wikify/rag/router.py b/wikify/rag/router.py
new file mode 100644
index 0000000..493d4cc
--- /dev/null
+++ b/wikify/rag/router.py
@@ -0,0 +1,208 @@
+"""Query routing — the core thesis of POC-2 made executable.
+
+*"Give me all the job descriptions"* is a **completeness** question: the right answer is a
+metadata filter on `Source Section.section_type` that returns every match, not a top-k
+similarity guess that silently under-recalls. This module decides, per question, which of
+the three retrieval legs to use:
+
+- `exhaustive` — filter on a section type, return **all** matches.
+- `semantic`   — fuzzy "something about Y", hybrid top-k.
+- `hybrid`     — a type filter *and* similarity ranking inside it.
+
+It also rewrites conversational follow-ups ("what about the second one?") into a
+standalone query, because retrieval has no memory — only the rewritten query is embedded.
+
+The classification runs on the cheap `classifier_model` through `engine.llm.chat_completion`
+— the same JSON-mode client the reranker uses, so routing shows up in the per-call cost
+metrics. When no key is configured (or the model misbehaves) routing falls back
+deterministically to `hybrid`: the union leg is never wrong, only less pointed, so
+retrieval must never hard-fail on the router.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import asdict, dataclass
+
+import frappe
+
+from wikify.engine import llm, settings
+from wikify.rag import usage
+
+# The router only ever sees a short tail of the conversation — enough to resolve "it" /
+# "the second one", not enough to blow up the cheap model's context.
+HISTORY_TURNS = 6
+
+FALLBACK_REASON = "Routing is unavailable, so I searched both by meaning and by section type."
+
+
+@dataclass
+class Route:
+	"""A routing decision. `reason` is rendered in the UI — write it for a human."""
+
+	intent: str  # "exhaustive" | "semantic" | "hybrid"
+	section_type: str | None
+	query: str  # rewritten, standalone
+	reason: str
+
+	def as_dict(self) -> dict:
+		return asdict(self)
+
+
+INTENTS = ("exhaustive", "semantic", "hybrid")
+
+SYSTEM_PROMPT = """You route questions against a wiki built from PDFs. Every section of \
+every document carries a `section_type` tag from a fixed taxonomy.
+
+Pick ONE intent:
+- "exhaustive" — the question asks for EVERY item of one kind. The giveaway is a universal \
+word: all, every, each, both, list the, how many, which ones are there, across all the \
+documents. Asking what the COLLECTION ITSELF holds is the same request even without one of \
+those words, because it sweeps every document to pull one thing out of each. Completeness \
+beats ranking, so this returns every matching section. Requires a section_type from the \
+taxonomy.
+- "hybrid" — an open question whose OWN WORDS name the kind of section it is about ("what \
+do the pay policies say about overtime"), or one that sets the SAME rule side by side \
+across a HANDFUL OF NAMED organisations, which asks for that kind of section in each of \
+them. If the sweep is over the whole collection rather than a named few, it is \
+"exhaustive", not "hybrid". Requires a section_type.
+- "semantic" — everything else. This is the DEFAULT.
+
+Naming the kind means using the taxonomy's own words or an obvious synonym for them — \
+"roles", "job descriptions", "pay policies". Naming a TOPIC is not naming a kind: \
+"rostering", "travel costs" and "supervision" are subjects that turn up inside several \
+kinds of section.
+
+Each type below carries a description of what it holds. Read it to work out WHICH type a \
+question names — never as a licence to filter. A question that merely mentions a topic \
+listed in a description has still not named a kind, because those topics are restated \
+inside role descriptions, training sections and procedures too.
+
+Choosing a filter is the risky move: a wrong section_type hides the answer completely, and \
+answers often span several types (a rostering rule can sit in a job description AND a pay \
+policy). So only filter when the user's own wording demands it. If you are weighing \
+"semantic" against "hybrid", pick "semantic".
+
+Worked examples, on a different corpus to the one you are routing:
+- "Show me every equipment maintenance procedure in these manuals." -> exhaustive, \
+equipment_and_facilities. A universal word plus a named kind.
+- "Which roles have to be registered with a professional body?" -> hybrid, \
+staff_roles_and_responsibilities. The question's own word is "roles".
+- "Put each employer's leave entitlement side by side." -> hybrid, \
+administrative_policies. Comparing one rule across organisations asks for that policy \
+section in every one of them.
+- "How do staff get their uniform costs reimbursed?" -> semantic, null. Expenses sit in \
+the pay policy AND in the role sections; a filter would hide half the answer.
+- "What should the team do if a controlled drug goes missing mid-shift?" -> semantic, \
+null. A what-happens-if scenario is retold across several kinds of section.
+
+Also rewrite the question into a STANDALONE query: resolve pronouns and references to \
+earlier turns ("what about the second one?" -> "the second job description in ..."). If \
+the question already stands alone, repeat it unchanged.
+
+Write `reason` as ONE plain sentence addressed to the user, explaining what you are doing \
+and why. It is shown in the interface. No jargon, no field names.
+
+Reply with ONLY a JSON object:
+{"intent": "...", "section_type": "<type_name or null>", "query": "...", "reason": "..."}"""
+
+
+def taxonomy_lines() -> list[dict]:
+	"""The Section Type taxonomy the router is allowed to choose from."""
+	return frappe.get_all(
+		"Section Type",
+		fields=["type_name", "label", "description"],
+		order_by="is_other asc, creation asc",
+	)
+
+
+def build_messages(question: str, project: str | None, history: list | None) -> list[dict]:
+	types = taxonomy_lines()
+	catalogue = "\n".join(
+		f"- {row['type_name']}: {row['label'] or row['type_name']}"
+		+ (f" — {row['description']}" if row.get("description") else "")
+		for row in types
+	)
+	context = f"Taxonomy (the only valid section_type values):\n{catalogue or '(empty)'}"
+	if project:
+		project_context = frappe.db.get_value("Wikify Project", project, "context_prompt")
+		if project_context:
+			context += f"\n\nWhat this wiki is about:\n{project_context}"
+
+	messages = [
+		{"role": "system", "content": SYSTEM_PROMPT},
+		{"role": "system", "content": context},
+	]
+	for turn in (history or [])[-HISTORY_TURNS:]:
+		role = turn.get("role")
+		content = (turn.get("content") or "").strip()
+		if role in ("user", "assistant") and content:
+			messages.append({"role": role, "content": content[:2000]})
+	messages.append({"role": "user", "content": question})
+	return messages
+
+
+def parse_decision(content: str) -> dict:
+	"""Read the model's JSON reply, tolerating a ```json fence or surrounding prose."""
+	text = (content or "").strip()
+	if text.startswith("```"):
+		text = text.split("```")[1]
+		text = text.removeprefix("json").strip()
+	start, end = text.find("{"), text.rfind("}")
+	if start == -1 or end == -1:
+		return {}
+	try:
+		return frappe.parse_json(text[start : end + 1]) or {}
+	except (ValueError, TypeError, json.JSONDecodeError):
+		return {}
+
+
+def fallback(question: str, reason: str = FALLBACK_REASON) -> Route:
+	return Route(intent="hybrid", section_type=None, query=question, reason=reason)
+
+
+def route(question: str, project: str | None = None, history: list | None = None) -> Route:
+	"""Classify intent + rewrite the question into a standalone query.
+
+	Never raises: any failure (no key, model down, unparseable reply, invented section
+	type) degrades to the `hybrid` union leg with an honest reason.
+	"""
+	question = (question or "").strip()
+	if not question:
+		return fallback(question, "There was no question to route.")
+	if not settings.openrouter_key():
+		return fallback(question)
+
+	try:
+		response = llm.chat_completion(
+			settings.get("classifier_model"),
+			build_messages(question, project, history),
+			label="rag_route",
+			response_format={"type": "json_object"},
+		)
+		content = response["choices"][0]["message"]["content"]
+		usage.add(response.get("usage"))
+	except Exception:
+		frappe.log_error(title="Wikify RAG router failed")
+		return fallback(question)
+
+	decision = parse_decision(content)
+	if not decision:
+		return fallback(question)
+
+	intent = (decision.get("intent") or "").strip().lower()
+	if intent not in INTENTS:
+		return fallback(question)
+
+	section_type = (decision.get("section_type") or "").strip() or None
+	# A hallucinated type would filter the corpus down to nothing, so an unknown type
+	# drops the filter rather than the results.
+	if section_type and not frappe.db.exists("Section Type", section_type):
+		section_type = None
+	# Both filtered legs are meaningless without a type to filter on.
+	if intent in ("exhaustive", "hybrid") and not section_type:
+		intent = "semantic"
+
+	rewritten = (decision.get("query") or "").strip() or question
+	reason = (decision.get("reason") or "").strip() or FALLBACK_REASON
+	return Route(intent=intent, section_type=section_type, query=rewritten, reason=reason)
diff --git a/wikify/rag/search.py b/wikify/rag/search.py
new file mode 100644
index 0000000..9e19231
--- /dev/null
+++ b/wikify/rag/search.py
@@ -0,0 +1,424 @@
+"""Retrieval — four modes over one table, then expansion back to whole sections.
+
+The thesis of POC-2 lives in `mode="filter"`: *"give me all the job descriptions"* is an
+exhaustive, structured intent, so it is answered by a metadata filter that returns **every**
+matching row — never a top-k similarity guess that silently drops the tail. The similarity
+modes (`vector` / `fts` / `hybrid`) are reserved for fuzzy "about X" questions.
+
+Two rules hold across all modes:
+
+- Metadata filters (project / document / type / the ACL project list) are pushed into the
+  LanceDB `.where()` clause so they pre-filter the scan. Filtering after top-k would
+  re-introduce exactly the recall hole the filter mode exists to close.
+- Hits are deduped up to their parent Source Section and carry the section's full markdown,
+  so a reader (human or LLM) never sees a chunk boundary.
+"""
+
+from __future__ import annotations
+
+import re
+from dataclasses import dataclass
+
+import frappe
+
+from wikify.rag import chunk, embed, store, usage
+
+MODES = ("vector", "fts", "hybrid", "filter")
+FTS_COLUMN = "text"
+
+
+class AclDecision:
+	"""A named non-list value for `allowed_projects`, so tracebacks read as English."""
+
+	def __init__(self, label: str):
+		self.label = label
+
+	def __repr__(self) -> str:
+		return f"search.{self.label}"
+
+
+# The ACL pre-filter is the one argument that enforces permissions, so it gets no usable
+# default: omitting it raises instead of quietly reading every project. A caller that
+# genuinely has no ACL to apply (a background rebuild, an eval run) says so by passing
+# `allowed_projects=ALL_PROJECTS`.
+#
+# Both are distinct objects on purpose. `ALL_PROJECTS` used to be `None`, which collided
+# with every "argument not supplied" default in the call chain — a caller that forgot to
+# thread the user's projects silently got the unscoped opt-out instead of the guard. Any
+# value that is neither a project list nor `ALL_PROJECTS` (including `None`) now throws.
+ALL_PROJECTS = AclDecision("ALL_PROJECTS")
+ACL_REQUIRED = AclDecision("ACL_REQUIRED")
+
+# Over-fetch chunks so dedupe-to-section still leaves `limit` distinct sections.
+CANDIDATE_MULTIPLIER = 5
+CANDIDATE_FLOOR = 50
+RERANK_CANDIDATES = 50
+RERANK_SNIPPET_CHARS = 700
+
+RESULT_COLUMNS = [
+	"id",
+	"section",
+	"source_document",
+	"section_type",
+	"hierarchy_path",
+	"page_start",
+	"page_end",
+	"wiki_route",
+	"text",
+]
+# The score column is requested explicitly: LanceDB only auto-projects it for now, and
+# warns that a future release will drop it from an explicit `select()`.
+VECTOR_COLUMNS = [*RESULT_COLUMNS, "_distance"]
+FTS_COLUMNS = [*RESULT_COLUMNS, "_score"]
+
+
+@dataclass
+class Hit:
+	chunk_id: str
+	section: str
+	source_document: str
+	document_title: str
+	title: str
+	text: str
+	section_type: str | None
+	hierarchy_path: str
+	page_start: int
+	page_end: int
+	wiki_route: str | None
+	score: float
+	vector_rank: int | None = None
+	fts_rank: int | None = None
+	rerank_score: float | None = None
+
+	def as_dict(self) -> dict:
+		return self.__dict__.copy()
+
+
+def assert_acl_decision(allowed_projects) -> None:
+	"""The single enforcement point: every retrieval must carry a deliberate ACL decision.
+
+	Anything that is not a project list or the explicit `ALL_PROJECTS` opt-out — an omitted
+	argument, a `None` threaded through from a caller's default — is a bug, never a licence
+	to search the whole site.
+	"""
+	if allowed_projects is ALL_PROJECTS or isinstance(allowed_projects, list | tuple):
+		return
+	frappe.throw(
+		"Retrieval needs an explicit `allowed_projects`: the list of projects this user may "
+		f"read, or search.ALL_PROJECTS to search unscoped on purpose. Got {allowed_projects!r}."
+	)
+
+
+def build_where(
+	project: str | None,
+	source_document: str | None,
+	section_type: str | None,
+	allowed_projects: list[str] | AclDecision,
+	include_title_only: bool = True,
+) -> str | None:
+	"""The `.where()` pre-filter clause, or None when nothing is scoped.
+
+	`allowed_projects` is the ACL leg: an empty list means "this user may read nothing",
+	which must scope to no rows rather than fall through to everything. Only the explicit
+	`ALL_PROJECTS` opt-out drops the clause.
+
+	`include_title_only=False` drops the breadcrumb sections that have no body of their own.
+	They are indexed so exhaustive filter mode can return them, but they answer nothing and
+	match on their title alone, so in a similarity leg they displace real content.
+	"""
+	clauses = []
+	if not include_title_only:
+		clauses.append("title_only = false")
+	if project:
+		clauses.append(f"project = {store.sql_literal(project)}")
+	if source_document:
+		clauses.append(f"source_document = {store.sql_literal(source_document)}")
+	if section_type:
+		clauses.append(f"section_type = {store.sql_literal(section_type)}")
+	if allowed_projects is not ALL_PROJECTS:
+		allowed = ", ".join(store.sql_literal(name) for name in allowed_projects)
+		clauses.append(f"project IN ({allowed})" if allowed else "1 = 0")
+	return " AND ".join(clauses) if clauses else None
+
+
+def scoped(query, where: str | None):
+	return query.where(where) if where else query
+
+
+def fts_query(query: str) -> str:
+	"""Strip punctuation the tantivy query parser treats as syntax (`:`, `?`, `-`, quotes)."""
+	return re.sub(r"[^\w\s]", " ", query or "").strip()
+
+
+def has_fts_index(table) -> bool:
+	return any(
+		table_index.index_type == "FTS" and FTS_COLUMN in (table_index.columns or [])
+		for table_index in table.list_indices()
+	)
+
+
+def run_vector(
+	table, query: str, where: str | None, limit: int, vector: list[float] | None = None
+) -> list[dict]:
+	"""`vector` lets a caller that already embedded `query` reuse it — embedding is the
+	single most expensive step in a search, and hybrid mode needs the same vector twice."""
+	return (
+		scoped(
+			table.search(vector if vector is not None else embed.embed_one(query)).select(VECTOR_COLUMNS),
+			where,
+		)
+		.limit(limit)
+		.to_list()
+	)
+
+
+def run_fts(table, query: str, where: str | None, limit: int) -> list[dict]:
+	text = fts_query(query)
+	if not text or not has_fts_index(table):
+		return []
+	return scoped(table.search(text, query_type="fts").select(FTS_COLUMNS), where).limit(limit).to_list()
+
+
+def run_hybrid(
+	table, query: str, where: str | None, limit: int, vector: list[float] | None = None
+) -> list[dict]:
+	"""LanceDB's native hybrid search — vector + FTS legs fused with reciprocal rank fusion.
+
+	Falls back to the vector leg when there is no FTS index yet (a project indexed by an
+	older build, or an empty table); a missing keyword leg must degrade, not raise.
+	"""
+	text = fts_query(query)
+	if vector is None:
+		vector = embed.embed_one(query)
+	if not text or not has_fts_index(table):
+		return run_vector(table, query, where, limit, vector=vector)
+	query_builder = table.search(query_type="hybrid").vector(vector).text(text).select(RESULT_COLUMNS)
+	return scoped(query_builder, where).limit(limit).to_list()
+
+
+def run_filter(table, where: str | None) -> list[dict]:
+	"""Exhaustive scan — every row matching the metadata filter, with no top-k truncation."""
+	return scoped(table.search().select(RESULT_COLUMNS), where).limit(None).to_list()
+
+
+def row_score(row: dict) -> float:
+	"""One comparable score per mode: RRF relevance, BM25 score, or inverted L2 distance."""
+	if row.get("_relevance_score") is not None:
+		return float(row["_relevance_score"])
+	if row.get("_score") is not None:
+		return float(row["_score"])
+	if row.get("_distance") is not None:
+		return 1.0 / (1.0 + float(row["_distance"]))
+	return 1.0
+
+
+def rank_by_chunk(rows: list[dict]) -> dict[str, int]:
+	"""chunk id → 1-based rank, so the UI can show *why* a section ranked."""
+	return {row["id"]: position for position, row in enumerate(rows, start=1)}
+
+
+def expand_to_sections(
+	rows: list[dict], vector_ranks: dict[str, int], fts_ranks: dict[str, int]
+) -> list[Hit]:
+	"""Dedupe chunk hits to their parent section and attach the full section markdown."""
+	best: dict[str, dict] = {}
+	matched_chunks: dict[str, list[str]] = {}
+	for row in rows:
+		score = row_score(row)
+		current = best.get(row["section"])
+		if current is None or score > current["score"]:
+			best[row["section"]] = {"row": row, "score": score}
+		matched_chunks.setdefault(row["section"], []).append(row["id"])
+
+	sections = {
+		section["name"]: section
+		for section in chunk.get_rows_by_name(
+			"Source Section",
+			list(best),
+			["name", "title", "markdown", "hierarchy_path", "section_type", "source_document"],
+		)
+	}
+	documents = {
+		document["name"]: document
+		for document in chunk.get_rows_by_name(
+			"Source Document",
+			[entry["row"]["source_document"] for entry in best.values()],
+			["name", "title"],
+		)
+	}
+
+	hits: list[Hit] = []
+	for section_name, entry in best.items():
+		row = entry["row"]
+		section = sections.get(section_name)
+		if section is None:
+			# The section was deleted since indexing — skip rather than show a dead citation.
+			continue
+		ranks = [
+			(vector_ranks.get(chunk_id), fts_ranks.get(chunk_id)) for chunk_id in matched_chunks[section_name]
+		]
+		vector_hits = [rank for rank, _ in ranks if rank]
+		fts_hits = [rank for _, rank in ranks if rank]
+		hits.append(
+			Hit(
+				chunk_id=row["id"],
+				section=section_name,
+				source_document=row["source_document"],
+				document_title=(documents.get(row["source_document"]) or {}).get("title")
+				or row["source_document"],
+				title=section.get("title") or section_name,
+				text=section.get("markdown") or row.get("text") or "",
+				section_type=section.get("section_type") or None,
+				hierarchy_path=section.get("hierarchy_path") or row.get("hierarchy_path") or "",
+				page_start=row.get("page_start") or 0,
+				page_end=row.get("page_end") or 0,
+				wiki_route=row.get("wiki_route") or None,
+				score=round(entry["score"], 6),
+				vector_rank=min(vector_hits) if vector_hits else None,
+				fts_rank=min(fts_hits) if fts_hits else None,
+			)
+		)
+	return hits
+
+
+def rerank_scores(query: str, hits: list[Hit], model: str) -> dict[int, float]:
+	"""Ask the cheap model to score each candidate 0-10 for answering `query`."""
+	from wikify.engine import llm
+
+	candidates = "\n\n".join(
+		f"[{position}] {hit.document_title}{chunk.CONTEXT_SEPARATOR}{hit.hierarchy_path or hit.title}\n"
+		f"{(hit.text or '')[:RERANK_SNIPPET_CHARS]}"
+		for position, hit in enumerate(hits)
+	)
+	response = llm.chat_completion(
+		model,
+		[
+			{
+				"role": "system",
+				"content": (
+					"You rank retrieved document sections by how well each one answers the "
+					'user question. Reply with JSON: {"scores": [{"id": <candidate number>, '
+					'"score": <0-10>}]} covering every candidate. No prose.'
+				),
+			},
+			{"role": "user", "content": f"Question: {query}\n\nCandidates:\n\n{candidates}"},
+		],
+		label="rag_rerank",
+		response_format={"type": "json_object"},
+	)
+	usage.add(response.get("usage"))
+	content = response["choices"][0]["message"]["content"]
+	parsed = frappe.parse_json(content) or {}
+	from frappe.utils.data import cint, flt
+
+	return {cint(item.get("id")): flt(item.get("score")) for item in parsed.get("scores") or []}
+
+
+def ranks_candidates(scores: dict[int, float]) -> bool:
+	"""False when the reply carries no ranking information, so it must not be read as one.
+
+	The cheap classifier intermittently *succeeds* and returns 0 for every candidate. Taken
+	at face value that is a confident "none of this is relevant", and `answer.below_floor`
+	then refuses a question the retriever had already answered correctly — the worst failure
+	this system has, because "the document doesn't say" is indistinguishable from the
+	document genuinely not covering it. One flat verdict across several candidates is the
+	reranker being unavailable, not the corpus being empty, so the caller keeps the fusion
+	order instead.
+	"""
+	present = [score for score in scores.values() if score is not None]
+	if len(present) < 2:
+		return bool(present)
+	return len(set(present)) > 1
+
+
+def rerank_hits(query: str, hits: list[Hit]) -> list[Hit]:
+	"""LLM rerank of the top candidates. Never fatal — the search path degrades to the
+	unreranked order when no OpenRouter key is configured, the call fails, or the reply
+	ranks nothing."""
+	from wikify.engine import llm, settings
+
+	if not hits or not llm.has_openrouter():
+		return hits
+
+	head, tail = hits[:RERANK_CANDIDATES], hits[RERANK_CANDIDATES:]
+	try:
+		scores = rerank_scores(query, head, settings.get("classifier_model"))
+	except Exception:
+		frappe.log_error(title="RAG rerank failed", message=frappe.get_traceback())
+		return hits
+
+	if not ranks_candidates(scores):
+		frappe.log_error(
+			title="RAG rerank returned a flat verdict",
+			message=f"Query: {query}\nCandidates: {len(head)}\nScores: {scores}",
+		)
+		return hits
+
+	for position, hit in enumerate(head):
+		hit.rerank_score = scores.get(position)
+	head.sort(
+		key=lambda hit: (hit.rerank_score if hit.rerank_score is not None else -1.0, hit.score), reverse=True
+	)
+	return head + tail
+
+
+def search(
+	query: str,
+	*,
+	project: str | None = None,
+	source_document: str | None = None,
+	section_type: str | None = None,
+	limit: int = 8,
+	mode: str = "hybrid",
+	rerank: bool = False,
+	allowed_projects: list[str] | AclDecision = ACL_REQUIRED,
+) -> list[Hit]:
+	"""Retrieve sections for `query`. See the module docstring for what each mode means.
+
+	`mode="filter"` ignores `limit` by design — it returns every section matching the
+	metadata filter, which is the completeness guarantee the whole POC rests on.
+	`allowed_projects` is the internal ACL pre-filter and has no usable default: pass the
+	list of readable projects, or `ALL_PROJECTS` to opt out of the ACL clause on purpose.
+	"""
+	if mode not in MODES:
+		frappe.throw(f"Unknown search mode '{mode}'. Expected one of: {', '.join(MODES)}.")
+	assert_acl_decision(allowed_projects)
+
+	table = store.chunks_table()
+	if table is None:
+		return []
+
+	where = build_where(
+		project, source_document, section_type, allowed_projects, include_title_only=mode == "filter"
+	)
+	candidate_limit = max(int(limit) * CANDIDATE_MULTIPLIER, CANDIDATE_FLOOR)
+
+	vector_ranks: dict[str, int] = {}
+	fts_ranks: dict[str, int] = {}
+	if mode == "filter":
+		rows = run_filter(table, where)
+	elif mode == "vector":
+		rows = run_vector(table, query, where, candidate_limit)
+		vector_ranks = rank_by_chunk(rows)
+	elif mode == "fts":
+		rows = run_fts(table, query, where, candidate_limit)
+		fts_ranks = rank_by_chunk(rows)
+	else:
+		# The fused result hides which leg found what, so the two legs are re-run to label
+		# each hit. The query is embedded once here and handed to both vector passes —
+		# re-embedding per pass is the expensive part, the extra scans are not.
+		query_vector = embed.embed_one(query)
+		rows = run_hybrid(table, query, where, candidate_limit, vector=query_vector)
+		vector_ranks = rank_by_chunk(run_vector(table, query, where, candidate_limit, vector=query_vector))
+		fts_ranks = rank_by_chunk(run_fts(table, query, where, candidate_limit))
+
+	hits = expand_to_sections(rows, vector_ranks, fts_ranks)
+	# Filter mode re-sorts by document/page below, so a rerank here would be an LLM call
+	# whose entire output is thrown away.
+	if rerank and query and mode != "filter":
+		hits = rerank_hits(query, hits)
+
+	if mode == "filter":
+		return sorted(hits, key=lambda hit: (hit.document_title, hit.page_start, hit.title))
+	hits.sort(key=lambda hit: hit.score, reverse=True)
+	return hits[: int(limit)]
diff --git a/wikify/rag/store.py b/wikify/rag/store.py
new file mode 100644
index 0000000..70e5ec7
--- /dev/null
+++ b/wikify/rag/store.py
@@ -0,0 +1,103 @@
+"""LanceDB storage seam — one embedded database under the site's private files.
+
+Living inside `sites/<site>/private/files/wikify_lance` keeps the index site-scoped and
+inside the normal backup/private boundary (never web-servable). One `chunks` table holds
+every project; project scoping is a `.where()` pre-filter, so cross-project queries stay
+a single scan and the ACL filter can be pushed into the same clause.
+
+The schema is declared explicitly rather than inferred from the first batch: inference
+gives `list<float64>` (variable length), which cannot be vector-indexed and silently
+disables `.search()`. The vector column must be `fixed_size_list(float32, 256)`.
+"""
+
+from __future__ import annotations
+
+import os
+
+import frappe
+
+from wikify.rag.embed import EMBED_DIM
+
+TABLE_NAME = "chunks"
+LANCE_DIRNAME = "wikify_lance"
+
+
+def lance_path() -> str:
+	"""Absolute path to the LanceDB directory (absolute: workers run from other cwds)."""
+	return os.path.abspath(frappe.get_site_path("private", "files", LANCE_DIRNAME))
+
+
+def connect():
+	"""Open (creating the directory if needed) the site's LanceDB connection."""
+	import lancedb
+
+	path = lance_path()
+	os.makedirs(path, exist_ok=True)
+	return lancedb.connect(path)
+
+
+def chunk_schema():
+	"""The `chunks` pyarrow schema. `section` is the parent-document retrieval anchor."""
+	import pyarrow as pa
+
+	return pa.schema(
+		[
+			pa.field("id", pa.string()),
+			pa.field("section", pa.string()),
+			pa.field("source_document", pa.string()),
+			pa.field("project", pa.string()),
+			pa.field("text", pa.string()),
+			pa.field("embed_text", pa.string()),
+			pa.field("section_type", pa.string()),
+			pa.field("hierarchy_path", pa.string()),
+			pa.field("page_start", pa.int32()),
+			pa.field("page_end", pa.int32()),
+			# Span provenance: the page the chunk was matched back to (0 / approximate when
+			# the match was ambiguous), its line span in the parent section's markdown, and
+			# its line span on that page. This is what makes a citation checkable.
+			pa.field("page_no", pa.int32()),
+			pa.field("page_approximate", pa.bool_()),
+			pa.field("line_start", pa.int32()),
+			pa.field("line_end", pa.int32()),
+			pa.field("page_line_start", pa.int32()),
+			pa.field("page_line_end", pa.int32()),
+			# Set on a section with no body of its own: returned by exhaustive filter mode,
+			# excluded from the similarity legs so a bare title cannot outrank real content.
+			pa.field("title_only", pa.bool_()),
+			pa.field("wiki_route", pa.string()),
+			pa.field("vector", pa.list_(pa.float32(), EMBED_DIM)),
+		]
+	)
+
+
+def chunks_table(create: bool = False):
+	"""The single `chunks` table, or `None` when it doesn't exist and `create` is False.
+
+	Read paths pass `create=False` so a site that has never indexed answers "no results"
+	instead of materialising an empty table on every search.
+	"""
+	db = connect()
+	if TABLE_NAME in db.table_names():
+		return db.open_table(TABLE_NAME)
+	if not create:
+		return None
+	return db.create_table(TABLE_NAME, schema=chunk_schema())
+
+
+def reset_chunks_table():
+	"""Drop and recreate `chunks` against the CURRENT schema, then return the empty table.
+
+	A LanceDB table keeps the schema it was created with, so a column added to
+	`chunk_schema()` never appears on an existing table and every read of it comes back
+	null. Adding a column is therefore a re-index, not a migration script — this drops the
+	table so the caller can rebuild each project into it.
+	"""
+	db = connect()
+	if TABLE_NAME in db.table_names():
+		db.drop_table(TABLE_NAME)
+	return db.create_table(TABLE_NAME, schema=chunk_schema())
+
+
+def sql_literal(value) -> str:
+	"""Quote a value for a LanceDB `.where()` clause (single quotes doubled)."""
+	return "'" + str(value).replace("'", "''") + "'"
diff --git a/wikify/rag/usage.py b/wikify/rag/usage.py
new file mode 100644
index 0000000..c8431b1
--- /dev/null
+++ b/wikify/rag/usage.py
@@ -0,0 +1,58 @@
+"""What one answer cost — token and money totals for the model calls it actually made.
+
+Answering a question spends up to three completions in three different modules (the
+router's classification, the reranker's scoring, the synthesis stream), so the total is
+folded into a thread-local accumulator rather than threaded back through every signature.
+
+Thread-local, not module-level, because the web worker serves requests on threads: a
+shared buffer would bill one user's question to another's. Outside a `collect()` block
+`add()` is a no-op, so the retrieval paths that are not part of an answer cost nothing to
+instrument.
+"""
+
+from __future__ import annotations
+
+import threading
+from collections.abc import Iterator
+from contextlib import contextmanager
+
+from frappe.utils.data import cint, flt
+
+_local = threading.local()
+
+
+def empty() -> dict:
+	return {"cost": 0.0, "prompt_tokens": 0, "completion_tokens": 0}
+
+
+@contextmanager
+def collect() -> Iterator[dict]:
+	"""Total the usage of every `add()` inside the block. Nesting reuses the outer total."""
+	if getattr(_local, "totals", None) is not None:
+		yield _local.totals
+		return
+
+	_local.totals = empty()
+	try:
+		yield _local.totals
+	finally:
+		_local.totals = None
+
+
+def add(usage) -> None:
+	"""Fold one completion's `usage` payload into the current total.
+
+	`usage` is whatever the client handed back: a dict from the OpenRouter REST client, or
+	litellm's usage object on the final stream chunk. Cost is only present when the
+	provider reports it — we never estimate it, an unpriced call adds 0.
+	"""
+	totals = getattr(_local, "totals", None)
+	if totals is None or not usage:
+		return
+	totals["cost"] += flt(read(usage, "cost"))
+	totals["prompt_tokens"] += cint(read(usage, "prompt_tokens"))
+	totals["completion_tokens"] += cint(read(usage, "completion_tokens"))
+
+
+def read(usage, key: str):
+	return usage.get(key) if isinstance(usage, dict) else getattr(usage, key, None)
diff --git a/wikify/seed.py b/wikify/seed.py
index afe3185..249f1ac 100644
--- a/wikify/seed.py
+++ b/wikify/seed.py
@@ -19,67 +19,82 @@
 		"staff_roles_and_responsibilities",
 		"Staff Roles & Responsibilities",
 		"#3b82f6",
-		"Job descriptions, role definitions, duties, reporting lines, and org structure.",
+		"Job descriptions and role profiles — one section per post: purpose of the role, "
+		"duties, reporting and supervision lines, required qualifications and licences, "
+		"and the staffing structure.",
 	),
 	(
 		"clinical_protocols",
 		"Clinical Protocols",
 		"#10b981",
-		"Clinical guidelines, care pathways, assessment and treatment protocols.",
+		"Clinical guidelines and care pathways — assessment, risk scoring, treatment steps, "
+		"escalation thresholds and review intervals for a named condition.",
 	),
 	(
 		"surgical_procedures",
 		"Surgical Procedures",
 		"#ef4444",
-		"Operative techniques, surgical steps, peri-operative and theatre procedures.",
+		"Operative and theatre practice — surgical steps, peri-operative checklists, swab "
+		"and instrument counts, and recovery.",
 	),
 	(
 		"patient_management",
 		"Patient Management",
 		"#8b5cf6",
-		"Admission, triage, monitoring, discharge, and ongoing patient-care management.",
+		"The patient or client journey — referral, admission, triage, care planning, "
+		"monitoring, review and discharge.",
 	),
 	(
 		"medication_management",
 		"Medication Management",
 		"#f59e0b",
-		"Prescribing, dosing, administration, storage, and reconciliation of medicines.",
+		"Medicines handling — prescribing, dosing, administration, controlled drugs, "
+		"storage, repeat prescriptions and reconciliation.",
 	),
 	(
 		"administrative_policies",
 		"Administrative Policies",
 		"#64748b",
-		"Governance, HR, finance, scheduling, and other non-clinical administrative policy.",
+		"Non-clinical policy sections — pay bands and salary scales, overtime, on-call and "
+		"unsocial-hours payments, pension, leave and other benefits, expenses and mileage, "
+		"working-hours and rota rules, and HR or finance governance.",
 	),
 	(
 		"equipment_and_facilities",
 		"Equipment & Facilities",
 		"#14b8a6",
-		"Devices, instruments, maintenance, supplies, and facility/environment management.",
+		"Devices, instruments and consumables — checks, maintenance, decontamination, "
+		"stock control, and the physical environment.",
 	),
 	(
 		"training_and_audits",
 		"Training & Audits",
 		"#ec4899",
-		"Education, competencies, induction, quality audits, and compliance reviews.",
+		"Becoming and staying competent — induction, mandatory training, competency "
+		"frameworks and sign-off, preceptorship, appraisal, and the audit and compliance cycle.",
 	),
 	(
 		"research_and_documentation",
 		"Research & Documentation",
 		"#6366f1",
-		"Research methods, evidence, record-keeping, forms, and documentation standards.",
+		"Evidence and record-keeping — research methods, references, forms, templates and "
+		"documentation standards.",
 	),
 	(
 		"emergency_procedures",
 		"Emergency Procedures",
 		"#f97316",
-		"Resuscitation, escalation, codes, and other urgent/emergency response procedures.",
+		"Urgent response — resuscitation, emergency calls and escalation, major incident "
+		"plans and other time-critical procedures.",
 	),
 	(
 		"other",
 		"Other",
 		"#9ca3af",
-		"Anything that does not fit the categories above (the catch-all).",
+		"Organisation overviews and front matter — who the provider is, its mission and "
+		"values, its sites, the services it offers and its size (beds, list size, annual "
+		'activity). An "about this organisation" or "who we are" section lives here, as '
+		"does anything that fits no category above.",
 	),
 ]
 
diff --git a/wikify/tests/fixtures/__init__.py b/wikify/tests/fixtures/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/wikify/tests/fixtures/demo_corpus.py b/wikify/tests/fixtures/demo_corpus.py
new file mode 100644
index 0000000..5a72085
--- /dev/null
+++ b/wikify/tests/fixtures/demo_corpus.py
@@ -0,0 +1,1653 @@
+"""Repeatable demo corpus for the RAG POC (`specs/poc-rag-CONTRACT.md`).
+
+Five healthcare-provider manuals whose sections deliberately **overlap in type**: each
+document carries three job descriptions, a compensation/benefits section, a
+qualifications-and-training section, an organisation overview, and one domain section.
+Fifteen `staff_roles_and_responsibilities` sections spread over five documents is the
+whole point — a naive top-k (k=8) vector search physically cannot return them all, while
+a `section_type` metadata filter returns exactly fifteen. That is the completeness thesis
+the POC demonstrates.
+
+Built as Source Document + Source Section rows directly rather than through the parse
+pipeline: no source PDFs for this domain exist in the repo, and a real parse is an
+LLM call per page. The bodies below are hand-written prose standing in for parser output.
+
+# ponytail: hand-authored corpus, replace with a real parsed PDF set once demo PDFs exist
+
+Usage (from the bench root):
+
+    bench --site wikify.localhost execute wikify.tests.fixtures.demo_corpus.seed_demo_corpus
+    bench --site wikify.localhost execute wikify.tests.fixtures.demo_corpus.inventory
+"""
+
+from __future__ import annotations
+
+import frappe
+from frappe.utils.nestedset import get_descendants_of
+
+from wikify.engine import store
+from wikify.engine.generate import generate_wiki
+from wikify.engine.loader.sectionizer import Section
+from wikify.seed import seed_section_types
+
+PROJECT_NAME = "Demo Corpus"
+SPACE_NAME = "Demo Corpus"
+SPACE_ROUTE = "demo-corpus"
+
+# --- Northfield General Hospital — Nursing Services Manual ---
+
+NORTHFIELD_OVERVIEW = """# Hospital Overview and Mission
+
+Northfield General Hospital is a 480-bed district general hospital serving a population of
+just under 400,000 people across the Northfield, Elmsworth and Carrow Vale districts. The
+hospital opened its current site in 1978 and has since grown to eleven inpatient wards, a
+twenty-four hour emergency department, a six-theatre elective surgical suite, and an
+outpatient centre handling approximately 210,000 appointments each year.
+
+Our stated mission is to deliver safe, timely and compassionate care as close to a
+patient's home as is clinically appropriate. Three commitments follow from that mission
+and govern every policy in this manual. First, no patient should wait for care because of
+an avoidable process delay. Second, every member of staff should be able to describe their
+own accountability without consulting a manager. Third, every clinical decision should be
+recorded well enough that the next clinician can safely continue the episode of care.
+
+The hospital is organised into four clinical divisions — Medicine, Surgery, Women's and
+Children's Health, and Diagnostics and Therapies — each led by a divisional director
+supported by a head of nursing and a divisional manager. Nursing services report
+professionally to the Chief Nurse and operationally to the divisional structure. This dual
+line is deliberate: professional standards, revalidation and clinical supervision stay
+with the nursing hierarchy even when day-to-day task allocation sits with the division.
+
+The Nursing Services Manual applies to every registered nurse, nursing associate and
+healthcare assistant employed by or contracted to the hospital, including agency and bank
+staff working a single shift.
+"""
+
+NORTHFIELD_BENEFITS = """# Pay Bands, Benefits and Leave Entitlements
+
+Nursing and healthcare support roles at Northfield General are paid on the nine-band
+national pay structure. Healthcare assistants enter at Band 2 and progress to Band 3 on
+completion of the Care Certificate and a supervised competency portfolio. Newly registered
+nurses are appointed at Band 5, senior and specialist nurses at Band 6, and ward sisters
+and charge nurses at Band 7. Progression within a band is by annual increment, subject to
+a satisfactory appraisal and completion of mandatory training.
+
+Unsocial hours are paid as a percentage enhancement on basic salary: 30 per cent for
+weekday nights and Saturdays, and 60 per cent for Sundays and public holidays. Staff at
+Band 8 and above are not eligible for enhancements and instead receive time off in lieu at
+the discretion of their divisional director.
+
+Annual leave begins at 27 days plus eight public holidays, rising to 29 days after five
+years of continuous service and 33 days after ten years. Leave is pro-rated for part-time
+contracts and must be requested at least six weeks in advance for periods longer than five
+consecutive working days.
+
+All staff are automatically enrolled in the hospital pension scheme at a contribution rate
+banded by salary, with the employer contributing 20.6 per cent. Additional benefits
+include a subsidised staff restaurant, an on-site nursery with 45 places allocated by
+ballot each September, an occupational health service offering same-week appointments, a
+cycle-to-work salary sacrifice scheme, and access to a confidential employee assistance
+helpline operating twenty-four hours a day.
+"""
+
+NORTHFIELD_WARD_SISTER = """# Job Description — Ward Sister / Charge Nurse
+
+**Band:** 7  **Reports to:** Matron, Divisional Head of Nursing  **Hours:** 37.5 per week
+
+**Purpose of the role.** The ward sister or charge nurse holds continuing 24-hour
+responsibility for the standard of nursing care delivered on a designated ward. The
+postholder is the visible clinical leader of the ward team, accountable for patient
+safety, staff performance, and the effective use of the ward's staffing and non-pay
+budget.
+
+**Principal duties.** Lead the nursing team on the ward, setting and monitoring standards
+of clinical practice against hospital policy and Nursing and Midwifery Council
+requirements. Produce and publish the ward roster at least six weeks in advance, ensuring
+every shift meets the agreed registered-nurse-to-patient ratio and skill mix. Conduct daily
+safety huddles and a structured ward round with the medical team, escalating deteriorating
+patients through the recognised pathway. Investigate incidents and complaints relating to
+the ward, complete the associated reports within ten working days, and share the learning
+with the team.
+
+Manage the ward's non-pay budget, authorise expenditure within delegated limits, and
+account for variance at the monthly divisional review. Undertake appraisals for all
+directly reporting staff, agree personal development plans, and manage sickness absence,
+capability and conduct issues in line with hospital procedure. Act as practice supervisor
+and assessor for student nurses on placement.
+
+**Person specification.** Registered Nurse with current NMC registration; minimum five
+years post-registration experience of which two are at Band 6; degree-level study or
+equivalent; evidence of leadership development; demonstrable experience of managing a
+roster and a devolved budget.
+"""
+
+NORTHFIELD_STAFF_NURSE = """# Job Description — Staff Nurse (Band 5)
+
+**Band:** 5  **Reports to:** Ward Sister / Charge Nurse  **Hours:** 37.5 per week, rotating
+
+**Purpose of the role.** The staff nurse delivers and coordinates direct nursing care for
+a defined group of patients throughout a shift, working within the NMC Code and the
+hospital's clinical policies. The postholder is the first point of professional
+accountability for the patients in their care during that shift.
+
+**Principal duties.** Assess, plan, implement and evaluate individualised nursing care,
+documenting each stage contemporaneously in the electronic patient record. Administer
+medicines in accordance with the hospital medicines policy, including the two-person check
+required for controlled drugs and intravenous preparations. Monitor and record vital
+signs, calculate early warning scores, and escalate deterioration to the nurse in charge
+and the medical team without delay.
+
+Take charge of the ward in the absence of the sister or charge nurse on a shift-by-shift
+basis once assessed as competent to do so. Give and receive structured handover at every
+shift change using the agreed handover tool. Prepare patients for investigations and
+theatre, receive them back, and carry out post-procedure observations to the specified
+frequency. Contribute to discharge planning from the day of admission, liaising with
+therapy, pharmacy and social care colleagues.
+
+Supervise healthcare assistants and student nurses allocated to the same patient group,
+delegating only tasks within the individual's assessed competence and retaining
+accountability for the delegation itself.
+
+**Person specification.** Registered Nurse with current NMC registration; ability to
+demonstrate safe medicines management; commitment to revalidation; evidence of
+continuing professional development within the last twelve months.
+"""
+
+NORTHFIELD_HCA = """# Job Description — Healthcare Assistant
+
+**Band:** 2, progressing to Band 3  **Reports to:** Staff Nurse and Ward Sister
+
+**Purpose of the role.** The healthcare assistant provides direct personal care and
+practical support to patients under the supervision of a registered nurse, contributing to
+a clean, safe and dignified ward environment. The role requires no prior professional
+registration; all clinical tasks are performed under delegation and within assessed
+competence.
+
+**Principal duties.** Assist patients with washing, dressing, oral care, continence care,
+toileting and mobility, always working in a way that preserves privacy and dignity. Serve
+meals and drinks, assist patients who need help to eat, and accurately record food and
+fluid intake on the relevant charts. Reposition patients on the agreed schedule and report
+any change in skin condition to the nurse in charge immediately.
+
+Take and record routine observations — temperature, pulse, respiratory rate, blood
+pressure, oxygen saturation and blood glucose — once assessed as competent, and report any
+reading outside the parameters set for that patient. Prepare beds and clinical areas,
+carry out cleaning tasks allocated to nursing staff, and restock consumables. Chaperone
+patients during examinations when asked. Escort patients to other departments and hand
+over relevant information on arrival.
+
+Answer call bells promptly and communicate with patients and relatives courteously,
+referring clinical questions to a registered nurse.
+
+**Person specification.** Good standard of literacy and numeracy; completion of the Care
+Certificate within twelve weeks of appointment; ability to work rotating shifts including
+nights, weekends and public holidays; evidence of a caring and respectful manner.
+"""
+
+NORTHFIELD_TRAINING = """# Recruitment, Qualifications and Mandatory Training
+
+Every nursing vacancy at Northfield General is recruited against the published job
+description and person specification, and every appointment is subject to satisfactory
+pre-employment checks: identity, right to work, professional registration verified
+directly with the NMC, an enhanced disclosure and barring check, two references covering
+the previous three years of employment, and occupational health clearance.
+
+Shortlisting is carried out by at least two people, one of whom must have completed
+recruitment and selection training within the last three years. Interview panels for Band
+6 and above include a member from outside the recruiting division. Values-based questions
+are mandatory for every clinical post, and their scores are recorded on the panel record
+retained for twelve months.
+
+All new starters attend a two-day corporate induction followed by a local induction
+completed within four weeks and signed off by the line manager. Registered nurses new to
+the organisation complete a supernumerary period of at least three shifts on their base
+ward, extended to ten shifts for newly qualified nurses entering the preceptorship
+programme.
+
+Mandatory training is refreshed on a rolling schedule: basic life support, moving and
+handling, infection prevention, safeguarding levels one and two, information governance,
+fire safety and equality and diversity annually; immediate life support, conflict
+resolution and safeguarding level three every three years for the staff groups that
+require them. Compliance is reported monthly to the divisional board, and any individual
+below 90 per cent compliance is placed on a documented recovery plan with their manager.
+"""
+
+NORTHFIELD_PRESSURE = """# Pressure Ulcer Prevention Protocol
+
+Every adult inpatient must have a pressure ulcer risk assessment completed within six
+hours of admission to the ward, repeated whenever the patient's condition changes and at
+minimum every seven days. The assessment combines a validated risk score with clinical
+judgement; a low score never overrides an obvious clinical concern.
+
+A full skin inspection is carried out at the time of the first assessment and at least
+daily thereafter, paying particular attention to the sacrum, heels, elbows, shoulder
+blades, and any skin in contact with a device such as an oxygen mask, catheter tubing or
+cast. Findings are recorded in the electronic record with the anatomical site and the
+category of any damage found.
+
+Patients assessed as at risk receive a documented repositioning schedule, normally two
+hourly for those unable to reposition themselves independently and four hourly for those
+with limited independent movement. A pressure-redistributing mattress is provided within
+four hours of the assessment for patients at high risk; the equipment library holds stock
+for this purpose and is contactable at all hours.
+
+Nutrition and hydration are reviewed alongside skin care, with referral to the dietitian
+where intake is inadequate for more than two days.
+
+Any pressure damage of category two or above acquired after admission is reported as an
+incident on the day it is identified, triggers a review by the tissue viability nurse
+within one working day, and is included in the ward's monthly harm-free care report.
+"""
+
+# --- Riverside Community Clinic — Staff Handbook ---
+
+RIVERSIDE_OVERVIEW = """# Who We Are: Riverside Community Clinic
+
+Riverside Community Clinic is a primary care practice with a registered list of
+approximately 14,200 patients, operating from a main site on Mill Street and a branch
+surgery in Hollowbrook that opens four days a week. The practice was formed in 2011 by the
+merger of two smaller partnerships and is now run by four GP partners supported by a
+salaried medical team, a nursing team, and a business and administrative team.
+
+Our purpose is straightforward: to give the people registered with us continuity of care
+from a named clinician, and to make routine access to that clinician simple enough that
+nobody delays seeking help. Continuity is measured every quarter and reported to the
+partnership; it is the single metric the practice treats as non-negotiable.
+
+Services provided directly by the clinic include general medical services, long-term
+condition reviews for diabetes, asthma, chronic obstructive pulmonary disease and
+hypertension, childhood and seasonal immunisations, cervical screening, minor surgery,
+contraceptive services including implant and coil fitting, and a weekly clinic for
+patients recently discharged from hospital.
+
+The practice also hosts visiting services provided by the wider health system: community
+physiotherapy on Tuesdays, a mental health practitioner on Wednesdays and Fridays, and a
+social prescribing link worker four days a week.
+
+This handbook applies to everyone working at the clinic, whether employed by the
+partnership, engaged as a locum, or placed with us as a student or apprentice. It should
+be read alongside the individual's contract of employment.
+"""
+
+RIVERSIDE_BENEFITS = """# Salary Scales, Pension and Employee Benefits
+
+Riverside Community Clinic sets salaries against an internal scale reviewed each April by
+the partnership and benchmarked against comparable practices in the region. Administrative
+roles occupy scales A to C, nursing and clinical support roles scales D to F, and salaried
+clinical roles are appointed on individually negotiated sessional rates. Every scale has
+four annual increment points; movement to the next point requires a completed appraisal
+and no live formal warning.
+
+Salaried GPs are contracted by session, with each session covering four hours and ten
+minutes of clinical time plus associated administration. The standard full-time commitment
+is eight sessions per week. Sessional rates include an allowance for professional expenses
+and one session per month of protected time for continuing professional development.
+
+All eligible employees are enrolled in the NHS Pension Scheme from their first day; those
+not eligible are enrolled in the practice's workplace pension with a five per cent
+employer contribution. Employees may opt out in writing at any time and are re-enrolled
+automatically every three years as the law requires.
+
+Annual leave is 25 days plus public holidays for administrative staff and 30 days plus
+public holidays for clinical staff, both pro-rated for part-time hours. The practice funds
+professional registration fees and indemnity cover for all clinical staff, one paid study
+day per year for administrative staff, free seasonal influenza vaccination, an interest
+free travel loan, and a discretionary annual bonus paid in December when the partnership's
+financial position allows.
+"""
+
+RIVERSIDE_PRACTICE_MANAGER = """# Job Description — Practice Manager
+
+**Reports to:** The GP Partners  **Hours:** 37 per week  **Scale:** Senior management
+
+**Purpose of the role.** The practice manager is responsible for the operational,
+financial and human resource management of the clinic, enabling the clinical team to
+deliver safe and effective care. The postholder is the senior non-clinical leader and
+deputises for the partners on all business matters.
+
+**Principal duties.** Manage the day-to-day running of both sites, including opening
+arrangements, appointment capacity, room allocation and the resolution of operational
+problems as they arise. Prepare the annual budget with the partners, monitor income and
+expenditure monthly, oversee payroll, and ensure claims for enhanced services and
+incentive schemes are submitted accurately and on time.
+
+Lead the administrative and reception teams: recruit, induct, appraise and develop staff;
+manage rotas and absence; and handle disciplinary and grievance matters in line with
+practice policy. Maintain the practice's compliance framework, including health and safety,
+fire, information governance, data protection and regulatory registration, and prepare the
+evidence required for inspection.
+
+Own the complaints process end to end, acknowledging within three working days and
+responding substantively within twenty working days, and present a quarterly complaints
+and significant events summary to the partnership meeting. Manage contracts with
+suppliers, the building lease, and the clinical system supplier.
+
+**Person specification.** Substantial management experience, ideally in primary care;
+demonstrable financial and budget management; strong working knowledge of employment law;
+experience of leading a team through change; qualification in management or equivalent
+experience.
+"""
+
+RIVERSIDE_GP = """# Job Description — General Practitioner (Salaried)
+
+**Reports to:** Lead GP Partner  **Commitment:** Six to eight sessions per week
+
+**Purpose of the role.** The salaried general practitioner provides general medical
+services to the practice's registered population, sharing fully in the clinical workload
+and in the practice's commitment to continuity of care.
+
+**Principal duties.** Undertake booked and on-the-day consultations, telephone and online
+consultations, and home visits for housebound patients within the practice area. Assess,
+diagnose, treat and where appropriate refer patients, working within the limits of
+professional competence and referring on when those limits are reached.
+
+Take responsibility for a named list of patients with complex needs, coordinating their
+care with community services and secondary care. Contribute to the daily duty rota,
+including the triage of urgent requests and the review of pathology results, hospital
+correspondence and repeat prescription requests generated by the clinical system.
+
+Participate in the practice's clinical governance programme: significant event analysis,
+prescribing review, clinical audit, and the multidisciplinary team meeting held on the
+first Wednesday of each month. Supervise and support the nursing team, pharmacist and
+paramedic practitioner on clinical questions arising in their own consultations.
+
+Maintain full and contemporaneous records in the clinical system, coding consultations
+consistently so that the practice's disease registers remain accurate.
+
+**Person specification.** Full GMC registration with a licence to practise and inclusion
+on the National Performers List; membership of a recognised indemnity organisation;
+evidence of annual appraisal and current revalidation status; commitment to working at
+both practice sites.
+"""
+
+RIVERSIDE_RECEPTIONIST = """# Job Description — Medical Receptionist
+
+**Reports to:** Reception Supervisor and Practice Manager  **Scale:** A to B
+
+**Purpose of the role.** The medical receptionist is the first point of contact between
+the practice and its patients. The postholder manages access to clinical appointments
+courteously and consistently, and handles confidential information with absolute
+discretion.
+
+**Principal duties.** Answer incoming telephone calls promptly, establish the reason for
+contact using the practice's agreed care navigation questions, and direct the patient to
+the most appropriate service — a GP, a nurse, a pharmacist, a community service, or
+self-care advice. Book, amend and cancel appointments across both sites, keeping the
+appointment book accurate at all times.
+
+Greet patients arriving at the desk, check and update their demographic details, and
+manage the flow of the waiting area including any delay announcements. Register new
+patients, process temporary resident forms, and prepare records for summarising.
+
+Handle repeat prescription requests received by any route, checking them against the
+clinical system and passing queries to the prescribing clinician. Receive, scan and
+workflow incoming correspondence to the correct clinician within one working day. Take
+messages accurately and pass urgent messages immediately rather than by workflow.
+
+Open and close the premises on a rotating basis, following the security checklist, and
+support colleagues at the branch surgery when cover is needed.
+
+**Person specification.** Good standard of general education; confident telephone manner;
+accurate keyboard skills; ability to remain calm with distressed or angry callers;
+understanding of confidentiality; willingness to complete care navigation training within
+three months of appointment.
+"""
+
+RIVERSIDE_TRAINING = """# Induction, Competency Framework and Annual Appraisal
+
+Every new member of staff at Riverside Community Clinic follows a structured induction
+lasting four weeks. Week one covers the practice, its sites, fire and security procedures,
+information governance, confidentiality, and the clinical system. Weeks two to four are
+role-specific and are signed off against a checklist held by the line manager; no member of
+staff works unsupervised on a task until the corresponding line of that checklist is
+signed.
+
+Clinical and administrative competencies are held in a single framework with four levels:
+aware, supervised, independent and able to teach. Reception staff are assessed on care
+navigation, registration, prescription handling and correspondence workflow. Nursing staff
+are assessed on immunisation, cervical screening, wound care, long-term condition review,
+and any extended skill such as coil fitting or spirometry. Competence at independent level
+expires after two years unless refreshed by observed practice.
+
+Mandatory training for all staff comprises basic life support and anaphylaxis, infection
+prevention and control, information governance, fire safety, health and safety,
+safeguarding children and adults at the level appropriate to the role, and equality and
+diversity. All are annual except safeguarding level three, which is three-yearly.
+
+Every employee has an annual appraisal with their line manager, preceded by a
+self-assessment and, for clinical staff, a review of at least one audit and one
+significant event. Objectives set at appraisal are reviewed at a documented mid-year
+check-in. Appraisal completion is reported to the partnership each October.
+"""
+
+RIVERSIDE_PRESCRIBING = """# Repeat Prescribing and Medicines Reconciliation
+
+Repeat prescribing at Riverside Community Clinic operates on a strict authorise-then-issue
+model. A medicine is added to a patient's repeat list only by a prescribing clinician, who
+at the same time sets the quantity, the number of issues permitted before review, and the
+review date. Administrative staff never add, amend or extend a repeat item.
+
+Requests are accepted in writing, through the online account, through the community
+pharmacy, or in person; they are not accepted by telephone except for housebound patients
+recorded as exempt. Requests are processed within two working days. The processing clerk
+checks the item against the repeat list, the requested quantity against the authorised
+quantity, and the review date; any mismatch is passed to the duty prescriber rather than
+issued.
+
+Every patient on four or more regular medicines, or on any medicine requiring monitoring,
+receives a structured medication review at least annually with the practice pharmacist.
+The review covers indication, effectiveness, safety, adherence and the patient's own
+priorities, and its outcome is recorded against the agreed codes.
+
+Medicines reconciliation is completed within two working days of receiving a hospital
+discharge summary. The pharmacist compares the discharge list against the practice record,
+resolves every discrepancy with the prescriber, updates the repeat list, and records what
+changed and why. Discharge summaries mentioning a high-risk medicine — anticoagulants,
+insulin, methotrexate, lithium or opioids — are prioritised the same day.
+"""
+
+# --- St Aubyn Maternity Unit — Operational Policy Manual ---
+
+AUBYN_OVERVIEW = """# St Aubyn Maternity Unit at a Glance
+
+St Aubyn Maternity Unit is a consultant-led maternity service co-located with an alongside
+midwifery-led birth centre, delivering approximately 3,100 babies each year. The unit
+comprises a twelve-room delivery suite, a four-room birth centre with two pools, a
+twenty-eight bed postnatal ward, a ten-bed antenatal ward, a triage and day assessment
+area open around the clock, and a level two neonatal unit on the same floor.
+
+The service covers the full maternity pathway: community antenatal care delivered from
+seven clinic bases, hospital antenatal clinics for women with additional needs, intrapartum
+care in either setting, postnatal care in hospital and at home for up to twenty-eight
+days, and a consultant-led birth reflections service for women who wish to discuss a
+previous birth.
+
+The unit's operating philosophy is that birth is a physiological event that sometimes
+needs medical help, not a medical event that sometimes proceeds normally. Practically this
+means continuity of carer is offered to every woman, that midwives lead care for women
+without complications with obstetric input available rather than imposed, and that any
+escalation between the two models is documented with a reason.
+
+Governance sits with a joint obstetric and midwifery board chaired alternately by the
+clinical director and the head of midwifery. The board reviews every case meeting the
+national reporting criteria, the monthly dashboard of clinical indicators, and the themes
+arising from feedback collected from women using the service.
+"""
+
+AUBYN_BENEFITS = """# Rostering, On-Call Payments and Maternity Benefits
+
+Midwifery staff at St Aubyn work a rolling twelve-week roster published eight weeks in
+advance. The standard pattern is long days and long nights of eleven and a half hours,
+with a maximum of three consecutive night shifts and a minimum rest period of forty-six
+hours after any run of nights. Requests are accepted up to a fair-shares limit of four
+per roster period and are allocated by a documented process rather than by seniority.
+
+Community midwives carry a separate on-call commitment averaging one weekday night per
+week and one weekend in six. On-call is paid as an availability allowance plus payment at
+the appropriate hourly rate for any period actually worked, with a guaranteed minimum
+call-out payment of two hours. A midwife called out after 02:00 is not rostered to a
+clinical shift before 14:00 the following day.
+
+Obstetric medical staff work a full shift rota compliant with the working time
+regulations, banded and reviewed annually by a joint monitoring exercise.
+
+The unit's own maternity, adoption and shared parental leave provisions follow national
+terms: occupational maternity pay of eight weeks at full pay, eighteen weeks at half pay
+plus statutory pay, and a further thirteen weeks of statutory pay, subject to twelve
+months continuous service at the qualifying week. Keeping-in-touch days are available and
+paid. Staff returning from maternity leave are entitled to a phased return over four weeks
+and to request flexible working, which the unit will refuse only for one of the statutory
+business reasons in writing.
+"""
+
+AUBYN_CONSULTANT = """# Job Description — Consultant Obstetrician
+
+**Grade:** Consultant  **Programmed activities:** 10 per week  **Accountable to:** Clinical
+Director for Women's Health
+
+**Purpose of the role.** The consultant obstetrician provides expert obstetric and
+gynaecological care, leads the multidisciplinary team on the delivery suite when rostered,
+and shares in the clinical governance and training responsibilities of the department.
+
+**Principal duties.** Deliver a weekly programme of consultant-led antenatal clinics
+including specialist clinics for diabetes in pregnancy and previous caesarean birth. Take
+consultant responsibility for the delivery suite on rostered days, attending the twice
+daily ward round, reviewing every woman with a complicated labour, and being immediately
+available for obstetric emergencies including instrumental birth and caesarean section.
+
+Provide a share of the department's on-call commitment on a one-in-eight rota, remaining
+within thirty minutes of the unit while on call. Undertake elective and emergency
+operating lists as required and maintain the surgical logbook expected for revalidation.
+
+Supervise and train specialty registrars and foundation doctors, provide named educational
+supervision for at least two trainees, and contribute to midwifery skills drills. Lead or
+co-lead at least one clinical audit per year and participate in the review of cases meeting
+national reporting criteria.
+
+**Person specification.** Full GMC registration with a licence to practise and entry on
+the specialist register or within six months of the expected date of entry; MRCOG or
+equivalent; evidence of advanced training in a subspecialty relevant to the department's
+needs; documented experience of teaching and audit; ability to meet the on-call travel
+requirement.
+"""
+
+AUBYN_COMMUNITY_MIDWIFE = """# Job Description — Community Midwife
+
+**Band:** 6  **Reports to:** Community Team Leader  **Base:** One of seven clinic bases
+
+**Purpose of the role.** The community midwife provides midwifery care to a named caseload
+of women through pregnancy, birth and the postnatal period, working autonomously within
+the NMC Code and referring to obstetric or neonatal colleagues where care needs step
+beyond midwifery-led practice.
+
+**Principal duties.** Carry a personal caseload of women, undertaking the booking
+appointment before the tenth week of pregnancy wherever possible, and completing the
+antenatal schedule of care at the intervals set by national guidance. Assess each woman's
+physical, psychological and social needs at every contact, and make onward referrals to
+obstetric, mental health, safeguarding, smoking cessation or social care services as
+indicated.
+
+Provide intrapartum care for home births and in the alongside birth centre as part of the
+on-call rota, including the transfer of care to the delivery suite where escalation is
+required. Provide postnatal care in the woman's home, including newborn examination where
+qualified, feeding support, and assessment for postnatal depression using the agreed tool.
+
+Maintain accurate contemporaneous records in the maternity information system, complete
+the statutory notification of birth, and contribute to the community team's caseload
+review meeting each fortnight.
+
+Act as practice supervisor and assessor for student midwives allocated to the team.
+
+**Person specification.** Registered Midwife with current NMC registration; ability to
+work autonomously and carry an on-call commitment; full driving licence and access to a
+vehicle; newborn examination qualification desirable; evidence of continuing professional
+development in the last twelve months.
+"""
+
+AUBYN_SUPPORT_WORKER = """# Job Description — Maternity Support Worker
+
+**Band:** 3  **Reports to:** Ward Manager or Community Team Leader
+
+**Purpose of the role.** The maternity support worker assists registered midwives in
+providing care to women and their babies in hospital and community settings, undertaking
+delegated clinical tasks within assessed competence and providing practical and emotional
+support to families.
+
+**Principal duties.** Support women with personal care, mobility and comfort after birth,
+and assist with the care of babies including bathing, nappy care and safe sleep advice.
+Provide practical infant feeding support under the direction of a midwife, in line with
+the unit's accredited infant feeding standards, and escalate any feeding concern promptly.
+
+Record maternal observations including temperature, pulse, blood pressure and respiratory
+rate, perform venepuncture and newborn blood spot sampling once assessed as competent, and
+report any result or observation outside the expected range to the midwife responsible for
+that woman. Prepare clinical rooms and equipment for antenatal clinics, chaperone during
+examinations, and maintain stock levels.
+
+Undertake home visits alongside or, for defined postnatal contacts, on behalf of the
+community midwife, following the visit plan and reporting back the same day. Support the
+running of parent education sessions and the antenatal clinic administration.
+
+**Person specification.** Level 3 qualification in maternity support or health and social
+care, or willingness to work towards it; completion of the Care Certificate; ability to
+work shifts including weekends; good written and verbal communication; understanding of
+the boundaries of the delegated role.
+"""
+
+AUBYN_TRAINING = """# Midwifery Preceptorship and Skills Drills Programme
+
+Every newly registered midwife joining St Aubyn enters a twelve-month preceptorship
+programme. The first four weeks are supernumerary and rotate through the delivery suite,
+the birth centre, the postnatal ward and a community team so the preceptee sees the whole
+pathway before carrying independent responsibility. A named preceptor is allocated on day
+one and protected time is rostered for a monthly meeting.
+
+Progress is assessed against a competency document covering intrapartum care,
+perineal repair, intravenous cannulation and drug administration, newborn assessment,
+emergency response and record keeping. Each competency is signed at supervised, then
+independent level by an assessor who has observed practice directly; self-declaration is
+not accepted for any clinical competency.
+
+Multidisciplinary skills drills run every week on the delivery suite and monthly in the
+community teams. The annual cycle covers shoulder dystocia, postpartum haemorrhage,
+maternal collapse and resuscitation, cord prolapse, eclampsia, neonatal resuscitation and
+breech birth. Attendance is mandatory: every clinical member of staff, including consultant
+obstetricians and anaesthetists, must attend the full annual set, and attendance is
+reported to the joint governance board each quarter.
+
+Mandatory training additionally includes fetal monitoring assessment, which must be passed
+annually by anyone interpreting a cardiotocograph, safeguarding at level three for all
+clinical staff, and the unit's own escalation and human factors session.
+"""
+
+AUBYN_HAEMORRHAGE = """# Obstetric Haemorrhage: Emergency Response
+
+Postpartum haemorrhage is declared when blood loss after birth reaches 500 millilitres and
+is continuing, or at any volume where the woman shows signs of cardiovascular compromise.
+Estimated loss is never used alone: all swabs, pads and drapes are weighed, and the
+running total is called aloud and recorded on the haemorrhage chart by a designated
+scribe.
+
+On declaration the emergency call is made using the unit's 2222 number stating "obstetric
+haemorrhage" and the location. The call summons the obstetric registrar and consultant,
+the anaesthetic registrar, a second midwife, the theatre team and a porter for urgent
+blood transport. The delivery suite coordinator attends every call and takes the role of
+team leader until a more senior clinician assumes it explicitly.
+
+Immediate management runs in parallel rather than in sequence: lie the woman flat and give
+high-flow oxygen; insert two large-bore cannulae and take a full blood count, coagulation
+screen and cross-match for four units; begin a warmed crystalloid infusion; rub up a
+uterine contraction and empty the bladder; and give uterotonic drugs in the order set out
+on the haemorrhage proforma.
+
+If bleeding continues beyond 1,000 millilitres the major haemorrhage protocol is
+activated, which releases the emergency blood pack from the transfusion laboratory without
+further authorisation. Transfer to theatre for examination under anaesthesia is made
+without delay where the cause is not controlled. A structured debrief with the woman and
+with the team is held within seventy-two hours.
+"""
+
+# --- Lakeside Surgical Centre — Theatre Operations Manual ---
+
+LAKESIDE_OVERVIEW = """# Lakeside Surgical Centre: Purpose and Services
+
+Lakeside Surgical Centre is a dedicated elective treatment centre with six operating
+theatres, two of which have laminar flow ventilation for joint replacement, a
+twenty-four bed day surgery unit and a thirty-two bed inpatient ward. The centre performs
+around 14,000 procedures a year and takes no emergency admissions, which is the single
+structural reason its cancellation rate stays below two per cent.
+
+Specialties operating at the centre are orthopaedics, general surgery, urology,
+gynaecology, ophthalmology and oral surgery. Anaesthetic services cover general, regional
+and local anaesthesia with sedation, supported by a four-bay recovery area staffed
+whenever a theatre is running.
+
+The centre's operating principle is separation: elective work is physically and
+organisationally separated from emergency work so that beds, staff and theatre time
+committed to a planned operation cannot be taken for another purpose. Every process in
+this manual exists to protect that separation or to protect the patient once inside it.
+
+Patients are admitted only after a completed pre-operative assessment, a documented
+anaesthetic review where indicated, and confirmation that the planned discharge
+arrangements are in place. Procedures are scheduled in lists built by the theatre
+scheduling team against published session times, with the order of the list set by the
+operating surgeon on clinical grounds — children, patients with diabetes and patients with
+learning disabilities are placed early unless there is a stated reason not to.
+
+The centre is registered with the regulator and inspected against the safe, effective,
+caring, responsive and well-led domains.
+"""
+
+LAKESIDE_BENEFITS = """# Theatre Staff Pay, Overtime and Benefits Policy
+
+Theatre practitioners at Lakeside are appointed at Band 5 on registration and progress to
+Band 6 on completion of the centre's specialist competency portfolio in either scrub,
+anaesthetics or recovery, which typically takes eighteen months. Theatre support workers
+are appointed at Band 2 and move to Band 3 on completion of their competency portfolio.
+Team leaders are Band 7 and the theatre manager Band 8a.
+
+Scheduled sessions run from 08:00 to 18:00 Monday to Friday, with a Saturday list rota
+operating three weekends in four. Saturday working attracts the standard enhancement;
+staff who work more than one Saturday in a four-week period receive a compensating rest day
+in the following period.
+
+Overlist running beyond the scheduled session is paid at the plain hourly rate for the
+first hour and at time and a half thereafter, and requires the theatre coordinator's
+authorisation before the list is extended. Unauthorised overruns are still paid, but are
+reported to the theatre manager as a scheduling exception so the underlying cause is
+addressed.
+
+Staff receive 27 days of annual leave plus public holidays, rising with service, and are
+required to take at least two weeks as a continuous block to support roster planning.
+Additional benefits include employer-funded professional registration fees, a fully funded
+place on one accredited course per person per year subject to service need, free on-site
+parking, subsidised meals when working an extended day, and access to the occupational
+health physiotherapy service without referral.
+"""
+
+LAKESIDE_SCRUB = """# Job Description — Theatre Scrub Practitioner
+
+**Band:** 5, progressing to 6  **Reports to:** Theatre Team Leader
+
+**Purpose of the role.** The scrub practitioner prepares for and assists at surgical
+procedures, maintaining the sterile field and accounting for every instrument, swab and
+sharp used during the operation. The postholder is accountable for the accuracy of the
+surgical count.
+
+**Principal duties.** Prepare the theatre and the instrument trolleys for each case on the
+list, checking instrument sets against the tray list and confirming the availability of
+implants, prostheses and specialist equipment before the patient is sent for. Participate
+fully in the team brief at the start of the list and in each of the three checklist
+moments for every patient.
+
+Perform the surgical scrub, gown and glove, and maintain the sterile field throughout,
+challenging any breach regardless of the seniority of the person who caused it.
+Anticipate and pass instruments, manage specimens and label them correctly at the point of
+removal, and handle sharps using a designated neutral zone.
+
+Carry out the count of swabs, needles, blades and instruments with a second registered
+practitioner before the procedure begins, at the closure of a cavity, and at the end of
+the operation, resolving any discrepancy before the patient leaves theatre.
+
+Support the safe positioning of the patient, the application of diathermy and tourniquets,
+and the documented checks each requires.
+
+**Person specification.** Registered Nurse or Operating Department Practitioner with
+current NMC or HCPC registration; completion of or willingness to complete the scrub
+competency portfolio; ability to participate in the Saturday list rota; meticulous
+attention to detail under time pressure.
+"""
+
+LAKESIDE_ODP = """# Job Description — Operating Department Practitioner (Anaesthetics)
+
+**Band:** 5, progressing to 6  **Reports to:** Anaesthetic Team Leader
+
+**Purpose of the role.** The anaesthetic ODP assists the anaesthetist through every phase
+of anaesthesia, prepares and checks the anaesthetic environment and equipment, and
+provides skilled support during induction, maintenance and emergence.
+
+**Principal duties.** Complete the full anaesthetic machine and equipment check before the
+first case of each list and record it, and confirm the availability and working order of
+the difficult airway trolley, suction, monitoring and warming devices. Prepare drugs with
+the anaesthetist, labelling every syringe at the moment of drawing up and applying the
+two-person check for controlled drugs.
+
+Receive the patient into the anaesthetic room, confirm identity, procedure, site and
+consent as part of the sign-in, and provide reassurance during induction. Assist with
+airway management including cricoid pressure and the passing of airway devices, with
+vascular access, with regional blocks, and with invasive monitoring insertion.
+
+Anticipate and respond to anaesthetic emergencies including failed intubation, anaphylaxis
+and malignant hyperthermia, knowing the location and contents of every emergency box in
+the department. Support safe transfer of the patient to the operating table and to
+recovery, handing over to the recovery practitioner using the agreed structure.
+
+Maintain stock in the anaesthetic room and report equipment faults through the asset
+system on the day they are found.
+
+**Person specification.** HCPC registration as an Operating Department Practitioner, or
+NMC registration with an anaesthetic nursing qualification; current adult and paediatric
+life support certification; ability to work at pace while remaining methodical.
+"""
+
+LAKESIDE_SUPPORT = """# Job Description — Theatre Support Worker
+
+**Band:** 2, progressing to Band 3  **Reports to:** Theatre Team Leader
+
+**Purpose of the role.** The theatre support worker keeps the operating department running
+by preparing rooms and equipment, moving patients safely, and supporting the registered
+team with delegated tasks that do not require registration.
+
+**Principal duties.** Prepare the theatre between cases: clean surfaces and equipment to
+the agreed standard, dispose of clinical waste and sharps correctly, restock consumables
+against the list held in each room, and confirm the room is ready to the practitioner in
+charge. Assist with the safe transfer and positioning of patients using the correct
+handling equipment, and apply patient warming and anti-embolism devices under direction.
+
+Collect patients from the ward with a registered member of staff, checking identity and
+the theatre checklist on collection, and return patients to recovery and the ward.
+Transport specimens to the laboratory promptly and record the transfer.
+
+Assist the scrub practitioner as a runner during the case, opening sterile packs onto the
+field without contaminating them, connecting suction and diathermy leads, and fetching
+additional instruments or implants when asked. Take used instrument sets to the
+decontamination hatch, complete the tracking record, and collect processed sets.
+
+**Person specification.** Good standard of literacy and numeracy; completion of the Care
+Certificate within twelve weeks; ability to stand for extended periods and to move
+equipment safely; understanding of asepsis sufficient to work near a sterile field;
+reliability and punctuality, as lists cannot start without the room being ready.
+"""
+
+LAKESIDE_TRAINING = """# Surgical Competency Sign-Off and Audit Cycle
+
+Nobody at Lakeside works unsupervised in a theatre role until the corresponding competency
+portfolio is signed. Portfolios exist for scrub, anaesthetic assistance, recovery, and
+support-worker duties, and each is structured as a set of observed practice records, a
+knowledge assessment and a reflective account. Sign-off requires two different assessors,
+at least one of whom must be a team leader.
+
+Speciality-specific sign-off sits on top of the core portfolio. A practitioner competent to
+scrub for general surgery is not thereby competent to scrub for arthroplasty; each
+specialty list has its own instrument, implant and positioning requirements, and the
+rostering system will not allocate a practitioner to a list for which they hold no
+sign-off. Competence lapses if a practitioner has not worked that specialty for six
+months, and is restored by three supervised cases.
+
+The department runs a rolling audit cycle with one topic per month. Fixed topics repeated
+annually are the surgical safety checklist, the swab and instrument count, surgical site
+infection surveillance, prophylactic antibiotic timing, venous thromboembolism assessment,
+specimen labelling accuracy and theatre start and turnaround times.
+
+Each audit is presented at the departmental governance meeting with an action plan naming
+an owner and a date, and every action is re-checked at the following meeting until closed.
+Where an audit shows compliance below 95 per cent on a safety-critical measure, a repeat
+audit is scheduled within eight weeks rather than the following year.
+"""
+
+LAKESIDE_CHECKLIST = """# WHO Surgical Safety Checklist and Count Procedure
+
+Every procedure carried out at Lakeside uses the five-part safety process: team brief
+before the list, sign-in before induction, time-out before incision, sign-out before the
+patient leaves theatre, and debrief at the end of the list. No part may be delegated to
+paperwork completed afterwards; each is a spoken exchange with the whole team stopped and
+attending.
+
+At the brief the list is read through case by case, naming each patient, the planned
+procedure and side, the anticipated duration, implants required, and any anticipated
+difficulty. Everyone present introduces themselves by name and role. At sign-in the
+patient confirms their identity, the procedure, the site and their consent, and allergies,
+airway risk, blood loss risk and anaesthetic equipment checks are confirmed aloud.
+
+Time-out happens with the patient draped and the surgeon present. The team confirms
+identity, procedure and marked site, the sterility of the instruments, prophylactic
+antibiotics given within the last sixty minutes, imaging displayed, and any critical step
+each discipline expects.
+
+The count of swabs, needles, blades, and separable instrument parts is performed by the
+scrub practitioner and a second registered practitioner: before the procedure, when any
+cavity is closed, and at the end. Counts are recorded on the theatre whiteboard as they
+occur. A discrepancy stops the closure: the field is searched, the bins and drapes are
+searched, and if the item is still unaccounted for an intra-operative radiograph is taken
+before the patient leaves the theatre.
+"""
+
+# --- Meridian Home Care Services — Employee Policy and Roles Manual ---
+
+MERIDIAN_OVERVIEW = """# Meridian Home Care: Company Overview
+
+Meridian Home Care Services is an independent domiciliary care provider supporting around
+620 people in their own homes across three neighbouring local authority areas. The company
+was founded in 2009, employs just over 300 care workers, and is registered with and
+inspected by the national care regulator.
+
+Meridian provides personal care, medication support, meal preparation, domestic support
+and companionship, ranging from a single thirty-minute call a day to a full package of
+four calls a day with waking or sleeping night cover. About sixty per cent of the work is
+commissioned by the three local authorities under framework contracts, and the remainder is
+arranged privately by individuals and families.
+
+The company's stated purpose is to make it possible for people to stay in their own homes
+for as long as that is what they want and it remains safe. The commitments that follow
+from that are: a small consistent team for each person supported, calls that arrive within
+a thirty-minute window of the agreed time, and a care plan written with the person rather
+than about them.
+
+Meridian operates from a central office with an on-call service covering every hour
+outside office time. Field supervision is provided by six care coordinators and three
+registered managers, each responsible for a geographic area. A digital care record is used
+at the point of care: every visit is checked in and out on the worker's phone, and every
+task and observation is recorded before the worker leaves.
+"""
+
+MERIDIAN_BENEFITS = """# Reward, Mileage and Employee Benefits
+
+Meridian pays care workers an hourly rate for all working time, including travel time
+between calls, which is calculated automatically by the scheduling system from the actual
+route rather than estimated. Rates are banded: standard rate for weekday daytime calls,
+an enhanced rate for evenings after 20:00 and for weekends, and a further enhancement for
+public holidays. Sleeping nights are paid as a flat allowance plus the hourly rate for any
+period the worker is woken and working.
+
+Mileage is paid at 45 pence per mile for the first 10,000 business miles in a tax year and
+25 pence thereafter, claimed automatically from the checked-in route and paid with the
+following month's salary. Public transport fares are reimbursed in full on production of a
+receipt. Business insurance for the worker's own vehicle is a condition of employment for
+roles requiring driving, and Meridian contributes a fixed annual sum towards its cost.
+
+Care workers accrue holiday at the statutory rate of 5.6 weeks pro-rated to hours worked,
+and are paid holiday at their average rate over the previous fifty-two weeks so that
+enhancements are properly reflected.
+
+Additional benefits include a workplace pension with a four per cent employer
+contribution, statutory sick pay from day one of absence with company sick pay after two
+years of service, a refer-a-friend payment of £250 paid after the new recruit completes
+six months, free enhanced disclosure checks, a paid day of leave on the employee's
+birthday, and access to a 24-hour counselling line.
+"""
+
+MERIDIAN_MANAGER = """# Job Description — Registered Care Manager
+
+**Reports to:** Operations Director  **Registration:** Named registered manager with the
+care regulator for the allocated area
+
+**Purpose of the role.** The registered care manager holds legal and regulatory
+responsibility for the quality and safety of the care delivered in their area, and leads
+the coordinators and care workers who deliver it.
+
+**Principal duties.** Ensure the service in the allocated area meets the fundamental
+standards at all times, and maintain the evidence that demonstrates it. Complete or
+delegate the initial assessment of every new person supported, agree the care plan and
+risk assessments with them and their family, and review each plan at least every six
+months or sooner if needs change.
+
+Lead the safeguarding response for the area: act as the point of referral for concerns
+raised by staff, make referrals to the local authority within agreed timescales,
+participate in strategy discussions, and complete internal investigations. Manage
+incidents, accidents and medication errors, ensuring each is recorded, investigated and
+learned from, and submit statutory notifications where the threshold is met.
+
+Recruit, induct, supervise and appraise the coordinators and care workers in the area,
+carrying out at least one observed practice check on every care worker each year and
+holding documented supervision quarterly. Manage capacity: accept or decline new packages
+against the area's actual staffing, and never accept a package that cannot be delivered
+consistently.
+
+**Person specification.** Level 5 Diploma in Leadership for Health and Social Care or
+equivalent; at least two years supervisory experience in a regulated care service; fitness
+to be registered with the regulator; full driving licence; strong understanding of
+safeguarding and mental capacity legislation.
+"""
+
+MERIDIAN_CARE_WORKER = """# Job Description — Community Care Worker
+
+**Reports to:** Care Coordinator  **Hours:** Zero-hours, part-time or full-time contracts
+available
+
+**Purpose of the role.** The community care worker delivers personal care and practical
+support to people in their own homes, following each person's care plan and treating their
+home, their routine and their preferences with respect.
+
+**Principal duties.** Deliver the calls allocated on the daily rota, checking in on arrival
+and out on leaving using the mobile application so that the office can see in real time
+that every person has been visited. Provide personal care including washing, showering,
+dressing, oral care, shaving, continence support and catheter or stoma care where trained,
+always in the manner recorded in the care plan.
+
+Support with medication according to the level recorded for that person — prompting,
+assisting or administering — signing the medication administration record at the time and
+never in advance. Report any refused, missed or wrongly available medicine to the office
+before leaving the property.
+
+Prepare meals and drinks, encourage adequate intake, and record what was eaten where
+monitoring is in place. Carry out agreed domestic tasks and light shopping. Support people
+to get to appointments, social activities and community groups where this is part of the
+package.
+
+Observe and report change: skin condition, mood, appetite, mobility, confusion, or
+anything about the home environment that appears unsafe. Complete the digital care record
+before leaving each call.
+
+**Person specification.** No prior experience required; completion of the Care Certificate
+within twelve weeks; ability to work to a rota including alternate weekends; driving
+licence and business insurance for rural rounds; patience, reliability and discretion.
+"""
+
+MERIDIAN_COORDINATOR = """# Job Description — Care Coordinator (Scheduling)
+
+**Reports to:** Registered Care Manager  **Base:** Central office with field visits
+
+**Purpose of the role.** The care coordinator builds and maintains the rota for their
+area, matching care workers to the people they support so that continuity, travel time and
+contracted hours all work — and holds the front line of the office's contact with workers
+and families.
+
+**Principal duties.** Produce the area rota at least one week in advance, allocating calls
+to the smallest practical team for each person supported and building in realistic travel
+time between addresses. Adjust the live rota through the day for sickness, traffic and
+changes in need, ensuring no call is missed and that any call running more than thirty
+minutes late is communicated to the person or their family.
+
+Act as first point of contact for care workers on shift, answering questions about tasks
+and equipment, and escalating clinical or safeguarding concerns to the registered manager
+immediately rather than resolving them alone. Carry a share of the out-of-hours on-call
+phone on a published rota.
+
+Complete introductory visits with new care workers so that the person supported meets
+their worker before the first solo call. Monitor call timing and duration data weekly and
+report exceptions. Maintain accurate records of worker availability, training expiry dates
+and vehicle documents, and stop allocation to any worker whose mandatory training or
+disclosure check has lapsed.
+
+**Person specification.** Experience in a scheduling, dispatch or coordination role;
+confident with rostering software and spreadsheets; excellent telephone manner under
+pressure; understanding of domiciliary care; willingness to undertake occasional care
+calls to cover emergencies.
+"""
+
+MERIDIAN_TRAINING = """# Care Certificate, Shadowing and Refresher Training Requirements
+
+No care worker at Meridian attends a call alone until three conditions are met: the
+five-day classroom induction is complete, a minimum of four shadowing shifts have been
+signed off by an experienced worker, and an observed practice check has been carried out
+by a coordinator or registered manager.
+
+The classroom induction covers the fifteen standards of the Care Certificate at
+introductory level, moving and handling with practical assessment, basic life support,
+safeguarding adults, medication support, infection prevention, food hygiene, and the
+digital care record. The full Care Certificate portfolio must then be completed within
+twelve weeks of the start date; failure to complete it without an agreed extension stops
+further shift allocation.
+
+Shadowing shifts are arranged in the geographic area the worker will actually cover and
+include at least one double-handed call and one medication call. The experienced worker
+records what the new worker did and did not do, and any gap identified is addressed before
+sign-off rather than noted for later.
+
+Refresher training runs on a fixed cycle: moving and handling, basic life support,
+safeguarding, medication and infection prevention annually; food hygiene, fire safety and
+mental capacity every two years. Specialist competencies — catheter care, stoma care,
+percutaneous feeding, end-of-life care and epilepsy rescue medication — are trained by the
+clinical lead and reassessed annually against a practical checklist. The scheduling system
+blocks allocation to any call requiring a competency the worker does not currently hold.
+"""
+
+MERIDIAN_CARE_PLANNING = """# Person-Centred Care Planning and Review
+
+Every person supported by Meridian has a care plan written with them, in their own words
+wherever possible, before the first call takes place. The plan is produced from an
+assessment visit carried out by a registered manager or a senior coordinator, and covers
+what the person can do independently, what they want help with, how they want that help
+given, and what matters to them beyond the tasks.
+
+The plan is structured by call rather than by task list, so a worker arriving for the
+morning call reads what that call involves, in what order, and what the person's
+preferences are — which side they prefer to be assisted from, how they take their tea, what
+they want to be called. Risk assessments sit alongside the plan and cover moving and
+handling, the home environment, medication, nutrition, skin integrity and lone working.
+
+A copy of the plan is held in the home and the same content is available in the digital
+care record on the worker's phone; the digital version is the master and any handwritten
+change must be entered in the system the same day.
+
+Plans are reviewed at six weeks after the package starts and at least every six months
+thereafter, with the person and, where they wish, a family member. A review is also
+triggered by any hospital admission, fall, safeguarding concern, medication error, or a
+request from the person or a worker. Every review records what changed, what stayed the
+same, and who agreed it.
+"""
+
+# --- corpus layout ---
+# Each section is (title, level, hierarchy_path, page_start, page_end, section_type, markdown).
+# Groups carry no body and no type: the wiki generator gives them a Contents rollup, and
+# leaving them untyped keeps a `section_type` filter returning exactly the leaf sections.
+
+GROUP = None
+
+DOCUMENTS: list[dict] = [
+	{
+		"title": "Northfield Hospital Nursing Manual",
+		"page_count": 48,
+		"sections": [
+			("Governance and Pay", 1, ["Governance and Pay"], 1, 12, GROUP, ""),
+			(
+				"Hospital Overview and Mission",
+				2,
+				["Governance and Pay", "Hospital Overview and Mission"],
+				2,
+				5,
+				"other",
+				NORTHFIELD_OVERVIEW,
+			),
+			(
+				"Pay Bands, Benefits and Leave Entitlements",
+				2,
+				["Governance and Pay", "Pay Bands, Benefits and Leave Entitlements"],
+				6,
+				12,
+				"administrative_policies",
+				NORTHFIELD_BENEFITS,
+			),
+			("Roles and Responsibilities", 1, ["Roles and Responsibilities"], 13, 30, GROUP, ""),
+			(
+				"Job Description — Ward Sister / Charge Nurse",
+				2,
+				["Roles and Responsibilities", "Job Description — Ward Sister / Charge Nurse"],
+				13,
+				18,
+				"staff_roles_and_responsibilities",
+				NORTHFIELD_WARD_SISTER,
+			),
+			(
+				"Job Description — Staff Nurse (Band 5)",
+				2,
+				["Roles and Responsibilities", "Job Description — Staff Nurse (Band 5)"],
+				19,
+				24,
+				"staff_roles_and_responsibilities",
+				NORTHFIELD_STAFF_NURSE,
+			),
+			(
+				"Job Description — Healthcare Assistant",
+				2,
+				["Roles and Responsibilities", "Job Description — Healthcare Assistant"],
+				25,
+				30,
+				"staff_roles_and_responsibilities",
+				NORTHFIELD_HCA,
+			),
+			("Practice Standards", 1, ["Practice Standards"], 31, 48, GROUP, ""),
+			(
+				"Recruitment, Qualifications and Mandatory Training",
+				2,
+				["Practice Standards", "Recruitment, Qualifications and Mandatory Training"],
+				31,
+				37,
+				"training_and_audits",
+				NORTHFIELD_TRAINING,
+			),
+			(
+				"Pressure Ulcer Prevention Protocol",
+				2,
+				["Practice Standards", "Pressure Ulcer Prevention Protocol"],
+				38,
+				48,
+				"clinical_protocols",
+				NORTHFIELD_PRESSURE,
+			),
+		],
+	},
+	{
+		"title": "Riverside Clinic Staff Handbook",
+		"page_count": 40,
+		"sections": [
+			("Governance and Pay", 1, ["Governance and Pay"], 1, 10, GROUP, ""),
+			(
+				"Who We Are: Riverside Community Clinic",
+				2,
+				["Governance and Pay", "Who We Are: Riverside Community Clinic"],
+				2,
+				5,
+				"other",
+				RIVERSIDE_OVERVIEW,
+			),
+			(
+				"Salary Scales, Pension and Employee Benefits",
+				2,
+				["Governance and Pay", "Salary Scales, Pension and Employee Benefits"],
+				6,
+				10,
+				"administrative_policies",
+				RIVERSIDE_BENEFITS,
+			),
+			("Roles and Responsibilities", 1, ["Roles and Responsibilities"], 11, 28, GROUP, ""),
+			(
+				"Job Description — Practice Manager",
+				2,
+				["Roles and Responsibilities", "Job Description — Practice Manager"],
+				11,
+				16,
+				"staff_roles_and_responsibilities",
+				RIVERSIDE_PRACTICE_MANAGER,
+			),
+			(
+				"Job Description — General Practitioner (Salaried)",
+				2,
+				["Roles and Responsibilities", "Job Description — General Practitioner (Salaried)"],
+				17,
+				22,
+				"staff_roles_and_responsibilities",
+				RIVERSIDE_GP,
+			),
+			(
+				"Job Description — Medical Receptionist",
+				2,
+				["Roles and Responsibilities", "Job Description — Medical Receptionist"],
+				23,
+				28,
+				"staff_roles_and_responsibilities",
+				RIVERSIDE_RECEPTIONIST,
+			),
+			("Practice Standards", 1, ["Practice Standards"], 29, 40, GROUP, ""),
+			(
+				"Induction, Competency Framework and Annual Appraisal",
+				2,
+				["Practice Standards", "Induction, Competency Framework and Annual Appraisal"],
+				29,
+				34,
+				"training_and_audits",
+				RIVERSIDE_TRAINING,
+			),
+			(
+				"Repeat Prescribing and Medicines Reconciliation",
+				2,
+				["Practice Standards", "Repeat Prescribing and Medicines Reconciliation"],
+				35,
+				40,
+				"medication_management",
+				RIVERSIDE_PRESCRIBING,
+			),
+		],
+	},
+	{
+		"title": "St Aubyn Maternity Policy Manual",
+		"page_count": 44,
+		"sections": [
+			("Unit Administration", 1, ["Unit Administration"], 1, 11, GROUP, ""),
+			(
+				"St Aubyn Maternity Unit at a Glance",
+				2,
+				["Unit Administration", "St Aubyn Maternity Unit at a Glance"],
+				2,
+				6,
+				"other",
+				AUBYN_OVERVIEW,
+			),
+			(
+				"Rostering, On-Call Payments and Maternity Benefits",
+				2,
+				["Unit Administration", "Rostering, On-Call Payments and Maternity Benefits"],
+				7,
+				11,
+				"administrative_policies",
+				AUBYN_BENEFITS,
+			),
+			("Roles and Responsibilities", 1, ["Roles and Responsibilities"], 12, 30, GROUP, ""),
+			(
+				"Job Description — Consultant Obstetrician",
+				2,
+				["Roles and Responsibilities", "Job Description — Consultant Obstetrician"],
+				12,
+				18,
+				"staff_roles_and_responsibilities",
+				AUBYN_CONSULTANT,
+			),
+			(
+				"Job Description — Community Midwife",
+				2,
+				["Roles and Responsibilities", "Job Description — Community Midwife"],
+				19,
+				24,
+				"staff_roles_and_responsibilities",
+				AUBYN_COMMUNITY_MIDWIFE,
+			),
+			(
+				"Job Description — Maternity Support Worker",
+				2,
+				["Roles and Responsibilities", "Job Description — Maternity Support Worker"],
+				25,
+				30,
+				"staff_roles_and_responsibilities",
+				AUBYN_SUPPORT_WORKER,
+			),
+			("Clinical Standards", 1, ["Clinical Standards"], 31, 44, GROUP, ""),
+			(
+				"Midwifery Preceptorship and Skills Drills Programme",
+				2,
+				["Clinical Standards", "Midwifery Preceptorship and Skills Drills Programme"],
+				31,
+				36,
+				"training_and_audits",
+				AUBYN_TRAINING,
+			),
+			(
+				"Obstetric Haemorrhage: Emergency Response",
+				2,
+				["Clinical Standards", "Obstetric Haemorrhage: Emergency Response"],
+				37,
+				44,
+				"emergency_procedures",
+				AUBYN_HAEMORRHAGE,
+			),
+		],
+	},
+	{
+		"title": "Lakeside Surgical Theatre Manual",
+		"page_count": 52,
+		"sections": [
+			(
+				"Centre Administration",
+				1,
+				["Centre Administration"],
+				1,
+				12,
+				GROUP,
+				"",
+			),
+			(
+				"Lakeside Surgical Centre: Purpose and Services",
+				2,
+				["Centre Administration", "Lakeside Surgical Centre: Purpose and Services"],
+				2,
+				6,
+				"other",
+				LAKESIDE_OVERVIEW,
+			),
+			(
+				"Theatre Staff Pay, Overtime and Benefits Policy",
+				2,
+				["Centre Administration", "Theatre Staff Pay, Overtime and Benefits Policy"],
+				7,
+				12,
+				"administrative_policies",
+				LAKESIDE_BENEFITS,
+			),
+			("Roles and Responsibilities", 1, ["Roles and Responsibilities"], 13, 34, GROUP, ""),
+			(
+				"Job Description — Theatre Scrub Practitioner",
+				2,
+				["Roles and Responsibilities", "Job Description — Theatre Scrub Practitioner"],
+				13,
+				19,
+				"staff_roles_and_responsibilities",
+				LAKESIDE_SCRUB,
+			),
+			(
+				"Job Description — Operating Department Practitioner (Anaesthetics)",
+				2,
+				[
+					"Roles and Responsibilities",
+					"Job Description — Operating Department Practitioner (Anaesthetics)",
+				],
+				20,
+				27,
+				"staff_roles_and_responsibilities",
+				LAKESIDE_ODP,
+			),
+			(
+				"Job Description — Theatre Support Worker",
+				2,
+				["Roles and Responsibilities", "Job Description — Theatre Support Worker"],
+				28,
+				34,
+				"staff_roles_and_responsibilities",
+				LAKESIDE_SUPPORT,
+			),
+			("Operational Standards", 1, ["Operational Standards"], 35, 52, GROUP, ""),
+			(
+				"Surgical Competency Sign-Off and Audit Cycle",
+				2,
+				["Operational Standards", "Surgical Competency Sign-Off and Audit Cycle"],
+				35,
+				42,
+				"training_and_audits",
+				LAKESIDE_TRAINING,
+			),
+			(
+				"WHO Surgical Safety Checklist and Count Procedure",
+				2,
+				["Operational Standards", "WHO Surgical Safety Checklist and Count Procedure"],
+				43,
+				52,
+				"surgical_procedures",
+				LAKESIDE_CHECKLIST,
+			),
+		],
+	},
+	{
+		"title": "Meridian Home Care Employee Manual",
+		"page_count": 38,
+		"sections": [
+			("Company Administration", 1, ["Company Administration"], 1, 10, GROUP, ""),
+			(
+				"Meridian Home Care: Company Overview",
+				2,
+				["Company Administration", "Meridian Home Care: Company Overview"],
+				2,
+				5,
+				"other",
+				MERIDIAN_OVERVIEW,
+			),
+			(
+				"Reward, Mileage and Employee Benefits",
+				2,
+				["Company Administration", "Reward, Mileage and Employee Benefits"],
+				6,
+				10,
+				"administrative_policies",
+				MERIDIAN_BENEFITS,
+			),
+			("Roles and Responsibilities", 1, ["Roles and Responsibilities"], 11, 28, GROUP, ""),
+			(
+				"Job Description — Registered Care Manager",
+				2,
+				["Roles and Responsibilities", "Job Description — Registered Care Manager"],
+				11,
+				17,
+				"staff_roles_and_responsibilities",
+				MERIDIAN_MANAGER,
+			),
+			(
+				"Job Description — Community Care Worker",
+				2,
+				["Roles and Responsibilities", "Job Description — Community Care Worker"],
+				18,
+				23,
+				"staff_roles_and_responsibilities",
+				MERIDIAN_CARE_WORKER,
+			),
+			(
+				"Job Description — Care Coordinator (Scheduling)",
+				2,
+				["Roles and Responsibilities", "Job Description — Care Coordinator (Scheduling)"],
+				24,
+				28,
+				"staff_roles_and_responsibilities",
+				MERIDIAN_COORDINATOR,
+			),
+			("Service Standards", 1, ["Service Standards"], 29, 38, GROUP, ""),
+			(
+				"Care Certificate, Shadowing and Refresher Training Requirements",
+				2,
+				["Service Standards", "Care Certificate, Shadowing and Refresher Training Requirements"],
+				29,
+				33,
+				"training_and_audits",
+				MERIDIAN_TRAINING,
+			),
+			(
+				"Person-Centred Care Planning and Review",
+				2,
+				["Service Standards", "Person-Centred Care Planning and Review"],
+				34,
+				38,
+				"patient_management",
+				MERIDIAN_CARE_PLANNING,
+			),
+		],
+	},
+]
+
+
+def build_sections(spec: dict) -> list[Section]:
+	"""Turn a document spec's tuples into the `Section` objects `store.replace_sections` eats."""
+	return [
+		Section(
+			title=title,
+			level=level,
+			hierarchy_path=list(path),
+			page_start=page_start,
+			page_end=page_end,
+			markdown=markdown.strip(),
+			section_type=section_type,
+		)
+		for title, level, path, page_start, page_end, section_type, markdown in spec["sections"]
+	]
+
+
+def get_or_create_project() -> str:
+	"""Get-or-create the demo project, keyed on `project_name`. Returns its name."""
+	existing = frappe.db.get_value("Wikify Project", {"project_name": PROJECT_NAME}, "name")
+	if existing:
+		return existing
+	return (
+		frappe.get_doc(
+			{
+				"doctype": "Wikify Project",
+				"project_name": PROJECT_NAME,
+				"description": "Five healthcare manuals with overlapping section types — the RAG POC corpus.",
+			}
+		)
+		.insert(ignore_permissions=True)
+		.name
+	)
+
+
+def get_or_create_space() -> str:
+	"""Get-or-create the demo Wiki Space, keyed on its route. Returns its name."""
+	existing = frappe.db.get_value("Wiki Space", {"route": SPACE_ROUTE}, "name")
+	if existing:
+		return existing
+	space = frappe.new_doc("Wiki Space")
+	space.space_name = SPACE_NAME
+	space.route = SPACE_ROUTE
+	space.is_published = 1
+	space.insert(ignore_permissions=True)
+	return space.name
+
+
+def get_or_create_document(spec: dict, project: str) -> str:
+	"""Get-or-create a Source Document by title within the demo project."""
+	existing = frappe.db.get_value("Source Document", {"title": spec["title"], "project": project}, "name")
+	if existing:
+		frappe.db.set_value("Source Document", existing, "page_count", spec["page_count"])
+		return existing
+	source_document = store.create_document(spec["title"], project=project)
+	store.set_page_count(source_document, spec["page_count"])
+	return source_document
+
+
+def sections_match(source_document: str, sections: list[Section]) -> bool:
+	"""True when the stored tree already equals the spec — lets a re-run skip the rebuild
+	(which would otherwise drop and recreate every wiki page)."""
+	stored = frappe.get_all(
+		"Source Section",
+		filters={"source_document": source_document},
+		fields=["title", "section_type", "page_start", "page_end", "hierarchy_path", "markdown"],
+		order_by="lft asc",
+	)
+	if len(stored) != len(sections):
+		return False
+	for row, section in zip(stored, sections, strict=True):
+		if (
+			row.title != section.title
+			or (row.section_type or None) != section.section_type
+			or row.page_start != section.page_start
+			or row.page_end != section.page_end
+			or (row.hierarchy_path or "") != " > ".join(section.hierarchy_path)
+			or (row.markdown or "") != section.markdown
+		):
+			return False
+	return True
+
+
+def prune_stale_documents(project: str) -> list[str]:
+	"""Drop demo-project documents no longer in the spec (a retitled document would
+	otherwise be left behind as a duplicate), taking their wiki subtree with them."""
+	wanted = {spec["title"] for spec in DOCUMENTS}
+	stale = [
+		row
+		for row in frappe.get_all(
+			"Source Document", filters={"project": project}, fields=["name", "title", "wiki_root_group"]
+		)
+		if row.title not in wanted
+	]
+	for row in stale:
+		if row.wiki_root_group and frappe.db.exists("Wiki Document", row.wiki_root_group):
+			# NestedSet needs leaves gone first, hence deepest (largest lft) first.
+			descendants = get_descendants_of("Wiki Document", row.wiki_root_group, ignore_permissions=True)
+			for name in frappe.get_all(
+				"Wiki Document", filters={"name": ["in", descendants]}, order_by="lft desc", pluck="name"
+			):
+				frappe.delete_doc("Wiki Document", name, ignore_permissions=True, force=True)
+			frappe.delete_doc("Wiki Document", row.wiki_root_group, ignore_permissions=True, force=True)
+		frappe.db.delete("Source Section", {"source_document": row.name})
+		frappe.delete_doc("Source Document", row.name, ignore_permissions=True, force=True)
+	return [row.title for row in stale]
+
+
+def seed_demo_corpus() -> dict:
+	"""Create (or refresh) the five-document demo corpus and project it into a Wiki Space.
+
+	Idempotent: documents are matched by title within the demo project, the section tree is
+	rebuilt only when it drifts from the spec, and wiki generation upserts pages against
+	each section's `wiki_document`. Returns the inventory.
+	"""
+	seed_section_types()
+	project = get_or_create_project()
+	space = get_or_create_space()
+	prune_stale_documents(project)
+
+	for spec in DOCUMENTS:
+		source_document = get_or_create_document(spec, project)
+		sections = build_sections(spec)
+		if not sections_match(source_document, sections):
+			store.replace_sections(source_document, sections)
+		generate_wiki(source_document, wiki_space=space)
+
+	frappe.db.commit()
+	return inventory()
+
+
+def section_routes() -> list[dict]:
+	"""(document, section, section_type, wiki route) for every demo section — the raw
+	material for the golden-question expected-source lists."""
+	project = frappe.db.get_value("Wikify Project", {"project_name": PROJECT_NAME}, "name")
+	documents = frappe.get_all(
+		"Source Document", filters={"project": project}, fields=["name", "title"], order_by="title asc"
+	)
+	title_of = {row.name: row.title for row in documents}
+	sections = frappe.get_all(
+		"Source Section",
+		filters={"source_document": ["in", list(title_of)], "is_group": 0},
+		fields=[
+			"name",
+			"source_document",
+			"title",
+			"section_type",
+			"page_start",
+			"page_end",
+			"wiki_document",
+		],
+		order_by="source_document asc, lft asc",
+	)
+	routes = {
+		row.name: row.route
+		for row in frappe.get_all(
+			"Wiki Document",
+			filters={"name": ["in", [s.wiki_document for s in sections if s.wiki_document]]},
+			fields=["name", "route"],
+		)
+	}
+	rows = [
+		{
+			"document": title_of[section.source_document],
+			"section": section.title,
+			"section_type": section.section_type,
+			"pages": f"{section.page_start}-{section.page_end}",
+			"route": f"/{routes.get(section.wiki_document, '')}",
+		}
+		for section in sections
+	]
+	for row in rows:
+		print(f"{row['section_type'] or '-':34} {row['route']}")
+	return rows
+
+
+def inventory() -> dict:
+	"""Print and return the corpus counts — the evidence the demo data is really there."""
+	project = frappe.db.get_value("Wikify Project", {"project_name": PROJECT_NAME}, "name")
+	documents = frappe.get_all(
+		"Source Document",
+		filters={"project": project} if project else {},
+		fields=["name", "title", "page_count", "status", "wiki_space"],
+		order_by="title asc",
+	)
+	sections = frappe.get_all(
+		"Source Section",
+		filters={"source_document": ["in", [d.name for d in documents]]} if documents else {"name": ""},
+		fields=["name", "title", "section_type", "is_group", "include_in_wiki", "wiki_document"],
+	)
+	by_type: dict[str, int] = {}
+	for section in sections:
+		key = section.section_type or ("(group)" if section.is_group else "(untyped)")
+		by_type[key] = by_type.get(key, 0) + 1
+
+	space_route = frappe.db.get_value("Wiki Space", {"route": SPACE_ROUTE}, "route")
+	result = {
+		"project": project,
+		"documents": len(documents),
+		"sections": len(sections),
+		"sections_by_type": dict(sorted(by_type.items(), key=lambda item: -item[1])),
+		"wiki_documents": len({s.wiki_document for s in sections if s.wiki_document}),
+		"included_in_wiki": sum(1 for s in sections if s.include_in_wiki),
+		"space_route": f"/{space_route}" if space_route else None,
+		"titles": [d.title for d in documents],
+		"totals_site_wide": {
+			"Source Document": frappe.db.count("Source Document"),
+			"Source Section": frappe.db.count("Source Section"),
+			"Wiki Document": frappe.db.count("Wiki Document"),
+		},
+	}
+	print(frappe.as_json(result, indent=2))
+	return result
diff --git a/wikify/tests/fixtures/icai_page_11_canonical.md b/wikify/tests/fixtures/icai_page_11_canonical.md
new file mode 100644
index 0000000..820e463
--- /dev/null
+++ b/wikify/tests/fixtures/icai_page_11_canonical.md
@@ -0,0 +1,35 @@
+## II. SURCHARGE
+
+Income-tax computed above would be increased by surcharge given under the following table:
+
+**Individual/HUF/AoP<sup>1</sup>/BoI and Artificial Juridical Person**
+
+```mermaid
+flowchart TD
+    A["Individual/HUF/AoP/BoI and Artificial Juridical Person"] --> B["paying tax under default tax regime u/s 115BAC"]
+    A --> C["exercising the option to shift out of the default tax regime and paying tax under the normal provisions of the Act"]
+```
+
+### paying tax under default tax regime u/s 115BAC
+
+| Particulars                                                                                                                                                                                                 | Rate of surcharge on income-tax |
+|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|
+| (i) TI (including dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 50 lakhs but ≤ ₹ 1 crore                                                                                              | 10%                             |
+| (ii) TI (including dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 1 crore but ≤ ₹ 2 crore                                                                                              | 15%                             |
+| (iii) TI (excluding dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 2 crore                                                                                                               | 25%                             |
+| Dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A                                                                                                                                   | Not exceeding 15%               |
+| (iv) TI (including dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 2 crore in cases not covered under (iii) above                                                                       | 15%                             |
+
+### exercising the option to shift out of the default tax regime and paying tax under the normal provisions of the Act
+
+| Particulars                                                                                                                                                                                                 | Rate of surcharge on income-tax |
+|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|---------------------------------|
+| (i) TI (including dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 50 lakhs but ≤ ₹ 1 crore                                                                                              | 10%                             |
+| (ii) TI (including dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 1 crore but ≤ ₹ 2 crore                                                                                              | 15%                             |
+| (iii) TI (excluding dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 2 crore but ≤ ₹ 5 crore                                                                                              | 25%                             |
+| Dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A                                                                                                                                   | Not exceeding 15%               |
+| (iv) TI (excluding dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 5 crore                                                                                                                 | 37%                             |
+| Dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A                                                                                                                                   | Not exceeding 15%               |
+| (v) TI (including dividend income and capital gains chargeable to tax u/s 111A, 112 and 112A) > ₹ 2 crore in cases not covered under (iii) and (iv) above                                                                 | 15%                             |
+
+<sup>1</sup> (other than an AOP consisting of only companies as members)
\ No newline at end of file
diff --git a/wikify/tests/test_ask_history.py b/wikify/tests/test_ask_history.py
new file mode 100644
index 0000000..7014a89
--- /dev/null
+++ b/wikify/tests/test_ask_history.py
@@ -0,0 +1,273 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""Ask conversation persistence + per-ask cost.
+
+Three things are worth asserting here: that one ask lands as a question/answer pair a
+later eval query can rejoin on `turn`, that the cost figure is the sum of all three LLM
+legs rather than synthesis alone, and that one user's conversation is invisible to
+another — which is enforced by the DocType's `if_owner` permission, not by a hand-rolled
+filter, so it has to be exercised as a real second user.
+"""
+
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.api import ask_history as api_ask_history
+from wikify.engine import llm as engine_llm
+from wikify.engine.store import cost_of
+from wikify.rag import history
+
+OTHER_USER = "ask-history-other@example.com"
+
+ROUTE_METRIC = {"label": "rag_route", "cost": 0.000021, "prompt_tokens": 900, "completion_tokens": 60}
+RERANK_METRIC = {"label": "rag_rerank", "cost": 0.000044, "prompt_tokens": 1800, "completion_tokens": 40}
+SYNTHESIS_METRIC = {
+	"label": "synthesis",
+	"cost": 0.004120,
+	"prompt_tokens": 5200,
+	"completion_tokens": 610,
+}
+ALL_LEGS = [ROUTE_METRIC, RERANK_METRIC, SYNTHESIS_METRIC]
+
+
+def make_project(label: str = "Ask History") -> str:
+	"""A uniquely-named project — `project_name` is unique and tests share a database."""
+	return (
+		frappe.get_doc(
+			{"doctype": "Wikify Project", "project_name": f"{label} {frappe.generate_hash(length=6)}"}
+		)
+		.insert()
+		.name
+	)
+
+
+def make_result(
+	answer: str = "There are five job descriptions [1].",
+	*,
+	intent: str = "exhaustive",
+	section_type: str | None = None,
+	refused: bool = False,
+	citations: list | None = None,
+) -> dict:
+	"""The dict `rag.answer.answer()` returns."""
+	return {
+		"answer": answer,
+		"citations": []
+		if refused
+		else (citations or [{"chunk_id": "sec-a::0", "title": "Backend Engineer"}]),
+		"route": {
+			"intent": intent,
+			"section_type": section_type,
+			"query": "list every job description",
+			"reason": "You asked for all of a kind of section.",
+		},
+		"refused": refused,
+	}
+
+
+def make_user(email: str) -> str:
+	if not frappe.db.exists("User", email):
+		user = frappe.get_doc(
+			{"doctype": "User", "email": email, "first_name": "Ask Tester", "send_welcome_email": 0}
+		).insert(ignore_permissions=True)
+		user.add_roles("Wiki Approver")
+	return email
+
+
+class TestAskHistoryTurns(FrappeTestCase):
+	def setUp(self):
+		self.project = make_project()
+
+	def test_first_turn_opens_a_titled_conversation(self):
+		session = history.record_turn(
+			None, "List every job description", make_result(), project=self.project, llm_metrics=ALL_LEGS
+		)
+		conversation = frappe.get_doc("Wikify Ask Session", session)
+		self.assertEqual(conversation.title, "List every job description")
+		self.assertEqual(conversation.project, self.project)
+		self.assertEqual(conversation.user, frappe.session.user)
+		self.assertEqual(conversation.message_count, 2)
+		self.assertIsNotNone(conversation.started_at)
+
+	def test_turn_number_pairs_the_question_with_its_answer(self):
+		session = history.record_turn(None, "First question", make_result(), llm_metrics=ALL_LEGS)
+		history.record_turn(session, "Second question", make_result(), llm_metrics=ALL_LEGS)
+
+		rows = frappe.get_all(
+			"Wikify Ask Message",
+			filters={"session": session},
+			fields=["role", "turn", "content"],
+			order_by="creation asc",
+		)
+		self.assertEqual(
+			[(row.role, row.turn) for row in rows],
+			[("question", 1), ("answer", 1), ("question", 2), ("answer", 2)],
+		)
+		self.assertEqual(rows[2].content, "Second question")
+
+	def test_answer_row_keeps_the_route_and_the_citations_it_stood_on(self):
+		citations = [{"chunk_id": "sec-a::0", "title": "Backend Engineer", "wiki_route": "/demo/roles"}]
+		session = history.record_turn(
+			None, "List every job description", make_result(citations=citations), llm_metrics=ALL_LEGS
+		)
+
+		answer_row = history.get_session(session)["messages"][1]
+		self.assertEqual(answer_row["route_intent"], "exhaustive")
+		self.assertEqual(answer_row["route_reason"], "You asked for all of a kind of section.")
+		self.assertEqual(answer_row["citations"], citations)
+		self.assertEqual(answer_row["refused"], 0)
+
+	def test_unknown_section_type_is_dropped_not_raised(self):
+		"""A type deleted since routing must not cost a turn that was already answered."""
+		session = history.record_turn(
+			None, "Which roles?", make_result(section_type="no_such_type_here"), llm_metrics=ALL_LEGS
+		)
+		self.assertIsNone(history.get_session(session)["messages"][1]["route_section_type"])
+
+	def test_refused_turn_is_logged_with_no_citations(self):
+		session = history.record_turn(
+			None, "Who won the cup?", make_result("I couldn't find this.", refused=True), llm_metrics=[]
+		)
+		answer_row = history.get_session(session)["messages"][1]
+		self.assertEqual(answer_row["refused"], 1)
+		self.assertEqual(answer_row["citations"], [])
+
+	def test_a_stale_session_id_opens_a_fresh_conversation(self):
+		"""ask() may still hold the id of a conversation the user has since deleted."""
+		session = history.record_turn(
+			"ASK-2026-99999", "List every job description", make_result(), llm_metrics=ALL_LEGS
+		)
+		self.assertNotEqual(session, "ASK-2026-99999")
+		self.assertTrue(frappe.db.exists("Wikify Ask Session", session))
+
+	def test_recent_turns_replay_as_router_history(self):
+		session = history.record_turn(None, "First question", make_result("First answer."), llm_metrics=[])
+		history.record_turn(session, "Second question", make_result("Second answer."), llm_metrics=[])
+
+		self.assertEqual(
+			history.recent_turns(session),
+			[
+				{"role": "user", "content": "First question"},
+				{"role": "assistant", "content": "First answer."},
+				{"role": "user", "content": "Second question"},
+				{"role": "assistant", "content": "Second answer."},
+			],
+		)
+
+	def test_deleting_a_conversation_removes_its_turns(self):
+		session = history.record_turn(None, "List every job description", make_result(), llm_metrics=ALL_LEGS)
+		history.delete_session(session)
+		self.assertEqual(frappe.db.count("Wikify Ask Message", {"session": session}), 0)
+
+
+class TestAskHistoryCost(FrappeTestCase):
+	def test_turn_metrics_sums_routing_rerank_and_synthesis(self):
+		"""The user wants cost per ask, so all three legs land in one figure."""
+		engine_calls: list[dict] = []
+		with patch.object(engine_llm, "get_metrics", side_effect=lambda: list(engine_calls)):
+			history.start_turn()
+			engine_calls.extend([ROUTE_METRIC, RERANK_METRIC])
+			history.record_litellm_metrics(
+				{"model": "claude-sonnet-4.6", "response_cost": SYNTHESIS_METRIC["cost"]},
+				SimpleNamespace(usage=SimpleNamespace(prompt_tokens=5200, completion_tokens=610)),
+				None,
+				None,
+			)
+			metrics = history.turn_metrics()
+
+		self.assertEqual([entry.get("label") for entry in metrics], ["rag_route", "rag_rerank", "synthesis"])
+		self.assertAlmostEqual(cost_of(metrics), 0.004185)
+		self.assertEqual(history.tokens_of(metrics), (7900, 710))
+
+	def test_watermark_excludes_a_previous_ask(self):
+		engine_calls = [ROUTE_METRIC]
+		with patch.object(engine_llm, "get_metrics", side_effect=lambda: list(engine_calls)):
+			history.start_turn()
+			engine_calls.append(RERANK_METRIC)
+			history.record_litellm_metrics({"response_cost": 0.001}, SimpleNamespace(usage=None), None, None)
+			metrics = history.turn_metrics()
+
+		self.assertEqual([entry.get("label") for entry in metrics], ["rag_rerank", "synthesis"])
+
+	def test_cost_lands_on_the_answer_row_and_rolls_up(self):
+		session = history.record_turn(None, "First question", make_result(), llm_metrics=ALL_LEGS)
+		history.record_turn(session, "Second question", make_result(), llm_metrics=[ROUTE_METRIC])
+
+		question_row, answer_row = history.get_session(session)["messages"][:2]
+		self.assertEqual(question_row["cost"], 0)
+		self.assertAlmostEqual(answer_row["cost"], 0.004185)
+		self.assertEqual((answer_row["prompt_tokens"], answer_row["completion_tokens"]), (7900, 710))
+
+		conversation = frappe.get_doc("Wikify Ask Session", session)
+		self.assertAlmostEqual(conversation.total_cost, 0.004206)
+		self.assertEqual(conversation.total_tokens, 9570)
+		self.assertEqual(conversation.message_count, 4)
+
+	def test_a_refused_turn_does_not_wait_for_synthesis_usage(self):
+		"""Nothing synthesises on a refusal, so the grace wait must be skipped."""
+		with patch.object(engine_llm, "get_metrics", return_value=[]):
+			history.start_turn()
+			with patch.object(history, "wait_for_synthesis_metrics") as waited:
+				history.record_turn(None, "Who won the cup?", make_result(refused=True))
+		waited.assert_not_called()
+
+
+class TestAskHistoryPermissions(FrappeTestCase):
+	def setUp(self):
+		self.other_user = make_user(OTHER_USER)
+		self.session = history.record_turn(
+			None, "Administrator's question", make_result(), llm_metrics=ALL_LEGS
+		)
+		self.addCleanup(frappe.set_user, "Administrator")
+
+	def test_another_user_cannot_list_or_read_the_conversation(self):
+		frappe.set_user(self.other_user)
+		self.assertNotIn(self.session, [row.name for row in history.list_sessions()])
+		with self.assertRaises(frappe.PermissionError):
+			history.get_session(self.session)
+
+	def test_another_user_cannot_append_to_the_conversation(self):
+		frappe.set_user(self.other_user)
+		with self.assertRaises(frappe.PermissionError):
+			history.record_turn(self.session, "Sneaking in", make_result(), llm_metrics=[])
+
+	def test_a_user_sees_their_own_conversation(self):
+		frappe.set_user(self.other_user)
+		own_session = history.record_turn(None, "My own question", make_result(), llm_metrics=[ROUTE_METRIC])
+
+		listed = [row.name for row in history.list_sessions()]
+		self.assertIn(own_session, listed)
+		self.assertNotIn(self.session, listed)
+		self.assertEqual(history.get_session(own_session)["title"], "My own question")
+
+	def test_system_manager_sees_every_conversation(self):
+		frappe.set_user(self.other_user)
+		own_session = history.record_turn(None, "My own question", make_result(), llm_metrics=[ROUTE_METRIC])
+
+		frappe.set_user("Administrator")
+		listed = {row.name for row in history.list_sessions()}
+		self.assertIn(own_session, listed)
+		self.assertIn(self.session, listed)
+
+
+class TestAskHistoryApi(FrappeTestCase):
+	def test_api_returns_the_conversation_and_its_turns(self):
+		project = make_project()
+		session = history.record_turn(
+			None, "List every job description", make_result(), project=project, llm_metrics=ALL_LEGS
+		)
+
+		listed = api_ask_history.list_sessions(project=project)
+		self.assertEqual([row.name for row in listed], [session])
+		self.assertAlmostEqual(listed[0].total_cost, 0.004185)
+
+		loaded = api_ask_history.get_session(session)
+		self.assertEqual(len(loaded["messages"]), 2)
+		self.assertEqual(loaded["messages"][1]["route_intent"], "exhaustive")
+
+		api_ask_history.delete_session(session)
+		self.assertFalse(frappe.db.exists("Wikify Ask Session", session))
diff --git a/wikify/tests/test_canonical_verdict.py b/wikify/tests/test_canonical_verdict.py
new file mode 100644
index 0000000..a24a889
--- /dev/null
+++ b/wikify/tests/test_canonical_verdict.py
@@ -0,0 +1,108 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""The verdict badge must describe the content the user is actually reading.
+
+Before this, `verdict` was frozen at the baseline parse: a page whose adopted remediation
+scored 0.99 still read "review 0.99". These cover the write seam (`store.set_canonical`)
+and the backfill that repairs already-parsed documents without re-parsing.
+"""
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.engine import settings, store
+from wikify.engine.verify import PageScore
+from wikify.patches.v1_0.backfill_canonical_verdict import execute as backfill_canonical_verdict
+
+
+def _page_score(composite: float, verdict: str) -> PageScore:
+	return PageScore(
+		page_no=1,
+		text_recall=0.0,
+		extra_ratio=0.0,
+		table_score=None,
+		judge_score=None,
+		composite=composite,
+		verdict=verdict,
+	)
+
+
+class TestCanonicalVerdict(FrappeTestCase):
+	def setUp(self):
+		self.source_document = frappe.get_doc(
+			{"doctype": "Source Document", "title": "Verdict Test", "page_count": 3}
+		).insert(ignore_permissions=True)
+		self.pass_threshold = float(settings.get("pass_threshold"))
+		self.escalate_threshold = float(settings.get("escalate_threshold"))
+
+	def add_page(self, page_no: int, baseline_composite: float) -> str:
+		"""A baseline-scored page (no rendered PNG — File inserts trip the test-mode
+		global-search assertion, and the verdict never touches the image)."""
+		page = frappe.new_doc("Source Page")
+		page.source_document = self.source_document.name
+		page.page_no = page_no
+		page.kind = "text"
+		page.baseline_markdown = f"baseline page {page_no}"
+		page.insert(ignore_permissions=True)
+		store.set_page_scores(page.name, _page_score(baseline_composite, "review"))
+		return page.name
+
+	def verdict_of(self, page_name: str) -> str:
+		return frappe.db.get_value("Source Page", page_name, "verdict")
+
+	def test_adopted_remediation_moves_the_verdict_off_the_baseline(self):
+		page_name = self.add_page(1, 0.0)
+		self.assertEqual(self.verdict_of(page_name), "review")
+
+		store.set_canonical(page_name, "remediated body", self.pass_threshold + 0.05, "vlm")
+
+		self.assertEqual(self.verdict_of(page_name), "pass")
+		# The baseline composite survives for audit; only the verdict tracks canonical.
+		row = frappe.db.get_value(
+			"Source Page", page_name, ["composite", "canonical_composite"], as_dict=True
+		)
+		self.assertEqual(row.composite, 0.0)
+		self.assertAlmostEqual(row.canonical_composite, self.pass_threshold + 0.05, places=3)
+
+	def test_a_genuinely_bad_canonical_still_flags(self):
+		bad = self.add_page(1, 0.999)
+		store.set_canonical(bad, "still mangled", self.escalate_threshold - 0.1, "vlm")
+		self.assertEqual(self.verdict_of(bad), "review")
+
+		middling = self.add_page(2, 0.999)
+		store.set_canonical(middling, "partly fixed", self.escalate_threshold + 0.01, "cleanup")
+		self.assertEqual(self.verdict_of(middling), "escalate")
+
+	def test_canonical_without_a_composite_leaves_the_verdict_alone(self):
+		"""`embed_page_image` swaps the markdown for the page image and scores nothing —
+		there is no new evidence, so the badge must not move."""
+		page_name = self.add_page(1, 0.999)
+		frappe.db.set_value("Source Page", page_name, "verdict", "pass")
+
+		store.set_canonical(page_name, "![Page 1](/files/page-0001.png)", None, "image")
+
+		self.assertEqual(self.verdict_of(page_name), "pass")
+
+	def test_backfill_repairs_stale_verdicts_and_is_idempotent(self):
+		stale = self.add_page(1, 0.0)
+		store.set_canonical(stale, "remediated body", self.pass_threshold + 0.05, "vlm")
+		# Re-freeze the pre-fix state: canonical is excellent, the badge says review.
+		frappe.db.set_value("Source Page", stale, "verdict", "review", update_modified=False)
+		genuinely_bad = self.add_page(2, 0.2)
+		store.set_canonical(genuinely_bad, "still mangled", 0.4, "vlm")
+		never_remediated = self.add_page(3, 0.999)
+		frappe.db.set_value("Source Page", never_remediated, "verdict", "pass", update_modified=False)
+
+		moved = backfill_canonical_verdict()
+
+		self.assertEqual(self.verdict_of(stale), "pass")
+		self.assertEqual(self.verdict_of(genuinely_bad), "review")
+		# canonical_composite is a Float (NOT NULL, default 0) — a page that was never
+		# remediated must not be badged off that phantom 0.
+		self.assertEqual(self.verdict_of(never_remediated), "pass")
+		self.assertGreaterEqual(moved.get("pass", 0), 1)
+
+		# Second run has nothing left to move, site-wide.
+		self.assertEqual(backfill_canonical_verdict(), {})
+		self.assertEqual(self.verdict_of(stale), "pass")
diff --git a/wikify/tests/test_diagrams.py b/wikify/tests/test_diagrams.py
new file mode 100644
index 0000000..1ac59e0
--- /dev/null
+++ b/wikify/tests/test_diagrams.py
@@ -0,0 +1,208 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""The mermaid gate + the ICAI page-11 correctness bar.
+
+Page 11 of the ICAI Final Paper 4 referencer is a surcharge rate table. The old pipeline sent
+it through `flowchart TD`, which hung the income slabs off one node and the surcharge rates off
+another as parallel dangling branches — nothing bound a slab to its rate, so a student revising
+from the output would quote the wrong statutory rate. These tests hold the two halves of the
+fix: the gate rejects that shape, and the markdown we now store binds each slab to exactly one
+rate in its own row.
+"""
+
+from __future__ import annotations
+
+import re
+import unittest
+from pathlib import Path
+
+from wikify.engine import diagrams
+
+# The real corrupt output measured on page 11 before the fix (verbatim shape).
+FLATTENED_TABLE_MERMAID = """flowchart TD
+	A["Individual/HUF/AoP/BoI"] --> F["Particulars"]
+	A --> G["Rate of surcharge on income-tax"]
+	F --> F1["(i) TI ... > Rs 50 lakhs but <= Rs 1 crore"]
+	F --> F2["(ii) TI ... > Rs 1 crore but <= Rs 2 crore"]
+	F --> F3["(iii) TI ... > Rs 2 crore but <= Rs 5 crore"]
+	F --> F4["(iv) TI ... > Rs 5 crore"]
+	G --> G1["10%"]
+	G --> G2["15%"]
+	G --> G3["25%"]
+	G --> G4["37%"]
+"""
+
+# Page 6 of `mhj80jag5u`, verbatim. The content is right — it is the unquoted label whose inner
+# `]` closes the node early that mermaid cannot read, and ICAI prints every statutory reference
+# exactly this way. Real mermaid 11 rejects this string and accepts the quoted repair of it.
+STATUTORY_BRACKET_MERMAID = """flowchart TD
+	A[CAPITAL ASSET<br>[Section 2(14)]] --> B[Property of any kind held by an assessee]
+	A --> C[Excludes stock-in-trade [Section 2(14)(a)]]
+	B --> D[Whether or not connected with his business or profession]
+"""
+
+GENUINE_FLOW_MERMAID = """flowchart TD
+	A["Individual/HUF/AoP/BoI and Artificial Juridical Person"] --> B["paying tax under default tax regime u/s 115BAC"]
+	A --> C["exercising the option to shift out of the default tax regime"]
+"""
+
+# `| cell | cell |` rows, separator rows excluded.
+_TABLE_ROW_RE = re.compile(r"^\s*\|(?!\s*[-:| ]+\|\s*$).*\|\s*$")
+_RATE_RE = re.compile(r"\b(?:not\s+exceeding\s+)?\d+(?:\.\d+)?%", re.IGNORECASE)
+_SLAB_RE = re.compile(r"₹\s*[\d.]+\s*(?:lakhs?|crores?)", re.IGNORECASE)
+
+
+def table_rows(markdown: str) -> list[list[str]]:
+	"""Cells of every pipe-table row in the markdown, in document order."""
+	rows = []
+	for line in markdown.splitlines():
+		if _TABLE_ROW_RE.match(line):
+			rows.append([cell.strip() for cell in line.strip().strip("|").split("|")])
+	return rows
+
+
+class TestMermaidGate(unittest.TestCase):
+	def test_flattened_table_is_rejected(self):
+		errors = diagrams.diagram_errors(FLATTENED_TABLE_MERMAID)
+		self.assertTrue(errors, "a grid flattened into parallel dangling branches must be rejected")
+		self.assertTrue(
+			any("bare values" in error or "parallel leaf groups" in error for error in errors),
+			f"expected a tabular-shape rejection, got {errors}",
+		)
+
+	def test_genuine_flow_is_kept(self):
+		self.assertEqual(diagrams.diagram_errors(GENUINE_FLOW_MERMAID), [])
+
+	def test_broken_syntax_is_rejected(self):
+		self.assertTrue(diagrams.diagram_errors('flowchart TD\n\tA["unbalanced] --> B["ok"]'))
+		self.assertTrue(diagrams.diagram_errors("sequenceDiagram\n\tA ->> B: hi"))
+		self.assertTrue(diagrams.diagram_errors('flowchart TD\n\tA["lonely node"]'))
+
+	def test_rejected_block_falls_back_to_the_source_crop(self):
+		markdown = f"# Surcharge\n\n```mermaid\n{FLATTENED_TABLE_MERMAID}```\n"
+		cleaned, notes = diagrams.remove_unverified_diagrams(markdown, "/files/page-0011.png")
+		self.assertNotIn("mermaid", cleaned)
+		self.assertIn("![Source region](/files/page-0011.png)", cleaned)
+		self.assertTrue(notes)
+
+	def test_a_surviving_table_needs_no_crop(self):
+		markdown = f"| Slab | Rate |\n|---|---|\n| A | 10% |\n\n```mermaid\n{FLATTENED_TABLE_MERMAID}```\n"
+		cleaned, _notes = diagrams.remove_unverified_diagrams(markdown, "/files/page-0011.png")
+		self.assertNotIn("![Source region]", cleaned)
+		self.assertIn("| A | 10% |", cleaned)
+
+	def test_valid_markdown_is_returned_untouched(self):
+		markdown = f"# Flow\n\n```mermaid\n{GENUINE_FLOW_MERMAID}```\n"
+		self.assertEqual(diagrams.remove_unverified_diagrams(markdown, "/files/x.png"), (markdown, []))
+
+
+class TestLabelRepair(unittest.TestCase):
+	"""Correct content must not be thrown away over a missing pair of quotes."""
+
+	def test_the_statutory_bracket_statement_is_the_one_that_used_to_be_rejected(self):
+		errors = diagrams.diagram_errors(STATUTORY_BRACKET_MERMAID)
+		self.assertIn(
+			"unparseable statement: 'A[CAPITAL ASSET<br>[Section 2(14)]] --> B[Property of any ki'",
+			errors,
+		)
+
+	def test_repair_makes_it_validate(self):
+		repaired = diagrams.quote_node_labels(STATUTORY_BRACKET_MERMAID)
+		self.assertEqual(diagrams.diagram_errors(repaired), [])
+		self.assertIn('A["CAPITAL ASSET<br>[Section 2(14)]"]', repaired)
+		self.assertIn('C["Excludes stock-in-trade [Section 2(14)(a)]"]', repaired)
+
+	def test_the_gate_repairs_instead_of_rejecting(self):
+		markdown = f"# Capital gains\n\n```mermaid\n{STATUTORY_BRACKET_MERMAID}```\n"
+		cleaned, notes = diagrams.remove_unverified_diagrams(markdown, "/files/page-0006.png")
+		self.assertNotIn("![Source region]", cleaned)
+		self.assertNotIn("mermaid rejected", "; ".join(notes))
+		self.assertEqual(notes, ["mermaid repaired: node labels quoted"])
+		self.assertIn("Section 2(14)", cleaned)
+		blocks = diagrams.mermaid_blocks(cleaned)
+		self.assertEqual(len(blocks), 1)
+		self.assertEqual(diagrams.diagram_errors(blocks[0]), [])
+
+	def test_repair_adds_quotes_and_nothing_else(self):
+		repaired = diagrams.quote_node_labels(STATUTORY_BRACKET_MERMAID)
+		self.assertEqual(repaired.replace('"', ""), STATUTORY_BRACKET_MERMAID)
+
+	def test_already_quoted_labels_are_left_alone(self):
+		self.assertEqual(diagrams.quote_node_labels(GENUINE_FLOW_MERMAID), GENUINE_FLOW_MERMAID)
+
+	def test_an_inner_quote_is_escaped_rather_than_doubled(self):
+		repaired = diagrams.quote_node_labels('flowchart TD\n\tA[He said "yes"] --> B[ok]\n')
+		self.assertIn('A["He said #quot;yes#quot;"]', repaired)
+		self.assertEqual(diagrams.diagram_errors(repaired), [])
+
+	def test_a_semicolon_inside_a_label_does_not_split_the_statement(self):
+		source = 'flowchart TD\n\tA["Rs 1,00,000; see proviso"] --> B["ok"]\n'
+		self.assertEqual(diagrams.statements(source), ['A["Rs 1,00,000; see proviso"] --> B["ok"]'])
+		self.assertEqual(diagrams.diagram_errors(source), [])
+
+	def test_ampersand_node_chaining_is_understood(self):
+		"""`A & B --> C` is valid mermaid (real mermaid 11 parses it) and used to be rejected as
+		an unparseable statement, which cost page 6 its diagram a second time."""
+		source = (
+			"flowchart TD\n"
+			'\tA["Land"] --> N & O & P\n'
+			'\tN & O & P --- S["These assets are hence, capital assets"]\n'
+		)
+		self.assertEqual(diagrams.diagram_errors(source), [])
+		_labels, edges, _broken = diagrams.parse_flowchart(source)
+		self.assertIn(("A", "N"), edges)
+		self.assertIn(("P", "S"), edges)
+
+	def test_an_ampersand_inside_a_label_is_not_a_node_separator(self):
+		self.assertEqual(diagrams.diagram_errors('flowchart TD\n\tA["Profit & Loss"] --> B["ok"]\n'), [])
+
+	def test_repair_never_rescues_a_flattened_table(self):
+		"""Quoting fixes syntax, never meaning — an unbound grid is still rejected."""
+		markdown = f"# Surcharge\n\n```mermaid\n{FLATTENED_TABLE_MERMAID}```\n"
+		cleaned, notes = diagrams.remove_unverified_diagrams(markdown, "/files/page-0011.png")
+		self.assertNotIn("mermaid", cleaned)
+		self.assertTrue(any("mermaid rejected" in note for note in notes))
+
+	def test_repair_never_rescues_broken_structure(self):
+		for source in ("flowchart TD\n\tA[lonely node]\n", "sequenceDiagram\n\tA ->> B: hi\n"):
+			cleaned, notes = diagrams.remove_unverified_diagrams(f"```mermaid\n{source}```\n", "")
+			self.assertNotIn("mermaid", cleaned)
+			self.assertTrue(any("mermaid rejected" in note for note in notes), notes)
+
+
+class TestIcaiPage11(unittest.TestCase):
+	"""The correctness bar: the stored page-11 markdown, as re-parsed by the fixed pipeline."""
+
+	@classmethod
+	def setUpClass(cls):
+		fixture = Path(__file__).parent / "fixtures" / "icai_page_11_canonical.md"
+		cls.markdown = fixture.read_text(encoding="utf-8")
+
+	def test_no_diagram_encodes_the_rate_table(self):
+		for source in diagrams.mermaid_blocks(self.markdown):
+			self.assertEqual(diagrams.diagram_errors(source), [], f"stored an ungated diagram: {source[:80]}")
+			self.assertFalse(_RATE_RE.search(source), "a surcharge rate is encoded in a flowchart")
+
+	def test_every_slab_binds_to_exactly_one_rate_in_its_own_row(self):
+		"""Cell adjacency: a row carrying an income slab carries its rate in the same row."""
+		slab_rows = [row for row in table_rows(self.markdown) if _SLAB_RE.search(" ".join(row))]
+		self.assertGreaterEqual(len(slab_rows), 6, "the slab rows themselves went missing")
+		for row in slab_rows:
+			rates = _RATE_RE.findall(" ".join(row))
+			self.assertEqual(len(rates), 1, f"row does not bind to exactly one rate: {row}")
+			# The rate must sit in a cell of its own, next to the slab — not narrated inside it.
+			self.assertTrue(
+				any(_RATE_RE.fullmatch(cell) for cell in row),
+				f"the rate is not in its own cell: {row}",
+			)
+
+	def test_statutory_references_and_rupee_amounts_are_verbatim(self):
+		for token in ("u/s 115BAC", "u/s 111A, 112 and 112A", "₹ 50 lakhs", "₹ 5 crore", "37%"):
+			self.assertIn(token, self.markdown)
+
+	def test_both_regimes_survive_with_their_own_rates(self):
+		"""The default regime tops out at 25%; only the normal-provisions one reaches 37%."""
+		default_regime, _, normal_regime = self.markdown.partition("exercising the option to shift out")
+		self.assertNotIn("37%", default_regime)
+		self.assertIn("37%", normal_regime)
diff --git a/wikify/tests/test_evidence.py b/wikify/tests/test_evidence.py
new file mode 100644
index 0000000..2c2b58c
--- /dev/null
+++ b/wikify/tests/test_evidence.py
@@ -0,0 +1,371 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""Span-level grounding (`wikify.rag.evidence`) + chunk provenance (`wikify.rag.chunk`).
+
+The load-bearing assertions:
+  - a located span reports line and character offsets into the ORIGINAL text, so the line
+    a student is told to check is the line that is actually there;
+  - a near-verbatim quote (smart quotes, en dash, rupee-sign vs Rs., re-flowed whitespace, dropped
+    bold markers) still resolves, and a FABRICATED quote does not — `verify_citations`
+    must call it unverified rather than pass it through;
+  - a chunk is pinned to the single page its text came from, and falls back to the section
+    page range flagged `page_approximate` when two pages are too close to call.
+"""
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.rag import chunk, evidence
+
+# Two pages of a rate table, in the shape the ICAI corpus stores them.
+PAGE_10 = """## AMT
+
+Alternate minimum tax is not attracted where the adjusted total income of the
+assessee does not exceed twenty lakh rupees.
+"""
+
+PAGE_11 = """## II. SURCHARGE
+
+Surcharge is levied on the amount of income-tax.
+
+| Total income | Rate of surcharge |
+| --- | --- |
+| Exceeding **Rs. 50 lakh** but not exceeding Rs. 1 crore | 10% |
+| Exceeding Rs. 1 crore but not exceeding Rs. 2 crore | 15% |
+| Exceeding Rs. 2 crore | 25% |
+
+Marginal relief is available in respect of the surcharge.
+"""
+
+SECTION_MARKDOWN = f"{PAGE_10}\n{PAGE_11}"
+
+PAGES = [
+	{"source_document": "DOC-1", "page_no": 10, "canonical_markdown": PAGE_10},
+	{"source_document": "DOC-1", "page_no": 11, "canonical_markdown": PAGE_11},
+]
+
+SECTION_ROW = {
+	"name": "SEC-1",
+	"source_document": "DOC-1",
+	"title": "AMT and surcharge",
+	"section_type": None,
+	"hierarchy_path": "Basic Concepts > Surcharge",
+	"page_start": 10,
+	"page_end": 11,
+	"markdown": SECTION_MARKDOWN,
+	"wiki_document": None,
+}
+
+
+def citation(source_document: str = "DOC-1", **overrides) -> dict:
+	base = {
+		"section": "SEC-1",
+		"source_document": source_document,
+		"text": SECTION_MARKDOWN,
+		"page_start": 10,
+		"page_end": 11,
+	}
+	base.update(overrides)
+	return base
+
+
+def seed_pages() -> str:
+	"""A real Source Document carrying PAGE_10 / PAGE_11, rolled back with the test.
+
+	`verify_citations` resolves a quote down to a page by reading `Source Page` out of the
+	database, so the page leg is only exercised against real rows — a dict fixture would
+	prove the matcher works and the query doesn't.
+	"""
+	project = frappe.get_doc(
+		{"doctype": "Wikify Project", "project_name": f"Evidence Test {frappe.generate_hash(length=8)}"}
+	).insert(ignore_permissions=True)
+	document = frappe.get_doc(
+		{"doctype": "Source Document", "title": "SARANSH", "project": project.name}
+	).insert(ignore_permissions=True)
+	for page in PAGES:
+		frappe.get_doc(
+			{
+				"doctype": "Source Page",
+				"source_document": document.name,
+				"page_no": page["page_no"],
+				"canonical_markdown": page["canonical_markdown"],
+			}
+		).insert(ignore_permissions=True)
+	return document.name
+
+
+class TestNormalise(FrappeTestCase):
+	def test_folds_the_variants_a_quote_actually_arrives_with(self):
+		normalised, _ = evidence.normalise("Exceeding\u00a0 **Rs. 1,00,000** \u2013 \u201cthe limit\u201d")
+		self.assertEqual(normalised, 'exceeding rs. 100000 - "the limit"')
+
+	def test_rupee_sign_and_rs_share_tokens(self):
+		self.assertEqual(evidence.get_tokens("\u20b92 crore"), ["rs", "2", "crore"])
+		self.assertEqual(evidence.get_tokens("Rs. 2 crore"), ["rs", "2", "crore"])
+
+	def test_offsets_point_back_into_the_original_text(self):
+		source = "a  **b**\nc"
+		normalised, offsets = evidence.normalise(source)
+		self.assertEqual(normalised, "a b c")
+		self.assertEqual(len(offsets), len(normalised))
+		self.assertEqual(source[offsets[normalised.index("b")]], "b")
+		self.assertEqual(source[offsets[-1]], "c")
+
+
+class TestLocateQuote(FrappeTestCase):
+	def test_exact_quote_reports_its_line_and_characters(self):
+		quote = "Marginal relief is available in respect of the surcharge."
+		located = evidence.locate_quote(quote, PAGE_11)
+
+		self.assertTrue(located["found"])
+		self.assertEqual(located["score"], 1.0)
+		self.assertEqual(PAGE_11[located["char_start"] : located["char_end"]], quote)
+		# The reported line must BE the line: read it back out of the source.
+		lines = PAGE_11.split("\n")
+		self.assertEqual(located["line_start"], located["line_end"])
+		self.assertEqual(lines[located["line_start"] - 1], quote)
+
+	def test_multi_line_quote_spans_the_lines_it_covers(self):
+		located = evidence.locate_quote(
+			"the adjusted total income of the assessee does not exceed twenty lakh rupees", PAGE_10
+		)
+		self.assertTrue(located["found"])
+		self.assertEqual((located["line_start"], located["line_end"]), (3, 4))
+
+	def test_near_verbatim_quote_still_resolves(self):
+		# Rupee sign for "Rs.", en dash, collapsed whitespace, bold markers gone.
+		located = evidence.locate_quote(
+			"Exceeding \u20b92 crore \u2013 25%", "| Exceeding **Rs. 2 crore** | 25% |"
+		)
+		self.assertTrue(located["found"])
+		self.assertGreaterEqual(located["score"], evidence.MIN_QUOTE_SCORE)
+
+	def test_fabricated_quote_is_not_located(self):
+		located = evidence.locate_quote(
+			"Surcharge on total income exceeding Rs. 2 crore is 37% under the default regime.",
+			SECTION_MARKDOWN,
+		)
+		self.assertFalse(located["found"])
+		self.assertEqual((located["line_start"], located["char_start"]), (0, 0))
+
+	def test_reads_the_body_off_a_chunk_or_a_row(self):
+		quote = "Marginal relief is available"
+		self.assertTrue(evidence.locate_quote(quote, {"markdown": PAGE_11})["found"])
+		self.assertTrue(evidence.locate_quote(quote, {"text": PAGE_11})["found"])
+		self.assertFalse(evidence.locate_quote(quote, "")["found"])
+		self.assertFalse(evidence.locate_quote("", PAGE_11)["found"])
+
+
+class TestFigureFidelity(FrappeTestCase):
+	"""Prose may drift; a figure may not.
+
+	Both cases here were found VERIFYING against the real ICAI page 6 before the figure gate
+	existed: fuzzy similarity is length-dependent, so a single wrong digit is a rounding error
+	to it and a statutory rate to a student.
+	"""
+
+	# The real line, ICAI SARANSH p.6, canonical_markdown line 30.
+	HEC_LINE = (
+		"*   HEC @4% on amount of income-tax (+) surcharge, if any OR (-) rebate u/s 87A, "
+		"if applicable, is levied."
+	)
+
+	def test_flipped_accounting_sign_is_not_verified(self):
+		# Scored 0.85 and verified before the gate: "(+)" and "(-)" carry no word tokens, so a
+		# token-similarity match cannot see the difference between adding and subtracting.
+		located = evidence.locate_quote(
+			self.HEC_LINE.replace("(+) surcharge", "(-) surcharge"), self.HEC_LINE
+		)
+		self.assertFalse(located["found"])
+		self.assertEqual(located["reason"], evidence.FIGURES_DIFFER)
+
+	def test_one_wrong_digit_in_a_long_quote_is_not_verified(self):
+		# The dangerous direction: padding the quote raises the fuzzy ratio, so the longer the
+		# citation the better a wrong rate hides. Length must not buy credibility.
+		long_source = f"{PAGE_10}\n{self.HEC_LINE} " + "Marginal relief is available. " * 12
+		fabricated = f"{self.HEC_LINE} " + "Marginal relief is available. " * 12
+		fabricated = fabricated.replace("@4%", "@6%")
+
+		similarity = evidence.best_token_window(
+			evidence.get_tokens(fabricated), evidence.tokens_with_offsets(*evidence.normalise(long_source))
+		)
+		self.assertGreater(similarity[2], evidence.MIN_QUOTE_SCORE, "the prose gate alone would pass this")
+
+		located = evidence.locate_quote(fabricated, long_source)
+		self.assertFalse(located["found"])
+		self.assertEqual(located["reason"], evidence.FIGURES_DIFFER)
+
+	def test_a_misquoted_statutory_reference_is_not_verified(self):
+		located = evidence.locate_quote(self.HEC_LINE.replace("87A", "87B"), self.HEC_LINE)
+		self.assertFalse(located["found"])
+		self.assertEqual(located["reason"], evidence.FIGURES_DIFFER)
+
+	def test_the_true_line_still_verifies(self):
+		# The gate must not be a blanket refusal — the control for every rejection above.
+		located = evidence.locate_quote(
+			"HEC @4% on amount of income-tax (+) surcharge, if any OR (-) rebate u/s 87A", self.HEC_LINE
+		)
+		self.assertTrue(located["found"])
+		self.assertIsNone(located["reason"])
+
+	def test_prose_may_still_drift_around_identical_figures(self):
+		self.assertEqual(evidence.get_figures("Rs. 1,00,000"), evidence.get_figures("\u20b91,00,000"))
+		located = evidence.locate_quote(
+			"Exceeding \u20b92 crore \u2013 25%", "| Exceeding **Rs. 2 crore** | 25% |"
+		)
+		self.assertTrue(located["found"])
+
+	def test_figures_are_compared_in_order(self):
+		source = "10% and 15%"
+		self.assertTrue(evidence.figures_agree(source, source, 0, len(source)))
+		self.assertFalse(evidence.figures_agree("15% and 10%", source, 0, len(source)))
+
+
+class TestResolvePage(FrappeTestCase):
+	def setUp(self):
+		self.page_index = evidence.build_page_index(PAGES)
+
+	def test_text_is_pinned_to_the_page_it_came_from(self):
+		resolved = evidence.resolve_page(
+			"| Exceeding Rs. 1 crore but not exceeding Rs. 2 crore | 15% |", self.page_index, 10
+		)
+		self.assertEqual(resolved["page_no"], 11)
+		self.assertFalse(resolved["page_approximate"])
+
+	def test_unmatched_text_falls_back_to_the_range_and_says_so(self):
+		resolved = evidence.resolve_page("dividends declared by a foreign company", self.page_index, 10)
+		self.assertEqual(resolved["page_no"], 10)
+		self.assertTrue(resolved["page_approximate"])
+
+	def test_no_pages_means_approximate_not_a_guess(self):
+		resolved = evidence.resolve_page("anything at all", [], 7)
+		self.assertEqual(resolved, {"page_no": 7, "page_approximate": True, "page_score": 0.0})
+
+
+class TestVerifyCitations(FrappeTestCase):
+	def setUp(self):
+		self.document = seed_pages()
+
+	def cited(self, **overrides) -> dict:
+		return citation(self.document, **overrides)
+
+	def test_real_quote_is_verified_and_carries_its_page_and_lines(self):
+		quote = "Exceeding Rs. 2 crore"
+		report = evidence.verify_citations(f'"{quote}" [1]', [self.cited(quote=quote)])
+
+		row = report["citations"][0]
+		self.assertEqual(row["status"], "verified")
+		self.assertTrue(row["cited"])
+		self.assertEqual(row["page_no"], 11)
+		self.assertFalse(row["page_approximate"])
+		self.assertGreater(row["page_line_start"], 0)
+		self.assertEqual(report["unverified"], 0)
+		self.assertTrue(report["grounded"])
+
+	def test_fabricated_quote_is_rejected(self):
+		report = evidence.verify_citations(
+			"", [self.cited(quote="Surcharge exceeding Rs. 5 crore is levied at 37%.")]
+		)
+		row = report["citations"][0]
+		self.assertEqual(row["status"], "unverified")
+		self.assertEqual(row["reason"], "quote not found in the cited source")
+		self.assertEqual(report["unverified"], 1)
+		self.assertFalse(report["grounded"])
+
+	def test_fabricated_quote_written_inline_in_the_answer_is_rejected(self):
+		answer = (
+			'The rate is 37%: "Surcharge exceeding Rs. 5 crore is levied at 37 per cent." [1]\n'
+			'The base rate is 25%: "Exceeding Rs. 2 crore | 25%" [1]'
+		)
+		report = evidence.verify_citations(answer, [self.cited()])
+
+		statuses = [row["status"] for row in report["quotes"]]
+		self.assertEqual(statuses, ["unverified", "verified"])
+		self.assertFalse(report["grounded"])
+
+	def test_marker_with_no_source_is_unverified(self):
+		report = evidence.verify_citations('"Marginal relief is available" [4]', [self.cited()])
+		self.assertEqual(report["quotes"][0]["reason"], "citation marker has no source")
+
+	def test_citation_without_a_quote_is_unquoted_not_verified(self):
+		report = evidence.verify_citations("", [self.cited()])
+		self.assertEqual(report["citations"][0]["status"], "unquoted")
+		self.assertEqual(report["verified"], 0)
+
+	def test_attach_keeps_the_unverified_quote_and_its_status(self):
+		fabricated = "Surcharge exceeding Rs. 5 crore is levied at 37%."
+		attached = evidence.attach_verified_quotes("", [self.cited(quote=fabricated)])
+
+		self.assertEqual(attached[0]["quote"], fabricated)
+		self.assertEqual(attached[0]["quote_status"], "unverified")
+		self.assertEqual(attached[0]["text"], SECTION_MARKDOWN)
+
+	def test_attach_pins_a_verified_quote_to_page_and_line(self):
+		attached = evidence.attach_verified_quotes("", [self.cited(quote="Exceeding Rs. 2 crore")])
+		self.assertEqual(attached[0]["quote_status"], "verified")
+		self.assertEqual(attached[0]["quote_page_no"], 11)
+		self.assertFalse(attached[0]["quote_page_approximate"])
+		self.assertGreater(attached[0]["quote_page_line_start"], 0)
+
+	def test_no_citations_reports_nothing_rather_than_throwing(self):
+		self.assertEqual(evidence.verify_citations("answer with no sources", [])["total"], 0)
+
+
+class TestChunkProvenance(FrappeTestCase):
+	def build(self):
+		return chunk.build_chunks(
+			[SECTION_ROW],
+			{"DOC-1": {"name": "DOC-1", "title": "SARANSH", "project": "PRJ-1"}},
+			{},
+			{"DOC-1": PAGES},
+		)
+
+	def test_chunk_resolves_to_a_single_page_not_the_range(self):
+		chunks = self.build()
+		self.assertTrue(chunks)
+		# The whole section is one chunk here, and it is mostly page 11.
+		self.assertIn(chunks[0].page_no, (10, 11))
+		self.assertGreaterEqual(chunks[0].line_start, 1)
+		self.assertGreaterEqual(chunks[0].line_end, chunks[0].line_start)
+
+	def test_line_span_indexes_the_section_markdown(self):
+		item = self.build()[0]
+		lines = SECTION_MARKDOWN.split("\n")
+		self.assertEqual(lines[item.line_start - 1].strip(), "## AMT")
+
+	def test_without_pages_every_chunk_is_flagged_approximate(self):
+		chunks = chunk.build_chunks([SECTION_ROW], {"DOC-1": {}}, {})
+		self.assertTrue(all(item.page_approximate for item in chunks))
+		self.assertTrue(all(item.page_no == SECTION_ROW["page_start"] for item in chunks))
+
+	def test_row_carries_every_provenance_field_the_schema_declares(self):
+		from wikify.rag import store
+
+		row = chunk.chunk_row(self.build()[0], [0.0] * 4)
+		self.assertEqual(set(row), {field.name for field in store.chunk_schema()})
+
+	def test_overlap_is_added_after_the_pieces_are_located(self):
+		markdown = "\n\n".join(f"Paragraph {index} " + "word " * 120 for index in range(4))
+		pieces = chunk.split_pieces(markdown)
+		self.assertGreater(len(pieces), 1)
+		self.assertEqual(chunk.split_markdown(markdown), chunk.add_overlap(pieces))
+		# Every un-overlapped piece is a contiguous run of the source, which is what makes
+		# its line span meaningful.
+		for piece in pieces:
+			self.assertTrue(evidence.locate_quote(piece, markdown)["found"])
+
+
+class TestPageLookup(FrappeTestCase):
+	def test_unknown_documents_read_nothing(self):
+		self.assertEqual(evidence.get_pages_by_document(["NOPE"], [1, 2]), {})
+		self.assertEqual(evidence.get_pages_by_document([], []), {})
+		self.assertEqual(chunk.get_pages_by_document(["NOPE"]), {})
+
+	def test_pages_come_back_grouped_by_document(self):
+		document = frappe.get_all("Source Page", fields=["source_document", "page_no"], limit=1)
+		if not document:
+			self.skipTest("no parsed Source Page on this site")
+		grouped = evidence.get_pages_by_document([document[0]["source_document"]], [document[0]["page_no"]])
+		self.assertEqual(list(grouped), [document[0]["source_document"]])
+		self.assertEqual(grouped[document[0]["source_document"]][0]["page_no"], document[0]["page_no"])
diff --git a/wikify/tests/test_page_propagation.py b/wikify/tests/test_page_propagation.py
new file mode 100644
index 0000000..c8054e9
--- /dev/null
+++ b/wikify/tests/test_page_propagation.py
@@ -0,0 +1,242 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""Page → section → index propagation: a fixed page must not leave stale copies behind.
+
+`Source Section.markdown` is a *copy* of its pages' canonical markdown, so re-remediating
+a page used to leave every section built from it — and every chunk indexed from those
+sections — serving the old text until somebody re-sectioned the whole document by hand.
+These cover the doc_event that closes it: change detection, per-document coalescing (so a
+bulk write queues one job, not one per page), the rebuild itself, the explicit re-index
+(`rebuild_section_markdown` writes via `frappe.db.set_value`, which fires no doc_event),
+and the requeue when a pass dies half-done.
+"""
+
+from contextlib import contextmanager
+from unittest.mock import patch
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.engine import store
+from wikify.engine.loader.sectionizer import Section
+from wikify.rag import events
+from wikify.tests import _cleanup
+
+
+@contextmanager
+def doc_events_live():
+	"""Let the handler run: `indexing_suspended()` gates out `in_test` by design."""
+	frappe.flags.in_test = False
+	try:
+		yield
+	finally:
+		frappe.flags.in_test = True
+
+
+def add_page(source_document: str, page_no: int, canonical: str) -> str:
+	"""A Source Page row without the rendered-PNG File (File inserts trip the test-mode
+	global-search assertion in this environment; propagation never touches the image)."""
+	page = frappe.new_doc("Source Page")
+	page.source_document = source_document
+	page.page_no = page_no
+	page.kind = "text"
+	page.baseline_markdown = f"baseline page {page_no}"
+	page.canonical_markdown = canonical
+	page.insert(ignore_permissions=True)
+	return page.name
+
+
+class TestPagePropagation(FrappeTestCase):
+	def setUp(self):
+		self.source_document = frappe.get_doc(
+			{"doctype": "Source Document", "title": "Propagation Test", "page_count": 4}
+		).insert(ignore_permissions=True)
+		self.pages = {
+			page_no: add_page(self.source_document.name, page_no, f"canonical page {page_no}")
+			for page_no in range(1, 5)
+		}
+		# Alpha owns pages 1-2 exclusively; Beta and Gamma share boundary page 3.
+		store.replace_sections(
+			self.source_document.name,
+			[
+				Section("1. Alpha", 1, ["1. Alpha"], 1, 2, "stale alpha body"),
+				Section("2. Beta", 1, ["2. Beta"], 3, 3, "stale beta body"),
+				Section("3. Gamma", 1, ["3. Gamma"], 3, 4, "stale gamma body"),
+			],
+		)
+		self.clear_markers()
+
+	def tearDown(self):
+		self.clear_markers()
+		_cleanup._delete_document_rows(self.source_document.name)
+		frappe.db.commit()
+
+	def clear_markers(self):
+		frappe.cache().delete_value(events.page_propagation_key(self.source_document.name))
+		events.take_dirty_pages(self.source_document.name)
+
+	def section_named(self, title: str) -> str:
+		return frappe.db.get_value(
+			"Source Section", {"source_document": self.source_document.name, "title": title}, "name"
+		)
+
+	def markdown_of(self, title: str) -> str:
+		return frappe.db.get_value("Source Section", self.section_named(title), "markdown")
+
+	def save_canonical(self, page_no: int, markdown: str) -> None:
+		page = frappe.get_doc("Source Page", self.pages[page_no])
+		page.canonical_markdown = markdown
+		page.save(ignore_permissions=True)
+
+	# --- the doc_event ------------------------------------------------------------------
+
+	def test_a_changed_page_marks_itself_dirty_and_queues_one_pass(self):
+		with doc_events_live(), patch("frappe.enqueue") as enqueue:
+			self.save_canonical(1, "canonical page 1 — surcharge slabs repaired")
+
+		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.events.propagate_dirty_pages")
+		self.assertEqual(enqueue.call_args.kwargs["source_document"], self.source_document.name)
+		self.assertEqual(enqueue.call_args.kwargs["queue"], "long")
+		self.assertEqual(events.take_dirty_pages(self.source_document.name), [1])
+
+	def test_a_save_that_leaves_canonical_markdown_alone_queues_nothing(self):
+		with doc_events_live(), patch("frappe.enqueue") as enqueue:
+			page = frappe.get_doc("Source Page", self.pages[1])
+			page.kind = "mixed"
+			page.save(ignore_permissions=True)
+
+		enqueue.assert_not_called()
+		self.assertEqual(events.take_dirty_pages(self.source_document.name), [])
+
+	def test_a_bulk_page_write_queues_one_pass_not_one_job_per_page(self):
+		"""A parse writes hundreds of pages; one job each would bury the long queue."""
+		with doc_events_live(), patch("frappe.enqueue") as enqueue:
+			for page_no in range(1, 5):
+				self.save_canonical(page_no, f"canonical page {page_no} — rewritten")
+			# The same page saved repeatedly still costs one pass, and one rebuild.
+			self.save_canonical(1, "canonical page 1 — rewritten twice")
+
+		self.assertEqual(enqueue.call_count, 1)
+		self.assertEqual(events.take_dirty_pages(self.source_document.name), [1, 2, 3, 4])
+
+	def test_a_new_page_queues_nothing_because_no_section_copied_it_yet(self):
+		with doc_events_live(), patch("frappe.enqueue") as enqueue:
+			add_page(self.source_document.name, 5, "canonical page 5")
+
+		enqueue.assert_not_called()
+
+	# --- the pass -----------------------------------------------------------------------
+
+	def test_the_pass_rebuilds_every_section_covering_a_changed_page(self):
+		marker = "SURCHARGE-MARKER-42"
+		store.set_canonical_markdown(self.pages[3], f"canonical page 3 {marker}")
+		frappe.cache().hset(events.DIRTY_PAGES_HASH, f"{self.source_document.name}:3", 1)
+
+		events.propagate_dirty_pages(self.source_document.name)
+
+		# Page 3 is a boundary page: both owners carry the fix, Alpha (pages 1-2) does not.
+		self.assertIn(marker, self.markdown_of("2. Beta"))
+		self.assertIn(marker, self.markdown_of("3. Gamma"))
+		self.assertNotIn(marker, self.markdown_of("1. Alpha"))
+		self.assertEqual(events.take_dirty_pages(self.source_document.name), [])
+
+	def test_the_pass_clears_its_marker_before_working_so_a_mid_run_edit_is_never_lost(self):
+		frappe.cache().set_value(
+			events.page_propagation_key(self.source_document.name),
+			"1",
+			expires_in_sec=events.PENDING_TTL_SECONDS,
+		)
+		frappe.cache().hset(events.DIRTY_PAGES_HASH, f"{self.source_document.name}:1", 1)
+
+		events.propagate_dirty_pages(self.source_document.name)
+
+		self.assertFalse(frappe.cache().get_value(events.page_propagation_key(self.source_document.name)))
+
+	def test_a_write_that_lost_the_claim_race_is_re_armed_not_stranded(self):
+		"""A 30-page bulk write went stale this way before the tail re-arm existed.
+
+		A writer that finds the marker set enqueues nothing — it banks on the queued pass
+		picking its pages up. If it marks them *after* that pass has already claimed, its
+		pages sit in the hash with no job behind them and never propagate.
+		"""
+		frappe.cache().hset(events.DIRTY_PAGES_HASH, f"{self.source_document.name}:1", 1)
+
+		def late_writer(source_document, pages):
+			events.mark_pages_dirty(source_document, [4])
+
+		with (
+			patch("wikify.rag.events.propagate_pages", side_effect=late_writer),
+			patch("frappe.enqueue") as enqueue,
+		):
+			events.propagate_dirty_pages(self.source_document.name)
+
+		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.events.propagate_dirty_pages")
+		self.assertEqual(events.take_dirty_pages(self.source_document.name), [4])
+
+	def test_a_marker_the_worker_already_cleared_does_not_swallow_the_next_change(self):
+		"""`cache.get_value` answers from process-local memory, so the writer that set the
+		marker never saw the worker clear it and coalesced later edits into a dead job."""
+		cache = frappe.cache()
+		key = events.page_propagation_key(self.source_document.name)
+		cache.set_value(key, "1", expires_in_sec=events.PENDING_TTL_SECONDS)
+		# What the worker's `delete_value` does to redis, without touching this process's
+		# local cache — the exact split that stranded a 30-page bulk write.
+		cache.unlink(cache.make_key(key))
+
+		with doc_events_live(), patch("frappe.enqueue") as enqueue:
+			self.save_canonical(1, "canonical page 1 — after the worker finished")
+
+		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.events.propagate_dirty_pages")
+
+	def test_a_claim_only_takes_the_document_it_names(self):
+		other = "some-other-document"
+		events.mark_pages_dirty(self.source_document.name, [1, 2])
+		events.mark_pages_dirty(other, [9])
+		try:
+			self.assertEqual(events.take_dirty_pages(self.source_document.name), [1, 2])
+			self.assertEqual(events.dirty_page_fields(other), [f"{other}:9"])
+		finally:
+			events.take_dirty_pages(other)
+
+	def test_a_failed_pass_requeues_the_pages_it_claimed(self):
+		"""A worker dying mid-pass used to leave section text new and the index old, silently."""
+		frappe.cache().hset(events.DIRTY_PAGES_HASH, f"{self.source_document.name}:1", 1)
+
+		with (
+			patch("wikify.engine.sectionize.rebuild_section_markdown", side_effect=RuntimeError("died")),
+			patch("frappe.enqueue") as enqueue,
+			self.assertRaises(RuntimeError),
+		):
+			events.propagate_dirty_pages(self.source_document.name)
+
+		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.events.propagate_dirty_pages")
+		self.assertTrue(frappe.cache().get_value(events.page_propagation_key(self.source_document.name)))
+		self.assertEqual(events.take_dirty_pages(self.source_document.name), [1])
+
+	# --- reaching the index -------------------------------------------------------------
+
+	def test_rebuilt_sections_are_pushed_into_the_index_one_by_one(self):
+		"""`set_section_markdown` fires no doc_event, so the reindex hook never sees it."""
+		with patch("wikify.rag.index.upsert_section") as upsert:
+			events.reindex_sections("PRJ-TEST", ["SEC-A", "SEC-B"])
+
+		self.assertEqual([call.args[0] for call in upsert.call_args_list], ["SEC-A", "SEC-B"])
+
+	def test_a_wide_fanout_queues_one_project_rebuild_instead_of_a_burst_of_upserts(self):
+		many = [f"SEC-{n}" for n in range(events.PROJECT_REBUILD_FANOUT + 1)]
+
+		with (
+			patch("wikify.rag.index.upsert_section") as upsert,
+			patch("wikify.rag.events.queue_project_rebuild") as queue_project,
+		):
+			events.reindex_sections("PRJ-TEST", many)
+
+		upsert.assert_not_called()
+		queue_project.assert_called_once_with("PRJ-TEST")
+
+	def test_a_document_outside_any_project_reindexes_nothing(self):
+		with patch("wikify.rag.index.upsert_section") as upsert:
+			events.reindex_sections(None, ["SEC-A"])
+
+		upsert.assert_not_called()
diff --git a/wikify/tests/test_rag_api.py b/wikify/tests/test_rag_api.py
new file mode 100644
index 0000000..48cdefa
--- /dev/null
+++ b/wikify/tests/test_rag_api.py
@@ -0,0 +1,558 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""POC-2 phase 1 — routing, grounded answering, the whitelisted API, and the reindex hook.
+
+Retrieval itself (embedding, LanceDB) is covered by the `rag/` unit tests; here the store
+is stubbed so these assert the layer above it: which leg a route picks, when we refuse,
+that a citation always resolves, and that the ACL reaches the store as a pre-filter rather
+than a post-hoc trim.
+"""
+
+import threading
+import time
+from types import SimpleNamespace
+from unittest.mock import patch
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.agent.registry import build_default_registry
+from wikify.api import rag as api_rag
+from wikify.engine import settings
+from wikify.rag import answer as rag_answer
+from wikify.rag import events as rag_events
+from wikify.rag import router as rag_router
+from wikify.rag import search as rag_search
+from wikify.rag import usage as rag_usage
+from wikify.rag.search import Hit
+
+
+def make_hit(title: str = "Backend Engineer", score: float = 0.03, rerank_score: float | None = None) -> Hit:
+	return Hit(
+		chunk_id=f"{title}::0",
+		section=f"sec-{title}",
+		source_document="doc-1",
+		document_title="Handbook",
+		title=title,
+		text=f"{title} body text.",
+		section_type="job_description",
+		hierarchy_path=f"Roles > {title}",
+		page_start=3,
+		page_end=4,
+		wiki_route=None,
+		score=score,
+		rerank_score=rerank_score,
+	)
+
+
+def make_project(label: str):
+	"""A uniquely-named project — `project_name` is unique and tests share a database."""
+	return frappe.get_doc(
+		{"doctype": "Wikify Project", "project_name": f"{label} {frappe.generate_hash(length=6)}"}
+	).insert()
+
+
+def llm_reply(content: str) -> dict:
+	"""An `engine.llm.chat_completion` response body carrying `content`."""
+	return {"choices": [{"message": {"content": content}}]}
+
+
+class TestRagRouter(FrappeTestCase):
+	def test_falls_back_to_hybrid_without_a_key(self):
+		with patch.object(settings, "openrouter_key", return_value=""):
+			decided = rag_router.route("all the job descriptions")
+		self.assertEqual(decided.intent, "hybrid")
+		self.assertIsNone(decided.section_type)
+		self.assertEqual(decided.query, "all the job descriptions")
+		self.assertTrue(decided.reason)
+
+	def test_falls_back_when_the_model_reply_is_unusable(self):
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_router.llm, "chat_completion", return_value=llm_reply("sorry, no idea")),
+		):
+			decided = rag_router.route("all the job descriptions")
+		self.assertEqual(decided.intent, "hybrid")
+
+	def test_exhaustive_intent_keeps_a_real_section_type(self):
+		section_type = frappe.get_all("Section Type", pluck="type_name", limit=1)[0]
+		reply = (
+			'```json\n{"intent": "exhaustive", "section_type": "%s", '
+			'"query": "every job description", "reason": "Listing them all."}\n```' % section_type
+		)
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_router.llm, "chat_completion", return_value=llm_reply(reply)),
+		):
+			decided = rag_router.route("give me all of them")
+		self.assertEqual(decided.intent, "exhaustive")
+		self.assertEqual(decided.section_type, section_type)
+		self.assertEqual(decided.query, "every job description")
+		self.assertEqual(decided.reason, "Listing them all.")
+
+	def test_invented_section_type_downgrades_instead_of_filtering_to_nothing(self):
+		reply = '{"intent": "exhaustive", "section_type": "not_a_real_type", "query": "q", "reason": "r"}'
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_router.llm, "chat_completion", return_value=llm_reply(reply)),
+		):
+			decided = rag_router.route("all the widgets")
+		self.assertEqual(decided.intent, "semantic")
+		self.assertIsNone(decided.section_type)
+
+	def test_history_is_passed_to_the_model_for_follow_up_rewriting(self):
+		reply = '{"intent": "semantic", "section_type": null, "query": "salary of the backend role", "reason": "r"}'
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_router.llm, "chat_completion", return_value=llm_reply(reply)) as completion,
+		):
+			decided = rag_router.route(
+				"what about the second one?",
+				None,
+				[
+					{"role": "user", "content": "list the roles"},
+					{"role": "assistant", "content": "1. QA 2. Backend"},
+				],
+			)
+		sent = completion.call_args[0][1]
+		self.assertIn("list the roles", [message["content"] for message in sent])
+		self.assertEqual(decided.query, "salary of the backend role")
+
+
+class TestRagAnswer(FrappeTestCase):
+	def test_refuses_when_retrieval_comes_back_empty(self):
+		with patch.object(rag_search, "search", return_value=[]):
+			result = rag_answer.answer(
+				"what is the capital of Mongolia", rerank=False, allowed_projects=["PRJ"]
+			)
+		self.assertTrue(result["refused"])
+		self.assertEqual(result["citations"], [])
+		self.assertIn("couldn't find this in the wiki", result["answer"])
+
+	def test_refuses_when_the_rerank_score_is_below_the_floor(self):
+		weak = [make_hit(rerank_score=1.0), make_hit("Other", rerank_score=0.0)]
+		with patch.object(rag_search, "search", return_value=weak):
+			result = rag_answer.answer("something unrelated", allowed_projects=["PRJ"])
+		self.assertTrue(result["refused"])
+
+	def test_answers_and_cites_when_retrieval_is_strong(self):
+		hits = [make_hit(rerank_score=9.0), make_hit("QA Engineer", rerank_score=8.0)]
+		with (
+			patch.object(rag_search, "search", return_value=hits),
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_answer, "generate", return_value="Two roles are listed [1][2]."),
+		):
+			result = rag_answer.answer("which roles are described", allowed_projects=["PRJ"])
+		self.assertFalse(result["refused"])
+		self.assertEqual(len(result["citations"]), 2)
+		self.assertEqual(result["answer"], "Two roles are listed [1][2].")
+
+	def test_citation_markers_without_a_source_are_dropped(self):
+		self.assertEqual(rag_answer.drop_unknown_citations("a [1] b [7] c", 2), "a [1] b  c")
+
+	def test_exhaustive_intent_uses_the_filter_leg(self):
+		decided = rag_router.Route("exhaustive", "job_description", "all job descriptions", "r")
+		with patch.object(rag_search, "search", return_value=[]) as search:
+			rag_answer.retrieve(decided, "PRJ", False, allowed_projects=["PRJ"])
+		kwargs = search.call_args.kwargs
+		self.assertEqual(kwargs["mode"], "filter")
+		self.assertEqual(kwargs["section_type"], "job_description")
+		self.assertEqual(kwargs["allowed_projects"], ["PRJ"])
+
+	def test_callbacks_deliver_route_then_citations_then_deltas(self):
+		order = []
+		hits = [make_hit(rerank_score=9.0)]
+
+		def fake_generate(question, context, model, on_delta):
+			on_delta("hello")
+			return "hello [1]"
+
+		with (
+			patch.object(rag_search, "search", return_value=hits),
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_answer, "generate", side_effect=fake_generate),
+		):
+			rag_answer.answer(
+				"which roles",
+				allowed_projects=["PRJ"],
+				on_route=lambda route: order.append("route"),
+				on_citations=lambda citations: order.append("citations"),
+				on_delta=lambda delta: order.append("delta"),
+			)
+		self.assertEqual(order, ["route", "citations", "delta"])
+
+
+class TestRagApi(FrappeTestCase):
+	def setUp(self):
+		self.project = make_project("RAG API Test")
+
+	def test_search_rejects_an_empty_query(self):
+		with self.assertRaises(frappe.ValidationError):
+			api_rag.search("   ")
+
+	def test_search_passes_the_acl_list_into_the_store(self):
+		with (
+			patch.object(rag_search, "search", return_value=[]) as search,
+			patch.object(api_rag, "readable_projects", return_value=["PRJ-ONLY-THIS"]),
+		):
+			result = api_rag.search("anything", use_router=False)
+		self.assertEqual(search.call_args.kwargs["allowed_projects"], ["PRJ-ONLY-THIS"])
+		self.assertIsNone(result["route"])
+		self.assertEqual(result["hits"], [])
+
+	def test_search_takes_the_section_type_from_the_route(self):
+		decided = rag_router.Route("exhaustive", "job_description", "all job descriptions", "r")
+		with (
+			patch.object(rag_search, "search", return_value=[]) as search,
+			patch.object(api_rag, "route_question", return_value=decided),
+		):
+			result = api_rag.search("give me all of them")
+		self.assertEqual(search.call_args.kwargs["section_type"], "job_description")
+		self.assertEqual(result["route"]["intent"], "exhaustive")
+
+	def test_reading_an_unreadable_project_is_refused(self):
+		# Guest stands in for any user without Wikify Project read: the scope guard throws
+		# and the ACL list resolves empty instead of blowing up on get_list.
+		frappe.set_user("Guest")
+		self.addCleanup(frappe.set_user, "Administrator")
+		with self.assertRaises(frappe.PermissionError):
+			api_rag.search("anything", project=self.project.name)
+		with self.assertRaises(frappe.PermissionError):
+			api_rag.reindex(self.project.name)
+		self.assertEqual(api_rag.readable_projects(), [])
+
+	def test_compare_runs_both_legs_over_the_same_acl(self):
+		naive_hits = [make_hit("Only One")]
+		routed_hits = [make_hit("Only One"), make_hit("And Another")]
+		decided = rag_router.Route("exhaustive", "job_description", "all job descriptions", "r")
+		with (
+			patch.object(rag_search, "search", return_value=naive_hits) as search,
+			patch.object(api_rag, "route_question", return_value=decided),
+			patch.object(rag_answer, "retrieve", return_value=routed_hits),
+		):
+			result = api_rag.compare("all the job descriptions")
+		self.assertEqual(search.call_args.kwargs["mode"], "vector")
+		self.assertEqual(len(result["naive"]), 1)
+		self.assertEqual(len(result["routed"]), 2)
+		self.assertEqual(result["route"]["intent"], "exhaustive")
+
+	def test_reindex_enqueues_on_the_long_queue(self):
+		with patch.object(frappe, "enqueue", return_value=SimpleNamespace(id="job-1")) as enqueue:
+			result = api_rag.reindex(self.project.name)
+		self.assertEqual(result["job"], "job-1")
+		self.assertEqual(enqueue.call_args.kwargs["queue"], "long")
+		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.index.rebuild_project")
+
+	def test_index_status_reports_only_readable_projects(self):
+		stats = {"chunks": 3, "sections": 2, "documents": 1, "indexed_at": "2026-01-01 00:00:00", "dim": 256}
+		with (
+			patch.object(api_rag, "readable_projects", return_value=["a", "b"]),
+			patch.object(api_rag.rag_index, "index_stats", return_value=stats),
+			patch.object(api_rag, "is_stale", return_value=False),
+		):
+			result = api_rag.index_status()
+		self.assertEqual(result["chunks"], 6)
+		self.assertEqual(result["sections"], 4)
+		self.assertFalse(result["stale"])
+
+
+class TestRagApiAgainstTheRealIndex(FrappeTestCase):
+	"""One end-to-end pass over the real LanceDB store — everything else here stubs it."""
+
+	def setUp(self):
+		from wikify.rag import index as rag_index
+
+		self.project = make_project("RAG Integration")
+		self.document = frappe.get_doc(
+			{"doctype": "Source Document", "title": "Integration Handbook", "project": self.project.name}
+		).insert()
+		self.section = frappe.get_doc(
+			{
+				"doctype": "Source Section",
+				"source_document": self.document.name,
+				"title": "Zarquon Protocol",
+				"hierarchy_path": "Protocols > Zarquon Protocol",
+				"page_start": 1,
+				"page_end": 1,
+				"markdown": "The Zarquon protocol requires two witnesses and a countersigned waiver.",
+			}
+		).insert()
+		rag_index.upsert_section(self.section.name)
+		self.addCleanup(rag_index.drop_section, self.section.name)
+
+	def test_search_finds_an_indexed_section_and_answer_cites_it(self):
+		result = api_rag.search("zarquon protocol witnesses", project=self.project.name, use_router=False)
+		self.assertEqual(result["hits"][0]["section"], self.section.name)
+		self.assertEqual(result["hits"][0]["document_title"], "Integration Handbook")
+
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch.object(rag_answer, "generate", return_value="Two witnesses are required [1]."),
+			patch.object(
+				rag_router, "route", return_value=rag_router.Route("semantic", None, "zarquon protocol", "r")
+			),
+		):
+			answered = rag_answer.answer(
+				"what does the zarquon protocol require",
+				project=self.project.name,
+				rerank=False,
+				allowed_projects=[self.project.name],
+			)
+		self.assertFalse(answered["refused"])
+		self.assertEqual(answered["citations"][0]["section"], self.section.name)
+
+	def test_a_project_outside_the_acl_returns_nothing(self):
+		result = api_rag.search("zarquon protocol witnesses", project=self.project.name, use_router=False)
+		self.assertTrue(result["hits"])
+		with patch.object(api_rag, "readable_projects", return_value=["some-other-project"]):
+			blocked = api_rag.search("zarquon protocol witnesses", use_router=False)
+		self.assertEqual(blocked["hits"], [])
+
+
+class TestRagAgentTool(FrappeTestCase):
+	def test_semantic_search_is_registered(self):
+		tool = build_default_registry()["semantic_search"]
+		self.assertEqual(tool.side, "server")
+		self.assertFalse(tool.confirm)
+		self.assertFalse(tool.mutates)
+
+	def test_tool_reuses_the_api_and_skips_the_router(self):
+		from wikify.agent.context import Ctx
+		from wikify.agent.tools import retrieve
+
+		payload = {"hits": [make_hit(rerank_score=9.0).as_dict()], "mode": "hybrid"}
+		with patch.object(api_rag, "search", return_value=payload) as search:
+			output = retrieve.semantic_search(Ctx(session="s", user="Administrator"), {"query": "roles"})
+		self.assertFalse(search.call_args.kwargs["use_router"])
+		self.assertIn("<sec-Backend Engineer>", output)
+
+
+class TestReindexHook(FrappeTestCase):
+	def setUp(self):
+		self.project = make_project("RAG Hook Test")
+		self.document = frappe.get_doc(
+			{"doctype": "Source Document", "title": "Hooked", "project": self.project.name}
+		).insert()
+		self.section = frappe.get_doc(
+			{"doctype": "Source Section", "source_document": self.document.name, "title": "A section"}
+		).insert()
+		frappe.cache().delete_value(rag_events.pending_key(self.project.name))
+
+	def test_changes_coalesce_into_one_queued_rebuild(self):
+		with (
+			patch.object(rag_events, "indexing_suspended", return_value=False),
+			patch.object(frappe, "enqueue") as enqueue,
+		):
+			rag_events.queue_reindex(self.section)
+			rag_events.queue_reindex(self.section)
+			rag_events.queue_reindex(self.section)
+		self.assertEqual(enqueue.call_count, 1)
+		self.assertEqual(enqueue.call_args.kwargs["queue"], "long")
+		self.assertEqual(enqueue.call_args.kwargs["project"], self.project.name)
+		self.assertTrue(enqueue.call_args.kwargs["enqueue_after_commit"])
+
+	def test_the_next_change_requeues_once_the_job_has_started(self):
+		with (
+			patch.object(rag_events, "indexing_suspended", return_value=False),
+			patch.object(frappe, "enqueue") as enqueue,
+		):
+			rag_events.queue_reindex(self.section)
+			with patch("wikify.rag.index.rebuild_project"):
+				rag_events.rebuild_pending_project(self.project.name)
+			rag_events.queue_reindex(self.section)
+		self.assertEqual(enqueue.call_count, 2)
+
+	def test_bulk_pipeline_runs_do_not_enqueue(self):
+		with patch.object(frappe, "enqueue") as enqueue:
+			rag_events.queue_reindex(self.section)
+		enqueue.assert_not_called()
+
+
+def llm_reply_with_usage(content: str, cost: float, prompt: int, completion: int) -> dict:
+	"""An `engine.llm.chat_completion` body that also carries OpenRouter's usage block."""
+	return {
+		"choices": [{"message": {"content": content}}],
+		"usage": {"cost": cost, "prompt_tokens": prompt, "completion_tokens": completion},
+	}
+
+
+def streamed_chunks(pieces: list[str], usage: dict | None):
+	"""A litellm stream: content chunks, then the choice-less usage chunk `include_usage` adds."""
+	for piece in pieces:
+		yield SimpleNamespace(choices=[SimpleNamespace(delta=SimpleNamespace(content=piece))], usage=None)
+	yield SimpleNamespace(choices=[], usage=SimpleNamespace(**usage) if usage else None)
+
+
+class TestAskCost(FrappeTestCase):
+	"""What a question costs is the sum of every call it made, not just the synthesis."""
+
+	def test_answer_totals_the_routing_and_synthesis_calls(self):
+		route_reply = llm_reply_with_usage(
+			'{"intent": "semantic", "section_type": null, "query": "which roles", "reason": "r"}',
+			cost=0.001,
+			prompt=100,
+			completion=10,
+		)
+		stream = streamed_chunks(
+			["Two roles [1]."], {"cost": 0.02, "prompt_tokens": 900, "completion_tokens": 300}
+		)
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch("wikify.engine.llm.chat_completion", return_value=route_reply),
+			patch("wikify.agent.llm.complete_with_tools", return_value=stream),
+			patch.object(rag_search, "search", return_value=[make_hit(rerank_score=9.0)]),
+		):
+			result = rag_answer.answer(
+				"which roles are described", allowed_projects=["PRJ"], on_delta=lambda delta: None
+			)
+
+		self.assertAlmostEqual(result["cost"], 0.021)
+		self.assertEqual(result["prompt_tokens"], 1000)
+		self.assertEqual(result["completion_tokens"], 310)
+		self.assertTrue(result["model"])
+
+	def test_a_refusal_still_reports_what_the_routing_call_cost(self):
+		route_reply = llm_reply_with_usage(
+			'{"intent": "semantic", "section_type": null, "query": "q", "reason": "r"}',
+			cost=0.0004,
+			prompt=80,
+			completion=12,
+		)
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch("wikify.engine.llm.chat_completion", return_value=route_reply),
+			patch.object(rag_search, "search", return_value=[]),
+		):
+			result = rag_answer.answer("what is the capital of Mongolia", allowed_projects=["PRJ"])
+
+		self.assertTrue(result["refused"])
+		self.assertAlmostEqual(result["cost"], 0.0004)
+		self.assertEqual(result["prompt_tokens"], 80)
+
+	def test_the_rerank_call_is_billed_to_the_question(self):
+		reply = llm_reply_with_usage(
+			'{"scores": [{"id": 0, "score": 9}]}', cost=0.0007, prompt=400, completion=20
+		)
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", return_value=reply),
+			rag_usage.collect() as spend,
+		):
+			rag_search.rerank_hits("which roles", [make_hit()])
+
+		self.assertAlmostEqual(spend["cost"], 0.0007)
+		self.assertEqual(spend["prompt_tokens"], 400)
+
+	def test_one_thread_is_never_billed_for_another(self):
+		"""The web worker answers on threads, so the accumulator must not be shared."""
+		totals = {}
+
+		def bill(name: str, cost: float):
+			with rag_usage.collect() as spend:
+				rag_usage.add({"cost": cost, "prompt_tokens": 1, "completion_tokens": 1})
+				time.sleep(0.05)
+				totals[name] = spend["cost"]
+
+		threads = [
+			threading.Thread(target=bill, args=("first", 0.5)),
+			threading.Thread(target=bill, args=("second", 0.25)),
+		]
+		for thread in threads:
+			thread.start()
+		for thread in threads:
+			thread.join()
+
+		self.assertEqual(totals, {"first": 0.5, "second": 0.25})
+
+	def test_spend_outside_an_answer_is_not_accumulated(self):
+		rag_usage.add({"cost": 1.0, "prompt_tokens": 5, "completion_tokens": 5})
+		with rag_usage.collect() as spend:
+			self.assertEqual(spend, rag_usage.empty())
+
+	def test_ask_publishes_the_cost_when_the_answer_finishes(self):
+		answered = {
+			"answer": "a [1]",
+			"citations": [],
+			"route": {"intent": "semantic"},
+			"refused": False,
+			"model": "anthropic/claude-sonnet-4.6",
+			"cost": 0.021,
+			"prompt_tokens": 1000,
+			"completion_tokens": 310,
+		}
+		with (
+			patch.object(rag_answer, "answer", return_value=dict(answered)),
+			patch.object(frappe, "publish_realtime") as publish,
+		):
+			result = api_rag.ask("which roles are described")
+
+		done = [call.args[1] for call in publish.call_args_list if call.args[1].get("done")]
+		self.assertEqual(len(done), 1)
+		self.assertAlmostEqual(done[0]["cost"], 0.021)
+		self.assertEqual(done[0]["prompt_tokens"], 1000)
+		self.assertEqual(done[0]["completion_tokens"], 310)
+		self.assertEqual(done[0]["model"], "anthropic/claude-sonnet-4.6")
+		self.assertAlmostEqual(result["cost"], 0.021)
+
+
+class TestSilentRerankFailure(FrappeTestCase):
+	"""A reranker that answers but ranks nothing must never produce a refusal.
+
+	Graded against the real corpus: "what are the slab rates under section 115BAC(1A)" is
+	answered at rank 2 by both the hybrid and the FTS leg, yet `ask` refused it because the
+	rerank call came back with 0.0 for all eight candidates.
+	"""
+
+	QUESTION = "what are the slab rates under section 115BAC(1A)"
+
+	def rerank_reply(self, scores: list[float]) -> dict:
+		items = [{"id": position, "score": score} for position, score in enumerate(scores)]
+		return llm_reply(frappe.as_json({"scores": items}))
+
+	def retrieved(self) -> list[Hit]:
+		return [make_hit("I. INCOME TAX RATES"), make_hit("II. SURCHARGE")]
+
+	def test_an_all_zero_verdict_keeps_the_fusion_order(self):
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply([0.0, 0.0])),
+		):
+			hits = rag_search.rerank_hits(self.QUESTION, self.retrieved())
+
+		self.assertEqual([hit.title for hit in hits], ["I. INCOME TAX RATES", "II. SURCHARGE"])
+		self.assertEqual([hit.rerank_score for hit in hits], [None, None])
+
+	def test_a_real_ranking_still_reorders(self):
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply([2.0, 9.0])),
+		):
+			hits = rag_search.rerank_hits(self.QUESTION, self.retrieved())
+
+		self.assertEqual([hit.title for hit in hits], ["II. SURCHARGE", "I. INCOME TAX RATES"])
+
+	def test_the_answer_is_not_refused_when_the_reranker_ranks_nothing(self):
+		"""End to end over the real rerank path: a flat verdict must still answer."""
+
+		def retrieve_and_rerank(query, **kwargs):
+			return rag_search.rerank_hits(query, self.retrieved())
+
+		with (
+			patch.object(settings, "openrouter_key", return_value="key"),
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply([0.0, 0.0])),
+			patch.object(rag_search, "search", side_effect=retrieve_and_rerank),
+			patch.object(rag_answer, "generate", return_value="The slabs are ... [1]"),
+		):
+			result = rag_answer.answer(self.QUESTION, rerank=True, allowed_projects=["PRJ"])
+
+		self.assertFalse(result["refused"])
+		self.assertEqual(len(result["citations"]), 2)
+
+	def test_a_working_reranker_can_still_refuse(self):
+		weak = [make_hit(rerank_score=1.0), make_hit("Other", rerank_score=0.5)]
+		with patch.object(rag_search, "search", return_value=weak):
+			result = rag_answer.answer("what is the capital of Mongolia", allowed_projects=["PRJ"])
+
+		self.assertTrue(result["refused"])
diff --git a/wikify/tests/test_rag_core.py b/wikify/tests/test_rag_core.py
new file mode 100644
index 0000000..48f4bea
--- /dev/null
+++ b/wikify/tests/test_rag_core.py
@@ -0,0 +1,636 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""RAG POC — the retrieval core (`wikify.rag`).
+
+Runs against a real LanceDB, redirected to a temp directory so the site's own index is
+never touched. The embedding model is real (static, numpy-only, no network after the
+first load) — mocking it would only prove the mock ranks things.
+
+The load-bearing assertions:
+  - `mode="filter"` is **exhaustive** — every matching section comes back, `limit` and
+    similarity notwithstanding. That is the whole thesis of POC-2.
+  - metadata / ACL filters are pre-filters: a scoped query can never surface a row from
+    another project, even when that project dominates the similarity ranking.
+  - `embed_text` carries the contextual prefix, `text` stays clean for display.
+  - rerank degrades to unreranked results instead of breaking the search path.
+"""
+
+import shutil
+import tempfile
+from unittest.mock import patch
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.engine import store as engine_store
+from wikify.engine.loader.sectionizer import Section
+from wikify.rag import chunk, index, search, store
+
+RECIPE_SECTIONS = [
+	("Coin recipe", "job_description", "Touch the coin and the score goes up by one point."),
+	("Timer recipe", "job_description", "A countdown variable starts at thirty and drops each second."),
+	("Sprite notes", "compensation", "Costumes and backdrops are swapped from the sprite pane."),
+]
+
+# The gold set for the recall test: five real role descriptions, deliberately written in
+# five different registers. The last two share almost no vocabulary with the question a
+# user actually asks ("give me all the job descriptions"), which is exactly why a top-k
+# vector search drops them.
+GOLD_ROLE_SECTIONS = [
+	("Backend Engineer", "Owns the ingestion service, its queues and its database migrations."),
+	("Frontend Engineer", "Builds the customer dashboard and keeps its accessibility audit green."),
+	("Payroll Administrator", "Runs the monthly payroll cycle and reconciles the statutory filings."),
+	("Falconry Handler", "Feeds the birds, cleans the mews and flies the peregrine at first light."),
+	("Kiln Operator", "Stacks greenware, watches the cone packs and holds the soak at temperature."),
+]
+
+# Distractors that echo the *words* of the question without being role descriptions at all.
+DISTRACTOR_TEMPLATE = (
+	"Job description formatting note {number}. Every job description in this handbook uses the "
+	"same job description template, and each description is reviewed before the job is posted."
+)
+
+
+def make_section(title, section_type, markdown, page=1):
+	return Section(
+		title=title,
+		level=1,
+		hierarchy_path=[title],
+		page_start=page,
+		page_end=page,
+		markdown=markdown,
+		section_type=section_type,
+	)
+
+
+def ensure_section_type(type_name):
+	if not frappe.db.exists("Section Type", type_name):
+		frappe.get_doc({"doctype": "Section Type", "type_name": type_name, "label": type_name}).insert(
+			ignore_permissions=True
+		)
+
+
+class TestRagCore(FrappeTestCase):
+	@classmethod
+	def setUpClass(cls):
+		super().setUpClass()
+		cls.lance_dir = tempfile.mkdtemp(prefix="wikify_lance_test_")
+		cls.lance_patch = patch.object(store, "lance_path", lambda: cls.lance_dir)
+		cls.lance_patch.start()
+
+	@classmethod
+	def tearDownClass(cls):
+		cls.lance_patch.stop()
+		shutil.rmtree(cls.lance_dir, ignore_errors=True)
+		super().tearDownClass()
+
+	def setUp(self):
+		# LanceDB writes aren't inside the test transaction, so the table is dropped per test
+		# — otherwise rows from a rolled-back project would still rank in the next one.
+		database = store.connect()
+		if store.TABLE_NAME in database.table_names():
+			database.drop_table(store.TABLE_NAME)
+
+		for type_name in ("job_description", "compensation"):
+			ensure_section_type(type_name)
+
+		# `project_name` is unique and the suite only rolls back per class, so each test gets
+		# its own pair of projects rather than colliding with the previous test's fixtures.
+		suffix = frappe.generate_hash(length=8)
+		self.project = frappe.get_doc(
+			{"doctype": "Wikify Project", "project_name": f"RAG Core Test {suffix}"}
+		).insert(ignore_permissions=True)
+		self.other_project = frappe.get_doc(
+			{"doctype": "Wikify Project", "project_name": f"RAG Core Other {suffix}"}
+		).insert(ignore_permissions=True)
+
+		self.document = frappe.get_doc(
+			{
+				"doctype": "Source Document",
+				"title": "Scratch Coins Worksheet",
+				"project": self.project.name,
+			}
+		).insert(ignore_permissions=True)
+		engine_store.replace_sections(
+			self.document.name,
+			[
+				make_section(title, section_type, markdown)
+				for title, section_type, markdown in RECIPE_SECTIONS
+			],
+		)
+
+		self.other_document = frappe.get_doc(
+			{
+				"doctype": "Source Document",
+				"title": "Other Project Handbook",
+				"project": self.other_project.name,
+			}
+		).insert(ignore_permissions=True)
+		engine_store.replace_sections(
+			self.other_document.name,
+			[make_section("Coin recipe elsewhere", "job_description", "The coin scores a point here too.")],
+		)
+
+		index.rebuild_project(self.project.name)
+		index.rebuild_project(self.other_project.name)
+
+	def section_named(self, title):
+		return frappe.db.get_value(
+			"Source Section", {"source_document": self.document.name, "title": title}, "name"
+		)
+
+	def add_document(self, title, project, sections):
+		"""A second Source Document with its own section list, indexed into the project."""
+		document = frappe.get_doc({"doctype": "Source Document", "title": title, "project": project}).insert(
+			ignore_permissions=True
+		)
+		engine_store.replace_sections(
+			document.name,
+			[
+				make_section(section_title, section_type, markdown)
+				for section_title, section_type, markdown in sections
+			],
+		)
+		index.rebuild_project(project)
+		return document
+
+	# --- chunking ---------------------------------------------------------------------
+
+	def test_embed_text_carries_context_prefix_and_text_stays_clean(self):
+		chunks = chunk.chunks_for_section(self.section_named("Coin recipe"))
+		self.assertEqual(len(chunks), 1)
+		built = chunks[0]
+		self.assertEqual(built.id, f"{built.section}::0")
+		self.assertTrue(
+			built.embed_text.startswith(f"Scratch Coins Worksheet{chunk.CONTEXT_SEPARATOR}Coin recipe\n\n"),
+			built.embed_text,
+		)
+		self.assertEqual(built.text, RECIPE_SECTIONS[0][2])
+		self.assertNotIn(chunk.CONTEXT_SEPARATOR, built.text)
+		self.assertEqual(built.project, self.project.name)
+		self.assertEqual(built.section_type, "job_description")
+
+	def test_oversized_section_splits_on_boundaries_with_overlap_and_whole_words(self):
+		paragraph = "the quick brown fox jumps over the lazy dog " * 40
+		markdown = f"## Alpha\n\n{paragraph}\n\n## Beta\n\n{paragraph}"
+		pieces = chunk.split_markdown(markdown)
+
+		self.assertGreater(len(pieces), 1)
+		words = set(markdown.split())
+		# A piece may exceed the target by the heading line it carries, plus the overlap tail.
+		budget = chunk.CHUNK_TARGET_CHARS + chunk.CHUNK_OVERLAP_CHARS + len("## Alpha") + 4
+		for piece in pieces:
+			self.assertLessEqual(len(piece), budget)
+			self.assertTrue(set(piece.split()) <= words, piece[:80])
+			# No mid-word cut, and no heading stranded without the body it introduces.
+			blocks = [block for block in piece.split("\n\n") if block.strip()]
+			self.assertFalse(all(chunk.is_heading(block) for block in blocks), piece)
+			self.assertFalse(chunk.is_heading(blocks[-1]), piece)
+		# Every piece after the first repeats the tail of its predecessor.
+		for position in range(1, len(pieces)):
+			overlap = pieces[position].split("\n\n")[0]
+			self.assertIn(overlap, pieces[position - 1])
+		self.assertCoversMarkdown(markdown, pieces)
+
+	def assertCoversMarkdown(self, markdown, pieces):
+		"""Splitting is lossless: no heading line and no word may go missing.
+
+		`text` is the FTS column, so anything dropped here stops being keyword-searchable —
+		and a dropped heading takes the section's own title out of the index with it.
+		"""
+		joined = "\n".join(pieces)
+		for line in markdown.splitlines():
+			if chunk.is_heading(line):
+				self.assertIn(line.strip(), joined, f"heading dropped: {line!r}")
+		missing = set(markdown.split()) - set(joined.split())
+		self.assertFalse(missing, f"tokens dropped: {sorted(missing)[:5]}")
+
+	def test_heading_survives_a_body_that_overflows_the_first_piece(self):
+		markdown = "# Job Description: Backend Engineer\n\n" + ("service ownership rota " * 250)
+		pieces = chunk.split_markdown(markdown)
+
+		self.assertGreater(len(pieces), 1)
+		self.assertCoversMarkdown(markdown, pieces)
+		self.assertIn("# Job Description: Backend Engineer", pieces[0])
+
+	def test_a_heading_stack_is_carried_not_dropped(self):
+		stack = "# Handbook\n\n## People\n\n### Backend Engineer"
+		markdown = f"{('preamble words ' * 120).strip()}\n\n{stack}\n\n" + ("duty roster line " * 200)
+		pieces = chunk.split_markdown(markdown)
+
+		self.assertCoversMarkdown(markdown, pieces)
+		# The stack travels together, onto the piece that holds the body it introduces.
+		with_stack = [piece for piece in pieces if "### Backend Engineer" in piece]
+		self.assertEqual(len(with_stack), 1)
+		self.assertIn("# Handbook", with_stack[0])
+		self.assertIn("## People", with_stack[0])
+
+	def test_a_body_of_nothing_but_headings_keeps_every_heading(self):
+		markdown = "\n\n".join(f"## Chapter {number}" for number in range(200))
+		pieces = chunk.split_markdown(markdown)
+
+		self.assertCoversMarkdown(markdown, pieces)
+		# The no-progress guard has to actually chunk them, not accumulate one giant piece.
+		self.assertGreater(len(pieces), 1)
+
+	def test_a_run_with_no_whitespace_is_cut_on_character_count(self):
+		markdown = "x" * 50000
+		pieces = chunk.split_markdown(markdown)
+
+		self.assertGreater(len(pieces), 1)
+		budget = chunk.CHUNK_TARGET_CHARS + chunk.CHUNK_OVERLAP_CHARS + 4
+		for piece in pieces:
+			self.assertLessEqual(len(piece), budget)
+		# Overlap repeats characters, so the total can only be >= the input length, never less.
+		self.assertGreaterEqual(sum(piece.count("x") for piece in pieces), 50000)
+
+	def test_short_section_is_one_chunk(self):
+		self.assertEqual(chunk.split_markdown("a short body"), ["a short body"])
+		self.assertEqual(chunk.split_markdown("   "), [])
+
+	def test_a_section_with_no_body_is_returned_by_filter_but_never_ranked(self):
+		"""Both halves of the title-only contract, which pull in opposite directions.
+
+		Filter mode promises 100% of the matching sections, so a row-less section is invisible
+		to it. But a body-less section matches on its title alone and then outranks real
+		content in every similarity leg — measured on the ICAI corpus, a 76-character section
+		titled almost exactly like the question took rank #1 in vector, hybrid and FTS at once
+		while carrying no answer. So it is indexed, flagged, and pre-filtered out of ranking.
+		"""
+		document = self.add_document(
+			"Empty Bodies",
+			self.project.name,
+			[
+				("Concessional tax rates under section 115BAC", "job_description", ""),
+				("Rates of tax", "job_description", "The concessional rate under section 115BAC is 10%."),
+			],
+		)
+		section = frappe.db.get_value(
+			"Source Section",
+			{"source_document": document.name, "title": "Concessional tax rates under section 115BAC"},
+			"name",
+		)
+
+		chunks = chunk.chunks_for_section(section)
+		self.assertEqual(len(chunks), 1)
+		self.assertEqual(chunks[0].text, "Concessional tax rates under section 115BAC")
+		self.assertTrue(chunks[0].title_only)
+
+		query = "concessional tax rates under section 115BAC"
+		filtered = search.search(
+			query,
+			project=self.project.name,
+			section_type="job_description",
+			mode="filter",
+			allowed_projects=search.ALL_PROJECTS,
+		)
+		self.assertIn("Concessional tax rates under section 115BAC", {hit.title for hit in filtered})
+
+		for mode in ("vector", "fts", "hybrid"):
+			titles = {
+				hit.title
+				for hit in search.search(
+					query, project=self.project.name, mode=mode, allowed_projects=search.ALL_PROJECTS
+				)
+			}
+			self.assertNotIn("Concessional tax rates under section 115BAC", titles, mode)
+			self.assertIn("Rates of tax", titles, mode)
+
+	def test_wiki_route_is_populated_from_the_linked_wiki_document(self):
+		page = frappe.get_doc(
+			{"doctype": "Wiki Document", "title": "Coin recipe", "route": "handbook/coin-recipe"}
+		).insert(ignore_permissions=True)
+		section = self.section_named("Coin recipe")
+		frappe.db.set_value("Source Section", section, "wiki_document", page.name)
+
+		self.assertEqual(chunk.chunks_for_section(section)[0].wiki_route, "handbook/coin-recipe")
+		self.assertIsNone(chunk.chunks_for_section(self.section_named("Timer recipe"))[0].wiki_route)
+
+	# --- indexing ---------------------------------------------------------------------
+
+	def test_rebuild_is_idempotent_and_drops_orphans(self):
+		first = index.rebuild_project(self.project.name)
+		second = index.rebuild_project(self.project.name)
+		self.assertEqual(first["chunks"], second["chunks"])
+		self.assertEqual(first["sections"], 3)
+
+		stats = index.index_stats(self.project.name)
+		self.assertEqual(stats["chunks"], first["chunks"])
+		self.assertEqual(stats["sections"], 3)
+		self.assertEqual(stats["documents"], 1)
+		self.assertEqual(stats["dim"], 256)
+		self.assertTrue(stats["indexed_at"])
+
+		frappe.db.delete("Source Section", {"name": self.section_named("Sprite notes")})
+		index.rebuild_project(self.project.name)
+		self.assertEqual(index.index_stats(self.project.name)["sections"], 2)
+
+	def test_upsert_and_drop_section(self):
+		section = self.section_named("Timer recipe")
+		engine_store.set_section_markdown(section, "A stopwatch counts down from sixty seconds.")
+		self.assertEqual(index.upsert_section(section), 1)
+
+		hits = search.search(
+			"stopwatch counting down",
+			project=self.project.name,
+			mode="vector",
+			allowed_projects=search.ALL_PROJECTS,
+		)
+		self.assertIn(section, [hit.section for hit in hits])
+
+		index.drop_section(section)
+		self.assertEqual(index.index_stats(self.project.name)["sections"], 2)
+
+	def test_upsert_removes_the_chunks_a_shrinking_section_leaves_behind(self):
+		"""Delete-then-add: a section that shrinks from N chunks to 1 must leave exactly 1 row."""
+		section = self.section_named("Timer recipe")
+		engine_store.set_section_markdown(section, "countdown timer variable " * 400)
+		self.assertGreater(index.upsert_section(section), 1)
+
+		engine_store.set_section_markdown(section, "A stopwatch counts down from sixty seconds.")
+		self.assertEqual(index.upsert_section(section), 1)
+
+		table = store.chunks_table()
+		remaining = (
+			table.search()
+			.select(["id", "section"])
+			.where(f"section = {store.sql_literal(section)}")
+			.limit(None)
+			.to_list()
+		)
+		self.assertEqual(len(remaining), 1)
+
+	def test_a_failed_rebuild_requeues_itself_instead_of_leaving_zero_rows(self):
+		"""A worker dying between the delete and the add used to leave a silently empty project."""
+		from wikify.rag import events
+
+		frappe.cache().delete_value(events.pending_key(self.project.name))
+		table = store.chunks_table(create=True)
+
+		with (
+			patch.object(type(table), "add", side_effect=RuntimeError("worker died")),
+			patch.object(store, "chunks_table", lambda create=False: table),
+			patch("frappe.enqueue") as enqueue,
+			self.assertRaises(RuntimeError),
+		):
+			index.rebuild_project(self.project.name)
+
+		self.assertTrue(frappe.cache().get_value(events.pending_key(self.project.name)))
+		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.events.rebuild_pending_project")
+		self.assertEqual(enqueue.call_args.kwargs["project"], self.project.name)
+		frappe.cache().delete_value(events.pending_key(self.project.name))
+
+	# --- search -----------------------------------------------------------------------
+
+	def test_filter_mode_is_exhaustive_and_ignores_limit(self):
+		hits = search.search(
+			"",
+			project=self.project.name,
+			section_type="job_description",
+			mode="filter",
+			limit=1,
+			allowed_projects=search.ALL_PROJECTS,
+		)
+		self.assertEqual({hit.title for hit in hits}, {"Coin recipe", "Timer recipe"})
+
+	def test_naive_vector_under_recalls_the_gold_set_that_filter_mode_returns_whole(self):
+		"""POC-2's success criterion: filter mode returns 100% of gold, top-k measurably misses.
+
+		The comparison is run at `limit=len(gold)` — the most generous honest budget for the
+		naive leg, since it is exactly the number of sections that should come back.
+		"""
+		question = "give me all the job descriptions"
+		roles = self.add_document(
+			"Roles Handbook",
+			self.project.name,
+			[(title, "job_description", markdown) for title, markdown in GOLD_ROLE_SECTIONS],
+		)
+		self.add_document(
+			"Formatting Handbook",
+			self.project.name,
+			[
+				(f"Formatting note {number}", "compensation", DISTRACTOR_TEMPLATE.format(number=number))
+				for number in range(20)
+			],
+		)
+
+		gold = {
+			hit.section
+			for hit in search.search(
+				question,
+				project=self.project.name,
+				source_document=roles.name,
+				section_type="job_description",
+				mode="filter",
+				allowed_projects=search.ALL_PROJECTS,
+			)
+		}
+		self.assertEqual(len(gold), len(GOLD_ROLE_SECTIONS))
+
+		naive = {
+			hit.section
+			for hit in search.search(
+				question,
+				project=self.project.name,
+				mode="vector",
+				limit=len(gold),
+				allowed_projects=search.ALL_PROJECTS,
+			)
+		}
+		# The naive leg spent its full budget and still came back short — it is under-recalling,
+		# not simply returning fewer rows than it was allowed.
+		self.assertEqual(len(naive), len(gold))
+		missed = gold - naive
+		self.assertTrue(missed, "naive top-k returned the whole gold set — fixture has no distractors")
+
+		exhaustive = {
+			hit.section
+			for hit in search.search(
+				question,
+				project=self.project.name,
+				section_type="job_description",
+				mode="filter",
+				limit=len(gold),
+				allowed_projects=search.ALL_PROJECTS,
+			)
+		}
+		self.assertTrue(gold <= exhaustive)
+
+	def test_similarity_modes_return_hits_with_rank_provenance(self):
+		query = "how does the score go up when touching the coin"
+		scope = {"project": self.project.name, "limit": 3, "allowed_projects": search.ALL_PROJECTS}
+
+		vector_hits = search.search(query, mode="vector", **scope)
+		self.assertTrue(vector_hits)
+		self.assertTrue(all(hit.vector_rank for hit in vector_hits))
+		self.assertTrue(all(hit.fts_rank is None for hit in vector_hits))
+
+		fts_hits = search.search(query, mode="fts", **scope)
+		self.assertTrue(fts_hits)
+		self.assertTrue(all(hit.fts_rank for hit in fts_hits))
+
+		hybrid_hits = search.search(query, mode="hybrid", **scope)
+		self.assertTrue(hybrid_hits)
+		self.assertTrue(any(hit.vector_rank and hit.fts_rank for hit in hybrid_hits))
+		self.assertEqual(hybrid_hits[0].title, "Coin recipe")
+
+	def test_hits_carry_the_full_parent_section_not_the_chunk(self):
+		long_body = "## Alpha\n\n" + ("the quick brown fox jumps over the lazy dog " * 60)
+		section = self.section_named("Sprite notes")
+		engine_store.set_section_markdown(section, long_body)
+		index.upsert_section(section)
+
+		hit = next(
+			hit
+			for hit in search.search(
+				"quick brown fox",
+				project=self.project.name,
+				mode="vector",
+				allowed_projects=search.ALL_PROJECTS,
+			)
+			if hit.section == section
+		)
+		self.assertEqual(hit.text, long_body)
+		self.assertEqual(hit.document_title, "Scratch Coins Worksheet")
+		self.assertEqual(hit.title, "Sprite notes")
+
+	def test_scoping_and_acl_are_pre_filters(self):
+		"""The foreign project is indexed with more rows than the candidate window holds.
+
+		That is what makes this a test of *pre*-filtering: filtering after the top-k would
+		spend the whole window on foreign rows and return a truncated in-project set.
+		"""
+		query = "the coin scores a point"
+		crowd = search.CANDIDATE_FLOOR + 10
+		self.add_document(
+			"Foreign Coin Manual",
+			self.other_project.name,
+			[
+				(f"Coin note {number}", "job_description", f"The coin scores a point, variation {number}.")
+				for number in range(crowd)
+			],
+		)
+		mine = set(
+			frappe.get_all("Source Section", filters={"source_document": self.document.name}, pluck="name")
+		)
+
+		unscoped = search.search(query, mode="vector", limit=10, allowed_projects=search.ALL_PROJECTS)
+		# The window really is crowded out by foreign rows: post-hoc filtering would have
+		# returned a truncated in-project set here, which is what the pre-filter prevents.
+		self.assertLess(len({hit.section for hit in unscoped} & mine), len(mine))
+
+		scoped = search.search(
+			query,
+			project=self.project.name,
+			mode="vector",
+			limit=10,
+			allowed_projects=search.ALL_PROJECTS,
+		)
+		self.assertEqual({hit.section for hit in scoped}, mine)
+
+		by_document = search.search(
+			query,
+			source_document=self.other_document.name,
+			mode="hybrid",
+			limit=10,
+			allowed_projects=search.ALL_PROJECTS,
+		)
+		self.assertEqual({hit.source_document for hit in by_document}, {self.other_document.name})
+
+		by_acl = search.search(
+			query, project=self.project.name, mode="vector", limit=10, allowed_projects=[self.project.name]
+		)
+		self.assertEqual({hit.section for hit in by_acl}, mine)
+
+		blocked = search.search(
+			query,
+			project=self.project.name,
+			mode="vector",
+			limit=10,
+			allowed_projects=[self.other_project.name],
+		)
+		self.assertEqual(blocked, [])
+
+		self.assertEqual(search.search(query, mode="filter", allowed_projects=[]), [])
+
+	def test_search_refuses_to_run_without_an_acl_decision(self):
+		"""Fail-open is the wrong default for the one argument that enforces permissions."""
+		with self.assertRaises(frappe.ValidationError):
+			search.search("the coin scores a point", project=self.project.name)
+
+	def test_unknown_mode_throws(self):
+		with self.assertRaises(frappe.ValidationError):
+			search.search("anything", mode="magic", allowed_projects=search.ALL_PROJECTS)
+
+	def test_missing_index_returns_no_hits(self):
+		with patch.object(store, "chunks_table", lambda create=False: None):
+			self.assertEqual(
+				search.search("anything", mode="hybrid", allowed_projects=search.ALL_PROJECTS), []
+			)
+
+	# --- rerank -----------------------------------------------------------------------
+
+	def test_filter_mode_does_not_pay_for_a_rerank_it_discards(self):
+		"""Filter mode re-sorts by document and page, so a rerank is a wasted LLM round-trip."""
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion") as chat_completion,
+		):
+			hits = search.search(
+				"give me all the job descriptions",
+				project=self.project.name,
+				section_type="job_description",
+				mode="filter",
+				rerank=True,
+				allowed_projects=search.ALL_PROJECTS,
+			)
+
+		self.assertTrue(hits)
+		chat_completion.assert_not_called()
+		self.assertTrue(all(hit.rerank_score is None for hit in hits))
+
+	def test_rerank_reorders_and_records_the_score(self):
+		hits = search.search(
+			"coin",
+			project=self.project.name,
+			mode="vector",
+			limit=3,
+			allowed_projects=search.ALL_PROJECTS,
+		)
+		self.assertGreater(len(hits), 1)
+		# Score the *last* candidate highest, so a no-op reranker can't pass this.
+		wanted = hits[-1].section
+		scores = [{"id": position, "score": 1.0} for position in range(len(hits))]
+		scores[-1]["score"] = 9.0
+		response = {"choices": [{"message": {"content": frappe.as_json({"scores": scores})}}]}
+
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", return_value=response),
+		):
+			reranked = search.rerank_hits("coin", hits)
+
+		self.assertEqual(reranked[0].section, wanted)
+		self.assertEqual(reranked[0].rerank_score, 9.0)
+
+	def test_rerank_degrades_without_a_key_and_when_the_call_fails(self):
+		scope = {
+			"project": self.project.name,
+			"mode": "vector",
+			"limit": 3,
+			"allowed_projects": search.ALL_PROJECTS,
+		}
+		order = [hit.section for hit in search.search("coin", **scope)]
+
+		with patch("wikify.engine.llm.has_openrouter", return_value=False):
+			unkeyed = search.search("coin", rerank=True, **scope)
+		self.assertEqual([hit.section for hit in unkeyed], order)
+		self.assertTrue(all(hit.rerank_score is None for hit in unkeyed))
+
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", side_effect=RuntimeError("openrouter down")),
+		):
+			failed = search.search("coin", rerank=True, **scope)
+		self.assertEqual([hit.section for hit in failed], order)
diff --git a/wikify/tests/test_rag_eval.py b/wikify/tests/test_rag_eval.py
new file mode 100644
index 0000000..90a36c8
--- /dev/null
+++ b/wikify/tests/test_rag_eval.py
@@ -0,0 +1,305 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""The retrieval eval harness (`wikify.rag.eval`) — and, through it, the POC-2 thesis.
+
+Two classes, deliberately split by what they need:
+
+- `TestEvalScoring` — the metric arithmetic. Pure, deterministic, no LanceDB and no LLM.
+  Completeness is the metric the whole POC rests on, so it gets tested as carefully as the
+  retrieval it measures: a leg that returns 14 of 15 job descriptions is 93% recall and
+  **not complete**, and that distinction has to survive a refactor.
+
+- `TestGoldenQuestions` — the real thing. Real index, real embeddings, real router, real
+  refusal check, scoped to the seeded Demo Corpus. Nothing is mocked: mocking the router
+  or the store here would only assert that the mock ranks things. It skips (loudly) when
+  the corpus is not seeded or no OpenRouter key is configured, because a routed leg with
+  no router is not the system under test.
+
+These tests never write to the index — they read the one `rag.index` built.
+"""
+
+import os
+import tempfile
+
+import frappe
+from frappe.tests import IntegrationTestCase
+
+from wikify.engine import settings
+from wikify.rag import eval as rag_eval
+from wikify.rag import index as rag_index
+from wikify.rag.search import Hit
+
+# The headline the POC is demonstrated on: 15 job descriptions across 5 documents, of which
+# a naive top-8 vector search can physically return at most 8. See specs/poc-rag-DEMO-DATA.md, G1.
+G1_EXPECTED_SOURCES = 15
+G1_NAIVE_LIMIT = 8
+
+# The demoed headline and golden question G1 ask the same thing in different words, and a
+# vector search is sensitive to exactly that: the demo phrasing ("...across all the
+# documents") puts 6 job descriptions in the naive top-8, G1's spec phrasing ("...across all
+# the PDFs") puts 5. Both are asserted, separately and by their own wording — averaging them
+# or asserting one number for both would hide a real ranking change behind a rounded mean.
+HEADLINE_QUERY = "give me all the job descriptions across all the documents"
+HEADLINE_NAIVE_CORRECT = 6
+G1_NAIVE_CORRECT = 5
+
+# Naive top-8 returns 8 sections whichever way the question is worded, so whatever it does not
+# get right is a miss against the full 15.
+G1_NAIVE_MISSES = G1_EXPECTED_SOURCES - G1_NAIVE_CORRECT
+HEADLINE_NAIVE_MISSES = G1_EXPECTED_SOURCES - HEADLINE_NAIVE_CORRECT
+
+
+def make_hit(section: str, title: str = "", document: str = "Doc", section_type: str | None = None) -> Hit:
+	return Hit(
+		chunk_id=f"{section}::0",
+		section=section,
+		source_document="SD-1",
+		document_title=document,
+		title=title or section,
+		text="",
+		section_type=section_type,
+		hierarchy_path="",
+		page_start=1,
+		page_end=2,
+		wiki_route=None,
+		score=0.5,
+	)
+
+
+def expected_sections(names: list[str]) -> list[dict]:
+	return [
+		{"name": name, "title": name, "document_title": "Doc", "section_type": "job_description"}
+		for name in names
+	]
+
+
+class TestEvalScoring(IntegrationTestCase):
+	"""The metric definitions, isolated from retrieval."""
+
+	def test_perfect_leg_is_complete(self):
+		expected = expected_sections(["A", "B"])
+		leg = rag_eval.score_leg(expected, [], [make_hit("A"), make_hit("B")])
+		self.assertEqual(leg["recall"], 1.0)
+		self.assertEqual(leg["precision"], 1.0)
+		self.assertTrue(leg["complete"])
+		self.assertEqual(leg["missed"], [])
+
+	def test_one_missing_source_is_high_recall_but_not_complete(self):
+		"""The distinction the whole thesis rests on: 93% recall, 0% complete."""
+		expected = expected_sections([f"S{number}" for number in range(15)])
+		hits = [make_hit(f"S{number}") for number in range(14)]
+		leg = rag_eval.score_leg(expected, [], hits)
+		self.assertEqual(leg["recall"], round(14 / 15, 4))
+		self.assertFalse(leg["complete"])
+		self.assertEqual([row["title"] for row in leg["missed"]], ["S14"])
+
+	def test_precision_counts_the_noise(self):
+		expected = expected_sections(["A", "B"])
+		hits = [make_hit("A"), make_hit("X"), make_hit("Y"), make_hit("Z")]
+		leg = rag_eval.score_leg(expected, [], hits)
+		self.assertEqual(leg["recall"], 0.5)
+		self.assertEqual(leg["precision"], 0.25)
+		self.assertFalse(leg["complete"])
+
+	def test_required_source_is_tracked_separately_from_recall(self):
+		"""A semantic answer that misses its bolded source is wrong even at 50% recall."""
+		expected = expected_sections(["Main", "Extra"])
+		leg = rag_eval.score_leg(expected, ["Main"], [make_hit("Extra")])
+		self.assertEqual(leg["recall"], 0.5)
+		self.assertFalse(leg["required_hit"])
+		self.assertTrue(rag_eval.score_leg(expected, ["Main"], [make_hit("Main")])["required_hit"])
+
+	def test_empty_leg_scores_zero_recall_and_misses_everything(self):
+		expected = expected_sections(["A", "B"])
+		leg = rag_eval.score_leg(expected, [], [])
+		self.assertEqual(leg["recall"], 0.0)
+		self.assertIsNone(leg["precision"])
+		self.assertFalse(leg["complete"])
+		self.assertEqual(len(leg["missed"]), 2)
+
+	def test_golden_questions_cover_the_declared_lanes(self):
+		"""Ground truth is transcribed, so its shape is asserted rather than trusted."""
+		kinds = [question["kind"] for question in rag_eval.GOLDEN_QUESTIONS]
+		self.assertEqual(len(rag_eval.GOLDEN_QUESTIONS), 12)
+		self.assertEqual(kinds.count("exhaustive"), 4)
+		self.assertEqual(kinds.count("semantic"), 5)
+		self.assertEqual(kinds.count("hybrid"), 2)
+		self.assertEqual(kinds.count("refusal"), 1)
+		self.assertEqual(len(rag_eval.GOLDEN_QUESTIONS[0]["expected"]), G1_EXPECTED_SOURCES)
+		self.assertEqual(
+			len({question["id"] for question in rag_eval.GOLDEN_QUESTIONS}),
+			len(rag_eval.GOLDEN_QUESTIONS),
+		)
+
+
+class TestGoldenQuestions(IntegrationTestCase):
+	"""The eval against the real, indexed Demo Corpus. One run, shared by every assertion."""
+
+	@classmethod
+	def setUpClass(cls):
+		super().setUpClass()
+		cls.project = frappe.db.get_value(
+			"Wikify Project", {"project_name": rag_eval.DEMO_PROJECT_NAME}, "name"
+		)
+		cls.results = None
+		if not cls.project:
+			return
+		if not rag_index.index_stats(cls.project)["chunks"]:
+			cls.project = None
+			return
+		if not settings.openrouter_key():
+			return
+		cls.results = rag_eval.run_eval(project=cls.project, k=G1_NAIVE_LIMIT)
+
+	def setUp(self):
+		if not self.project:
+			self.skipTest(
+				"Demo Corpus is not seeded or not indexed on this site — run "
+				"wikify.tests.fixtures.demo_corpus.seed_demo_corpus then rag.index.rebuild_project"
+			)
+		if self.results is None:
+			self.skipTest("No OpenRouter key configured, so the routed leg cannot be evaluated")
+
+	def question(self, question_id: str) -> dict:
+		return next(row for row in self.results["questions"] if row["id"] == question_id)
+
+	def test_ground_truth_resolves_against_the_real_corpus(self):
+		"""Every transcribed expected-source title must be a real section — no silent zeroes."""
+		for row in self.results["questions"]:
+			declared = next(q for q in rag_eval.GOLDEN_QUESTIONS if q["id"] == row["id"])
+			self.assertEqual(
+				len(row["expected_sources"]),
+				len(declared["expected"]),
+				f"{row['id']}: expected sources did not all resolve to Source Sections",
+			)
+
+	def test_g1_reproduces_the_headline_naive_vs_routed_numbers(self):
+		row = self.question("G1")
+		naive, routed = row["modes"]["naive"], row["modes"]["routed"]
+
+		self.assertEqual(row["route"]["intent"], "exhaustive")
+		self.assertEqual(row["route"]["section_type"], "staff_roles_and_responsibilities")
+
+		self.assertEqual(naive["returned_count"], G1_NAIVE_LIMIT)
+		self.assertEqual(routed["returned_count"], G1_EXPECTED_SOURCES)
+
+		self.assertEqual(naive["correct_count"], G1_NAIVE_CORRECT)
+		self.assertEqual(naive["recall"], round(G1_NAIVE_CORRECT / G1_EXPECTED_SOURCES, 4))
+		self.assertEqual(routed["recall"], 1.0)
+		self.assertFalse(naive["complete"])
+		self.assertTrue(routed["complete"])
+		self.assertEqual(len(naive["missed"]), G1_NAIVE_MISSES)
+		self.assertEqual(len(routed["missed"]), 0)
+
+	def test_g1_naive_misses_whole_documents_that_routed_covers(self):
+		row = self.question("G1")
+		self.assertLess(row["modes"]["naive"]["documents_covered"], 5)
+		self.assertEqual(row["modes"]["routed"]["documents_covered"], 5)
+
+	def test_routed_beats_naive_on_exhaustive_completeness(self):
+		"""The thesis in one assertion: routing wins the completeness lane, not just recall."""
+		naive = self.results["aggregate"]["naive"]
+		routed = self.results["aggregate"]["routed"]
+		self.assertGreater(routed["exhaustive_complete_count"], naive["exhaustive_complete_count"])
+		self.assertGreater(routed["exhaustive_recall"], naive["exhaustive_recall"])
+		self.assertGreater(routed["found_total"], naive["found_total"])
+		self.assertLess(routed["missed_total"], naive["missed_total"])
+
+	def test_routed_never_loses_ground_on_a_golden_question(self):
+		"""Routing may tie naive, but a route that *reduces* recall is a regression."""
+		regressions = [
+			(row["id"], row["modes"]["naive"]["recall"], row["modes"]["routed"]["recall"])
+			for row in self.results["questions"]
+			if row["expected_sources"] and row["modes"]["routed"]["recall"] < row["modes"]["naive"]["recall"]
+		]
+		self.assertEqual(regressions, [], f"routed retrieval lost recall on: {regressions}")
+
+	def test_exhaustive_questions_use_the_filter_leg(self):
+		"""An exhaustive intent must return every section of its type, not a top-k slice."""
+		for row in self.results["questions"]:
+			if row["route"] and row["route"]["intent"] == "exhaustive":
+				section_type = row["route"]["section_type"]
+				in_corpus = frappe.get_all(
+					"Source Section",
+					filters={
+						"section_type": section_type,
+						"source_document": ["in", self.corpus_documents()],
+						"is_group": 0,
+					},
+					pluck="name",
+				)
+				self.assertEqual(
+					row["modes"]["routed"]["returned_count"],
+					len(in_corpus),
+					f"{row['id']}: filter leg did not return every {section_type} section",
+				)
+
+	def corpus_documents(self) -> list[str]:
+		return frappe.get_all("Source Document", filters={"project": self.project}, pluck="name")
+
+	def test_g4_reaches_the_other_catch_all(self):
+		"""G4 was the question the router lost, and it is pinned as fixed.
+
+		"Which organisations does this corpus cover" is an exhaustive ask over
+		`section_type="other"`. The router used to read it as a general question and take the
+		semantic leg, because a catch-all labelled only "anything that does not fit above"
+		gave the model no way to know organisation overviews are filed there. The Section Type
+		descriptions now say what each type actually holds, so the exhaustive filter runs and
+		returns all five overviews.
+
+		`route()` is an LLM call, so this is a *behavioural* pin: if the catch-all description
+		is watered down again, or the prompt stops distinguishing a corpus-wide sweep from a
+		comparison across a named few, this goes red instead of silently under-recalling.
+		"""
+		row = self.question("G4")
+		self.assertEqual(row["expected_intent"], "exhaustive")
+		self.assertEqual(row["route"]["intent"], "exhaustive")
+		self.assertEqual(row["route"]["section_type"], "other")
+		self.assertTrue(row["modes"]["routed"]["complete"])
+		self.assertFalse(row["modes"]["naive"]["complete"])
+
+	def test_g12_is_refused_rather_than_answered(self):
+		row = self.question("G12")
+		self.assertTrue(row["expect_refusal"])
+		self.assertTrue(row["refused"], "G12 must refuse — the corpus has no cyber security plan")
+
+	def test_compare_query_reproduces_the_demoed_headline(self):
+		"""The number quoted in the demo: naive 8 hits over 4 of 5 documents, routed 15 over 5."""
+		comparison = rag_eval.compare_query(HEADLINE_QUERY, project=self.project)
+		self.assertEqual(comparison["naive"]["count"], G1_NAIVE_LIMIT)
+		self.assertEqual(comparison["routed"]["count"], G1_EXPECTED_SOURCES)
+		self.assertEqual(comparison["routed"]["documents_covered"], 5)
+		self.assertEqual(comparison["naive"]["documents_covered"], 4)
+		correct = [
+			hit
+			for hit in comparison["naive"]["hits"]
+			if hit["section_type"] == "staff_roles_and_responsibilities"
+		]
+		self.assertEqual(len(correct), HEADLINE_NAIVE_CORRECT)
+		self.assertEqual(len(comparison["missed_by_naive"]), HEADLINE_NAIVE_MISSES)
+		# The diff must be sections routed found and naive did not — never the other way round.
+		naive_sections = {hit["section"] for hit in comparison["naive"]["hits"]}
+		for missed in comparison["missed_by_naive"]:
+			self.assertNotIn(missed["section"], naive_sections)
+
+	def test_results_are_json_serializable(self):
+		"""The /rag-lab UI and the scorecard both consume this straight as JSON."""
+		encoded = frappe.as_json(self.results)
+		self.assertIn("G1", encoded)
+		self.assertEqual(frappe.parse_json(encoded)["project"], self.project)
+
+	def test_scorecard_is_self_contained_and_names_the_missed_sources(self):
+		with tempfile.TemporaryDirectory() as directory:
+			path = os.path.join(directory, "rag-eval.html")
+			rag_eval.write_scorecard(self.results, path)
+			with open(path, encoding="utf-8") as scorecard:
+				markup = scorecard.read()
+
+		# No CDN, no sibling assets — the file has to survive being emailed on its own.
+		self.assertNotIn("http://", markup)
+		self.assertNotIn("https://", markup)
+		self.assertNotIn("<script", markup)
+		# The naive leg's misses are the point of the picture, so they must be in it.
+		for missed in self.question("G1")["modes"]["naive"]["missed"]:
+			self.assertIn(rag_eval.escape(missed["title"]), markup)
diff --git a/wikify/tests/test_regions.py b/wikify/tests/test_regions.py
new file mode 100644
index 0000000..72f8ae1
--- /dev/null
+++ b/wikify/tests/test_regions.py
@@ -0,0 +1,104 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""Per-region shape classification and the page kind derived from it.
+
+The gate this replaces was `chars < 250 AND drawings > 40`, which on a born-digital
+diagram-heavy manual never fired: 235 of 236 ICAI pages carried both a full text layer and
+heavy vector art, so every one was filed `text` and the visual path was unreachable.
+"""
+
+from __future__ import annotations
+
+import unittest
+
+import fitz  # PyMuPDF
+
+from wikify.engine import pdf_utils, regions
+
+
+def grid_page(doc, rows: int = 6, columns: int = 4):
+	"""A ruled grid of filled cells with a paragraph of prose above it."""
+	page = doc.new_page()
+	for line in range(6):
+		page.insert_text(
+			(72, 90 + line * 14), "Rates of surcharge for the assessment year. " * 2, fontsize=11
+		)
+	for row in range(rows):
+		for column in range(columns):
+			x = 72 + column * 110
+			y = 200 + row * 26
+			page.draw_rect(fitz.Rect(x, y, x + 105, y + 24), color=(0, 0, 0), fill=(0.9, 0.9, 1), width=0.5)
+			page.insert_text((x + 4, y + 16), f"r{row}c{column}", fontsize=9)
+	return page
+
+
+def flow_page(doc):
+	"""Labelled boxes joined by connector lines — a genuine flow, not a grid."""
+	page = doc.new_page()
+	page.insert_text((72, 90), "Decision tree for opting out of the default regime.", fontsize=11)
+	for index in range(4):
+		top = 150 + index * 90
+		page.draw_rect(fitz.Rect(200, top, 350, top + 60), color=(0, 0, 0), width=1)
+		page.insert_text((208, top + 34), f"step {index}", fontsize=10)
+		if index:
+			page.draw_line(fitz.Point(275, top - 30), fitz.Point(275, top))
+	return page
+
+
+def prose_page(doc):
+	page = doc.new_page()
+	page.insert_text((72, 90), "Plain prose with no artwork at all.\n" * 20, fontsize=11)
+	return page
+
+
+class TestRegions(unittest.TestCase):
+	def setUp(self):
+		self.doc = fitz.open()
+		self.addCleanup(self.doc.close)
+
+	def shapes(self, page) -> list[str]:
+		return [region.shape for region in regions.find_regions(page)]
+
+	def test_a_grid_is_found_as_a_table(self):
+		page = grid_page(self.doc)
+		tables = [region for region in regions.find_regions(page) if region.shape == regions.TABLE]
+		self.assertEqual(len(tables), 1)
+		self.assertGreaterEqual(tables[0].rows, 4)
+		self.assertGreaterEqual(tables[0].columns, 3)
+		self.assertTrue(tables[0].substantial)
+
+	def test_labelled_boxes_are_a_flow_not_a_table(self):
+		shapes = self.shapes(flow_page(self.doc))
+		self.assertIn(regions.FLOW, shapes)
+		self.assertNotIn(regions.TABLE, shapes)
+
+	def test_a_text_page_with_heavy_art_is_mixed_not_text(self):
+		"""The regression the old AND-gate could not express."""
+		page = grid_page(self.doc)
+		self.assertGreater(len(page.get_text("text").strip()), 250)
+		self.assertEqual(pdf_utils.classify_page(page, 250, 40), "mixed")
+
+	def test_a_plain_prose_page_stays_text(self):
+		self.assertEqual(pdf_utils.classify_page(prose_page(self.doc), 250, 40), "text")
+
+	def test_a_textless_diagram_page_stays_visual(self):
+		page = self.doc.new_page()
+		for index in range(60):
+			x, y = 50 + (index % 10) * 45, 120 + (index // 10) * 60
+			page.draw_rect(fitz.Rect(x, y, x + 35, y + 45), color=(0, 0, 0), width=1)
+		self.assertEqual(pdf_utils.classify_page(page, 250, 40), "visual")
+
+	def test_a_flow_engulfed_by_a_grid_is_not_announced_twice(self):
+		"""Connectors chain through a table's area; announcing that span as a second `flow`
+		region is exactly the hint that talks the model into a flowchart."""
+		page = grid_page(self.doc, rows=10, columns=5)
+		self.assertEqual(self.shapes(page).count(regions.FLOW), 0)
+
+	def test_the_shape_hint_names_the_grid_and_forbids_a_flowchart(self):
+		hint = regions.shape_hint(regions.find_regions(grid_page(self.doc)))
+		self.assertIn("table region", hint)
+		self.assertIn("never a flowchart", hint)
+
+	def test_the_shape_hint_is_empty_for_a_prose_page(self):
+		self.assertEqual(regions.shape_hint(regions.find_regions(prose_page(self.doc))), "")
diff --git a/wikify/tests/test_remediate_adoption.py b/wikify/tests/test_remediate_adoption.py
new file mode 100644
index 0000000..a0ef3d2
--- /dev/null
+++ b/wikify/tests/test_remediate_adoption.py
@@ -0,0 +1,92 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""Adoption floors: a remediation may only become canonical if it is a better read of the page.
+
+Page 6 of the second ICAI referencer shipped `# TIE TIT Molo` — ten characters of OCR noise — as
+its canonical markdown at composite 0.058, over a VLM read of the same page. Cleanup's own
+eligibility test only asks whether recall regressed, and recall measured against an equally empty
+baseline had not, so the worst candidate on the page won by default. These tests hold the two
+floors that now sit under every method's rules.
+"""
+
+from __future__ import annotations
+
+import unittest
+
+from wikify.engine.remediate import content_chars, pick_winner, with_page_crop
+from wikify.engine.verify import PageScore
+
+
+def candidate(method: str, markdown: str, composite: float, eligible: bool = True) -> tuple:
+	score = PageScore(
+		page_no=6,
+		text_recall=0.0,
+		extra_ratio=0.0,
+		table_score=None,
+		judge_score=None,
+		composite=composite,
+		verdict="review",
+	)
+	return (method, markdown, score, eligible)
+
+
+GOOD_MARKDOWN = (
+	"# Capital asset\n\nA capital asset means property of any kind held by an assessee, "
+	"whether or not connected with his business or profession [Section 2(14)].\n"
+)
+OCR_NOISE = "# TIE TIT Molo"
+
+
+class TestAdoptionFloors(unittest.TestCase):
+	def test_near_empty_candidate_never_beats_one_with_content(self):
+		"""The live regression: cleanup passed its recall test, and still must not be adopted."""
+		candidates = [candidate("vlm", GOOD_MARKDOWN, 0.82), candidate("cleanup", OCR_NOISE, 0.058)]
+		winner = pick_winner(candidates, baseline_composite=0.05, baseline_markdown="")
+		self.assertEqual(winner[0], "vlm")
+
+	def test_near_empty_candidate_is_not_adopted_even_when_it_is_the_only_one(self):
+		candidates = [candidate("cleanup", OCR_NOISE, 0.058)]
+		self.assertIsNone(pick_winner(candidates, baseline_composite=0.31, baseline_markdown=GOOD_MARKDOWN))
+
+	def test_a_dramatically_worse_candidate_is_never_adopted(self):
+		candidates = [candidate("vlm", GOOD_MARKDOWN, 0.91), candidate("cleanup", GOOD_MARKDOWN, 0.40)]
+		winner = pick_winner(candidates, baseline_composite=0.30, baseline_markdown=GOOD_MARKDOWN)
+		self.assertEqual(winner[0], "vlm")
+
+	def test_a_candidate_below_the_baseline_it_came_from_is_never_adopted(self):
+		candidates = [candidate("cleanup", GOOD_MARKDOWN, 0.40)]
+		self.assertIsNone(pick_winner(candidates, baseline_composite=0.88, baseline_markdown=GOOD_MARKDOWN))
+
+	def test_a_genuinely_blank_page_still_adopts_its_best_read(self):
+		"""Both floors are relative — they must not strand a page whose source really is bare."""
+		candidates = [candidate("cleanup", "# Notes", 0.62)]
+		winner = pick_winner(candidates, baseline_composite=0.20, baseline_markdown="")
+		self.assertEqual(winner[0], "cleanup")
+
+	def test_ineligible_candidates_stay_ineligible(self):
+		candidates = [candidate("vlm", GOOD_MARKDOWN, 0.95, eligible=False)]
+		self.assertIsNone(pick_winner(candidates, baseline_composite=0.30, baseline_markdown=""))
+
+
+class TestContentChars(unittest.TestCase):
+	def test_ocr_noise_does_not_read_as_content(self):
+		self.assertEqual(content_chars(OCR_NOISE), len("TIETITMolo"))
+
+	def test_a_crop_on_its_own_does_not_read_as_content(self):
+		self.assertEqual(content_chars("![Source page](/files/page-0006.png)"), 0)
+
+	def test_a_diagram_reads_as_content(self):
+		markdown = '```mermaid\nflowchart TD\n\tA["Capital asset"] --> B["Property of any kind"]\n```'
+		self.assertGreater(content_chars(markdown), 40)
+
+
+class TestPageCropFallback(unittest.TestCase):
+	def test_a_near_empty_page_keeps_its_crop(self):
+		self.assertIn("![Source page](/files/p.png)", with_page_crop(OCR_NOISE, "/files/p.png"))
+
+	def test_a_page_with_content_is_left_alone(self):
+		self.assertEqual(with_page_crop(GOOD_MARKDOWN, "/files/p.png"), GOOD_MARKDOWN)
+
+	def test_no_image_means_no_change(self):
+		self.assertEqual(with_page_crop(OCR_NOISE, ""), OCR_NOISE)
diff --git a/wikify/tests/test_sectionize.py b/wikify/tests/test_sectionize.py
index 7c48535..fd7ec80 100644
--- a/wikify/tests/test_sectionize.py
+++ b/wikify/tests/test_sectionize.py
@@ -10,7 +10,8 @@
 
 from wikify.engine import parse_pdf, remediate_pdf
 from wikify.engine.loader.cleanup import clean_pages, strip_outer_markdown_fence
-from wikify.engine.loader.sectionizer import sectionize
+from wikify.engine.loader.sectionizer import MAX_TITLE_LENGTH, running_header_titles, sectionize
+from wikify.rag.chunk import build_chunks
 from wikify.tests.test_parse_pipeline import _make_sample_pdf
 from wikify.tests.test_remediate_pipeline import _MERMAID, _fake_chat
 
@@ -127,6 +128,60 @@ def test_clean_pages_strips_signoff_footer_block(self):
 		self.assertIn("real body", cleaned[1])
 		self.assertIn("more body", cleaned[2])
 
+	def test_title_is_clipped_to_the_storable_length(self):
+		# An over-length title aborted `replace_sections` mid-loop with
+		# CharacterLengthExceededError, dropping every later section (ICAI: pages 20-236).
+		long_title = "DETERMINATION OF RESIDENTIAL STATUS OF " + "HINDU UNDIVIDED FAMILY " * 8
+		secs = sectionize([(1, f"## {long_title}\nbody")])
+		self.assertLessEqual(len(secs[0].title), MAX_TITLE_LENGTH)
+		self.assertTrue(secs[0].title.endswith("…"))
+		self.assertTrue(secs[0].title.startswith("DETERMINATION OF RESIDENTIAL STATUS"))
+		self.assertNotIn(" …", secs[0].title)  # clipped on a word boundary, no dangling space
+
+	def test_clipped_title_fits_the_data_column(self):
+		# The loader is frappe-free, so its mirror of the Data column cap is pinned here.
+		self.assertEqual(MAX_TITLE_LENGTH, frappe.db.VARCHAR_LEN)
+
+	def test_long_titled_sections_all_reach_the_database(self):
+		# The regression that mattered: every section after the long heading survived.
+		pages = [
+			(1, "## Chapter One\nfirst"),
+			(2, f"## {'X' * 200}\nsecond"),
+			(3, "## Chapter Three\nthird"),
+		]
+		secs = sectionize(pages)
+		self.assertEqual(len(secs), 3)
+		self.assertEqual(secs[-1].title, "Chapter Three")
+		self.assertEqual(max(s.page_end for s in secs), 3)
+
+	def test_running_page_header_opens_one_section_not_one_per_page(self):
+		# An unnumbered chapter header re-stamped at the top of every page is furniture
+		# after its first appearance — the body of later pages continues the open section.
+		pages = [
+			(1, "# TRANSFER PRICING\n## Arm's Length Price\nalp body"),
+			(2, "# TRANSFER PRICING\nmore alp body"),
+			(3, "# TRANSFER PRICING\nstill more alp body"),
+		]
+		secs = sectionize(pages)
+		self.assertEqual([s.title for s in secs], ["TRANSFER PRICING", "Arm's Length Price"])
+		alp = secs[1]
+		self.assertEqual(alp.page_end, 3)
+		self.assertIn("still more alp body", alp.markdown)
+		# The header text itself is dropped, not folded into the body.
+		self.assertNotIn("TRANSFER PRICING", alp.markdown)
+
+	def test_heading_repeated_mid_page_is_not_a_running_header(self):
+		# Repetition alone isn't the signal — a genuine heading deep in the page stays.
+		pages = [
+			(page, f"## Chapter {page}\nlead in\nfiller\nfiller\n## Notes\nnote body") for page in (1, 2, 3)
+		]
+		self.assertNotIn("Notes", running_header_titles(pages))
+		self.assertEqual(len([s for s in sectionize(pages) if s.title == "Notes"]), 3)
+
+	def test_heading_on_two_pages_is_not_a_running_header(self):
+		pages = [(1, "# Intro\na"), (2, "# Intro\nb")]
+		self.assertEqual(running_header_titles(pages), set())
+
 	def test_clean_pages_keeps_data_row_mentioning_approved_by_once(self):
 		# A genuine data row that merely mentions one sign-off phrase is NOT furniture.
 		table = "| Step | Status |\n|---|---|\n| Reviewed and approved by committee | done |"
@@ -136,6 +191,37 @@ def test_clean_pages_keeps_data_row_mentioning_approved_by_once(self):
 		self.assertIn("|---|---|", cleaned[1])  # the real table separator survives
 
 
+class TestEmptySectionsAreFlagged(FrappeTestCase):
+	"""A breadcrumb section with no body is indexed, but flagged out of the ranking legs."""
+
+	def test_section_without_markdown_is_chunked_as_title_only(self):
+		rows = [
+			{
+				"name": "SEC-EMPTY",
+				"source_document": "SD-1",
+				"title": "Concessional tax rates under section 115BAC(1A)",
+				"hierarchy_path": "Basic Concepts",
+				"markdown": "",
+				"page_start": 8,
+				"page_end": 8,
+			},
+			{
+				"name": "SEC-BODY",
+				"source_document": "SD-1",
+				"title": "Conditions",
+				"hierarchy_path": "Basic Concepts > Conditions",
+				"markdown": "The conditions are as follows.",
+				"page_start": 8,
+				"page_end": 8,
+			},
+		]
+		chunks = build_chunks(rows, {"SD-1": {"title": "Referencer", "project": "PRJ-1"}}, {})
+		self.assertEqual([chunk.section for chunk in chunks], ["SEC-EMPTY", "SEC-BODY"])
+		self.assertEqual([chunk.title_only for chunk in chunks], [True, False])
+		# The flagged row carries its title so a filter-mode citation still reads as something.
+		self.assertEqual(chunks[0].text, "Concessional tax rates under section 115BAC(1A)")
+
+
 class TestSectionizeIntegration(FrappeTestCase):
 	"""parse/remediate build the Source Section NestedSet tree."""
 
diff --git a/wikify/wikify/doctype/source_page/source_page.json b/wikify/wikify/doctype/source_page/source_page.json
index 3eb7300..bd83f5d 100644
--- a/wikify/wikify/doctype/source_page/source_page.json
+++ b/wikify/wikify/doctype/source_page/source_page.json
@@ -56,7 +56,7 @@
    "fieldtype": "Select",
    "in_list_view": 1,
    "label": "Kind",
-   "options": "text\nvisual",
+   "options": "text\nmixed\nvisual",
    "read_only": 1
   },
   {
diff --git a/wikify/wikify/doctype/wikify_ask_message/__init__.py b/wikify/wikify/doctype/wikify_ask_message/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.json b/wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.json
new file mode 100644
index 0000000..92e6f72
--- /dev/null
+++ b/wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.json
@@ -0,0 +1,190 @@
+{
+ "actions": [],
+ "allow_rename": 0,
+ "autoname": "hash",
+ "creation": "2026-08-10 00:00:00",
+ "doctype": "DocType",
+ "engine": "InnoDB",
+ "field_order": [
+  "session",
+  "role",
+  "turn",
+  "content",
+  "route_section",
+  "route_intent",
+  "route_section_type",
+  "column_break_route",
+  "route_reason",
+  "refused",
+  "sources_section",
+  "citations",
+  "cost_section",
+  "model",
+  "took_ms",
+  "column_break_cost",
+  "prompt_tokens",
+  "completion_tokens",
+  "cost"
+ ],
+ "fields": [
+  {
+   "fieldname": "session",
+   "fieldtype": "Link",
+   "in_list_view": 1,
+   "label": "Session",
+   "options": "Wikify Ask Session",
+   "reqd": 1,
+   "search_index": 1
+  },
+  {
+   "fieldname": "role",
+   "fieldtype": "Select",
+   "in_list_view": 1,
+   "in_standard_filter": 1,
+   "label": "Role",
+   "options": "question\nanswer",
+   "reqd": 1
+  },
+  {
+   "default": "0",
+   "description": "Pairs the question row with its answer row, so an eval query joins a turn on one integer instead of on creation order.",
+   "fieldname": "turn",
+   "fieldtype": "Int",
+   "label": "Turn",
+   "non_negative": 1
+  },
+  {
+   "fieldname": "content",
+   "fieldtype": "Long Text",
+   "label": "Content"
+  },
+  {
+   "fieldname": "route_section",
+   "fieldtype": "Section Break",
+   "label": "Routing"
+  },
+  {
+   "description": "Which retrieval leg the router chose. Indexed: \"which questions did we route wrong\" is the primary eval query.",
+   "fieldname": "route_intent",
+   "fieldtype": "Select",
+   "in_standard_filter": 1,
+   "label": "Route Intent",
+   "options": "\nexhaustive\nsemantic\nhybrid",
+   "search_index": 1
+  },
+  {
+   "fieldname": "route_section_type",
+   "fieldtype": "Link",
+   "in_standard_filter": 1,
+   "label": "Route Section Type",
+   "options": "Section Type"
+  },
+  {
+   "fieldname": "column_break_route",
+   "fieldtype": "Column Break"
+  },
+  {
+   "description": "The one-line justification shown in the interface.",
+   "fieldname": "route_reason",
+   "fieldtype": "Small Text",
+   "label": "Route Reason"
+  },
+  {
+   "default": "0",
+   "description": "The answer refused because nothing retrieved cleared the score floor.",
+   "fieldname": "refused",
+   "fieldtype": "Check",
+   "in_standard_filter": 1,
+   "label": "Refused"
+  },
+  {
+   "fieldname": "sources_section",
+   "fieldtype": "Section Break",
+   "label": "Sources"
+  },
+  {
+   "description": "The cited Hit dicts (JSON) exactly as the interface rendered them — kept so a later pass can ask which sources an answer actually stood on.",
+   "fieldname": "citations",
+   "fieldtype": "Long Text",
+   "label": "Citations"
+  },
+  {
+   "fieldname": "cost_section",
+   "fieldtype": "Section Break",
+   "label": "Cost"
+  },
+  {
+   "fieldname": "model",
+   "fieldtype": "Data",
+   "in_standard_filter": 1,
+   "label": "Model"
+  },
+  {
+   "fieldname": "took_ms",
+   "fieldtype": "Int",
+   "label": "Took (ms)",
+   "non_negative": 1
+  },
+  {
+   "fieldname": "column_break_cost",
+   "fieldtype": "Column Break"
+  },
+  {
+   "default": "0",
+   "fieldname": "prompt_tokens",
+   "fieldtype": "Int",
+   "label": "Prompt Tokens",
+   "non_negative": 1
+  },
+  {
+   "default": "0",
+   "fieldname": "completion_tokens",
+   "fieldtype": "Int",
+   "label": "Completion Tokens",
+   "non_negative": 1
+  },
+  {
+   "default": "0",
+   "description": "LLM spend (USD) for this one ask, summed across routing, rerank and synthesis.",
+   "fieldname": "cost",
+   "fieldtype": "Float",
+   "in_list_view": 1,
+   "label": "Cost (USD)",
+   "non_negative": 1,
+   "precision": "6"
+  }
+ ],
+ "index_web_pages_for_search": 1,
+ "links": [],
+ "modified": "2026-08-10 00:00:00",
+ "modified_by": "Administrator",
+ "module": "Wikify",
+ "name": "Wikify Ask Message",
+ "naming_rule": "Random",
+ "owner": "Administrator",
+ "permissions": [
+  {
+   "create": 1,
+   "delete": 1,
+   "email": 1,
+   "export": 1,
+   "print": 1,
+   "read": 1,
+   "report": 1,
+   "role": "System Manager",
+   "share": 1,
+   "write": 1
+  },
+  {
+   "create": 1,
+   "delete": 1,
+   "if_owner": 1,
+   "read": 1,
+   "role": "All",
+   "write": 1
+  }
+ ],
+ "sort_field": "creation",
+ "sort_order": "ASC",
+ "states": []
+}
diff --git a/wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.py b/wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.py
new file mode 100644
index 0000000..913517f
--- /dev/null
+++ b/wikify/wikify/doctype/wikify_ask_message/wikify_ask_message.py
@@ -0,0 +1,10 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+from __future__ import annotations
+
+from frappe.model.document import Document
+
+
+class WikifyAskMessage(Document):
+	pass
diff --git a/wikify/wikify/doctype/wikify_ask_session/__init__.py b/wikify/wikify/doctype/wikify_ask_session/__init__.py
new file mode 100644
index 0000000..e69de29
diff --git a/wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.json b/wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.json
new file mode 100644
index 0000000..601a45d
--- /dev/null
+++ b/wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.json
@@ -0,0 +1,137 @@
+{
+ "actions": [],
+ "allow_rename": 0,
+ "autoname": "naming_series:",
+ "creation": "2026-08-10 00:00:00",
+ "doctype": "DocType",
+ "engine": "InnoDB",
+ "field_order": [
+  "naming_series",
+  "title",
+  "user",
+  "column_break_meta",
+  "project",
+  "started_at",
+  "totals_section",
+  "message_count",
+  "total_tokens",
+  "column_break_totals",
+  "total_cost"
+ ],
+ "fields": [
+  {
+   "default": "ASK-.YYYY.-.#####",
+   "fieldname": "naming_series",
+   "fieldtype": "Select",
+   "hidden": 1,
+   "label": "Naming Series",
+   "options": "ASK-.YYYY.-.#####"
+  },
+  {
+   "description": "Taken from the first question of the conversation; editable.",
+   "fieldname": "title",
+   "fieldtype": "Data",
+   "in_list_view": 1,
+   "label": "Title"
+  },
+  {
+   "description": "Denormalized from owner so the eval set can group by asker without a join.",
+   "fieldname": "user",
+   "fieldtype": "Link",
+   "in_standard_filter": 1,
+   "label": "User",
+   "options": "User",
+   "read_only": 1
+  },
+  {
+   "fieldname": "column_break_meta",
+   "fieldtype": "Column Break"
+  },
+  {
+   "description": "The project the conversation was scoped to, if any.",
+   "fieldname": "project",
+   "fieldtype": "Link",
+   "in_standard_filter": 1,
+   "label": "Project",
+   "options": "Wikify Project"
+  },
+  {
+   "fieldname": "started_at",
+   "fieldtype": "Datetime",
+   "label": "Started At",
+   "read_only": 1
+  },
+  {
+   "fieldname": "totals_section",
+   "fieldtype": "Section Break",
+   "label": "Totals"
+  },
+  {
+   "default": "0",
+   "fieldname": "message_count",
+   "fieldtype": "Int",
+   "in_list_view": 1,
+   "label": "Message Count",
+   "non_negative": 1,
+   "read_only": 1
+  },
+  {
+   "default": "0",
+   "description": "Prompt + completion tokens across every leg of every ask in this conversation.",
+   "fieldname": "total_tokens",
+   "fieldtype": "Int",
+   "label": "Total Tokens",
+   "non_negative": 1,
+   "read_only": 1
+  },
+  {
+   "fieldname": "column_break_totals",
+   "fieldtype": "Column Break"
+  },
+  {
+   "default": "0",
+   "description": "Cumulative LLM spend (USD) for this conversation: routing + rerank + synthesis.",
+   "fieldname": "total_cost",
+   "fieldtype": "Float",
+   "in_list_view": 1,
+   "label": "Total Cost (USD)",
+   "non_negative": 1,
+   "precision": "6",
+   "read_only": 1
+  }
+ ],
+ "index_web_pages_for_search": 1,
+ "links": [],
+ "modified": "2026-08-10 00:00:00",
+ "modified_by": "Administrator",
+ "module": "Wikify",
+ "name": "Wikify Ask Session",
+ "naming_rule": "By \"Naming Series\" field",
+ "owner": "Administrator",
+ "permissions": [
+  {
+   "create": 1,
+   "delete": 1,
+   "email": 1,
+   "export": 1,
+   "print": 1,
+   "read": 1,
+   "report": 1,
+   "role": "System Manager",
+   "share": 1,
+   "write": 1
+  },
+  {
+   "create": 1,
+   "delete": 1,
+   "if_owner": 1,
+   "read": 1,
+   "role": "All",
+   "write": 1
+  }
+ ],
+ "sort_field": "modified",
+ "sort_order": "DESC",
+ "states": [],
+ "title_field": "title"
+}
diff --git a/wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.py b/wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.py
new file mode 100644
index 0000000..c04487e
--- /dev/null
+++ b/wikify/wikify/doctype/wikify_ask_session/wikify_ask_session.py
@@ -0,0 +1,17 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+from __future__ import annotations
+
+import frappe
+from frappe.model.document import Document
+
+
+class WikifyAskSession(Document):
+	def before_insert(self) -> None:
+		self.user = self.user or frappe.session.user
+		self.started_at = self.started_at or frappe.utils.now_datetime()
+
+	def on_trash(self) -> None:
+		"""Cascade-delete this conversation's turns (they aren't a child table)."""
+		frappe.db.delete("Wikify Ask Message", {"session": self.name})

From 27c85ec3723ab4eaf5ef27b0283180d35127123e Mon Sep 17 00:00:00 2001
From: rl0007 <rl0007@users.noreply.github.com>
Date: Tue, 11 Aug 2026 09:01:17 +0530
Subject: [PATCH 4/8] docs(rag): add PR screenshots captured from
 wikify.localhost

Twelve images covering the Ask flow with citations, page/line provenance, the
cost meter, the naive-vs-routed comparison, the refusal state, a rendered
mermaid diagram, the reconstructed ICAI surcharge tables, the page verdict
list, and three mobile views plus dark mode.
---
 .../media/pr/01-ask-answer-citations.png       | Bin 0 -> 260345 bytes
 .../media/pr/02-source-card-provenance.png     | Bin 0 -> 187982 bytes
 docs/implementation/media/pr/03-cost-meter.png | Bin 0 -> 46894 bytes
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 create mode 100644 docs/implementation/media/pr/01-ask-answer-citations.png
 create mode 100644 docs/implementation/media/pr/02-source-card-provenance.png
 create mode 100644 docs/implementation/media/pr/03-cost-meter.png
 create mode 100644 docs/implementation/media/pr/04-rag-lab-compare.png
 create mode 100644 docs/implementation/media/pr/05-refusal.png
 create mode 100644 docs/implementation/media/pr/06-mermaid-render.png
 create mode 100644 docs/implementation/media/pr/07-icai-surcharge-tables.png
 create mode 100644 docs/implementation/media/pr/08-page-list-verdicts.png
 create mode 100644 docs/implementation/media/pr/09-mobile-ask.png
 create mode 100644 docs/implementation/media/pr/10-mobile-rag-lab.png
 create mode 100644 docs/implementation/media/pr/11-mobile-bottom-nav.png
 create mode 100644 docs/implementation/media/pr/12-ask-answer-dark.png

diff --git a/docs/implementation/media/pr/01-ask-answer-citations.png b/docs/implementation/media/pr/01-ask-answer-citations.png
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HcmV?d00001


From 32e1154f7a782a375b57fb9cdebf694f6bc68c33 Mon Sep 17 00:00:00 2001
From: rl0007 <rl0007@users.noreply.github.com>
Date: Tue, 11 Aug 2026 12:42:42 +0530
Subject: [PATCH 5/8] fix(rag): correct reranking, cost accounting, and
 retrieval efficiency
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Three defects found by testing the Ask path against two real ICAI documents,
plus the cleanup pass that followed.

Reranking — answers were being refused that the corpus covered
- `search()` re-sorted by fusion score after reranking and then applied the
  limit, so the reranked winner was pushed back to its fusion position and
  sliced off. "Slab rates under section 115BAC(1A)" scored 8.0 at fusion rank
  10 and never reached the answer; the question was refused. One `rank_key`
  now drives both the rerank and the slice.
- Grading 35 candidates in one call makes the classifier emit a run of zeros.
  Batching at 10 returns real scores. A batch whose reply skips candidates is
  dropped whole rather than letting the missing ones read as a confident 0.
- Refusal now needs two legs to agree: a below-floor rerank is overruled by a
  strong embedding match. Calibrated on both corpora — answerable questions
  peak 0.56-0.63, unanswerable 0.39-0.45. Genuine refusals still refuse.
- When the embedding leg overrules, citations carry `rerank_score: null`
  rather than 0.0, so the UI stops showing a dead relevance chip.

Cost — the reported figure was ~15x too low
Only the price printed on the response body was counted. The router and
reranker use the REST client, which returns cost inline; synthesis goes
through litellm, which reports price to a callback on its own thread. So the
meter added $0.00 for the leg carrying ~92% of the spend. A `CustomLogger`
keyed on `litellm_call_id` now bills each call back to the collector that
started it, and the second accounting mechanism in `history.py` is deleted —
session totals are summed from message rows so the header cannot drift.
Verified against OpenRouter's own generation records: an ICAI question costs
$0.047, not the $0.003 previously reported.

Efficiency
- Rerank batches run concurrently. `frappe.local` is unbound on a pool thread,
  so the API key is resolved on the calling thread and passed in. Measured
  19.81s -> 16.00s median per ask, with identical call count and spend.
- `index_status` did one LanceDB connect and one full scan per readable
  project; now one scan with a single `project IN (...)`.
- Page propagation rebuilt the whole FTS index once per section and re-read
  every page of the document each time; now one batched upsert and one FTS
  rebuild, loading only the relevant pages.
- Sectionising fired the reindex hook per row (~592 redis round trips, 295 of
  them no-ops); it now suspends per-row indexing and queues one rebuild.
- `get_page_image()` ran per page inside the remediation loop; the field is
  read with the rest of the row.
- Mermaid sources were parsed up to nine times per page; now once.
- `find_regions` read each page's drawings twice.

Reuse and dead code
Duplicate `get_pages_by_document` / batched-IN / page-line-span helpers
collapsed to one definition each; `useIsMobile` folded into `useMediaQuery` so
the app has one media-query registry; AskWiki reuses `MarkdownPreview`, which
also gives Ask answers mermaid rendering they never had; the eval harness now
calls production's `retrieve()` and `compare()` instead of copying them, which
is the drift it exists to catch. Removed an unreachable `EvalScoreboard`
branch, a dead `fts` relevance basis, an unused `page_regions` parameter, and
a `wait_for_prices` call that could add 5s to a failing ask without affecting
any reported number.

Streaming answers no longer re-parse the whole markdown per token.

References to five spec files that are not in this repo are rewritten to point
at the code that holds the information.
---
 frontend/src/components/AppShell.vue          |   2 +-
 frontend/src/components/Explore.vue           |   2 +-
 frontend/src/components/MarkdownPreview.vue   |   8 +-
 .../src/components/rag/EvalScoreboard.vue     |  36 +---
 frontend/src/components/rag/RelevanceTag.vue  |   8 +-
 frontend/src/composables/useIsMobile.js       |  21 --
 frontend/src/composables/useMediaQuery.js     |   7 +
 frontend/src/composables/useRag.js            |  66 +++---
 frontend/src/pages/AskWiki.vue                |  48 +++--
 frontend/src/pages/ImportDetail.vue           |   2 +-
 frontend/src/pages/ProjectDetail.vue          |   2 +-
 frontend/src/pages/ProjectList.vue            |   2 +-
 wikify/agent/session.py                       |  20 +-
 wikify/agent/tools/retrieve.py                |   9 +-
 wikify/api/rag.py                             |  47 ++---
 wikify/engine/diagrams.py                     |  37 ++--
 wikify/engine/llm.py                          |   7 +-
 wikify/engine/pdf_utils.py                    |   5 +-
 wikify/engine/regions.py                      |  37 ++--
 wikify/engine/remediate.py                    |  21 +-
 wikify/engine/store.py                        |  63 ++++--
 wikify/rag/__init__.py                        |   2 +-
 wikify/rag/answer.py                          | 114 ++++++++--
 wikify/rag/chunk.py                           |  71 +++----
 wikify/rag/eval.py                            | 107 +++-------
 wikify/rag/events.py                          |  11 +-
 wikify/rag/evidence.py                        |  70 ++++---
 wikify/rag/history.py                         | 165 ++++-----------
 wikify/rag/index.py                           |  41 +++-
 wikify/rag/search.py                          | 194 ++++++++++++++----
 wikify/rag/usage.py                           | 132 +++++++++++-
 wikify/tests/fixtures/demo_corpus.py          |  13 +-
 wikify/tests/test_ask_history.py              | 156 ++++++--------
 wikify/tests/test_evidence.py                 |   2 +-
 wikify/tests/test_page_propagation.py         |  19 +-
 wikify/tests/test_rag_api.py                  |  97 ++++++---
 wikify/tests/test_rag_core.py                 | 130 +++++++++++-
 wikify/tests/test_rag_eval.py                 |   7 +-
 38 files changed, 1041 insertions(+), 740 deletions(-)
 delete mode 100644 frontend/src/composables/useIsMobile.js

diff --git a/frontend/src/components/AppShell.vue b/frontend/src/components/AppShell.vue
index 0d47bd7..d83cdf5 100644
--- a/frontend/src/components/AppShell.vue
+++ b/frontend/src/components/AppShell.vue
@@ -10,7 +10,7 @@ import {
 import { computed, onMounted, ref, watch } from "vue";
 import { RouterLink, useRoute } from "vue-router";
 import { useTheme } from "@/utils/useTheme";
-import { useIsMobile } from "@/composables/useIsMobile";
+import { useIsMobile } from "@/composables/useMediaQuery";
 import { session } from "@/data/session";
 import AgentChatPanel from "@/components/AgentChatPanel.vue";
 import AppSettingsDialog from "@/components/AppSettingsDialog.vue";
diff --git a/frontend/src/components/Explore.vue b/frontend/src/components/Explore.vue
index bf8623c..594d9e5 100644
--- a/frontend/src/components/Explore.vue
+++ b/frontend/src/components/Explore.vue
@@ -3,7 +3,7 @@ import { computed, ref, watch, onMounted, onUnmounted } from "vue";
 import { Badge, Button, useCall, toast } from "frappe-ui";
 import { useSocket } from "@/socket";
 import TypeChip from "@/components/TypeChip.vue";
-import { useIsMobile } from "@/composables/useIsMobile";
+import { useIsMobile } from "@/composables/useMediaQuery";
 import { actionButtonProps } from "@/utils/actionButton";
 
 const props = defineProps({
diff --git a/frontend/src/components/MarkdownPreview.vue b/frontend/src/components/MarkdownPreview.vue
index ef6be63..d0716ac 100644
--- a/frontend/src/components/MarkdownPreview.vue
+++ b/frontend/src/components/MarkdownPreview.vue
@@ -8,10 +8,16 @@ import { renderMermaidIn } from "@/utils/mermaid";
 
 const props = defineProps({
 	content: { type: String, default: "" },
+	// Optional post-processor over the rendered markup, for callers that inject their own
+	// interactive markup into the answer (Ask turns `[1]` markers into citation chips).
+	decorate: { type: Function, default: null },
 });
 
 const container = ref(null);
-const html = computed(() => marked.parse(props.content || "", { async: false }));
+const html = computed(() => {
+	const rendered = marked.parse(props.content || "", { async: false });
+	return props.decorate ? props.decorate(rendered) : rendered;
+});
 
 async function renderDiagrams() {
 	await nextTick();
diff --git a/frontend/src/components/rag/EvalScoreboard.vue b/frontend/src/components/rag/EvalScoreboard.vue
index c967ef6..96c0fb9 100644
--- a/frontend/src/components/rag/EvalScoreboard.vue
+++ b/frontend/src/components/rag/EvalScoreboard.vue
@@ -1,19 +1,6 @@
 <script setup>
-// Recall@k per golden question. The eval endpoint is not in the frozen contract yet, so
-// this renders an explicitly-labelled placeholder until a later slice wires it up.
-import { computed } from "vue";
-import CoverageBar from "@/components/rag/CoverageBar.vue";
-
-const props = defineProps({
-	rows: { type: Array, default: () => [] },
-	loading: { type: Boolean, default: false },
-});
-
-const averageRecall = computed(() => {
-	if (!props.rows.length) return null;
-	const total = props.rows.reduce((sum, row) => sum + (row.recall || 0), 0);
-	return total / props.rows.length;
-});
+// Recall@k per golden question. There is no eval endpoint yet, so this renders an
+// explicitly-labelled placeholder until a later slice wires it up.
 </script>
 
 <template>
@@ -22,26 +9,9 @@ const averageRecall = computed(() => {
 			<span class="lucide-target size-4 text-ink-gray-5" aria-hidden="true" />
 			<h2 class="text-base font-medium text-ink-gray-9">Eval scoreboard</h2>
 			<span class="text-xs text-ink-gray-5">recall@k per golden question</span>
-			<div class="flex-1" />
-			<span v-if="averageRecall != null" class="text-sm text-ink-gray-7">
-				avg {{ (averageRecall * 100).toFixed(0) }}%
-			</span>
 		</header>
 
-		<div v-if="rows.length" class="divide-y divide-outline-gray-1">
-			<div
-				v-for="row in rows"
-				:key="row.question"
-				class="flex flex-col gap-2 px-4 py-3 sm:flex-row sm:items-center sm:gap-4"
-			>
-				<p class="min-w-0 flex-1 truncate text-base text-ink-gray-8">{{ row.question }}</p>
-				<div class="w-full sm:w-48">
-					<CoverageBar :value="row.recall" :total="1" tone="good" label="recall" />
-				</div>
-			</div>
-		</div>
-
-		<div v-else class="flex flex-col items-center gap-2 px-4 py-10 text-center">
+		<div class="flex flex-col items-center gap-2 px-4 py-10 text-center">
 			<span class="lucide-flask-conical size-6 text-ink-gray-4" aria-hidden="true" />
 			<p class="text-base text-ink-gray-7">Not wired up yet</p>
 			<p class="max-w-md text-sm text-ink-gray-5">
diff --git a/frontend/src/components/rag/RelevanceTag.vue b/frontend/src/components/rag/RelevanceTag.vue
index ffc6cf9..4ce24e7 100644
--- a/frontend/src/components/rag/RelevanceTag.vue
+++ b/frontend/src/components/rag/RelevanceTag.vue
@@ -16,7 +16,7 @@ const props = defineProps({
 	rank: { type: Number, default: null },
 	total: { type: Number, default: 0 },
 	score: { type: Number, default: null },
-	basis: { type: String, default: "hybrid" }, // exhaustive | hybrid | vector | fts
+	basis: { type: String, default: "hybrid" }, // exhaustive | hybrid | vector
 	// Set when every hit in the list shares one score, whatever the route said it was.
 	unrankedSet: { type: Boolean, default: false },
 });
@@ -40,12 +40,6 @@ const BASES = {
 		chip: "bg-surface-gray-3 text-ink-gray-8",
 		hint: "Position by embedding similarity to the question. A rank within these results, not a confidence score.",
 	},
-	fts: {
-		label: "keyword rank",
-		icon: "lucide-type",
-		chip: "bg-surface-gray-3 text-ink-gray-8",
-		hint: "Position by full-text (BM25) keyword match. A rank within these results, not a confidence score.",
-	},
 };
 
 const basis = computed(() => BASES[props.basis] || BASES.hybrid);
diff --git a/frontend/src/composables/useIsMobile.js b/frontend/src/composables/useIsMobile.js
deleted file mode 100644
index dbf23dd..0000000
--- a/frontend/src/composables/useIsMobile.js
+++ /dev/null
@@ -1,21 +0,0 @@
-// Viewport-based desktop/mobile switch for the app shell. Mirrors frappe-ui's own
-// `breakpoints.smaller('sm')` (Tailwind sm = 640px) but via a bare `matchMedia`
-// listener so the app doesn't take a direct `@vueuse/core` dependency. Shared module
-// state — one media query drives every caller.
-import { onUnmounted, ref } from "vue";
-
-const query = window.matchMedia("(max-width: 639px)");
-const isMobile = ref(query.matches);
-
-function onChange(event) {
-	isMobile.value = event.matches;
-}
-
-query.addEventListener("change", onChange);
-
-export function useIsMobile() {
-	// The listener lives for the app's lifetime (shared state); `onUnmounted` is a no-op
-	// safety net for the rare case this is used outside a persistent shell.
-	onUnmounted(() => {});
-	return isMobile;
-}
diff --git a/frontend/src/composables/useMediaQuery.js b/frontend/src/composables/useMediaQuery.js
index 7082b5f..c9c8b16 100644
--- a/frontend/src/composables/useMediaQuery.js
+++ b/frontend/src/composables/useMediaQuery.js
@@ -32,3 +32,10 @@ export function useMediaQuery(query) {
 export function useIsNarrow() {
 	return useMediaQuery("(max-width: 1023px)");
 }
+
+// Phone-width switch for the app shell and page headers. Mirrors frappe-ui's own
+// `breakpoints.smaller('sm')` (Tailwind sm = 640px): below it the shell swaps to the
+// mobile chrome and header actions drop their labels.
+export function useIsMobile() {
+	return useMediaQuery("(max-width: 639px)");
+}
diff --git a/frontend/src/composables/useRag.js b/frontend/src/composables/useRag.js
index 57a5f0a..0867e74 100644
--- a/frontend/src/composables/useRag.js
+++ b/frontend/src/composables/useRag.js
@@ -6,7 +6,7 @@ import { useCall, useList } from "frappe-ui";
 import { useSocket } from "@/socket";
 
 // `ask` streams over one shared channel and tags every payload with the session it
-// belongs to (see the contract), unlike the agent loop's `wikify_agent_*:<sid>` events.
+// belongs to, unlike the agent loop's per-session `wikify_agent_*:<sid>` events.
 const ANSWER_CHANNEL = "wikify_rag_answer";
 
 function newSessionId() {
@@ -76,28 +76,29 @@ export function useProjectOptions() {
 // Kept at module scope, not per-instance: the layout remounts these pages on a viewport
 // breakpoint change, and a remount must not throw away the question, the project scope or
 // the results the user is reading.
-const askState = {
-	question: ref(""),
-	project: ref(""),
-	askedQuestion: ref(""),
-	sources: ref([]),
-	answerText: ref(""),
-	route: ref(null),
-	refused: ref(false),
-	tookMs: ref(null),
-	errorText: ref(""),
-	// Spend on the last answer, and the running total for this browser session. The
-	// backend may not report cost yet, so these stay null until a payload carries them
-	// rather than defaulting to a zero that would read as "free".
-	usage: ref(null),
-	sessionCost: ref(0),
-	// True when the request never reached the server, so nothing was retrieved. The page
-	// must then show the failure alone — empty "Sources 0 / No answer" panels would claim
-	// a search happened and came back empty.
-	failed: ref(false),
-	streaming: ref(false),
-	sessionId: ref(null),
-};
+const question = ref("");
+const project = ref("");
+const askedQuestion = ref("");
+const sources = ref([]);
+const answerText = ref("");
+const route = ref(null);
+const refused = ref(false);
+const tookMs = ref(null);
+const errorText = ref("");
+// Spend on the last answer, and the running total for this browser session. The backend
+// may not report cost yet, so these stay null until a payload carries them rather than
+// defaulting to a zero that would read as "free".
+const usage = ref(null);
+const sessionCost = ref(0);
+const streaming = ref(false);
+const sessionId = ref(null);
+
+// True when the request failed and nothing was retrieved. The page must then show the
+// failure alone — empty "Sources 0 / No answer" panels would claim a search happened and
+// came back empty.
+const failed = computed(
+	() => Boolean(errorText.value) && !sources.value.length && !answerText.value,
+);
 
 // The request itself is module state too, and for a stronger reason than the results:
 // `useCall` aborts its in-flight fetch whenever it is re-executed, and a component-scoped
@@ -111,23 +112,6 @@ const askCall = useCall({
 	immediate: false,
 });
 
-const {
-	question,
-	project,
-	askedQuestion,
-	sources,
-	answerText,
-	route,
-	refused,
-	tookMs,
-	errorText,
-	failed,
-	usage,
-	sessionCost,
-	streaming,
-	sessionId,
-} = askState;
-
 function handleAnswerEvent(payload) {
 	// Strict match: realtime is per-user, not per-tab, so another tab's (or another
 	// session's) answer would otherwise stream into this one.
@@ -161,7 +145,6 @@ function reset() {
 	refused.value = false;
 	tookMs.value = null;
 	errorText.value = "";
-	failed.value = false;
 	usage.value = null;
 }
 
@@ -180,7 +163,6 @@ async function ask() {
 		});
 		if (askCall.error) {
 			errorText.value = errorMessage(askCall.error);
-			failed.value = !sources.value.length && !answerText.value;
 			return;
 		}
 		if (!response) return;
diff --git a/frontend/src/pages/AskWiki.vue b/frontend/src/pages/AskWiki.vue
index 8263d32..52ab856 100644
--- a/frontend/src/pages/AskWiki.vue
+++ b/frontend/src/pages/AskWiki.vue
@@ -2,9 +2,9 @@
 // /ask — ask a question of the indexed wiki. Sources land before the answer (they are
 // published first), the routing decision is shown up front, and citation chips in the
 // answer jump to the source they came from.
-import { computed, nextTick, ref } from "vue";
-import { marked } from "marked";
+import { computed, nextTick, onUnmounted, ref, watch } from "vue";
 import { Button, FormControl, PageHeader } from "frappe-ui";
+import MarkdownPreview from "@/components/MarkdownPreview.vue";
 import CostMeter from "@/components/rag/CostMeter.vue";
 import RouteBadge from "@/components/rag/RouteBadge.vue";
 import SourceCard from "@/components/rag/SourceCard.vue";
@@ -42,17 +42,39 @@ const elapsed = computed(() =>
 	tookMs.value >= 1000 ? `${(tookMs.value / 1000).toFixed(1)} s` : `${tookMs.value} ms`,
 );
 
+// The answer streams a token at a time, and every delta would otherwise re-parse the whole
+// accumulated markdown. Rendering trails the text by one 50 ms tick — below the cadence a
+// reader can see, but it collapses hundreds of full re-parses into a handful.
+const RENDER_INTERVAL_MS = 50;
+// Seeded from the answer already in module state: a remount mid-read must show it at
+// once, not blank to "No answer returned" until the first tick lands.
+const renderedText = ref(answerText.value);
+let renderTimer = null;
+
+watch(answerText, (text) => {
+	if (!text) {
+		clearTimeout(renderTimer);
+		renderTimer = null;
+		renderedText.value = "";
+		return;
+	}
+	if (renderTimer) return;
+	renderTimer = setTimeout(() => {
+		renderTimer = null;
+		renderedText.value = answerText.value;
+	}, RENDER_INTERVAL_MS);
+});
+
+onUnmounted(() => clearTimeout(renderTimer));
+
 // Citation markers `[1]` become clickable chips that scroll to their source card.
-const answerHtml = computed(() => {
-	const markdown = answerText.value || "";
-	if (!markdown) return "";
-	const rendered = marked.parse(markdown, { async: false });
+function addCitationChips(rendered) {
 	return rendered.replace(
 		/\[(\d+)\]/g,
 		(match, number) =>
 			`<button type="button" class="rag-citation" data-citation="${number}">${number}</button>`,
 	);
-});
+}
 
 async function scrollToSource(index) {
 	highlightedSource.value = index;
@@ -129,12 +151,10 @@ function handleAnswerClick(event) {
 			The connection dropped mid-answer — showing everything that streamed in.
 		</p>
 
-		<!-- The routing decision, up front -->
 		<div v-if="hasResult && (route || streaming)" class="mt-4">
 			<RouteBadge :route="route" :loading="streaming && !route" />
 		</div>
 
-		<!-- Nothing asked yet -->
 		<div
 			v-if="!hasResult && !failed"
 			class="mt-10 flex flex-col items-center gap-3 rounded-lg border border-dashed border-outline-gray-2 px-6 py-16 text-center"
@@ -188,7 +208,6 @@ function handleAnswerClick(event) {
 				</div>
 			</section>
 
-			<!-- Answer -->
 			<section class="order-1 min-w-0 lg:order-2 lg:col-span-7">
 				<div class="mb-2 flex flex-wrap items-center gap-x-3 gap-y-1">
 					<h2 class="text-base font-medium text-ink-gray-9">Answer</h2>
@@ -222,11 +241,12 @@ function handleAnswerClick(event) {
 				</div>
 
 				<div
-					v-else-if="answerHtml"
-					class="rag-answer prose prose-sm dark:prose-invert max-w-none rounded-lg border border-outline-gray-2 bg-surface-elevation-1 px-4 py-4"
+					v-else-if="renderedText"
+					class="rag-answer rounded-lg border border-outline-gray-2 bg-surface-elevation-1 px-4 py-4"
 					@click="handleAnswerClick"
-					v-html="answerHtml"
-				/>
+				>
+					<MarkdownPreview :content="renderedText" :decorate="addCitationChips" />
+				</div>
 
 				<!-- "No answer returned" is only true when the request completed and the
 				     model said nothing. If the connection dropped, the answer was lost in
diff --git a/frontend/src/pages/ImportDetail.vue b/frontend/src/pages/ImportDetail.vue
index 1d22162..73ca1fd 100644
--- a/frontend/src/pages/ImportDetail.vue
+++ b/frontend/src/pages/ImportDetail.vue
@@ -13,7 +13,7 @@ import {
 	useList,
 } from "frappe-ui";
 import { useSocket } from "@/socket";
-import { useIsMobile } from "@/composables/useIsMobile";
+import { useIsMobile } from "@/composables/useMediaQuery";
 import { actionButtonProps } from "@/utils/actionButton";
 import { statusTheme, isActive } from "@/utils/status";
 import PageReview from "@/components/PageReview.vue";
diff --git a/frontend/src/pages/ProjectDetail.vue b/frontend/src/pages/ProjectDetail.vue
index 4151fd3..6a77e7b 100644
--- a/frontend/src/pages/ProjectDetail.vue
+++ b/frontend/src/pages/ProjectDetail.vue
@@ -3,7 +3,7 @@ import { ref, watch } from "vue";
 import { Badge, Button, PageHeader, useDoc } from "frappe-ui";
 import ImportList from "@/pages/ImportList.vue";
 import NewImportDialog from "@/components/NewImportDialog.vue";
-import { useIsMobile } from "@/composables/useIsMobile";
+import { useIsMobile } from "@/composables/useMediaQuery";
 import { actionButtonProps } from "@/utils/actionButton";
 import { setProject } from "@/data/agentContext";
 
diff --git a/frontend/src/pages/ProjectList.vue b/frontend/src/pages/ProjectList.vue
index 839eec8..e3751bd 100644
--- a/frontend/src/pages/ProjectList.vue
+++ b/frontend/src/pages/ProjectList.vue
@@ -12,7 +12,7 @@ import {
 	useCall,
 	useList,
 } from "frappe-ui";
-import { useIsMobile } from "@/composables/useIsMobile";
+import { useIsMobile } from "@/composables/useMediaQuery";
 import { actionButtonProps } from "@/utils/actionButton";
 import { clear as clearAgentContext } from "@/data/agentContext";
 
diff --git a/wikify/agent/session.py b/wikify/agent/session.py
index d53b91e..a715f81 100644
--- a/wikify/agent/session.py
+++ b/wikify/agent/session.py
@@ -14,6 +14,19 @@
 # How many prior messages to replay as context each turn.
 HISTORY_LIMIT = 40
 
+# Both session doctypes cap `title` at 140; 120 leaves the trimmed line visibly short of it.
+TITLE_CHARS = 120
+
+
+def session_title(first_message: str | None) -> str:
+	"""A conversation's title: the first line of its opening message, trimmed.
+
+	Shared with the Ask surface (`rag.history`) so the two conversation lists cannot title
+	themselves differently.
+	"""
+	lines = (first_message or "").strip().splitlines()
+	return lines[0][:TITLE_CHARS] if lines else ""
+
 
 def get_or_create(
 	session_id: str | None,
@@ -126,9 +139,8 @@ def set_running(session: str, value: bool) -> None:
 def touch(session: str, *, first_user_message: str | None = None) -> None:
 	"""Bump `last_interaction_on`; set a title from the first user message if unset."""
 	values = {"last_interaction_on": now_datetime()}
-	if first_user_message and not frappe.db.get_value("Wikify Agent Session", session, "title"):
-		title = first_user_message.strip().splitlines()[0][:120] if first_user_message.strip() else None
-		if title:
-			values["title"] = title
+	title = session_title(first_user_message)
+	if title and not frappe.db.get_value("Wikify Agent Session", session, "title"):
+		values["title"] = title
 	frappe.db.set_value("Wikify Agent Session", session, values)
 	frappe.db.commit()
diff --git a/wikify/agent/tools/retrieve.py b/wikify/agent/tools/retrieve.py
index 7d93d40..098b0a7 100644
--- a/wikify/agent/tools/retrieve.py
+++ b/wikify/agent/tools/retrieve.py
@@ -15,7 +15,7 @@
 
 from wikify.agent.context import Ctx
 from wikify.agent.registry import Tool
-from wikify.rag.chunk import CONTEXT_SEPARATOR
+from wikify.rag import search as rag_search
 
 # Excerpts are trimmed hard — the model pulls the full body with read_section when a hit
 # looks worth reading.
@@ -25,13 +25,10 @@
 def format_hits(hits: list[dict], mode: str) -> str:
 	lines = [f"{len(hits)} result(s) ({mode}):"]
 	for position, hit in enumerate(hits, start=1):
-		pages = f"p.{hit['page_start']}"
-		if hit.get("page_end") and hit["page_end"] != hit["page_start"]:
-			pages = f"p.{hit['page_start']}-{hit['page_end']}"
 		type_label = f" ({hit['section_type']})" if hit.get("section_type") else ""
-		crumb = f"{hit['document_title']}{CONTEXT_SEPARATOR}{hit['hierarchy_path'] or hit['title']}"
 		lines.append(
-			f"\n[{position}] {crumb}{type_label} [{pages}] <{hit['section']}> score={hit['score']:.4f}"
+			f"\n[{position}] {rag_search.crumb(hit)}{type_label} [{rag_search.page_label(hit)}] "
+			f"<{hit['section']}> score={hit['score']:.4f}"
 		)
 		text = hit.get("text") or ""
 		lines.append(text if len(text) <= EXCERPT_LIMIT else text[:EXCERPT_LIMIT] + "…")
diff --git a/wikify/api/rag.py b/wikify/api/rag.py
index 788c9ed..373b953 100644
--- a/wikify/api/rag.py
+++ b/wikify/api/rag.py
@@ -20,7 +20,6 @@
 from frappe.utils.data import cint, sbool
 
 from wikify.rag import answer as rag_answer
-from wikify.rag import embed
 from wikify.rag import history as rag_history
 from wikify.rag import index as rag_index
 from wikify.rag import search as rag_search
@@ -28,10 +27,6 @@
 
 STREAM_EVENT = "wikify_rag_answer"
 
-# The naive leg of `compare`: plain vector top-k with no filter and no routing — exactly
-# what a textbook RAG pipeline does, which is the baseline the thesis argues against.
-NAIVE_LIMIT = 8
-
 
 def readable_projects() -> list[str]:
 	"""Wikify Projects the current user may read (`get_list` applies permissions).
@@ -126,7 +121,6 @@ def ask(
 
 	user = frappe.session.user
 	started = time.monotonic()
-	rag_history.start_turn()
 
 	def publish(payload: dict) -> None:
 		frappe.publish_realtime(STREAM_EVENT, {"session": session, **payload}, user=user)
@@ -188,28 +182,21 @@ def index_status(project: str | None = None) -> dict:
 	"""
 	assert_readable(project)
 	projects = [project] if project else readable_projects()
-	# ponytail: one index scan per readable project, replace with a single grouped scan
-	# once a site carries more than a handful of projects.
-	totals = {"chunks": 0, "sections": 0, "documents": 0, "indexed_at": None, "dim": embed.EMBED_DIM}
-	for name in projects:
-		stats = rag_index.index_stats(name)
-		for key in ("chunks", "sections", "documents"):
-			totals[key] += stats[key]
-		if stats["indexed_at"] and (not totals["indexed_at"] or stats["indexed_at"] > totals["indexed_at"]):
-			totals["indexed_at"] = stats["indexed_at"]
-	totals["stale"] = is_stale(project, totals["indexed_at"])
+	totals = rag_index.index_stats(projects)
+	totals["stale"] = is_stale(projects, totals["indexed_at"])
 	return totals
 
 
-def is_stale(project: str | None, indexed_at) -> bool:
+def is_stale(projects: list[str], indexed_at) -> bool:
 	"""True when any in-scope section was edited after the index was last written."""
 	if not indexed_at:
 		return True
-	filters: dict = {"modified": [">", indexed_at]}
-	projects = [project] if project else readable_projects()
 	if not projects:
 		return False
-	filters["source_document"] = ["in", documents_in_projects(projects)]
+	filters = {
+		"modified": [">", indexed_at],
+		"source_document": ["in", documents_in_projects(projects)],
+	}
 	return bool(frappe.get_all("Source Section", filters=filters, limit=1, pluck="name"))
 
 
@@ -241,22 +228,12 @@ def compare(query: str, project: str | None = None) -> dict:
 		frappe.throw(_("Enter a query to compare."))
 	assert_readable(project)
 
-	allowed = readable_projects()
-	naive = rag_search.search(
-		query,
-		project=project,
-		limit=NAIVE_LIMIT,
-		mode="vector",
-		allowed_projects=allowed,
-	)
-	decided = route_question(query, project)
-	routed = rag_answer.retrieve(decided, project, False, allowed_projects=allowed)
-	found_by_naive = {hit.section for hit in naive}
+	comparison = rag_answer.compare(query, project, readable_projects())
 	return {
-		"naive": [hit.as_dict() for hit in naive],
-		"routed": [hit.as_dict() for hit in routed],
-		"route": decided.as_dict(),
+		"naive": [hit.as_dict() for hit in comparison["naive"]],
+		"routed": [hit.as_dict() for hit in comparison["routed"]],
+		"route": comparison["route"].as_dict(),
 		# The headline number: the sections the routed leg found that naive top-k never saw.
 		# Precomputed here so the interface can highlight them without re-deriving the diff.
-		"missed_by_naive": [hit.section for hit in routed if hit.section not in found_by_naive],
+		"missed_by_naive": [hit.section for hit in comparison["missed_by_naive"]],
 	}
diff --git a/wikify/engine/diagrams.py b/wikify/engine/diagrams.py
index c66ed77..38894e0 100644
--- a/wikify/engine/diagrams.py
+++ b/wikify/engine/diagrams.py
@@ -200,17 +200,9 @@ def parse_flowchart(source: str) -> tuple[dict[str, str], list[tuple[str, str]],
 	return labels, edges, broken
 
 
-def syntax_errors(source: str) -> list[str]:
-	"""Everything that would stop this block parsing or rendering. Empty means it is safe."""
-	body = (source or "").strip()
-	if not body:
-		return ["empty diagram"]
-	header = body.splitlines()[0]
-	if not _HEADER_RE.match(header):
-		return [f"unsupported diagram type: {header.strip()[:40]!r}"]
-
+def syntax_errors(labels: dict[str, str], edges: list[tuple[str, str]], broken: list[str]) -> list[str]:
+	"""Everything that would stop a parsed block rendering. Empty means it is safe."""
 	errors: list[str] = []
-	labels, edges, broken = parse_flowchart(body)
 	if broken:
 		errors.append(f"unparseable statement: {broken[0][:60]!r}")
 	for node_id, label in labels.items():
@@ -220,8 +212,10 @@ def syntax_errors(source: str) -> list[str]:
 			errors.append(f"literal \\n in node {node_id} — mermaid needs <br>")
 	if not edges:
 		errors.append("no edges — a flowchart with no connections carries no structure")
-	elif len(components(labels, edges)) > 1:
-		errors.append(f"disconnected: {len(components(labels, edges))} unlinked fragments")
+		return errors
+	fragments = components(labels, edges)
+	if len(fragments) > 1:
+		errors.append(f"disconnected: {len(fragments)} unlinked fragments")
 	return errors
 
 
@@ -248,9 +242,8 @@ def components(labels: dict[str, str], edges: list[tuple[str, str]]) -> list[set
 	return found
 
 
-def tabular_signals(source: str) -> list[str]:
+def tabular_signals(labels: dict[str, str], edges: list[tuple[str, str]]) -> list[str]:
 	"""Signs this "flowchart" is really a grid whose row↔value binding has been lost."""
-	labels, edges, _ = parse_flowchart(source)
 	if not edges:
 		return []
 	children: dict[str, list[str]] = {}
@@ -291,8 +284,20 @@ def tabular_signals(source: str) -> list[str]:
 
 
 def diagram_errors(source: str) -> list[str]:
-	"""Every reason this mermaid block must not be stored."""
-	return syntax_errors(source) + tabular_signals(source)
+	"""Every reason this mermaid block must not be stored.
+
+	Parsed once for both verdicts: `remediate_pdf` runs this gate over every candidate of
+	every page, so a source that parsed itself twice per verdict cost up to nine parses a page.
+	"""
+	body = (source or "").strip()
+	if not body:
+		return ["empty diagram"]
+	header = body.splitlines()[0]
+	if not _HEADER_RE.match(header):
+		return [f"unsupported diagram type: {header.strip()[:40]!r}"]
+
+	labels, edges, broken = parse_flowchart(body)
+	return syntax_errors(labels, edges, broken) + tabular_signals(labels, edges)
 
 
 def has_table(markdown: str) -> bool:
diff --git a/wikify/engine/llm.py b/wikify/engine/llm.py
index 2714851..9bd6d63 100644
--- a/wikify/engine/llm.py
+++ b/wikify/engine/llm.py
@@ -49,13 +49,18 @@ def chat_completion(
 	response_format: dict | None = None,
 	max_tokens: int | None = None,
 	timeout: int = 120,
+	api_key: str = "",
 ) -> dict:
 	"""POST a chat completion to OpenRouter, recording latency + token cost.
 
 	Returns the parsed JSON response body (same shape as the OpenAI chat API, so
 	callers read `resp["choices"][0]["message"]["content"]`).
+
+	`api_key` is for callers running this on a worker thread: resolving the key reads
+	`Wikify Settings`, and `frappe.local` is unbound off the request thread, so the caller
+	resolves it once up front and hands it down. Omit it and it is resolved here as usual.
 	"""
-	key = settings.openrouter_key()
+	key = api_key or settings.openrouter_key()
 	if not key:
 		raise RuntimeError("OPENROUTER key not set; cloud features unavailable.")
 
diff --git a/wikify/engine/pdf_utils.py b/wikify/engine/pdf_utils.py
index 9a407b1..7e25fbe 100644
--- a/wikify/engine/pdf_utils.py
+++ b/wikify/engine/pdf_utils.py
@@ -29,7 +29,6 @@ def classify_page(
 	page,
 	min_chars: int = config.VISUAL_MIN_CHARS,
 	min_drawings: int = config.VISUAL_MIN_DRAWINGS,
-	page_regions: list | None = None,
 ) -> str:
 	"""Page type: `visual` | `mixed` | `text`.
 
@@ -39,9 +38,9 @@ def classify_page(
 	filed `text` — the visual path was unreachable. `regions.classify_page` answers it from the
 	page's shape regions instead, and adds `mixed`: a trustworthy text layer *plus* substantial
 	table/diagram ink, which needs a vision model to recover structure but must go on being
-	scored against its text layer. Callers pass `page_regions` when they already have them.
+	scored against its text layer.
 	"""
-	return regions.classify_page(page, min_chars, min_drawings, page_regions)
+	return regions.classify_page(page, min_chars, min_drawings)
 
 
 def render_png(page, dpi: int = config.RENDER_DPI) -> bytes:
diff --git a/wikify/engine/regions.py b/wikify/engine/regions.py
index b089c69..9641b95 100644
--- a/wikify/engine/regions.py
+++ b/wikify/engine/regions.py
@@ -283,19 +283,19 @@ def label_count(labels: list[tuple], region_box: tuple) -> int:
 	return sum(1 for box in labels if overlap_fraction(box, [region_box]) > 0.6)
 
 
-def graphic_regions(page, blocks: list, occupied: list[tuple]) -> list[Region]:
+def graphic_regions(
+	page_box: tuple, blocks: list, graphics: list[tuple], occupied: list[tuple]
+) -> list[Region]:
 	"""Clusters of non-grid ink, each classified `flow` or `figure`.
 
 	Labelled boxes wired together are a flow — mermaid's actual job. Wordless ink is a figure,
 	where a crop of the original is the only faithful representation we can offer.
+
+	`graphics` is the page's furniture-free ink, passed in rather than re-derived: reading it
+	means `page.get_drawings()`, which costs ~25ms on a drawing-heavy page.
 	"""
-	page_box = tuple(float(value) for value in page.rect)
 	labels = text_boxes(blocks)
-	boxes = [
-		box
-		for box in graphic_boxes(page, blocks)
-		if not is_furniture(box, page_box) and overlap_fraction(box, occupied) <= 0.5
-	]
+	boxes = [box for box in graphics if overlap_fraction(box, occupied) <= 0.5]
 
 	cells: set[tuple[int, int]] = set()
 	for box in boxes:
@@ -316,9 +316,8 @@ def graphic_regions(page, blocks: list, occupied: list[tuple]) -> list[Region]:
 	return regions
 
 
-def prose_region(page, blocks: list, occupied: list[tuple]) -> Region | None:
+def prose_region(page_box: tuple, blocks: list, occupied: list[tuple]) -> Region | None:
 	"""The union of text blocks outside every graphic region, if any survive."""
-	page_box = tuple(float(value) for value in page.rect)
 	box = None
 	for candidate in text_boxes(blocks):
 		if is_furniture(candidate, page_box) or overlap_fraction(candidate, occupied) > 0.5:
@@ -339,19 +338,24 @@ def prose_region(page, blocks: list, occupied: list[tuple]) -> Region | None:
 
 
 def find_regions(page) -> list[Region]:
-	"""Shape-classified regions of one page: grids first, then the remaining ink, then prose."""
+	"""Shape-classified regions of one page: grids first, then the remaining ink, then prose.
+
+	The page's blocks and its ink are read once here and threaded down — the grid pass and
+	the graphic pass used to call `page.get_drawings()` each, doubling the most expensive
+	step in the analysis for a list both of them already had.
+	"""
 	blocks = page.get_text("blocks")
 	page_box = tuple(float(value) for value in page.rect)
-	boxes = [box for box in graphic_boxes(page, blocks) if not is_furniture(box, page_box)]
-	regions = table_regions(page_box, boxes)
-	regions.extend(graphic_regions(page, blocks, [region.bbox for region in regions]))
-	prose = prose_region(page, blocks, [region.bbox for region in regions])
+	graphics = [box for box in graphic_boxes(page, blocks) if not is_furniture(box, page_box)]
+	regions = table_regions(page_box, graphics)
+	regions.extend(graphic_regions(page_box, blocks, graphics, [region.bbox for region in regions]))
+	prose = prose_region(page_box, blocks, [region.bbox for region in regions])
 	if prose:
 		regions.append(prose)
 	return regions
 
 
-def classify_page(page, min_chars: int, min_drawings: int, regions: list[Region] | None = None) -> str:
+def classify_page(page, min_chars: int, min_drawings: int) -> str:
 	"""Page kind from its regions: `visual` | `mixed` | `text`.
 
 	`visual` keeps its old meaning — no usable text layer, so the rendered image is the only
@@ -359,8 +363,7 @@ def classify_page(page, min_chars: int, min_drawings: int, regions: list[Region]
 	layer *and* substantial table/diagram ink, which still needs a vision model to recover the
 	structure but must go on being scored against its text layer.
 	"""
-	if regions is None:
-		regions = find_regions(page)
+	regions = find_regions(page)
 	prose_chars = len(page.get_text("text").strip())
 	graphics = [region for region in regions if region.shape in (TABLE, FLOW, FIGURE)]
 	if prose_chars < min_chars and (graphics or page.get_images()):
diff --git a/wikify/engine/remediate.py b/wikify/engine/remediate.py
index 2e2f4d5..ff57e0c 100644
--- a/wikify/engine/remediate.py
+++ b/wikify/engine/remediate.py
@@ -63,17 +63,22 @@ def with_page_crop(markdown: str, page_image: str | None) -> str:
 	return f"{markdown}\n\n![Source page]({page_image})".strip()
 
 
+def best_composite(candidates: list[tuple], baseline_composite: float) -> float:
+	"""The best read of the page anyone managed — the bar adoption and escalation both use."""
+	return max([baseline_composite or 0.0, *(candidate[2].composite for candidate in candidates)])
+
+
 def pick_winner(candidates: list[tuple], baseline_composite: float, baseline_markdown: str) -> tuple | None:
 	"""Best adopt-eligible candidate: vlm when it also matches/beats cleanup's composite
 	(cleanup's composite is depressed by intended furniture removal, so a tie goes to
 	vlm), else the eligible cleanup, else None (keep baseline)."""
-	best_composite = max([baseline_composite or 0.0, *(c[2].composite for c in candidates)])
+	best = best_composite(candidates, baseline_composite)
 	best_chars = max([content_chars(baseline_markdown), *(content_chars(c[1]) for c in candidates)])
 	eligible = [
 		c
 		for c in candidates
 		if c[3]
-		and c[2].composite >= best_composite * ADOPTION_COMPOSITE_RATIO
+		and c[2].composite >= best * ADOPTION_COMPOSITE_RATIO
 		and (content_chars(c[1]) >= MIN_CANONICAL_CHARS or content_chars(c[1]) >= best_chars)
 	]
 	vlm_candidate = next((c for c in eligible if c[0] == "vlm"), None)
@@ -134,8 +139,10 @@ def remediate_pdf(
 			page = doc[p["page_no"] - 1]
 			gt = page.get_text("text")
 			kind = p["kind"]
-			page_image = store.get_page_image(p["name"]) or ""
+			page_image = p["image"] or ""
 			data_url = pdf_utils.png_to_data_url(pdf_utils.render_png(page, dpi=dpi))
+			# Regions are the page's shape analysis — computed once and reused for the hint.
+			page_regions = regions.find_regions(page)
 
 			use_judge = judge_all or kind == "visual"
 			img = data_url if use_judge else None
@@ -160,7 +167,7 @@ def remediate_pdf(
 					data_url,
 					project_context=project_context,
 					instruction=instruction,
-					shape_hint=regions.shape_hint(regions.find_regions(page)),
+					shape_hint=regions.shape_hint(page_regions),
 				)
 				# Nothing unverified is stored: a diagram that will not parse after repair, or one
 				# carrying the signature of a table flattened into dangling branches, is dropped in
@@ -193,10 +200,10 @@ def remediate_pdf(
 					errors.append(f"cleanup failed: {e}")
 
 			winner = pick_winner(candidates, base_ps.composite, base_md)
-			best_composite = max([base_ps.composite, *(c[2].composite for c in candidates)])
-			if best_composite < float(settings.get("escalate_threshold")):
+			best = best_composite(candidates, base_ps.composite)
+			if best < float(settings.get("escalate_threshold")):
 				errors.append(
-					f"every candidate scored below the escalate threshold (best {best_composite}) — "
+					f"every candidate scored below the escalate threshold (best {best}) — "
 					"kept the best available read; this page needs a human"
 				)
 			# Record the adopted candidate; when nothing is adopted, record the vlm attempt
diff --git a/wikify/engine/store.py b/wikify/engine/store.py
index 1440671..2ce5d2e 100644
--- a/wikify/engine/store.py
+++ b/wikify/engine/store.py
@@ -128,11 +128,14 @@ def add_document_cost(source_document: str, cost: float) -> None:
 
 
 def get_pages(source_document: str) -> list[dict]:
-	"""Pages of a doc (ordered) with the fields remediation needs to route + re-score."""
+	"""Pages of a doc (ordered) with the fields remediation needs to route + re-score.
+
+	`image` rides along because the remediation loop needs every page's crop URL — read per
+	page it was one query per page of the document."""
 	return frappe.get_all(
 		"Source Page",
 		filters={"source_document": source_document},
-		fields=["name", "page_no", "kind", "baseline_markdown", "verdict", "composite"],
+		fields=["name", "page_no", "kind", "baseline_markdown", "verdict", "composite", "image"],
 		order_by="page_no asc",
 	)
 
@@ -327,28 +330,54 @@ def replace_sections(source_document: str, sections) -> int:
 	clear, then insert in document order, resolving each section's parent by its
 	hierarchy path (the parent always precedes it). NestedSet manages `lft`/`rgt`;
 	`is_group` is set for any section another section nests under. Returns the count.
+
+	The whole rebuild indexes ONCE, at the end. Every insert fires `Source Section`'s
+	reindex hook, so sectionising a 296-section document cost ~600 redis round trips to
+	coalesce down to the single project rebuild this queues directly — half of them the
+	no-op "already queued" branch.
 	"""
+	from wikify.rag import events
+
 	# Full rebuild: raw-delete this doc's sections (other docs' subtrees are
 	# independent number-spaces, so NestedSet stays consistent without a global rebuild).
 	frappe.db.delete("Source Section", {"source_document": source_document})
 
 	parent_paths = {tuple(s.hierarchy_path[:-1]) for s in sections if len(s.hierarchy_path) > 1}
 	path_to_name: dict[tuple[str, ...], str] = {}
-	for idx, sec in enumerate(sections):
-		doc = frappe.new_doc("Source Section")
-		doc.source_document = source_document
-		doc.parent_source_section = path_to_name.get(tuple(sec.hierarchy_path[:-1]))
-		doc.is_group = 1 if tuple(sec.hierarchy_path) in parent_paths else 0
-		doc.title = sec.title
-		doc.section_type = sec.section_type
-		doc.level = sec.level
-		doc.hierarchy_path = " > ".join(sec.hierarchy_path)
-		doc.page_start = sec.page_start
-		doc.page_end = sec.page_end
-		doc.sort_order = idx
-		doc.markdown = sec.markdown
-		doc.insert(ignore_permissions=True)
-		path_to_name[tuple(sec.hierarchy_path)] = doc.name
+	suspended_by_caller = events.indexing_suspended()
+	# Restored rather than cleared: an outer bulk context may already hold the flag, and
+	# clearing it here would re-arm per-row indexing for the rest of that context.
+	skip_reindex_was = frappe.flags.wikify_skip_reindex
+	frappe.flags.wikify_skip_reindex = True
+	try:
+		for idx, sec in enumerate(sections):
+			doc = frappe.new_doc("Source Section")
+			doc.source_document = source_document
+			doc.parent_source_section = path_to_name.get(tuple(sec.hierarchy_path[:-1]))
+			doc.is_group = 1 if tuple(sec.hierarchy_path) in parent_paths else 0
+			doc.title = sec.title
+			doc.section_type = sec.section_type
+			doc.level = sec.level
+			doc.hierarchy_path = " > ".join(sec.hierarchy_path)
+			doc.page_start = sec.page_start
+			doc.page_end = sec.page_end
+			doc.sort_order = idx
+			doc.markdown = sec.markdown
+			doc.insert(ignore_permissions=True)
+			path_to_name[tuple(sec.hierarchy_path)] = doc.name
+	finally:
+		frappe.flags.wikify_skip_reindex = skip_reindex_was
+		# Queued even when an insert threw: the tree is already partly rewritten, and the
+		# per-row hook this replaces would have queued a rebuild for every row that landed.
+		# A document outside any project has nothing to scope an index to; it becomes
+		# searchable when it is assigned to one (which reindexes then).
+		project = (
+			None
+			if suspended_by_caller
+			else frappe.db.get_value("Source Document", source_document, "project")
+		)
+		if project:
+			events.queue_project_rebuild(project)
 
 	# 0.5: the tree (and its page spans) just changed wholesale — rebuild the
 	# document's reference edges against it. Runs after all inserts so every
diff --git a/wikify/rag/__init__.py b/wikify/rag/__init__.py
index da0660c..152faf4 100644
--- a/wikify/rag/__init__.py
+++ b/wikify/rag/__init__.py
@@ -1,6 +1,6 @@
 """Retrieval core for the RAG POC — chunking, embedding, LanceDB storage and search.
 
-Layout mirrors the frozen contract in `specs/poc-rag-CONTRACT.md`:
+Layout:
 
 - `embed`  — model2vec static embeddings (256-dim, numpy-only, no API key).
 - `store`  — the LanceDB connection + the single `chunks` table (explicit pyarrow schema).
diff --git a/wikify/rag/answer.py b/wikify/rag/answer.py
index 8559482..be1eeda 100644
--- a/wikify/rag/answer.py
+++ b/wikify/rag/answer.py
@@ -19,7 +19,6 @@
 from wikify.engine import settings
 from wikify.rag import evidence, usage
 from wikify.rag import search as rag_search
-from wikify.rag.chunk import CONTEXT_SEPARATOR
 from wikify.rag.router import Route, route
 
 # Which retrieval leg each intent takes. "filter" returns every match (completeness);
@@ -30,6 +29,8 @@
 # An exhaustive answer is allowed to be long, but a runaway type (e.g. "other") must not
 # push a thousand chunks into the prompt.
 EXHAUSTIVE_LIMIT = 60
+# The naive leg of `compare`: plain vector top-k with no filter and no routing.
+NAIVE_LIMIT = 8
 
 # RRF fusion scores are RANK-based: a top-ranked irrelevant chunk scores exactly what a
 # top-ranked perfect one does (measured: 0.0328 for both an on-topic and a nonsense query
@@ -39,8 +40,17 @@
 MIN_RERANK_SCORE = 3.0
 # Raw vector distance does carry signal (measured: 0.51 on-topic vs 0.33 for nonsense).
 MIN_VECTOR_SCORE = 0.36
-# ponytail: both floors are calibrated against the demo corpus with potion-base-8M; recheck
-# them from the eval harness (recall@k per golden question) if the embedder or corpus changes.
+# The reranker must never be able to mute the product on its own, because it fails silently:
+# it answers, it looks healthy, and it scores every candidate 0. So a below-floor rerank only
+# refuses when the embedding leg agrees, and a strong embedding match overrules it outright.
+# Deliberately well clear of MIN_VECTOR_SCORE — overruling a refusal takes real confidence,
+# not merely "good enough to answer from". Measured on potion-base-8M: questions the corpus
+# answers peak at 0.56-0.63 (ICAI) and 0.60 (demo), questions it does not at 0.39-0.45 (ICAI)
+# and 0.44 (demo).
+STRONG_VECTOR_SCORE = 0.5
+# ponytail: all three floors are calibrated against potion-base-8M on the demo + ICAI
+# corpora; recheck them from the eval harness (recall@k per golden question) if the embedder
+# or corpus changes.
 
 REFUSAL = (
 	"I couldn't find this in the wiki. Nothing in the indexed documents is close enough to "
@@ -71,52 +81,122 @@ def retrieve(
 	project: str | None,
 	rerank: bool,
 	allowed_projects=rag_search.ACL_REQUIRED,
+	top_k: int = TOP_K,
 ) -> list:
 	"""Run the leg the route chose. Exhaustive returns ALL matches, not a top-k.
 
 	`allowed_projects` carries the caller's ACL decision straight through to `search()`,
-	which is where an omitted one throws.
+	which is where an omitted one throws. `top_k` widens the similarity legs for a caller
+	measuring recall at a different k (the eval harness) — the exhaustive leg ignores it,
+	because completeness is the whole point of that mode.
 	"""
 	mode = MODE_FOR_INTENT[decided.intent]
 	return rag_search.search(
 		decided.query,
 		project=project,
 		section_type=decided.section_type,
-		limit=EXHAUSTIVE_LIMIT if mode == "filter" else TOP_K,
+		limit=EXHAUSTIVE_LIMIT if mode == "filter" else top_k,
 		mode=mode,
 		rerank=rerank,
 		allowed_projects=allowed_projects,
 	)
 
 
+def naive_retrieve(query: str, project: str | None, allowed_projects, limit: int = NAIVE_LIMIT) -> list:
+	"""The baseline: raw question, plain vector top-k, no router and no metadata filter.
+
+	This is what a textbook RAG pipeline does, and it is the thing the POC argues against —
+	so it is defined once and both the demo API and the eval harness measure the same leg.
+	"""
+	return rag_search.search(
+		query, project=project, limit=limit, mode="vector", allowed_projects=allowed_projects
+	)
+
+
+def compare(
+	query: str,
+	project: str | None,
+	allowed_projects,
+	naive_limit: int = NAIVE_LIMIT,
+	top_k: int = TOP_K,
+) -> dict:
+	"""Naive top-k beside the routed leg, plus the sections only routing found.
+
+	Hits and the `Route` come back as objects; each caller shapes its own payload. The demo
+	endpoint and the eval scorecard both read this, so the headline number they show can
+	never be computed two different ways — which is the exact drift the eval exists to catch.
+	"""
+	naive = naive_retrieve(query, project, allowed_projects, naive_limit)
+	decided = route(query, project)
+	routed = retrieve(decided, project, False, allowed_projects, top_k=top_k)
+	found_by_naive = {hit.section for hit in naive}
+	return {
+		"route": decided,
+		"naive": naive,
+		"routed": routed,
+		"missed_by_naive": [hit for hit in routed if hit.section not in found_by_naive],
+	}
+
+
+def best_vector_score(hits: list) -> float | None:
+	"""The closest embedding match in the result set, or None when this leg has no say."""
+	similarities = [hit.vector_score for hit in hits if hit.vector_score is not None]
+	return max(similarities) if similarities else None
+
+
 def below_floor(hits: list, mode: str) -> bool:
 	"""True when nothing retrieved is good enough to answer from.
 
+	Refusing is the most damaging thing this system does when it is wrong — to a reader
+	"I couldn't find this in the wiki" is indistinguishable from the document not covering
+	it — so it takes two independent legs to agree, never the reranker alone.
+
 	A `filter` leg is an exact metadata match — it either matched or it didn't — so only
 	emptiness refuses there, and the same holds for the rank-scored fusion legs.
 	"""
 	if not hits:
 		return True
-	# Only a real ranking may refuse. `search.rerank_hits` leaves every score unset when the
-	# reranker answers but ranks nothing, so a silent rerank failure degrades to the fusion
-	# order here rather than turning into "the wiki doesn't cover this".
-	reranked = [hit.rerank_score for hit in hits if hit.rerank_score is not None]
-	if reranked:
-		return max(reranked) < MIN_RERANK_SCORE
+	# `search.rerank_hits` leaves every score unset when the reranker carries no verdict, so
+	# a silent rerank failure degrades to the fusion order here rather than turning into
+	# "the wiki doesn't cover this".
+	if any(hit.rerank_score is not None for hit in hits):
+		return rerank_below_floor(hits) and not overrules_rerank(hits)
 	if mode == "vector":
 		return max(hit.score for hit in hits) < MIN_VECTOR_SCORE
 	return False
 
 
+def rerank_below_floor(hits: list) -> bool:
+	"""True when the reranker scored every candidate under the floor."""
+	reranked = [hit.rerank_score for hit in hits if hit.rerank_score is not None]
+	return bool(reranked) and max(reranked) < MIN_RERANK_SCORE
+
+
+def overrules_rerank(hits: list) -> bool:
+	"""True when the embedding leg is confident enough to veto a below-floor rerank."""
+	best = best_vector_score(hits)
+	return best is not None and best >= STRONG_VECTOR_SCORE
+
+
+def rerank_overruled(hits: list) -> bool:
+	"""The reranker wanted to refuse and the embedding leg would not let it."""
+	return rerank_below_floor(hits) and overrules_rerank(hits)
+
+
+def clear_rerank_scores(citations: list[dict]) -> None:
+	"""Drop the rerank numbers from the cards. Used when the embedding leg overruled the
+	rerank: those scores are demonstrably wrong, and a card reading "rerank 0.0" next to an
+	answer the reranker tried to suppress is worse than no number at all. `None` is the
+	signal the UI hides on."""
+	for citation in citations:
+		citation["rerank_score"] = None
+
+
 def format_context(hits: list) -> str:
 	"""The numbered excerpt block the model cites against — `[n]` is the hit's position."""
 	blocks = []
 	for position, hit in enumerate(hits, start=1):
-		pages = f"p.{hit.page_start}"
-		if hit.page_end and hit.page_end != hit.page_start:
-			pages = f"p.{hit.page_start}-{hit.page_end}"
-		crumb = f"{hit.document_title}{CONTEXT_SEPARATOR}{hit.hierarchy_path or hit.title}"
-		header = f"[{position}] {crumb} ({pages})"
+		header = f"[{position}] {rag_search.crumb(hit)} ({rag_search.page_label(hit)})"
 		if hit.section_type:
 			header += f" — type: {hit.section_type}"
 		blocks.append(f"{header}\n{hit.text}")
@@ -199,6 +279,8 @@ def answer(
 		refused = below_floor(hits, mode) or not settings.openrouter_key()
 
 		citations = [] if refused else [hit.as_dict() for hit in hits]
+		if citations and rerank_overruled(hits):
+			clear_rerank_scores(citations)
 		if on_citations:
 			on_citations(citations)
 
diff --git a/wikify/rag/chunk.py b/wikify/rag/chunk.py
index 6a54c16..4fdf538 100644
--- a/wikify/rag/chunk.py
+++ b/wikify/rag/chunk.py
@@ -33,10 +33,6 @@
 CHUNK_TARGET_CHARS = 1200
 CHUNK_OVERLAP_CHARS = 150
 
-# Sections are read in slices so a project with thousands of them can't build an
-# `IN (...)` list the database refuses (and so memory stays bounded).
-BATCH_SIZE = 500
-
 SECTION_FIELDS = [
 	"name",
 	"source_document",
@@ -77,16 +73,6 @@ class Chunk:
 	title_only: bool = False
 
 
-def get_rows_by_name(doctype: str, names: list[str], fields: list[str]) -> list[dict]:
-	"""`get_all` over a name list, sliced into batches — one query per batch, no N+1."""
-	unique = [name for name in dict.fromkeys(names) if name]
-	rows: list[dict] = []
-	for start in range(0, len(unique), BATCH_SIZE):
-		batch = unique[start : start + BATCH_SIZE]
-		rows.extend(frappe.get_all(doctype, filters={"name": ["in", batch]}, fields=fields))
-	return rows
-
-
 def is_heading(block: str) -> bool:
 	return block.lstrip().startswith("#")
 
@@ -225,22 +211,14 @@ def contextual_prefix(document_title: str, hierarchy_path: str) -> str:
 	return CONTEXT_SEPARATOR.join(crumbs)
 
 
-def get_pages_by_document(document_names: list[str]) -> dict[str, list[dict]]:
-	"""Every parsed page of the given documents, grouped — one query per batch, no N+1."""
-	pages: dict[str, list[dict]] = {}
-	for row in get_rows_by_name("Source Document", document_names, ["name"]):
-		pages.setdefault(row["name"], [])
-	names = list(pages)
-	for start in range(0, len(names), BATCH_SIZE):
-		batch = frappe.get_all(
-			"Source Page",
-			filters={"source_document": ["in", names[start : start + BATCH_SIZE]]},
-			fields=["source_document", "page_no", "canonical_markdown"],
-			order_by="page_no asc",
-		)
-		for row in batch:
-			pages[row["source_document"]].append(row)
-	return pages
+def section_pages(rows: list[dict]) -> list[int]:
+	"""Every page number the given section rows declare — the pages provenance will read."""
+	wanted: set[int] = set()
+	for row in rows:
+		start = int(row.get("page_start") or 0)
+		if start:
+			wanted.update(range(start, max(start, int(row.get("page_end") or start)) + 1))
+	return sorted(wanted)
 
 
 def section_page_index(row: dict, pages: list[dict]) -> list[tuple[int, set]]:
@@ -267,13 +245,9 @@ def resolve_provenance(piece: str, markdown: str, row: dict, page_index, pages:
 	}
 	if resolved["page_approximate"]:
 		return provenance
-	for page in pages:
-		if page["page_no"] != resolved["page_no"]:
-			continue
-		on_page = evidence.locate_quote(piece, page)
-		if on_page["found"]:
-			provenance["page_line_start"] = on_page["line_start"]
-			provenance["page_line_end"] = on_page["line_end"]
+	provenance["page_line_start"], provenance["page_line_end"] = evidence.page_line_span(
+		piece, pages, resolved["page_no"]
+	)
 	return provenance
 
 
@@ -336,7 +310,7 @@ def resolve_context(rows: list[dict]) -> tuple[dict[str, dict], dict[str, str]]:
 	"""(documents by name, wiki route by Wiki Document name) for a batch of section rows."""
 	documents = {
 		document["name"]: document
-		for document in get_rows_by_name(
+		for document in evidence.get_rows_by_name(
 			"Source Document",
 			[row["source_document"] for row in rows],
 			["name", "title", "project"],
@@ -344,7 +318,7 @@ def resolve_context(rows: list[dict]) -> tuple[dict[str, dict], dict[str, str]]:
 	}
 	wiki_routes = {
 		page["name"]: page["route"]
-		for page in get_rows_by_name(
+		for page in evidence.get_rows_by_name(
 			"Wiki Document",
 			[row.get("wiki_document") for row in rows],
 			["name", "route"],
@@ -353,12 +327,23 @@ def resolve_context(rows: list[dict]) -> tuple[dict[str, dict], dict[str, str]]:
 	return documents, wiki_routes
 
 
-def chunks_for_section(section_name: str) -> list[Chunk]:
-	rows = frappe.get_all("Source Section", filters={"name": section_name}, fields=SECTION_FIELDS)
+def chunks_for_sections(section_names: list[str]) -> list[Chunk]:
+	"""Chunks for a batch of sections, reading each document's pages once for the whole batch.
+
+	Per-section chunking used to re-read every page of the parent document once per section,
+	so a ten-section propagation pass over a 236-page manual loaded 2,360 page rows to use
+	a couple of dozen. Only the pages the batch's sections declare are fetched.
+	"""
+	rows = evidence.get_rows_by_name("Source Section", section_names, SECTION_FIELDS)
 	if not rows:
 		return []
 	documents, wiki_routes = resolve_context(rows)
-	return build_chunks(rows, documents, wiki_routes, get_pages_by_document([rows[0]["source_document"]]))
+	pages = evidence.get_pages_by_document([row["source_document"] for row in rows], section_pages(rows))
+	return build_chunks(rows, documents, wiki_routes, pages)
+
+
+def chunks_for_section(section_name: str) -> list[Chunk]:
+	return chunks_for_sections([section_name])
 
 
 def chunks_for_project(project: str) -> list[Chunk]:
@@ -375,7 +360,7 @@ def chunks_for_project(project: str) -> list[Chunk]:
 	if not rows:
 		return []
 	documents, wiki_routes = resolve_context(rows)
-	return build_chunks(rows, documents, wiki_routes, get_pages_by_document(document_names))
+	return build_chunks(rows, documents, wiki_routes, evidence.get_pages_by_document(document_names))
 
 
 def chunk_row(item: Chunk, vector: list[float]) -> dict:
diff --git a/wikify/rag/eval.py b/wikify/rag/eval.py
index c138ef6..885fddf 100644
--- a/wikify/rag/eval.py
+++ b/wikify/rag/eval.py
@@ -1,6 +1,6 @@
 """Retrieval evaluation — the POC-2 thesis measured, not asserted.
 
-Twelve golden questions over the Demo Corpus (`specs/poc-rag-DEMO-DATA.md`) are run down
+Twelve golden questions over the Demo Corpus (`GOLDEN_QUESTIONS` below) are run down
 two legs and scored side by side:
 
 - **naive**  — plain top-k vector search on the raw question, no router, no filter. This is
@@ -14,13 +14,12 @@
 descriptions is 40% recall and 0% complete, and for "give me all the job descriptions"
 only the second number describes what the user actually got.
 
-**Why the golden questions are transcribed here rather than parsed out of the markdown.**
-The expected-source lists in `poc-rag-DEMO-DATA.md` live in free prose ("**Job Description
-— Community Midwife** (St Aubyn); ... (Meridian)"), not in a machine-readable table, so a
-parser would be a regex over English that rots silently the first time someone rewords a
-paragraph. Instead the ground truth is transcribed below and `resolve_expected()` checks
-every title against the real Source Sections at run time — a transcription typo or a
-renamed section is a loud failure, not a quiet zero.
+**Why the ground truth is transcribed here rather than parsed from prose.** Expected-source
+lists written as English ("**Job Description — Community Midwife** (St Aubyn); ...
+(Meridian)") can only be read by a regex over prose, which rots silently the first time
+someone rewords a paragraph. Instead `GOLDEN_QUESTIONS` below carries the ground truth
+literally and `resolve_expected()` checks every title against the real Source Sections at
+run time — a transcription typo or a renamed section is a loud failure, not a quiet zero.
 
 Usage (from the bench root):
 
@@ -49,17 +48,6 @@
 MODES = ("naive", "routed")
 SCORECARD_PATH = ("docs", "implementation", "media", "rag-eval.html")
 
-# The routed leg mirrors production exactly: same intent → mode map and same exhaustive
-# ceiling `answer()` uses, so the eval cannot flatter a strategy the app does not run.
-MODE_FOR_INTENT = rag_answer.MODE_FOR_INTENT
-EXHAUSTIVE_LIMIT = rag_answer.EXHAUSTIVE_LIMIT
-
-# The harness runs headless against one explicitly named project, so it opts out of the ACL
-# pre-filter on purpose rather than pretending to be a logged-in user. Imported directly, not
-# via `getattr` with a fallback: a fallback would silently reinstate the unscoped default this
-# sentinel exists to prevent.
-ALL_PROJECTS = rag_search.ALL_PROJECTS
-
 JD = "staff_roles_and_responsibilities"
 PAY = "administrative_policies"
 TRAINING = "training_and_audits"
@@ -92,8 +80,8 @@
 ]
 
 # `expected` is the ground-truth source set (Source Section titles, unique across the demo
-# corpus). `required` is the subset the spec bolds — the source a semantic answer is wrong
-# without. `exhaustive` marks the lane where recall is scored against the COMPLETE set
+# corpus). `required` is the subset a semantic answer is wrong without.
+# `exhaustive` marks the lane where recall is scored against the COMPLETE set
 # rather than as recall@k.
 GOLDEN_QUESTIONS = [
 	{
@@ -273,9 +261,6 @@
 ]
 
 
-# --- ground truth resolution ----------------------------------------------------------
-
-
 def demo_project() -> str:
 	"""The Demo Corpus project name. Autonames are not stable across sites, so look it up."""
 	project = frappe.db.get_value("Wikify Project", {"project_name": DEMO_PROJECT_NAME}, "name")
@@ -347,32 +332,6 @@ def hit_summary(hit) -> dict:
 	}
 
 
-# --- retrieval legs -------------------------------------------------------------------
-
-
-def naive_hits(question_text: str, project: str, k: int) -> list:
-	"""The baseline: raw question, plain vector top-k, no router and no metadata filter."""
-	return rag_search.search(
-		question_text, project=project, limit=k, mode="vector", allowed_projects=ALL_PROJECTS
-	)
-
-
-def routed_hits(decided, project: str, k: int) -> list:
-	"""The routed leg — the same intent → mode mapping `rag.answer.retrieve()` uses."""
-	mode = MODE_FOR_INTENT.get(decided.intent, "hybrid")
-	return rag_search.search(
-		decided.query,
-		project=project,
-		section_type=decided.section_type,
-		limit=EXHAUSTIVE_LIMIT if mode == "filter" else k,
-		mode=mode,
-		allowed_projects=ALL_PROJECTS,
-	)
-
-
-# --- scoring --------------------------------------------------------------------------
-
-
 def score_leg(expected: list[dict], required: list[str], hits: list) -> dict:
 	"""recall@k, precision@k, and completeness for one retrieval leg of one question."""
 	expected_names = {section["name"] for section in expected}
@@ -423,9 +382,6 @@ def aggregate(rows: list[dict], mode: str) -> dict:
 	}
 
 
-# --- the harness ----------------------------------------------------------------------
-
-
 def run_eval(
 	project: str | None = None,
 	k: int = DEFAULT_K,
@@ -469,16 +425,14 @@ def run_eval(
 			"expect_refusal": bool(question.get("expect_refusal")),
 		}
 		if "naive" in modes:
-			row["modes"]["naive"] = score_leg(
-				expected, question["required"], naive_hits(question["question"], project, k)
-			)
+			naive = rag_answer.naive_retrieve(question["question"], project, rag_search.ALL_PROJECTS, limit=k)
+			row["modes"]["naive"] = score_leg(expected, question["required"], naive)
 		if "routed" in modes:
-			row["modes"]["routed"] = score_leg(
-				expected, question["required"], routed_hits(decided, project, k)
-			)
+			routed = rag_answer.retrieve(decided, project, False, rag_search.ALL_PROJECTS, top_k=k)
+			row["modes"]["routed"] = score_leg(expected, question["required"], routed)
 		if check_refusals and question.get("expect_refusal"):
 			synthesised = rag_answer.answer(
-				question["question"], project=project, allowed_projects=ALL_PROJECTS
+				question["question"], project=project, allowed_projects=rag_search.ALL_PROJECTS
 			)
 			row["refused"] = bool(synthesised["refused"])
 		rows.append(row)
@@ -501,19 +455,17 @@ def run_eval(
 def compare_query(query: str, project: str | None = None, k: int = DEFAULT_K) -> dict:
 	"""One query, both legs, plus the diff — the structure `/rag-lab` renders.
 
-	The sibling of `wikify.api.rag.compare`; this one is the harness-side, permission-free
-	version used by the eval and the scorecard, and it adds the document-coverage counts
-	the visual comparison leans on.
+	The retrieval is `rag.answer.compare`, the same code `wikify.api.rag.compare` runs; this
+	is the harness-side, permission-free wrapper that adds the document-coverage counts the
+	visual comparison leans on.
 	"""
 	query = (query or "").strip()
 	if not query:
 		frappe.throw("Enter a query to compare.")
 	project = project or demo_project()
 
-	naive = naive_hits(query, project, k)
-	decided = route_question(query, project)
-	routed = routed_hits(decided, project, k)
-	found_by_naive = {hit.section for hit in naive}
+	comparison = rag_answer.compare(query, project, rag_search.ALL_PROJECTS, naive_limit=k, top_k=k)
+	decided = comparison["route"]
 	total_documents = len(frappe.get_all("Source Document", filters={"project": project}, pluck="name"))
 
 	return {
@@ -523,23 +475,21 @@ def compare_query(query: str, project: str | None = None, k: int = DEFAULT_K) ->
 		"route": decided.as_dict(),
 		"naive": {
 			"mode": "vector",
-			"hits": [hit_summary(hit) for hit in naive],
-			"count": len(naive),
-			"documents_covered": len({hit.document_title for hit in naive}),
+			"hits": [hit_summary(hit) for hit in comparison["naive"]],
+			"count": len(comparison["naive"]),
+			"documents_covered": len({hit.document_title for hit in comparison["naive"]}),
 		},
 		"routed": {
-			"mode": MODE_FOR_INTENT.get(decided.intent, "hybrid"),
-			"hits": [hit_summary(hit) for hit in routed],
-			"count": len(routed),
-			"documents_covered": len({hit.document_title for hit in routed}),
+			"mode": rag_answer.MODE_FOR_INTENT[decided.intent],
+			"hits": [hit_summary(hit) for hit in comparison["routed"]],
+			"count": len(comparison["routed"]),
+			"documents_covered": len({hit.document_title for hit in comparison["routed"]}),
 		},
 		"documents_in_project": total_documents,
-		"missed_by_naive": [hit_summary(hit) for hit in routed if hit.section not in found_by_naive],
+		"missed_by_naive": [hit_summary(hit) for hit in comparison["missed_by_naive"]],
 	}
 
 
-# --- HTML scorecard -------------------------------------------------------------------
-
 SCORECARD_CSS = """
 :root { color-scheme: light; }
 * { box-sizing: border-box; }
@@ -782,9 +732,6 @@ def write_scorecard(results: dict, path: str | None = None) -> str:
 	return path
 
 
-# --- headless entry point -------------------------------------------------------------
-
-
 def format_summary(results: dict) -> str:
 	"""The terminal view — the same numbers the scorecard shows, pasteable into a report."""
 	lines = [
diff --git a/wikify/rag/events.py b/wikify/rag/events.py
index 9761a2b..9deb3b1 100644
--- a/wikify/rag/events.py
+++ b/wikify/rag/events.py
@@ -31,8 +31,9 @@
 # saved ten times costs one rebuild.
 DIRTY_PAGES_HASH = "wikify_rag_dirty_pages"
 
-# Past this many sections in one pass, one project rebuild beats N `upsert_section` calls —
-# each of those re-creates the whole FTS index, so the per-section path stops paying off.
+# Past this many sections in one pass, one project rebuild beats a scoped `upsert_sections`:
+# the batch still re-embeds every section it touches, so at some width re-embedding the whole
+# project costs the same and leaves the index consistent in one commit instead of two.
 PROJECT_REBUILD_FANOUT = 10
 
 # ponytail: coalescing rebuilds the WHOLE project, so a one-word title fix re-embeds every
@@ -108,9 +109,6 @@ def rebuild_pending_project(project: str) -> None:
 	frappe.db.commit()
 
 
-# --- page → section → index propagation ------------------------------------------------
-
-
 def queue_page_propagation(doc, method: str | None = None) -> None:
 	"""`Source Page` doc_event handler — coalesce a section rebuild onto the long queue."""
 	if indexing_suspended():
@@ -244,5 +242,4 @@ def reindex_sections(project: str | None, section_names: list[str]) -> None:
 	if len(section_names) > PROJECT_REBUILD_FANOUT:
 		queue_project_rebuild(project)
 		return
-	for section_name in section_names:
-		index.upsert_section(section_name)
+	index.upsert_sections(section_names)
diff --git a/wikify/rag/evidence.py b/wikify/rag/evidence.py
index b94791f..129ed8a 100644
--- a/wikify/rag/evidence.py
+++ b/wikify/rag/evidence.py
@@ -367,32 +367,62 @@ def resolve_page(text: str, page_index: list[tuple[int, set, str]], fallback_pag
 	return {"page_no": best_page, "page_approximate": False, "page_score": round(best_score, 4)}
 
 
-def get_pages_by_document(document_names: list[str], page_numbers: list[int]) -> dict[str, list[dict]]:
-	"""Source Pages for the cited documents, one query per batch — never per citation."""
-	documents = [name for name in dict.fromkeys(document_names) if name]
-	numbers = sorted({number for number in page_numbers if number})
-	if not documents or not numbers:
-		return {}
+def get_rows_by_name(doctype: str, names: list[str], fields: list[str]) -> list[dict]:
+	"""`get_all` over a name list, sliced into batches — one query per batch, no N+1.
 
+	Lives here rather than next to either caller because `chunk` imports this module, so
+	this is the lowest point in the retrieval stack both sides can reach.
+	"""
+	unique = [name for name in dict.fromkeys(names) if name]
 	rows: list[dict] = []
-	for start in range(0, len(documents), BATCH_SIZE):
+	for start in range(0, len(unique), BATCH_SIZE):
 		rows.extend(
 			frappe.get_all(
-				"Source Page",
-				filters={
-					"source_document": ["in", documents[start : start + BATCH_SIZE]],
-					"page_no": ["in", numbers],
-				},
-				fields=PAGE_FIELDS,
-				order_by="page_no asc",
+				doctype, filters={"name": ["in", unique[start : start + BATCH_SIZE]]}, fields=fields
 			)
 		)
+	return rows
+
+
+def get_pages_by_document(
+	document_names: list[str], page_numbers: list[int] | None = None
+) -> dict[str, list[dict]]:
+	"""Source Pages for the given documents, grouped — one query per batch, never per row.
+
+	`page_numbers` narrows the scan to the pages the caller will actually read; omit it for
+	every page of each document. An empty page list is a real answer ("no pages wanted"),
+	so it returns nothing rather than falling through to the whole document.
+	"""
+	documents = [name for name in dict.fromkeys(document_names) if name]
+	numbers = None if page_numbers is None else sorted({number for number in page_numbers if number})
+	if not documents or numbers == []:
+		return {}
+
 	grouped: dict[str, list[dict]] = {}
-	for row in rows:
-		grouped.setdefault(row["source_document"], []).append(row)
+	for start in range(0, len(documents), BATCH_SIZE):
+		filters: dict = {"source_document": ["in", documents[start : start + BATCH_SIZE]]}
+		if numbers is not None:
+			filters["page_no"] = ["in", numbers]
+		for row in frappe.get_all("Source Page", filters=filters, fields=PAGE_FIELDS, order_by="page_no asc"):
+			grouped.setdefault(row["source_document"], []).append(row)
 	return grouped
 
 
+def page_line_span(text: str, pages: list[dict], page_no: int) -> tuple[int, int]:
+	"""Where `text` sits on one page of a document, as (line start, line end).
+
+	(0, 0) when the page is not in `pages` or the text cannot be located on it — an
+	unresolved span is reported as unknown, never guessed.
+	"""
+	for page in pages:
+		if page["page_no"] != page_no:
+			continue
+		located = locate_quote(text, page)
+		if located["found"]:
+			return located["line_start"], located["line_end"]
+	return 0, 0
+
+
 def page_range(citation: dict) -> list[int]:
 	start = int(citation.get("page_start") or 0)
 	end = int(citation.get("page_end") or start)
@@ -431,13 +461,7 @@ def check_quote(quote: str, citation: dict, pages_by_document: dict[str, list[di
 	if result["page_approximate"]:
 		return result
 
-	for page in pages:
-		if page["page_no"] != result["page_no"]:
-			continue
-		on_page = locate_quote(quote, page)
-		if on_page["found"]:
-			result["page_line_start"] = on_page["line_start"]
-			result["page_line_end"] = on_page["line_end"]
+	result["page_line_start"], result["page_line_end"] = page_line_span(quote, pages, result["page_no"])
 	return result
 
 
diff --git a/wikify/rag/history.py b/wikify/rag/history.py
index ecac78a..009a567 100644
--- a/wikify/rag/history.py
+++ b/wikify/rag/history.py
@@ -10,28 +10,23 @@
 answers actually rest on" — which is why `route_intent` is indexed and why a `turn`
 integer pairs a question row with its answer row instead of leaving that to creation order.
 
-Cost is summed across all three legs of one ask:
-
-- **routing** and **rerank** go through `engine.llm.chat_completion`, which already records
-  cost + tokens into its own metrics buffer — read here by watermark rather than by
-  `reset_metrics()`, because a parse job sharing the process would lose its buffer.
-- **synthesis** goes through litellm (`agent/llm.complete_with_tools`), which reports usage
-  only through a success callback; `add_litellm_metrics_callback` registers one.
+What a turn cost is not measured here. `rag.usage` accumulates all three legs of the ask
+— routing, rerank and synthesis — and `answer()` returns that total, so the figure logged
+is character-for-character the one the reader was shown. A second, independently derived
+total is how the log and the answer came to disagree by 13x.
 """
 
 from __future__ import annotations
 
 import json
-import threading
-import time
 
 import frappe
 from frappe import _
+from frappe.query_builder.functions import Count, Sum
 from frappe.utils.data import cint, flt
 
 from wikify.agent import llm as agent_llm
-from wikify.engine import llm as engine_llm
-from wikify.engine.store import cost_of
+from wikify.agent.session import session_title
 
 # How many prior turns the router replays to rewrite a follow-up question.
 HISTORY_TURNS = 10
@@ -39,99 +34,6 @@
 # Newest-first page of conversations for the sidebar.
 SESSION_PAGE_LENGTH = 50
 
-# litellm hands a streamed completion to its success callback from a thread pool
-# (`executor.submit(self.logging_obj.success_handler, ...)` at end-of-stream), so the
-# synthesis usage can land a few milliseconds after `answer()` has already returned.
-# The answer is fully streamed to the user by then, so waiting briefly for it costs the
-# reader nothing and is the difference between logging the whole ask and logging only
-# its two cheap legs.
-SYNTHESIS_GRACE_SECONDS = 0.3
-SYNTHESIS_POLL_SECONDS = 0.02
-
-_metrics_lock = threading.Lock()
-_litellm_metrics: list[dict] = []
-_callback_registered = False
-
-# Watermarks into both buffers, per thread, taken by `start_turn()`.
-_watermarks: dict[int, tuple[int, int]] = {}
-
-
-# --- cost capture -------------------------------------------------------------------
-
-
-def add_litellm_metrics_callback() -> None:
-	"""Register the litellm success callback once, so synthesis reports cost + tokens."""
-	global _callback_registered
-	if _callback_registered:
-		return
-
-	import litellm
-
-	litellm.success_callback = [*litellm.success_callback, record_litellm_metrics]
-	_callback_registered = True
-
-
-def record_litellm_metrics(kwargs, response, start_time, end_time) -> None:
-	"""litellm success callback — mirrors an `engine.llm` metrics entry.
-
-	Never raise: litellm swallows callback errors, but a broken callback would still cost
-	every agent completion a traceback in the logs.
-	"""
-	try:
-		usage = getattr(response, "usage", None)
-		with _metrics_lock:
-			_litellm_metrics.append(
-				{
-					"label": "synthesis",
-					"model": kwargs.get("model"),
-					"cost": kwargs.get("response_cost"),
-					"prompt_tokens": getattr(usage, "prompt_tokens", None),
-					"completion_tokens": getattr(usage, "completion_tokens", None),
-				}
-			)
-	except Exception:
-		frappe.log_error(title="Wikify ask cost callback failed")
-
-
-def start_turn() -> None:
-	"""Mark where this ask's LLM calls begin. Call immediately before `answer()`."""
-	add_litellm_metrics_callback()
-	with _metrics_lock:
-		# ponytail: both buffers are process-wide, so a second ask running concurrently in
-		# the same worker can inflate this turn's cost; give engine.llm a per-turn buffer
-		# if per-ask cost ever has to be billable rather than indicative.
-		_watermarks[threading.get_ident()] = (len(engine_llm.get_metrics()), len(_litellm_metrics))
-
-
-def turn_metrics(*, expect_synthesis: bool = True) -> list[dict]:
-	"""Every LLM call recorded since `start_turn()` — routing, rerank and synthesis."""
-	engine_mark, litellm_mark = _watermarks.pop(threading.get_ident(), (0, 0))
-	if expect_synthesis:
-		wait_for_synthesis_metrics(litellm_mark)
-	with _metrics_lock:
-		synthesis = _litellm_metrics[litellm_mark:]
-		del _litellm_metrics[litellm_mark:]
-	return engine_llm.get_metrics()[engine_mark:] + synthesis
-
-
-def wait_for_synthesis_metrics(litellm_mark: int) -> None:
-	deadline = time.monotonic() + SYNTHESIS_GRACE_SECONDS
-	while time.monotonic() < deadline:
-		with _metrics_lock:
-			if len(_litellm_metrics) > litellm_mark:
-				return
-		time.sleep(SYNTHESIS_POLL_SECONDS)
-
-
-def tokens_of(metrics: list[dict]) -> tuple[int, int]:
-	"""Prompt and completion tokens across a metrics buffer."""
-	prompt = sum(cint(entry.get("prompt_tokens")) for entry in metrics)
-	completion = sum(cint(entry.get("completion_tokens")) for entry in metrics)
-	return prompt, completion
-
-
-# --- conversations ------------------------------------------------------------------
-
 
 def start_session(project: str | None = None, title: str | None = None) -> str:
 	"""Open a conversation owned by the current user and return its name."""
@@ -140,17 +42,11 @@ def start_session(project: str | None = None, title: str | None = None) -> str:
 
 	session = frappe.new_doc("Wikify Ask Session")
 	session.project = project
-	session.title = summarize(title) if title else None
+	session.title = session_title(title) or None
 	session.insert()
 	return session.name
 
 
-def summarize(question: str) -> str:
-	"""A conversation title: the first line of the first question, trimmed."""
-	first_line = (question or "").strip().splitlines()
-	return first_line[0][:120] if first_line else ""
-
-
 def record_turn(
 	session: str | None,
 	question: str,
@@ -159,14 +55,13 @@ def record_turn(
 	project: str | None = None,
 	took_ms: int | None = None,
 	model: str | None = None,
-	llm_metrics: list[dict] | None = None,
 ) -> str:
 	"""Persist one ask — the question, the answer, its route, citations and cost.
 
-	`result` is the dict `rag.answer.answer()` returns. Opens a conversation when `session`
-	is empty — or names one that no longer exists, the same tolerance
-	`agent.session.get_or_create` has — and returns the conversation name either way, so
-	the caller can hand it back to the interface as one line at the end of `ask()`.
+	`result` is the dict `rag.answer.answer()` returns, cost and tokens included. Opens a
+	conversation when `session` is empty — or names one that no longer exists, the same
+	tolerance `agent.session.get_or_create` has — and returns the conversation name either
+	way, so the caller can hand it back to the interface as one line at the end of `ask()`.
 	"""
 	if not session or not frappe.db.exists("Wikify Ask Session", session):
 		session = start_session(project=project, title=question)
@@ -181,9 +76,9 @@ def record_turn(
 	# the router degrades on an unknown type the same way.
 	if section_type and not frappe.db.exists("Section Type", section_type):
 		section_type = None
-	metrics = llm_metrics if llm_metrics is not None else turn_metrics(expect_synthesis=not refused)
-	prompt_tokens, completion_tokens = tokens_of(metrics)
-	cost = flt(cost_of(metrics), 6)
+	prompt_tokens = cint(result.get("prompt_tokens"))
+	completion_tokens = cint(result.get("completion_tokens"))
+	cost = flt(result.get("cost"), 6)
 	turn = cint(frappe.db.count("Wikify Ask Message", {"session": session, "role": "question"})) + 1
 
 	append_message(session, "question", question, turn=turn)
@@ -197,21 +92,43 @@ def record_turn(
 		route_reason=route.get("reason"),
 		citations=result.get("citations") or [],
 		refused=refused,
-		took_ms=cint(took_ms or result.get("took_ms")),
+		took_ms=cint(took_ms),
 		model=model or agent_llm.resolve_model(project=conversation.project),
 		prompt_tokens=prompt_tokens,
 		completion_tokens=completion_tokens,
 		cost=cost,
 	)
 
-	conversation.title = conversation.title or summarize(question)
-	conversation.message_count = cint(conversation.message_count) + 2
-	conversation.total_tokens = cint(conversation.total_tokens) + prompt_tokens + completion_tokens
-	conversation.total_cost = flt(flt(conversation.total_cost) + cost, 6)
+	conversation.title = conversation.title or session_title(question)
+	set_totals(conversation)
 	conversation.save()
 	return session
 
 
+def set_totals(conversation) -> None:
+	"""Re-total a conversation from its own turns.
+
+	Summed rather than incremented so the header cannot drift from the rows it claims to
+	total — a deleted turn, a failed insert or a legacy row totalled by the old
+	double-counting cost path all heal on the next turn.
+	"""
+	message = frappe.qb.DocType("Wikify Ask Message")
+	totals = (
+		frappe.qb.from_(message)
+		.select(
+			Count(message.name).as_("messages"),
+			Sum(message.cost).as_("cost"),
+			Sum(message.prompt_tokens).as_("prompt_tokens"),
+			Sum(message.completion_tokens).as_("completion_tokens"),
+		)
+		.where(message.session == conversation.name)
+		.run(as_dict=True)[0]
+	)
+	conversation.message_count = cint(totals.messages)
+	conversation.total_tokens = cint(totals.prompt_tokens) + cint(totals.completion_tokens)
+	conversation.total_cost = flt(totals.cost, 6)
+
+
 def append_message(session: str, role: str, content: str, **values) -> str:
 	"""Insert one `Wikify Ask Message` row and return its name."""
 	if values.get("citations") is not None:
diff --git a/wikify/rag/index.py b/wikify/rag/index.py
index 9410f05..d3326ce 100644
--- a/wikify/rag/index.py
+++ b/wikify/rag/index.py
@@ -85,18 +85,35 @@ def rebuild_project(project: str) -> dict:
 	}
 
 
-def upsert_section(section_name: str) -> int:
-	"""Re-index one section (after an edit/split/merge). Returns the chunk count written."""
-	chunks = chunk.chunks_for_section(section_name)
+def upsert_sections(section_names: list[str]) -> int:
+	"""Re-index a batch of sections (after an edit/split/merge, or a page propagation pass).
+
+	One delete, one add and one FTS rebuild for the whole batch. Done per section, a
+	ten-section propagation pass rebuilt the entire full-text index ten times and re-read
+	every page of the document ten times over — the cost was in the fan-out, not the work.
+	Returns the chunk count written. Sized for the handful of sections one edit or one
+	propagation pass touches — `events.PROJECT_REBUILD_FANOUT` sends anything wider to a
+	whole-project rebuild rather than building an unbounded `IN (...)` clause here.
+	"""
+	names = [name for name in dict.fromkeys(section_names) if name]
+	if not names:
+		return 0
+	chunks = chunk.chunks_for_sections(names)
 	table = store.chunks_table(create=True)
 	rows = chunk_rows(chunks) if chunks else []
-	table.delete(f"section = {store.sql_literal(section_name)}")
+	scoped = ", ".join(store.sql_literal(name) for name in names)
+	table.delete(f"section IN ({scoped})")
 	if rows:
 		table.add(rows)
 	refresh_fts_index(table)
 	return len(rows)
 
 
+def upsert_section(section_name: str) -> int:
+	"""Re-index one section. Returns the chunk count written."""
+	return upsert_sections([section_name])
+
+
 def drop_section(section_name: str) -> None:
 	table = store.chunks_table()
 	if table is None:
@@ -113,16 +130,22 @@ def indexed_at(table) -> str | None:
 	return timestamp.isoformat() if timestamp else None
 
 
-def index_stats(project: str | None = None) -> dict:
-	"""Counts for the index-status card. `project=None` covers the whole site."""
+def index_stats(projects: list[str] | None = None) -> dict:
+	"""Counts for the index-status card. `projects=None` covers the whole site.
+
+	One scan for the whole scope, aggregated in Python: asking per project meant a fresh
+	LanceDB connection and a full table read for every project the reader could see, which
+	is the same rows re-scanned N times to produce one card.
+	"""
 	table = store.chunks_table()
 	empty = {"chunks": 0, "sections": 0, "documents": 0, "indexed_at": None, "dim": embed.EMBED_DIM}
-	if table is None:
+	if table is None or projects == []:
 		return empty
 
 	query = table.search().select(["section", "source_document"]).limit(None)
-	if project:
-		query = query.where(f"project = {store.sql_literal(project)}")
+	if projects is not None:
+		allowed = ", ".join(store.sql_literal(name) for name in projects)
+		query = query.where(f"project IN ({allowed})")
 	rows = query.to_list()
 	if not rows:
 		return empty
diff --git a/wikify/rag/search.py b/wikify/rag/search.py
index 9e19231..f7cce12 100644
--- a/wikify/rag/search.py
+++ b/wikify/rag/search.py
@@ -17,11 +17,13 @@
 from __future__ import annotations
 
 import re
+from concurrent.futures import ThreadPoolExecutor
 from dataclasses import dataclass
 
 import frappe
+from frappe.utils.data import cint, flt
 
-from wikify.rag import chunk, embed, store, usage
+from wikify.rag import chunk, embed, evidence, store, usage
 
 MODES = ("vector", "fts", "hybrid", "filter")
 FTS_COLUMN = "text"
@@ -54,6 +56,18 @@ def __repr__(self) -> str:
 CANDIDATE_FLOOR = 50
 RERANK_CANDIDATES = 50
 RERANK_SNIPPET_CHARS = 700
+# Asked to grade a whole candidate list in one reply the classifier degenerates into a run
+# of zeros. Measured on PRJ-2026-00002: "what is the surcharge rate when total income
+# exceeds 2 crore" scored 0.0 across all 35 candidates in a single call, and found the
+# answering section at 8.0 when the same 35 candidates were graded ten at a time.
+RERANK_BATCH_SIZE = 10
+# The batches are independent HTTP POSTs, so they run concurrently and the rerank costs one
+# batch of latency rather than all of them. What may NOT cross into a pool thread is frappe:
+# `frappe.local` is unbound there, so the OpenRouter key is resolved on the calling thread and
+# each batch's usage is billed on it too (`usage` is thread-local by design — see `rag.usage`).
+# ponytail: fixed pool width, sized for the ~4 batches a 35-candidate rerank produces; make it
+# proportional to the batch count if RERANK_CANDIDATES ever grows past a couple of hundred.
+RERANK_WORKERS = 4
 
 RESULT_COLUMNS = [
 	"id",
@@ -89,11 +103,37 @@ class Hit:
 	vector_rank: int | None = None
 	fts_rank: int | None = None
 	rerank_score: float | None = None
+	# The embedding leg's absolute 0-1 similarity, kept alongside the rank-based fusion
+	# score because it is the one retrieval number that means the same thing across
+	# queries — `answer.below_floor` reads it as a second opinion on the reranker.
+	vector_score: float | None = None
 
 	def as_dict(self) -> dict:
 		return self.__dict__.copy()
 
 
+def hit_value(hit, key: str):
+	"""One field of a hit, whether it is still a `Hit` or already `as_dict()`ed.
+
+	The two surfaces that label a hit sit either side of the whitelisted API — synthesis
+	holds Hits, the agent tool holds the JSON it returned — and they must print the same
+	label, so the labelling reads both shapes rather than being written twice.
+	"""
+	return hit.get(key) if isinstance(hit, dict) else getattr(hit, key, None)
+
+
+def page_label(hit) -> str:
+	"""A hit's page span as it is shown: `p.5`, or `p.5-7` when it runs across pages."""
+	start, end = hit_value(hit, "page_start"), hit_value(hit, "page_end")
+	return f"p.{start}-{end}" if end and end != start else f"p.{start}"
+
+
+def crumb(hit) -> str:
+	"""The breadcrumb: document title, then the section's ancestor path (or its own title)."""
+	path = hit_value(hit, "hierarchy_path") or hit_value(hit, "title")
+	return f"{hit_value(hit, 'document_title')}{chunk.CONTEXT_SEPARATOR}{path}"
+
+
 def assert_acl_decision(allowed_projects) -> None:
 	"""The single enforcement point: every retrieval must carry a deliberate ACL decision.
 
@@ -217,8 +257,17 @@ def rank_by_chunk(rows: list[dict]) -> dict[str, int]:
 	return {row["id"]: position for position, row in enumerate(rows, start=1)}
 
 
+def similarity_by_chunk(rows: list[dict]) -> dict[str, float]:
+	"""chunk id → embedding similarity, read off the vector rows the ranking pass already
+	scanned. Free: no extra query, the rows are in hand."""
+	return {row["id"]: row_score(row) for row in rows}
+
+
 def expand_to_sections(
-	rows: list[dict], vector_ranks: dict[str, int], fts_ranks: dict[str, int]
+	rows: list[dict],
+	vector_ranks: dict[str, int],
+	fts_ranks: dict[str, int],
+	vector_scores: dict[str, float],
 ) -> list[Hit]:
 	"""Dedupe chunk hits to their parent section and attach the full section markdown."""
 	best: dict[str, dict] = {}
@@ -232,7 +281,7 @@ def expand_to_sections(
 
 	sections = {
 		section["name"]: section
-		for section in chunk.get_rows_by_name(
+		for section in evidence.get_rows_by_name(
 			"Source Section",
 			list(best),
 			["name", "title", "markdown", "hierarchy_path", "section_type", "source_document"],
@@ -240,7 +289,7 @@ def expand_to_sections(
 	}
 	documents = {
 		document["name"]: document
-		for document in chunk.get_rows_by_name(
+		for document in evidence.get_rows_by_name(
 			"Source Document",
 			[entry["row"]["source_document"] for entry in best.values()],
 			["name", "title"],
@@ -259,6 +308,11 @@ def expand_to_sections(
 		]
 		vector_hits = [rank for rank, _ in ranks if rank]
 		fts_hits = [rank for _, rank in ranks if rank]
+		similarities = [
+			vector_scores[chunk_id]
+			for chunk_id in matched_chunks[section_name]
+			if vector_scores.get(chunk_id) is not None
+		]
 		hits.append(
 			Hit(
 				chunk_id=row["id"],
@@ -276,65 +330,114 @@ def expand_to_sections(
 				score=round(entry["score"], 6),
 				vector_rank=min(vector_hits) if vector_hits else None,
 				fts_rank=min(fts_hits) if fts_hits else None,
+				vector_score=round(max(similarities), 6) if similarities else None,
 			)
 		)
 	return hits
 
 
-def rerank_scores(query: str, hits: list[Hit], model: str) -> dict[int, float]:
-	"""Ask the cheap model to score each candidate 0-10 for answering `query`."""
+RERANK_SYSTEM_PROMPT = (
+	"You rank retrieved document sections by how well each one answers the "
+	'user question. Reply with JSON: {"scores": [{"id": <candidate number>, '
+	'"score": <0-10>}]} covering every candidate. No prose.'
+)
+
+
+def score_batch(
+	query: str, hits: list[Hit], offset: int, model: str, api_key: str = ""
+) -> tuple[dict[int, float], dict | None]:
+	"""Score one batch of candidates 0-10, keyed by each one's position in the FULL list.
+
+	A reply that skips candidates is dropped whole: the missing ones would otherwise read as
+	an unscored 0 and outrank nothing, which is the same silent zero this batching exists to
+	prevent.
+
+	Returns the scores and the completion's usage payload rather than folding the usage in
+	here: this runs on a pool thread and `usage` is thread-local, so only the caller can bill
+	it. A dropped batch still reports its usage — the call was made and it was paid for.
+	"""
 	from wikify.engine import llm
 
 	candidates = "\n\n".join(
-		f"[{position}] {hit.document_title}{chunk.CONTEXT_SEPARATOR}{hit.hierarchy_path or hit.title}\n"
+		f"[{offset + position}] {hit.document_title}{chunk.CONTEXT_SEPARATOR}{hit.hierarchy_path or hit.title}\n"
 		f"{(hit.text or '')[:RERANK_SNIPPET_CHARS]}"
 		for position, hit in enumerate(hits)
 	)
 	response = llm.chat_completion(
 		model,
 		[
-			{
-				"role": "system",
-				"content": (
-					"You rank retrieved document sections by how well each one answers the "
-					'user question. Reply with JSON: {"scores": [{"id": <candidate number>, '
-					'"score": <0-10>}]} covering every candidate. No prose.'
-				),
-			},
+			{"role": "system", "content": RERANK_SYSTEM_PROMPT},
 			{"role": "user", "content": f"Question: {query}\n\nCandidates:\n\n{candidates}"},
 		],
 		label="rag_rerank",
 		response_format={"type": "json_object"},
+		api_key=api_key,
 	)
-	usage.add(response.get("usage"))
-	content = response["choices"][0]["message"]["content"]
-	parsed = frappe.parse_json(content) or {}
-	from frappe.utils.data import cint, flt
+	parsed = frappe.parse_json(response["choices"][0]["message"]["content"]) or {}
+	scores = {cint(item.get("id")): flt(item.get("score")) for item in parsed.get("scores") or []}
+	complete = set(range(offset, offset + len(hits))) <= set(scores)
+	return (scores if complete else {}), response.get("usage")
 
-	return {cint(item.get("id")): flt(item.get("score")) for item in parsed.get("scores") or []}
 
+def rerank_scores(query: str, hits: list[Hit], model: str) -> dict[int, float]:
+	"""Ask the cheap model to score every candidate 0-10 for answering `query`.
 
-def ranks_candidates(scores: dict[int, float]) -> bool:
-	"""False when the reply carries no ranking information, so it must not be read as one.
+	Graded in batches of `RERANK_BATCH_SIZE` — see the constant for the measurement that
+	forced it, and `RERANK_WORKERS` for why the batches run concurrently. The candidate
+	numbers stay global across batches so a score always maps back to the same hit.
+	"""
+	from wikify.engine import settings
+
+	api_key = settings.openrouter_key()
+	offsets = range(0, len(hits), RERANK_BATCH_SIZE)
+	with ThreadPoolExecutor(max_workers=RERANK_WORKERS) as pool:
+		batches = list(
+			pool.map(
+				lambda offset: score_batch(
+					query, hits[offset : offset + RERANK_BATCH_SIZE], offset, model, api_key
+				),
+				offsets,
+			)
+		)
 
-	The cheap classifier intermittently *succeeds* and returns 0 for every candidate. Taken
-	at face value that is a confident "none of this is relevant", and `answer.below_floor`
-	then refuses a question the retriever had already answered correctly — the worst failure
-	this system has, because "the document doesn't say" is indistinguishable from the
-	document genuinely not covering it. One flat verdict across several candidates is the
-	reranker being unavailable, not the corpus being empty, so the caller keeps the fusion
-	order instead.
+	scores: dict[int, float] = {}
+	for batch_scores, batch_usage in batches:
+		usage.add(batch_usage)
+		scores.update(batch_scores)
+	return scores
+
+
+def usable_verdict(scores: dict[int, float], expected: int) -> bool:
+	"""False when the reply carries no verdict at all, so it must not be read as one.
+
+	Two shapes are not verdicts. A reply that covers only part of the candidate list lost
+	whichever batch failed, so the survivors would be ranked against nothing. And one
+	identical non-zero score across every candidate is the model declining to discriminate.
+
+	An all-zero reply IS a verdict — "none of these are relevant" — and is trusted as one,
+	because the false all-zeros that made this system refuse answerable questions came from
+	over-long candidate lists (see `RERANK_BATCH_SIZE`), not from the model's judgement.
+	`answer.below_floor` never lets it refuse alone: the embedding leg has to agree.
 	"""
-	present = [score for score in scores.values() if score is not None]
-	if len(present) < 2:
-		return bool(present)
-	return len(set(present)) > 1
+	if len(scores) < expected:
+		return False
+	present = set(scores.values())
+	# "Declining to discriminate" needs something to discriminate between, so a lone
+	# candidate's score is always taken at face value.
+	return len(scores) < 2 or len(present) > 1 or present == {0.0}
+
+
+def rank_key(hit: Hit) -> tuple[float, float]:
+	"""How hits are ordered once a rerank has run: the model's judgement first, the fusion
+	score as the tiebreak. Candidates past `RERANK_CANDIDATES` were never judged and sort
+	below the ones that were."""
+	return (hit.rerank_score if hit.rerank_score is not None else -1.0, hit.score)
 
 
 def rerank_hits(query: str, hits: list[Hit]) -> list[Hit]:
 	"""LLM rerank of the top candidates. Never fatal — the search path degrades to the
 	unreranked order when no OpenRouter key is configured, the call fails, or the reply
-	ranks nothing."""
+	carries no verdict."""
 	from wikify.engine import llm, settings
 
 	if not hits or not llm.has_openrouter():
@@ -347,18 +450,16 @@ def rerank_hits(query: str, hits: list[Hit]) -> list[Hit]:
 		frappe.log_error(title="RAG rerank failed", message=frappe.get_traceback())
 		return hits
 
-	if not ranks_candidates(scores):
+	if not usable_verdict(scores, len(head)):
 		frappe.log_error(
-			title="RAG rerank returned a flat verdict",
+			title="RAG rerank returned no verdict",
 			message=f"Query: {query}\nCandidates: {len(head)}\nScores: {scores}",
 		)
 		return hits
 
 	for position, hit in enumerate(head):
 		hit.rerank_score = scores.get(position)
-	head.sort(
-		key=lambda hit: (hit.rerank_score if hit.rerank_score is not None else -1.0, hit.score), reverse=True
-	)
+	head.sort(key=rank_key, reverse=True)
 	return head + tail
 
 
@@ -395,11 +496,13 @@ def search(
 
 	vector_ranks: dict[str, int] = {}
 	fts_ranks: dict[str, int] = {}
+	vector_scores: dict[str, float] = {}
 	if mode == "filter":
 		rows = run_filter(table, where)
 	elif mode == "vector":
 		rows = run_vector(table, query, where, candidate_limit)
 		vector_ranks = rank_by_chunk(rows)
+		vector_scores = similarity_by_chunk(rows)
 	elif mode == "fts":
 		rows = run_fts(table, query, where, candidate_limit)
 		fts_ranks = rank_by_chunk(rows)
@@ -409,10 +512,12 @@ def search(
 		# re-embedding per pass is the expensive part, the extra scans are not.
 		query_vector = embed.embed_one(query)
 		rows = run_hybrid(table, query, where, candidate_limit, vector=query_vector)
-		vector_ranks = rank_by_chunk(run_vector(table, query, where, candidate_limit, vector=query_vector))
+		vector_rows = run_vector(table, query, where, candidate_limit, vector=query_vector)
+		vector_ranks = rank_by_chunk(vector_rows)
+		vector_scores = similarity_by_chunk(vector_rows)
 		fts_ranks = rank_by_chunk(run_fts(table, query, where, candidate_limit))
 
-	hits = expand_to_sections(rows, vector_ranks, fts_ranks)
+	hits = expand_to_sections(rows, vector_ranks, fts_ranks, vector_scores)
 	# Filter mode re-sorts by document/page below, so a rerank here would be an LLM call
 	# whose entire output is thrown away.
 	if rerank and query and mode != "filter":
@@ -420,5 +525,10 @@ def search(
 
 	if mode == "filter":
 		return sorted(hits, key=lambda hit: (hit.document_title, hit.page_start, hit.title))
-	hits.sort(key=lambda hit: hit.score, reverse=True)
+	# `rank_key` falls back to the fusion score, so an unreranked search is ordered exactly
+	# as before. Sorting on `hit.score` here used to discard the rerank outright: the
+	# reranked winner was pushed back into fusion position and then cut by the `limit`
+	# slice, which is how "slab rates under section 115BAC(1A)" scored 8.0 at fusion rank 10
+	# and was never returned at all.
+	hits.sort(key=rank_key, reverse=True)
 	return hits[: int(limit)]
diff --git a/wikify/rag/usage.py b/wikify/rag/usage.py
index c8431b1..bf05ae6 100644
--- a/wikify/rag/usage.py
+++ b/wikify/rag/usage.py
@@ -8,35 +8,81 @@
 shared buffer would bill one user's question to another's. Outside a `collect()` block
 `add()` is a no-op, so the retrieval paths that are not part of an answer cost nothing to
 instrument.
+
+The money arrives by two different roads because the two clients report it differently:
+
+- the OpenRouter REST client (`engine.llm`) returns `usage.cost` on the response itself,
+  folded in by `add()`;
+- litellm (synthesis) puts the price nowhere on the response — it reports it only to a
+  callback, after the stream has ended, on one of its own pool threads. `LitellmSpend`
+  therefore ties each litellm call to the collector that started it in `log_pre_api_call`
+  (which litellm runs inline, on the calling thread) and bills it back by the same call id
+  when the price lands. Only the money comes from the callback; the tokens are already on
+  the response.
 """
 
 from __future__ import annotations
 
 import threading
+import time
 from collections.abc import Iterator
 from contextlib import contextmanager
+from dataclasses import dataclass, field
 
+import litellm
 from frappe.utils.data import cint, flt
+from litellm.integrations.custom_logger import CustomLogger
+
+# litellm prices a streamed completion a few milliseconds after the stream ends, so a
+# collector with a call outstanding waits for it. Generous, because reporting a cost of
+# zero for the leg that carries ~95% of the spend is far worse than a short pause on an
+# answer the reader has already been streamed.
+SPEND_GRACE_SECONDS = 5.0
+SPEND_POLL_SECONDS = 0.01
 
 _local = threading.local()
+_lock = threading.Lock()
+
+# litellm call id -> the collector that started it. Module-level, because the callback
+# that resolves it runs on a different thread than the one that made the call.
+_awaiting_price: dict[str, Collector] = {}
+_callback_registered = False
 
 
 def empty() -> dict:
 	return {"cost": 0.0, "prompt_tokens": 0, "completion_tokens": 0}
 
 
+@dataclass
+class Collector:
+	"""One question's running total, plus the litellm calls it is still owed a price for."""
+
+	totals: dict = field(default_factory=empty)
+	pending: set[str] = field(default_factory=set)
+
+
 @contextmanager
 def collect() -> Iterator[dict]:
 	"""Total the usage of every `add()` inside the block. Nesting reuses the outer total."""
-	if getattr(_local, "totals", None) is not None:
-		yield _local.totals
+	if getattr(_local, "collector", None) is not None:
+		yield _local.collector.totals
 		return
 
-	_local.totals = empty()
+	collector = Collector()
+	_local.collector = collector
+	add_litellm_callback()
 	try:
-		yield _local.totals
+		yield collector.totals
 	finally:
-		_local.totals = None
+		# No wait here: `answer()` reads the totals INSIDE the block, so a price that lands
+		# after it has read them cannot reach any reported number — waiting for one would
+		# only add up to the whole grace period to a turn that is already finished. `add()`
+		# does the waiting, at the one moment the figure is still going somewhere.
+		with _lock:
+			for call_id in collector.pending:
+				_awaiting_price.pop(call_id, None)
+			collector.pending.clear()
+		_local.collector = None
 
 
 def add(usage) -> None:
@@ -45,14 +91,80 @@ def add(usage) -> None:
 	`usage` is whatever the client handed back: a dict from the OpenRouter REST client, or
 	litellm's usage object on the final stream chunk. Cost is only present when the
 	provider reports it — we never estimate it, an unpriced call adds 0.
+
+	Folding also collects any litellm price this block is still owed, because
+	`answer()` reads the total inside the `collect()` block, immediately after the last
+	`add()` — waiting at the end of the block would be too late to be in the number.
 	"""
-	totals = getattr(_local, "totals", None)
-	if totals is None or not usage:
+	collector = getattr(_local, "collector", None)
+	if collector is None:
+		return
+	wait_for_prices(collector)
+	if not usage:
 		return
-	totals["cost"] += flt(read(usage, "cost"))
-	totals["prompt_tokens"] += cint(read(usage, "prompt_tokens"))
-	totals["completion_tokens"] += cint(read(usage, "completion_tokens"))
+	collector.totals["cost"] += flt(read(usage, "cost"))
+	collector.totals["prompt_tokens"] += cint(read(usage, "prompt_tokens"))
+	collector.totals["completion_tokens"] += cint(read(usage, "completion_tokens"))
 
 
 def read(usage, key: str):
 	return usage.get(key) if isinstance(usage, dict) else getattr(usage, key, None)
+
+
+def wait_for_prices(collector: Collector) -> None:
+	"""Wait for litellm to price the calls this collector started. No-op when it owes none.
+
+	# ponytail: a price that never arrives costs the ask the whole grace period, since
+	# litellm reporting neither success nor failure is the only way out; give the
+	# completion its own timeout if a provider ever goes quiet on us.
+	"""
+	deadline = time.monotonic() + SPEND_GRACE_SECONDS
+	while time.monotonic() < deadline:
+		with _lock:
+			if not collector.pending:
+				return
+		time.sleep(SPEND_POLL_SECONDS)
+
+
+class LitellmSpend(CustomLogger):
+	"""Bills each litellm completion to the collector that started it."""
+
+	def log_pre_api_call(self, model, messages, kwargs):
+		# Runs inline on the calling thread, which is the only moment the call and the
+		# question that made it are known to be the same piece of work.
+		collector = getattr(_local, "collector", None)
+		call_id = kwargs.get("litellm_call_id")
+		if collector is None or not call_id:
+			return
+		with _lock:
+			collector.pending.add(call_id)
+			_awaiting_price[call_id] = collector
+
+	def log_success_event(self, kwargs, response_obj, start_time, end_time):
+		self.add_price(kwargs.get("litellm_call_id"), kwargs.get("response_cost"))
+
+	def log_failure_event(self, kwargs, response_obj, start_time, end_time):
+		self.add_price(kwargs.get("litellm_call_id"), None)
+
+	def add_price(self, call_id: str | None, cost) -> None:
+		"""Add one completion's price to its collector, exactly once.
+
+		Popping the call id is what makes it once: litellm may report the same streamed
+		completion more than once, and a repeated price would bill the reader twice.
+		"""
+		with _lock:
+			collector = _awaiting_price.pop(call_id, None)
+			if collector is None:
+				return
+			collector.pending.discard(call_id)
+			collector.totals["cost"] += flt(cost)
+
+
+def add_litellm_callback() -> None:
+	"""Register the cost logger once per process."""
+	global _callback_registered
+	with _lock:
+		if _callback_registered:
+			return
+		litellm.callbacks = [*litellm.callbacks, LitellmSpend()]
+		_callback_registered = True
diff --git a/wikify/tests/fixtures/demo_corpus.py b/wikify/tests/fixtures/demo_corpus.py
index 5a72085..68c8b18 100644
--- a/wikify/tests/fixtures/demo_corpus.py
+++ b/wikify/tests/fixtures/demo_corpus.py
@@ -1,4 +1,4 @@
-"""Repeatable demo corpus for the RAG POC (`specs/poc-rag-CONTRACT.md`).
+"""Repeatable demo corpus for the RAG POC.
 
 Five healthcare-provider manuals whose sections deliberately **overlap in type**: each
 document carries three job descriptions, a compensation/benefits section, a
@@ -34,8 +34,6 @@
 SPACE_NAME = "Demo Corpus"
 SPACE_ROUTE = "demo-corpus"
 
-# --- Northfield General Hospital — Nursing Services Manual ---
-
 NORTHFIELD_OVERVIEW = """# Hospital Overview and Mission
 
 Northfield General Hospital is a 480-bed district general hospital serving a population of
@@ -239,8 +237,6 @@
 within one working day, and is included in the ward's monthly harm-free care report.
 """
 
-# --- Riverside Community Clinic — Staff Handbook ---
-
 RIVERSIDE_OVERVIEW = """# Who We Are: Riverside Community Clinic
 
 Riverside Community Clinic is a primary care practice with a registered list of
@@ -446,8 +442,6 @@
 insulin, methotrexate, lithium or opioids — are prioritised the same day.
 """
 
-# --- St Aubyn Maternity Unit — Operational Policy Manual ---
-
 AUBYN_OVERVIEW = """# St Aubyn Maternity Unit at a Glance
 
 St Aubyn Maternity Unit is a consultant-led maternity service co-located with an alongside
@@ -647,8 +641,6 @@
 with the team is held within seventy-two hours.
 """
 
-# --- Lakeside Surgical Centre — Theatre Operations Manual ---
-
 LAKESIDE_OVERVIEW = """# Lakeside Surgical Centre: Purpose and Services
 
 Lakeside Surgical Centre is a dedicated elective treatment centre with six operating
@@ -852,8 +844,6 @@
 before the patient leaves the theatre.
 """
 
-# --- Meridian Home Care Services — Employee Policy and Roles Manual ---
-
 MERIDIAN_OVERVIEW = """# Meridian Home Care: Company Overview
 
 Meridian Home Care Services is an independent domiciliary care provider supporting around
@@ -1057,7 +1047,6 @@
 same, and who agreed it.
 """
 
-# --- corpus layout ---
 # Each section is (title, level, hierarchy_path, page_start, page_end, section_type, markdown).
 # Groups carry no body and no type: the wiki generator gives them a Contents rollup, and
 # leaving them untyped keeps a `section_type` filter returning exactly the leaf sections.
diff --git a/wikify/tests/test_ask_history.py b/wikify/tests/test_ask_history.py
index 7014a89..08e526e 100644
--- a/wikify/tests/test_ask_history.py
+++ b/wikify/tests/test_ask_history.py
@@ -4,34 +4,26 @@
 """Ask conversation persistence + per-ask cost.
 
 Three things are worth asserting here: that one ask lands as a question/answer pair a
-later eval query can rejoin on `turn`, that the cost figure is the sum of all three LLM
-legs rather than synthesis alone, and that one user's conversation is invisible to
-another — which is enforced by the DocType's `if_owner` permission, not by a hand-rolled
-filter, so it has to be exercised as a real second user.
-"""
+later eval query can rejoin on `turn`, that the logged cost is the figure the ask itself
+reported and that a conversation totals its own turns, and that one user's conversation
+is invisible to another — which is enforced by the DocType's `if_owner` permission, not
+by a hand-rolled filter, so it has to be exercised as a real second user.
 
-from types import SimpleNamespace
-from unittest.mock import patch
+What the ask *costs* is measured in `rag.usage` and asserted in `test_ask_cost`.
+"""
 
 import frappe
 from frappe.tests.utils import FrappeTestCase
 
 from wikify.api import ask_history as api_ask_history
-from wikify.engine import llm as engine_llm
-from wikify.engine.store import cost_of
 from wikify.rag import history
 
 OTHER_USER = "ask-history-other@example.com"
 
-ROUTE_METRIC = {"label": "rag_route", "cost": 0.000021, "prompt_tokens": 900, "completion_tokens": 60}
-RERANK_METRIC = {"label": "rag_rerank", "cost": 0.000044, "prompt_tokens": 1800, "completion_tokens": 40}
-SYNTHESIS_METRIC = {
-	"label": "synthesis",
-	"cost": 0.004120,
-	"prompt_tokens": 5200,
-	"completion_tokens": 610,
-}
-ALL_LEGS = [ROUTE_METRIC, RERANK_METRIC, SYNTHESIS_METRIC]
+# One ask's three legs — routing, rerank, synthesis — as `answer()` reports them.
+ALL_LEGS_COST = 0.004185
+ALL_LEGS_PROMPT_TOKENS = 7900
+ALL_LEGS_COMPLETION_TOKENS = 710
 
 
 def make_project(label: str = "Ask History") -> str:
@@ -52,9 +44,15 @@ def make_result(
 	section_type: str | None = None,
 	refused: bool = False,
 	citations: list | None = None,
+	cost: float = ALL_LEGS_COST,
+	prompt_tokens: int = ALL_LEGS_PROMPT_TOKENS,
+	completion_tokens: int = ALL_LEGS_COMPLETION_TOKENS,
 ) -> dict:
 	"""The dict `rag.answer.answer()` returns."""
 	return {
+		"cost": cost,
+		"prompt_tokens": prompt_tokens,
+		"completion_tokens": completion_tokens,
 		"answer": answer,
 		"citations": []
 		if refused
@@ -83,9 +81,7 @@ def setUp(self):
 		self.project = make_project()
 
 	def test_first_turn_opens_a_titled_conversation(self):
-		session = history.record_turn(
-			None, "List every job description", make_result(), project=self.project, llm_metrics=ALL_LEGS
-		)
+		session = history.record_turn(None, "List every job description", make_result(), project=self.project)
 		conversation = frappe.get_doc("Wikify Ask Session", session)
 		self.assertEqual(conversation.title, "List every job description")
 		self.assertEqual(conversation.project, self.project)
@@ -94,8 +90,8 @@ def test_first_turn_opens_a_titled_conversation(self):
 		self.assertIsNotNone(conversation.started_at)
 
 	def test_turn_number_pairs_the_question_with_its_answer(self):
-		session = history.record_turn(None, "First question", make_result(), llm_metrics=ALL_LEGS)
-		history.record_turn(session, "Second question", make_result(), llm_metrics=ALL_LEGS)
+		session = history.record_turn(None, "First question", make_result())
+		history.record_turn(session, "Second question", make_result())
 
 		rows = frappe.get_all(
 			"Wikify Ask Message",
@@ -111,9 +107,7 @@ def test_turn_number_pairs_the_question_with_its_answer(self):
 
 	def test_answer_row_keeps_the_route_and_the_citations_it_stood_on(self):
 		citations = [{"chunk_id": "sec-a::0", "title": "Backend Engineer", "wiki_route": "/demo/roles"}]
-		session = history.record_turn(
-			None, "List every job description", make_result(citations=citations), llm_metrics=ALL_LEGS
-		)
+		session = history.record_turn(None, "List every job description", make_result(citations=citations))
 
 		answer_row = history.get_session(session)["messages"][1]
 		self.assertEqual(answer_row["route_intent"], "exhaustive")
@@ -123,14 +117,12 @@ def test_answer_row_keeps_the_route_and_the_citations_it_stood_on(self):
 
 	def test_unknown_section_type_is_dropped_not_raised(self):
 		"""A type deleted since routing must not cost a turn that was already answered."""
-		session = history.record_turn(
-			None, "Which roles?", make_result(section_type="no_such_type_here"), llm_metrics=ALL_LEGS
-		)
+		session = history.record_turn(None, "Which roles?", make_result(section_type="no_such_type_here"))
 		self.assertIsNone(history.get_session(session)["messages"][1]["route_section_type"])
 
 	def test_refused_turn_is_logged_with_no_citations(self):
 		session = history.record_turn(
-			None, "Who won the cup?", make_result("I couldn't find this.", refused=True), llm_metrics=[]
+			None, "Who won the cup?", make_result("I couldn't find this.", refused=True)
 		)
 		answer_row = history.get_session(session)["messages"][1]
 		self.assertEqual(answer_row["refused"], 1)
@@ -138,15 +130,13 @@ def test_refused_turn_is_logged_with_no_citations(self):
 
 	def test_a_stale_session_id_opens_a_fresh_conversation(self):
 		"""ask() may still hold the id of a conversation the user has since deleted."""
-		session = history.record_turn(
-			"ASK-2026-99999", "List every job description", make_result(), llm_metrics=ALL_LEGS
-		)
+		session = history.record_turn("ASK-2026-99999", "List every job description", make_result())
 		self.assertNotEqual(session, "ASK-2026-99999")
 		self.assertTrue(frappe.db.exists("Wikify Ask Session", session))
 
 	def test_recent_turns_replay_as_router_history(self):
-		session = history.record_turn(None, "First question", make_result("First answer."), llm_metrics=[])
-		history.record_turn(session, "Second question", make_result("Second answer."), llm_metrics=[])
+		session = history.record_turn(None, "First question", make_result("First answer."))
+		history.record_turn(session, "Second question", make_result("Second answer."))
 
 		self.assertEqual(
 			history.recent_turns(session),
@@ -159,69 +149,57 @@ def test_recent_turns_replay_as_router_history(self):
 		)
 
 	def test_deleting_a_conversation_removes_its_turns(self):
-		session = history.record_turn(None, "List every job description", make_result(), llm_metrics=ALL_LEGS)
+		session = history.record_turn(None, "List every job description", make_result())
 		history.delete_session(session)
 		self.assertEqual(frappe.db.count("Wikify Ask Message", {"session": session}), 0)
 
 
 class TestAskHistoryCost(FrappeTestCase):
-	def test_turn_metrics_sums_routing_rerank_and_synthesis(self):
-		"""The user wants cost per ask, so all three legs land in one figure."""
-		engine_calls: list[dict] = []
-		with patch.object(engine_llm, "get_metrics", side_effect=lambda: list(engine_calls)):
-			history.start_turn()
-			engine_calls.extend([ROUTE_METRIC, RERANK_METRIC])
-			history.record_litellm_metrics(
-				{"model": "claude-sonnet-4.6", "response_cost": SYNTHESIS_METRIC["cost"]},
-				SimpleNamespace(usage=SimpleNamespace(prompt_tokens=5200, completion_tokens=610)),
-				None,
-				None,
-			)
-			metrics = history.turn_metrics()
-
-		self.assertEqual([entry.get("label") for entry in metrics], ["rag_route", "rag_rerank", "synthesis"])
-		self.assertAlmostEqual(cost_of(metrics), 0.004185)
-		self.assertEqual(history.tokens_of(metrics), (7900, 710))
-
-	def test_watermark_excludes_a_previous_ask(self):
-		engine_calls = [ROUTE_METRIC]
-		with patch.object(engine_llm, "get_metrics", side_effect=lambda: list(engine_calls)):
-			history.start_turn()
-			engine_calls.append(RERANK_METRIC)
-			history.record_litellm_metrics({"response_cost": 0.001}, SimpleNamespace(usage=None), None, None)
-			metrics = history.turn_metrics()
-
-		self.assertEqual([entry.get("label") for entry in metrics], ["rag_rerank", "synthesis"])
-
-	def test_cost_lands_on_the_answer_row_and_rolls_up(self):
-		session = history.record_turn(None, "First question", make_result(), llm_metrics=ALL_LEGS)
-		history.record_turn(session, "Second question", make_result(), llm_metrics=[ROUTE_METRIC])
+	def test_the_logged_cost_is_the_one_the_ask_reported(self):
+		"""The row and the answer must carry the same figure — they once differed by 13x."""
+		result = make_result()
+		session = history.record_turn(None, "First question", result)
 
 		question_row, answer_row = history.get_session(session)["messages"][:2]
 		self.assertEqual(question_row["cost"], 0)
-		self.assertAlmostEqual(answer_row["cost"], 0.004185)
-		self.assertEqual((answer_row["prompt_tokens"], answer_row["completion_tokens"]), (7900, 710))
+		self.assertAlmostEqual(answer_row["cost"], result["cost"])
+		self.assertEqual(
+			(answer_row["prompt_tokens"], answer_row["completion_tokens"]),
+			(result["prompt_tokens"], result["completion_tokens"]),
+		)
+
+	def test_a_conversation_totals_exactly_its_own_turns(self):
+		session = history.record_turn(None, "First question", make_result())
+		history.record_turn(
+			session,
+			"Second question",
+			make_result(cost=0.000021, prompt_tokens=900, completion_tokens=60),
+		)
 
 		conversation = frappe.get_doc("Wikify Ask Session", session)
-		self.assertAlmostEqual(conversation.total_cost, 0.004206)
-		self.assertEqual(conversation.total_tokens, 9570)
-		self.assertEqual(conversation.message_count, 4)
+		messages = history.get_session(session)["messages"]
+		self.assertAlmostEqual(conversation.total_cost, sum(row["cost"] for row in messages))
+		self.assertEqual(
+			conversation.total_tokens,
+			sum(row["prompt_tokens"] + row["completion_tokens"] for row in messages),
+		)
+		self.assertEqual(conversation.message_count, len(messages))
+
+	def test_a_drifted_total_heals_on_the_next_turn(self):
+		"""Totals are summed, not incremented, so a legacy over-counted row corrects itself."""
+		session = history.record_turn(None, "First question", make_result())
+		frappe.db.set_value("Wikify Ask Session", session, "total_cost", 9.99)
+
+		history.record_turn(session, "Second question", make_result())
 
-	def test_a_refused_turn_does_not_wait_for_synthesis_usage(self):
-		"""Nothing synthesises on a refusal, so the grace wait must be skipped."""
-		with patch.object(engine_llm, "get_metrics", return_value=[]):
-			history.start_turn()
-			with patch.object(history, "wait_for_synthesis_metrics") as waited:
-				history.record_turn(None, "Who won the cup?", make_result(refused=True))
-		waited.assert_not_called()
+		conversation = frappe.get_doc("Wikify Ask Session", session)
+		self.assertAlmostEqual(conversation.total_cost, ALL_LEGS_COST * 2)
 
 
 class TestAskHistoryPermissions(FrappeTestCase):
 	def setUp(self):
 		self.other_user = make_user(OTHER_USER)
-		self.session = history.record_turn(
-			None, "Administrator's question", make_result(), llm_metrics=ALL_LEGS
-		)
+		self.session = history.record_turn(None, "Administrator's question", make_result())
 		self.addCleanup(frappe.set_user, "Administrator")
 
 	def test_another_user_cannot_list_or_read_the_conversation(self):
@@ -233,11 +211,13 @@ def test_another_user_cannot_list_or_read_the_conversation(self):
 	def test_another_user_cannot_append_to_the_conversation(self):
 		frappe.set_user(self.other_user)
 		with self.assertRaises(frappe.PermissionError):
-			history.record_turn(self.session, "Sneaking in", make_result(), llm_metrics=[])
+			history.record_turn(self.session, "Sneaking in", make_result())
 
 	def test_a_user_sees_their_own_conversation(self):
 		frappe.set_user(self.other_user)
-		own_session = history.record_turn(None, "My own question", make_result(), llm_metrics=[ROUTE_METRIC])
+		own_session = history.record_turn(
+			None, "My own question", make_result(cost=0.000021, prompt_tokens=900, completion_tokens=60)
+		)
 
 		listed = [row.name for row in history.list_sessions()]
 		self.assertIn(own_session, listed)
@@ -246,7 +226,9 @@ def test_a_user_sees_their_own_conversation(self):
 
 	def test_system_manager_sees_every_conversation(self):
 		frappe.set_user(self.other_user)
-		own_session = history.record_turn(None, "My own question", make_result(), llm_metrics=[ROUTE_METRIC])
+		own_session = history.record_turn(
+			None, "My own question", make_result(cost=0.000021, prompt_tokens=900, completion_tokens=60)
+		)
 
 		frappe.set_user("Administrator")
 		listed = {row.name for row in history.list_sessions()}
@@ -257,9 +239,7 @@ def test_system_manager_sees_every_conversation(self):
 class TestAskHistoryApi(FrappeTestCase):
 	def test_api_returns_the_conversation_and_its_turns(self):
 		project = make_project()
-		session = history.record_turn(
-			None, "List every job description", make_result(), project=project, llm_metrics=ALL_LEGS
-		)
+		session = history.record_turn(None, "List every job description", make_result(), project=project)
 
 		listed = api_ask_history.list_sessions(project=project)
 		self.assertEqual([row.name for row in listed], [session])
diff --git a/wikify/tests/test_evidence.py b/wikify/tests/test_evidence.py
index 2c2b58c..a49ef35 100644
--- a/wikify/tests/test_evidence.py
+++ b/wikify/tests/test_evidence.py
@@ -360,7 +360,7 @@ class TestPageLookup(FrappeTestCase):
 	def test_unknown_documents_read_nothing(self):
 		self.assertEqual(evidence.get_pages_by_document(["NOPE"], [1, 2]), {})
 		self.assertEqual(evidence.get_pages_by_document([], []), {})
-		self.assertEqual(chunk.get_pages_by_document(["NOPE"]), {})
+		self.assertEqual(evidence.get_pages_by_document(["NOPE"]), {})
 
 	def test_pages_come_back_grouped_by_document(self):
 		document = frappe.get_all("Source Page", fields=["source_document", "page_no"], limit=1)
diff --git a/wikify/tests/test_page_propagation.py b/wikify/tests/test_page_propagation.py
index c8054e9..ba37c21 100644
--- a/wikify/tests/test_page_propagation.py
+++ b/wikify/tests/test_page_propagation.py
@@ -89,8 +89,6 @@ def save_canonical(self, page_no: int, markdown: str) -> None:
 		page.canonical_markdown = markdown
 		page.save(ignore_permissions=True)
 
-	# --- the doc_event ------------------------------------------------------------------
-
 	def test_a_changed_page_marks_itself_dirty_and_queues_one_pass(self):
 		with doc_events_live(), patch("frappe.enqueue") as enqueue:
 			self.save_canonical(1, "canonical page 1 — surcharge slabs repaired")
@@ -126,8 +124,6 @@ def test_a_new_page_queues_nothing_because_no_section_copied_it_yet(self):
 
 		enqueue.assert_not_called()
 
-	# --- the pass -----------------------------------------------------------------------
-
 	def test_the_pass_rebuilds_every_section_covering_a_changed_page(self):
 		marker = "SURCHARGE-MARKER-42"
 		store.set_canonical_markdown(self.pages[3], f"canonical page 3 {marker}")
@@ -214,20 +210,21 @@ def test_a_failed_pass_requeues_the_pages_it_claimed(self):
 		self.assertTrue(frappe.cache().get_value(events.page_propagation_key(self.source_document.name)))
 		self.assertEqual(events.take_dirty_pages(self.source_document.name), [1])
 
-	# --- reaching the index -------------------------------------------------------------
+	def test_rebuilt_sections_are_pushed_into_the_index_in_one_batch(self):
+		"""`set_section_markdown` fires no doc_event, so the reindex hook never sees it.
 
-	def test_rebuilt_sections_are_pushed_into_the_index_one_by_one(self):
-		"""`set_section_markdown` fires no doc_event, so the reindex hook never sees it."""
-		with patch("wikify.rag.index.upsert_section") as upsert:
+		One call, not one per section: each `upsert_sections` rebuilds the whole FTS index.
+		"""
+		with patch("wikify.rag.index.upsert_sections") as upsert:
 			events.reindex_sections("PRJ-TEST", ["SEC-A", "SEC-B"])
 
-		self.assertEqual([call.args[0] for call in upsert.call_args_list], ["SEC-A", "SEC-B"])
+		upsert.assert_called_once_with(["SEC-A", "SEC-B"])
 
 	def test_a_wide_fanout_queues_one_project_rebuild_instead_of_a_burst_of_upserts(self):
 		many = [f"SEC-{n}" for n in range(events.PROJECT_REBUILD_FANOUT + 1)]
 
 		with (
-			patch("wikify.rag.index.upsert_section") as upsert,
+			patch("wikify.rag.index.upsert_sections") as upsert,
 			patch("wikify.rag.events.queue_project_rebuild") as queue_project,
 		):
 			events.reindex_sections("PRJ-TEST", many)
@@ -236,7 +233,7 @@ def test_a_wide_fanout_queues_one_project_rebuild_instead_of_a_burst_of_upserts(
 		queue_project.assert_called_once_with("PRJ-TEST")
 
 	def test_a_document_outside_any_project_reindexes_nothing(self):
-		with patch("wikify.rag.index.upsert_section") as upsert:
+		with patch("wikify.rag.index.upsert_sections") as upsert:
 			events.reindex_sections(None, ["SEC-A"])
 
 		upsert.assert_not_called()
diff --git a/wikify/tests/test_rag_api.py b/wikify/tests/test_rag_api.py
index 48cdefa..54130d2 100644
--- a/wikify/tests/test_rag_api.py
+++ b/wikify/tests/test_rag_api.py
@@ -28,7 +28,12 @@
 from wikify.rag.search import Hit
 
 
-def make_hit(title: str = "Backend Engineer", score: float = 0.03, rerank_score: float | None = None) -> Hit:
+def make_hit(
+	title: str = "Backend Engineer",
+	score: float = 0.03,
+	rerank_score: float | None = None,
+	vector_score: float | None = None,
+) -> Hit:
 	return Hit(
 		chunk_id=f"{title}::0",
 		section=f"sec-{title}",
@@ -43,6 +48,7 @@ def make_hit(title: str = "Backend Engineer", score: float = 0.03, rerank_score:
 		wiki_route=None,
 		score=score,
 		rerank_score=rerank_score,
+		vector_score=vector_score,
 	)
 
 
@@ -228,7 +234,7 @@ def test_compare_runs_both_legs_over_the_same_acl(self):
 		decided = rag_router.Route("exhaustive", "job_description", "all job descriptions", "r")
 		with (
 			patch.object(rag_search, "search", return_value=naive_hits) as search,
-			patch.object(api_rag, "route_question", return_value=decided),
+			patch.object(rag_answer, "route", return_value=decided),
 			patch.object(rag_answer, "retrieve", return_value=routed_hits),
 		):
 			result = api_rag.compare("all the job descriptions")
@@ -244,14 +250,16 @@ def test_reindex_enqueues_on_the_long_queue(self):
 		self.assertEqual(enqueue.call_args.kwargs["queue"], "long")
 		self.assertEqual(enqueue.call_args.args[0], "wikify.rag.index.rebuild_project")
 
-	def test_index_status_reports_only_readable_projects(self):
-		stats = {"chunks": 3, "sections": 2, "documents": 1, "indexed_at": "2026-01-01 00:00:00", "dim": 256}
+	def test_index_status_scans_the_readable_projects_once(self):
+		"""One scan for the whole readable scope — not one per project."""
+		stats = {"chunks": 6, "sections": 4, "documents": 2, "indexed_at": "2026-01-01 00:00:00", "dim": 256}
 		with (
 			patch.object(api_rag, "readable_projects", return_value=["a", "b"]),
-			patch.object(api_rag.rag_index, "index_stats", return_value=stats),
+			patch.object(api_rag.rag_index, "index_stats", return_value=stats) as index_stats,
 			patch.object(api_rag, "is_stale", return_value=False),
 		):
 			result = api_rag.index_status()
+		index_stats.assert_called_once_with(["a", "b"])
 		self.assertEqual(result["chunks"], 6)
 		self.assertEqual(result["sections"], 4)
 		self.assertFalse(result["stale"])
@@ -497,11 +505,13 @@ def test_ask_publishes_the_cost_when_the_answer_finishes(self):
 
 
 class TestSilentRerankFailure(FrappeTestCase):
-	"""A reranker that answers but ranks nothing must never produce a refusal.
+	"""The reranker must never be able to mute the product on its own.
 
 	Graded against the real corpus: "what are the slab rates under section 115BAC(1A)" is
 	answered at rank 2 by both the hybrid and the FTS leg, yet `ask` refused it because the
-	rerank call came back with 0.0 for all eight candidates.
+	rerank scored every candidate 0.0. Refusing on that alone is the worst thing this system
+	does when it is wrong — a reader cannot tell "the reranker died" from "the document does
+	not cover this" — so a below-floor rerank now needs the embedding leg to agree.
 	"""
 
 	QUESTION = "what are the slab rates under section 115BAC(1A)"
@@ -510,48 +520,75 @@ def rerank_reply(self, scores: list[float]) -> dict:
 		items = [{"id": position, "score": score} for position, score in enumerate(scores)]
 		return llm_reply(frappe.as_json({"scores": items}))
 
-	def retrieved(self) -> list[Hit]:
-		return [make_hit("I. INCOME TAX RATES"), make_hit("II. SURCHARGE")]
-
-	def test_an_all_zero_verdict_keeps_the_fusion_order(self):
-		with (
-			patch("wikify.engine.llm.has_openrouter", return_value=True),
-			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply([0.0, 0.0])),
-		):
-			hits = rag_search.rerank_hits(self.QUESTION, self.retrieved())
-
-		self.assertEqual([hit.title for hit in hits], ["I. INCOME TAX RATES", "II. SURCHARGE"])
-		self.assertEqual([hit.rerank_score for hit in hits], [None, None])
+	def retrieved(self, vector_score: float | None = None) -> list[Hit]:
+		return [
+			make_hit("I. INCOME TAX RATES", vector_score=vector_score),
+			make_hit("II. SURCHARGE", vector_score=vector_score),
+		]
 
-	def test_a_real_ranking_still_reorders(self):
+	def rerank(self, scores: list[float], vector_score: float | None = None) -> list[Hit]:
 		with (
 			patch("wikify.engine.llm.has_openrouter", return_value=True),
-			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply([2.0, 9.0])),
+			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply(scores)),
 		):
-			hits = rag_search.rerank_hits(self.QUESTION, self.retrieved())
+			return rag_search.rerank_hits(self.QUESTION, self.retrieved(vector_score))
 
-		self.assertEqual([hit.title for hit in hits], ["II. SURCHARGE", "I. INCOME TAX RATES"])
-
-	def test_the_answer_is_not_refused_when_the_reranker_ranks_nothing(self):
-		"""End to end over the real rerank path: a flat verdict must still answer."""
+	def answer_over_the_rerank_path(self, scores: list[float], vector_score: float | None) -> dict:
+		"""End to end through the real rerank path, with only the store and the synthesis stubbed."""
 
 		def retrieve_and_rerank(query, **kwargs):
-			return rag_search.rerank_hits(query, self.retrieved())
+			return rag_search.rerank_hits(query, self.retrieved(vector_score))
 
 		with (
 			patch.object(settings, "openrouter_key", return_value="key"),
 			patch("wikify.engine.llm.has_openrouter", return_value=True),
-			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply([0.0, 0.0])),
+			patch("wikify.engine.llm.chat_completion", return_value=self.rerank_reply(scores)),
 			patch.object(rag_search, "search", side_effect=retrieve_and_rerank),
 			patch.object(rag_answer, "generate", return_value="The slabs are ... [1]"),
 		):
-			result = rag_answer.answer(self.QUESTION, rerank=True, allowed_projects=["PRJ"])
+			return rag_answer.answer(self.QUESTION, rerank=True, allowed_projects=["PRJ"])
+
+	def test_an_all_zero_verdict_keeps_the_fusion_order(self):
+		"""Zero for everything separates nothing, so the fusion order has to survive it."""
+		hits = self.rerank([0.0, 0.0])
+		self.assertEqual([hit.title for hit in hits], ["I. INCOME TAX RATES", "II. SURCHARGE"])
+
+	def test_an_identical_non_zero_verdict_is_discarded(self):
+		"""One repeated score is the model declining to judge — not a ranking, not a refusal."""
+		hits = self.rerank([5.0, 5.0])
+		self.assertEqual([hit.rerank_score for hit in hits], [None, None])
+
+	def test_a_real_ranking_still_reorders(self):
+		hits = self.rerank([2.0, 9.0])
+		self.assertEqual([hit.title for hit in hits], ["II. SURCHARGE", "I. INCOME TAX RATES"])
+
+	def test_the_answer_is_not_refused_when_the_reranker_zeroes_a_hit_the_corpus_answers(self):
+		"""The 115BAC casualty: an all-zero rerank over a section the embedding leg matched
+		strongly must fall back to the fusion order and answer, never refuse."""
+		result = self.answer_over_the_rerank_path([0.0, 0.0], vector_score=0.61)
 
 		self.assertFalse(result["refused"])
 		self.assertEqual(len(result["citations"]), 2)
 
+	def test_an_overruled_rerank_reports_no_score_rather_than_zero(self):
+		"""The scores are known-bad once the embedding leg overrules them, and `null` is what
+		the source card hides on — a "rerank 0.0" chip reads as a dead reranker."""
+		result = self.answer_over_the_rerank_path([0.0, 0.0], vector_score=0.61)
+
+		self.assertTrue(all(citation["rerank_score"] is None for citation in result["citations"]))
+
+	def test_a_genuinely_uncovered_question_is_still_refused(self):
+		"""Both legs agree there is nothing here, so the honest answer is still to refuse."""
+		result = self.answer_over_the_rerank_path([0.0, 0.0], vector_score=0.39)
+
+		self.assertTrue(result["refused"])
+		self.assertEqual(result["citations"], [])
+
 	def test_a_working_reranker_can_still_refuse(self):
-		weak = [make_hit(rerank_score=1.0), make_hit("Other", rerank_score=0.5)]
+		weak = [
+			make_hit(rerank_score=1.0, vector_score=0.31),
+			make_hit("Other", rerank_score=0.5, vector_score=0.30),
+		]
 		with patch.object(rag_search, "search", return_value=weak):
 			result = rag_answer.answer("what is the capital of Mongolia", allowed_projects=["PRJ"])
 
diff --git a/wikify/tests/test_rag_core.py b/wikify/tests/test_rag_core.py
index 48f4bea..af35c2c 100644
--- a/wikify/tests/test_rag_core.py
+++ b/wikify/tests/test_rag_core.py
@@ -16,6 +16,7 @@
   - rerank degrades to unreranked results instead of breaking the search path.
 """
 
+import re
 import shutil
 import tempfile
 from unittest.mock import patch
@@ -27,6 +28,50 @@
 from wikify.engine.loader.sectionizer import Section
 from wikify.rag import chunk, index, search, store
 
+CANDIDATE_HEADER = re.compile(r"^\[(\d+)\] ", re.MULTILINE)
+
+
+def candidate_ids(messages: list[dict]) -> list[int]:
+	"""The candidate numbers a rerank prompt actually offered the model."""
+	return [int(number) for number in CANDIDATE_HEADER.findall(messages[1]["content"])]
+
+
+def rerank_favouring(crumb: str):
+	"""A reranker that scores the candidate whose breadcrumb is `crumb` 9.0 and everything
+	else 0.0, batch by batch — so a `search` that quietly re-sorts by fusion score fails."""
+
+	def reply(model, messages, **kwargs):
+		content = messages[1]["content"]
+		headers = [
+			(int(match.group(1)), content[match.end() : content.index("\n", match.end())])
+			for match in CANDIDATE_HEADER.finditer(content)
+		]
+		scores = [
+			{"id": number, "score": 9.0 if header.endswith(crumb) else 0.0} for number, header in headers
+		]
+		return {"choices": [{"message": {"content": frappe.as_json({"scores": scores})}}]}
+
+	return reply
+
+
+def make_bare_hit(title: str, score: float = 0.03) -> search.Hit:
+	"""A Hit with no store behind it — for the rerank paths, which only read title and text."""
+	return search.Hit(
+		chunk_id=f"{title}::0",
+		section=f"sec-{title}",
+		source_document="doc-1",
+		document_title="Handbook",
+		title=title,
+		text=f"{title} body text.",
+		section_type=None,
+		hierarchy_path=title,
+		page_start=1,
+		page_end=1,
+		wiki_route=None,
+		score=score,
+	)
+
+
 RECIPE_SECTIONS = [
 	("Coin recipe", "job_description", "Touch the coin and the score goes up by one point."),
 	("Timer recipe", "job_description", "A countdown variable starts at thirty and drops each second."),
@@ -155,8 +200,6 @@ def add_document(self, title, project, sections):
 		index.rebuild_project(project)
 		return document
 
-	# --- chunking ---------------------------------------------------------------------
-
 	def test_embed_text_carries_context_prefix_and_text_stays_clean(self):
 		chunks = chunk.chunks_for_section(self.section_named("Coin recipe"))
 		self.assertEqual(len(chunks), 1)
@@ -307,15 +350,13 @@ def test_wiki_route_is_populated_from_the_linked_wiki_document(self):
 		self.assertEqual(chunk.chunks_for_section(section)[0].wiki_route, "handbook/coin-recipe")
 		self.assertIsNone(chunk.chunks_for_section(self.section_named("Timer recipe"))[0].wiki_route)
 
-	# --- indexing ---------------------------------------------------------------------
-
 	def test_rebuild_is_idempotent_and_drops_orphans(self):
 		first = index.rebuild_project(self.project.name)
 		second = index.rebuild_project(self.project.name)
 		self.assertEqual(first["chunks"], second["chunks"])
 		self.assertEqual(first["sections"], 3)
 
-		stats = index.index_stats(self.project.name)
+		stats = index.index_stats([self.project.name])
 		self.assertEqual(stats["chunks"], first["chunks"])
 		self.assertEqual(stats["sections"], 3)
 		self.assertEqual(stats["documents"], 1)
@@ -324,7 +365,7 @@ def test_rebuild_is_idempotent_and_drops_orphans(self):
 
 		frappe.db.delete("Source Section", {"name": self.section_named("Sprite notes")})
 		index.rebuild_project(self.project.name)
-		self.assertEqual(index.index_stats(self.project.name)["sections"], 2)
+		self.assertEqual(index.index_stats([self.project.name])["sections"], 2)
 
 	def test_upsert_and_drop_section(self):
 		section = self.section_named("Timer recipe")
@@ -340,7 +381,7 @@ def test_upsert_and_drop_section(self):
 		self.assertIn(section, [hit.section for hit in hits])
 
 		index.drop_section(section)
-		self.assertEqual(index.index_stats(self.project.name)["sections"], 2)
+		self.assertEqual(index.index_stats([self.project.name])["sections"], 2)
 
 	def test_upsert_removes_the_chunks_a_shrinking_section_leaves_behind(self):
 		"""Delete-then-add: a section that shrinks from N chunks to 1 must leave exactly 1 row."""
@@ -381,8 +422,6 @@ def test_a_failed_rebuild_requeues_itself_instead_of_leaving_zero_rows(self):
 		self.assertEqual(enqueue.call_args.kwargs["project"], self.project.name)
 		frappe.cache().delete_value(events.pending_key(self.project.name))
 
-	# --- search -----------------------------------------------------------------------
-
 	def test_filter_mode_is_exhaustive_and_ignores_limit(self):
 		hits = search.search(
 			"",
@@ -569,8 +608,6 @@ def test_missing_index_returns_no_hits(self):
 				search.search("anything", mode="hybrid", allowed_projects=search.ALL_PROJECTS), []
 			)
 
-	# --- rerank -----------------------------------------------------------------------
-
 	def test_filter_mode_does_not_pay_for_a_rerank_it_discards(self):
 		"""Filter mode re-sorts by document and page, so a rerank is a wasted LLM round-trip."""
 		with (
@@ -634,3 +671,74 @@ def test_rerank_degrades_without_a_key_and_when_the_call_fails(self):
 		):
 			failed = search.search("coin", rerank=True, **scope)
 		self.assertEqual([hit.section for hit in failed], order)
+
+	def test_search_returns_the_reranked_winner_not_the_fusion_winner(self):
+		"""The rerank has to survive the `limit` slice, or paying for it is theatre.
+
+		`search` used to re-sort by the fusion score after reranking, which put the reranked
+		winner back where fusion had it and then cut it off with everything past `limit`. On
+		PRJ-2026-00002 that is how "what are the slab rates under section 115BAC(1A)" scored
+		8.0 at fusion rank 10, never reached the answer, and was refused.
+		"""
+		scope = {"project": self.project.name, "mode": "hybrid", "allowed_projects": search.ALL_PROJECTS}
+		fusion_order = search.search("coin", limit=50, **scope)
+		self.assertGreater(len(fusion_order), 1)
+		outsider = fusion_order[-1]
+
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch(
+				"wikify.engine.llm.chat_completion",
+				side_effect=rerank_favouring(outsider.hierarchy_path or outsider.title),
+			),
+		):
+			hits = search.search("coin", limit=1, rerank=True, **scope)
+
+		self.assertEqual([hit.section for hit in hits], [outsider.section])
+		self.assertEqual(hits[0].rerank_score, 9.0)
+
+	def test_the_rerank_grades_candidates_in_batches(self):
+		"""A single call over a long candidate list comes back all zeros — see
+		`search.RERANK_BATCH_SIZE`. Candidate numbers stay global across the batches."""
+		hits = [make_bare_hit(f"Section {position}") for position in range(25)]
+
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch(
+				"wikify.engine.llm.chat_completion", side_effect=rerank_favouring("Section 24")
+			) as chat_completion,
+		):
+			reranked = search.rerank_hits("coin", hits)
+
+		self.assertEqual(chat_completion.call_count, 3)
+		# Sorted, because the batches run concurrently (`search.RERANK_WORKERS`) — what must
+		# hold is that they partition the candidate list, not the order they come back in.
+		batches = sorted(candidate_ids(call.args[1]) for call in chat_completion.call_args_list)
+		self.assertEqual(batches, [list(range(0, 10)), list(range(10, 20)), list(range(20, 25))])
+		self.assertEqual(reranked[0].title, "Section 24")
+
+	def test_a_batch_that_skips_candidates_is_dropped_whole(self):
+		"""A partial reply would leave the unscored candidates reading as a confident 0."""
+		hits = [make_bare_hit(f"Section {position}") for position in range(3)]
+		partial = {"choices": [{"message": {"content": frappe.as_json({"scores": [{"id": 0, "score": 9}]})}}]}
+
+		with (
+			patch("wikify.engine.llm.has_openrouter", return_value=True),
+			patch("wikify.engine.llm.chat_completion", return_value=partial),
+		):
+			reranked = search.rerank_hits("coin", hits)
+
+		self.assertEqual([hit.title for hit in reranked], ["Section 0", "Section 1", "Section 2"])
+		self.assertTrue(all(hit.rerank_score is None for hit in reranked))
+
+	def test_the_similarity_leg_reports_an_absolute_score(self):
+		"""`answer.below_floor` needs a number that means the same thing across queries, so
+		the vector leg's similarity rides along even when fusion decides the order."""
+		scope = {"project": self.project.name, "allowed_projects": search.ALL_PROJECTS, "limit": 5}
+		for mode in ("hybrid", "vector"):
+			hits = search.search("coin", mode=mode, **scope)
+			self.assertTrue(hits, mode)
+			self.assertTrue(all(0.0 < hit.vector_score <= 1.0 for hit in hits), mode)
+
+		keyword_only = search.search("coin", mode="fts", **scope)
+		self.assertTrue(all(hit.vector_score is None for hit in keyword_only))
diff --git a/wikify/tests/test_rag_eval.py b/wikify/tests/test_rag_eval.py
index 90a36c8..722f032 100644
--- a/wikify/tests/test_rag_eval.py
+++ b/wikify/tests/test_rag_eval.py
@@ -31,13 +31,14 @@
 from wikify.rag.search import Hit
 
 # The headline the POC is demonstrated on: 15 job descriptions across 5 documents, of which
-# a naive top-8 vector search can physically return at most 8. See specs/poc-rag-DEMO-DATA.md, G1.
+# a naive top-8 vector search can physically return at most 8. Golden question G1 in
+# `wikify.rag.eval.GOLDEN_QUESTIONS` asks for exactly this set.
 G1_EXPECTED_SOURCES = 15
 G1_NAIVE_LIMIT = 8
 
 # The demoed headline and golden question G1 ask the same thing in different words, and a
 # vector search is sensitive to exactly that: the demo phrasing ("...across all the
-# documents") puts 6 job descriptions in the naive top-8, G1's spec phrasing ("...across all
+# documents") puts 6 job descriptions in the naive top-8, G1's phrasing ("...across all
 # the PDFs") puts 5. Both are asserted, separately and by their own wording — averaging them
 # or asserting one number for both would hide a real ranking change behind a rounded mean.
 HEADLINE_QUERY = "give me all the job descriptions across all the documents"
@@ -145,7 +146,7 @@ def setUpClass(cls):
 		cls.results = None
 		if not cls.project:
 			return
-		if not rag_index.index_stats(cls.project)["chunks"]:
+		if not rag_index.index_stats([cls.project])["chunks"]:
 			cls.project = None
 			return
 		if not settings.openrouter_key():

From 7b57e75730fc79cdb49e5f15c4a81cb25ef3df3d Mon Sep 17 00:00:00 2001
From: rl0007 <rl0007@users.noreply.github.com>
Date: Tue, 11 Aug 2026 18:40:28 +0530
Subject: [PATCH 6/8] fix(agent): stop search_sections reporting content as
 missing
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

Asked "how many job descriptions are in the Demo Corpus project?", the agent
answered "there are none". There are 15. Two faults produced the same sentence,
so a correct chain of reasoning ended in a false statement.

- The `query` argument was a raw substring filter over title and hierarchy
  path. Questions are plural ("job descriptions"), titles are singular
  ("Job Description — Theatre Scrub Practitioner"), so every match was
  filtered away. It is now a narrowing hint: token-wise, normalised for case,
  punctuation and trailing plurals, and when it matches nothing the full set
  is returned with the filter reported as ignored rather than as absence.

- The model passed the project title, not its id. `Ctx.default_document`
  already guarded against exactly this, with a comment noting models echo
  display labels instead of bare ids; the project field had no such guard.
  Added `Ctx.default_project`, and closed the same hole in `semantic_search`.

Three outcomes that were indistinguishable now read differently: an unknown
section type (with the near matches, and an explicit note that this is not
evidence the content is missing), a valid type that is empty in scope (naming
the scope and which types do have content), and a filter that removed
everything (naming how many it removed). Every reply carries the in-scope
count and the resolved scope, so absence cannot be inferred from silence.

Also closes a fixture leak: types created by tests that drive the agent loop
survived rollback because the loop commits mid-turn, and were being offered to
users as real taxonomy suggestions.
---
 wikify/agent/context.py        |  14 +++
 wikify/agent/tools/read.py     | 170 +++++++++++++++++++++++++++------
 wikify/agent/tools/retrieve.py |   2 +-
 wikify/api/explore.py          |  12 +--
 wikify/tests/_cleanup.py       |   6 ++
 wikify/tests/test_agent.py     | 124 ++++++++++++++++++++++++
 6 files changed, 291 insertions(+), 37 deletions(-)

diff --git a/wikify/agent/context.py b/wikify/agent/context.py
index 2c9b41f..97fe9f4 100644
--- a/wikify/agent/context.py
+++ b/wikify/agent/context.py
@@ -46,6 +46,20 @@ def default_document(self, explicit: str | None = None) -> str | None:
 			return explicit
 		return self.source_document
 
+	def default_project(self, explicit: str | None = None) -> str | None:
+		"""A tool's `project` arg, resolved from a display name, falling back to the scope.
+
+		Same failure `default_document` guards, one field over: the user says "the Demo
+		Corpus project" and the model passes that title, but the id is `PRJ-2026-00003`.
+		An unresolved title scoped the query to zero documents, which the model then read
+		as "this content does not exist".
+		"""
+		if not explicit:
+			return self.project
+		if frappe.db.exists("Wikify Project", explicit):
+			return explicit
+		return frappe.db.get_value("Wikify Project", {"project_name": explicit}, "name") or self.project
+
 	def default_import(self, explicit: str | None = None) -> str | None:
 		"""The Wikify Import owning the (resolved) document — needed by pipeline jobs.
 
diff --git a/wikify/agent/tools/read.py b/wikify/agent/tools/read.py
index 9ed6c7c..96048ff 100644
--- a/wikify/agent/tools/read.py
+++ b/wikify/agent/tools/read.py
@@ -7,6 +7,9 @@
 
 from __future__ import annotations
 
+import re
+from difflib import get_close_matches
+
 import frappe
 from frappe import _
 
@@ -15,6 +18,7 @@
 
 # Keep tool results bounded — the model can ask for a narrower slice if it needs more.
 _BODY_LIMIT = 6000
+_WORD_SEPARATOR = re.compile(r"[^a-z0-9]+")
 
 
 def _truncate(text: str) -> str:
@@ -129,48 +133,146 @@ def _list_section_types(ctx: Ctx, args: dict) -> str:
 	return "\n".join(lines)
 
 
+def search_terms(text: str) -> list[str]:
+	"""Lowercase alphanumeric tokens with a trailing plural `s` dropped.
+
+	Applied to BOTH sides of the comparison, so the way a user asks ("job descriptions")
+	still matches the way titles are stored ("Job Description — Staff Nurse").
+	"""
+	# ponytail: only the trailing-`s` plural is folded, reach for a real stemmer if
+	# `-ies`/`-es` mismatches start costing recall
+	terms = []
+	for token in _WORD_SEPARATOR.split((text or "").lower()):
+		if token:
+			terms.append(token[:-1] if len(token) > 3 and token.endswith("s") else token)
+	return terms
+
+
+def matches_terms(section: dict, terms: list[str]) -> bool:
+	"""True when every query term appears in the section's path/title (normalised)."""
+	haystack = " ".join(search_terms(f"{section.get('hierarchy_path') or ''} {section.get('title') or ''}"))
+	return all(term in haystack for term in terms)
+
+
+def format_section_groups(groups: list[dict]) -> str:
+	lines: list[str] = []
+	for group in groups:
+		lines.append(f"# {group['doc_title']} ({group['source_document']})")
+		for section in group["sections"]:
+			pages = f" [p.{section['page_start']}-{section['page_end']}]" if section.get("page_start") else ""
+			lines.append(f"  - {section['hierarchy_path'] or section['title']}{pages} <{section['name']}>")
+	return "\n".join(lines)
+
+
+def describe_scope(project: str | None, source_document: str | None) -> str:
+	if source_document:
+		return _("document {0}").format(source_document)
+	if project:
+		return _("project {0}").format(project)
+	return _("any project")
+
+
+def unknown_type_hint(section_type: str) -> str:
+	"""Message for a `section_type` that is not in the taxonomy, with the near matches.
+
+	Distinguishing this from "the type exists but is empty" is the whole point: a model
+	that guesses `job_description` must learn the key is wrong, not that the content is
+	missing.
+	"""
+	known = frappe.get_all("Section Type", fields=["type_name", "label", "description"])
+	terms = search_terms(section_type)
+	scored = []
+	for row in known:
+		haystack = " ".join(search_terms(f"{row.type_name} {row.label or ''} {row.description or ''}"))
+		hits = sum(1 for term in terms if term in haystack)
+		if hits:
+			scored.append((hits, row.type_name))
+	names = [row.type_name for row in known]
+	near = [entry[1] for entry in sorted(scored, reverse=True)] or get_close_matches(
+		section_type, names, n=5, cutoff=0.4
+	)
+	if near:
+		return _(
+			"'{0}' is not a Section Type in this taxonomy — so this is NOT evidence that the "
+			"content is missing. Closest existing types: {1}. Retry with one of those, or call "
+			"list_section_types for the full taxonomy."
+		).format(section_type, ", ".join(near[:5]))
+	return _(
+		"'{0}' is not a Section Type in this taxonomy — this says nothing about whether the "
+		"content exists. Call list_section_types and retry with a real type."
+	).format(section_type)
+
+
 def _search_sections(ctx: Ctx, args: dict) -> str:
 	"""Explore-style cross-document lookup, reusing `api.explore.sections_by_type`.
 
 	With `section_type`, returns the matching sections grouped by document (optionally
-	scoped to `project` / the attached `source_document`, and filtered by a `query`
-	substring on title/path). Without a type, lists the taxonomy counts so the model can
-	pick a type to drill into.
+	scoped to `project` / the attached `source_document`). Without a type, lists the
+	taxonomy counts so the model can pick a type to drill into.
+
+	`query` is a NARROWING HINT, not a hard filter: when it matches nothing the full set
+	is returned with a note. The tool's contract is "find me the sections" — silently
+	returning nothing because the user's wording differs from the stored titles reads to
+	the model as "that type is empty" and it then states that as fact.
 	"""
-	from wikify.api.explore import sections_by_type, type_summary
+	from wikify.api.explore import UNTAGGED, sections_by_type, type_summary
 
 	section_type = args.get("section_type")
-	project = args.get("project") or ctx.project
+	requested_project = args.get("project")
+	project = ctx.default_project(requested_project)
 	source_document = ctx.default_document(args.get("source_document"))
-	query = (args.get("query") or "").strip().lower()
+	query = (args.get("query") or "").strip()
+	scope = describe_scope(project, source_document)
+	if requested_project and not project:
+		scope = _("{0} ('{1}' matches no project, so the search was NOT scoped to it)").format(
+			scope, requested_project
+		)
 
 	if not section_type:
 		summary = type_summary(source_document=source_document, project=project)
 		lines = ["Available section types (pass `section_type` to drill in):"]
 		lines += [f"- {s['type_name']}: {s['count']}" for s in summary if s["count"]]
-		return "\n".join(lines) if len(lines) > 1 else _("No tagged sections in this scope yet.")
+		if len(lines) == 1:
+			return _("No tagged sections in {0} yet.").format(scope)
+		return "\n".join(lines)
+
+	if section_type != UNTAGGED and not frappe.db.exists("Section Type", section_type):
+		return unknown_type_hint(section_type)
 
 	groups = sections_by_type(section_type, source_document=source_document, project=project)
-	lines: list[str] = []
-	total = 0
-	for g in groups:
-		secs = g["sections"]
-		if query:
-			secs = [
-				s
-				for s in secs
-				if query in (s.get("title") or "").lower() or query in (s.get("hierarchy_path") or "").lower()
-			]
-		if not secs:
-			continue
-		lines.append(f"# {g['doc_title']} ({g['source_document']})")
-		for s in secs:
-			pages = f" [p.{s['page_start']}-{s['page_end']}]" if s.get("page_start") else ""
-			lines.append(f"  - {s['hierarchy_path'] or s['title']}{pages} <{s['name']}>")
-			total += 1
-	if not lines:
-		return _("No sections of type {0} match.").format(section_type)
-	return f"{total} section(s) of type {section_type}:\n" + "\n".join(lines)
+	available = sum(len(group["sections"]) for group in groups)
+	if not available:
+		summary = type_summary(source_document=source_document, project=project)
+		populated = ", ".join(f"{s['type_name']}: {s['count']}" for s in summary if s["count"])
+		message = _("'{0}' exists in the taxonomy but has no sections in {1}.").format(section_type, scope)
+		return f"{message} " + (
+			_("Types that do have sections here: {0}.").format(populated)
+			if populated
+			else _("No section in this scope carries any type yet.")
+		)
+
+	if not query:
+		header = _("{0} section(s) of type {1} in {2}:").format(available, section_type, scope)
+		return f"{header}\n{format_section_groups(groups)}"
+
+	terms = search_terms(query)
+	narrowed = [
+		{**group, "sections": [row for row in group["sections"] if matches_terms(row, terms)]}
+		for group in groups
+	]
+	narrowed = [group for group in narrowed if group["sections"]]
+	matched = sum(len(group["sections"]) for group in narrowed)
+	if not matched:
+		header = _(
+			"Type {0} has {1} section(s) in {2}, but none of their titles or paths contain "
+			'"{3}" — the query filter was IGNORED (it is a hint, not proof of absence). '
+			"All {1} are listed below:"
+		).format(section_type, available, scope, query)
+		return f"{header}\n{format_section_groups(groups)}"
+	header = _('{0} of {1} section(s) of type {2} in {3} match "{4}":').format(
+		matched, available, section_type, scope, query
+	)
+	return f"{header}\n{format_section_groups(narrowed)}"
 
 
 def _read_rendered_preview(ctx: Ctx, args: dict) -> str:
@@ -325,14 +427,22 @@ def _read_wiki_page(ctx: Ctx, args: dict) -> str:
 		side="server",
 		description=(
 			"Find sections across documents by Section Type (Explore-style). Optionally scope "
-			"to a project or the attached document, and filter by a title/path substring. "
-			"Omit section_type to list the available types with counts."
+			"to a project or the attached document, and narrow with a title/path `query`. "
+			"Omit section_type to list the available types with counts. The reply always "
+			"states how many sections the type has in scope — read that count before "
+			"concluding anything is absent."
 		),
 		parameters={
 			"type": "object",
 			"properties": {
 				"section_type": {"type": "string", "description": "Section Type to filter by."},
-				"query": {"type": "string", "description": "Optional substring filter on title/path."},
+				"query": {
+					"type": "string",
+					"description": (
+						"Optional narrowing hint matched against title/path. It never hides the "
+						"type's sections — when it matches none, all of them are returned."
+					),
+				},
 				"project": {"type": "string", "description": "Optional Wikify Project to scope to."},
 				"source_document": {"type": "string", "description": "Optional single-document scope."},
 			},
diff --git a/wikify/agent/tools/retrieve.py b/wikify/agent/tools/retrieve.py
index 098b0a7..134547e 100644
--- a/wikify/agent/tools/retrieve.py
+++ b/wikify/agent/tools/retrieve.py
@@ -43,7 +43,7 @@ def semantic_search(ctx: Ctx, args: dict) -> str:
 		return _("Provide a `query` to search for.")
 	result = search(
 		query,
-		project=args.get("project") or ctx.project,
+		project=ctx.default_project(args.get("project")),
 		source_document=ctx.default_document(args.get("source_document")),
 		section_type=args.get("section_type"),
 		limit=args.get("limit") or 8,
diff --git a/wikify/api/explore.py b/wikify/api/explore.py
index d3cd05b..a567ce6 100644
--- a/wikify/api/explore.py
+++ b/wikify/api/explore.py
@@ -14,7 +14,7 @@
 import frappe
 from frappe.query_builder.functions import Count
 
-_UNTAGGED = "__untagged__"  # sentinel for sections classification hasn't reached yet
+UNTAGGED = "__untagged__"  # sentinel for sections classification hasn't reached yet
 
 
 def _scope(source_document: str | None) -> dict:
@@ -46,7 +46,7 @@ def _counts(scope: list | None) -> dict[str, int]:
 		column = getattr(table, field)
 		query = query.where(column.isin(value) if op == "in" else column == value)
 	rows = query.run(as_dict=True)
-	return {r["section_type"] or _UNTAGGED: r["count"] for r in rows}
+	return {r["section_type"] or UNTAGGED: r["count"] for r in rows}
 
 
 @frappe.whitelist()
@@ -79,14 +79,14 @@ def type_summary(source_document: str | None = None, project: str | None = None)
 		}
 		for t in types
 	]
-	if counts.get(_UNTAGGED):
+	if counts.get(UNTAGGED):
 		summary.append(
 			{
-				"type_name": _UNTAGGED,
+				"type_name": UNTAGGED,
 				"label": "Untagged",
 				"color": "#cbd5e1",
 				"is_other": 0,
-				"count": counts[_UNTAGGED],
+				"count": counts[UNTAGGED],
 			}
 		)
 	return summary
@@ -103,7 +103,7 @@ def sections_by_type(
 	single document (`source_document`) or a project (`project`), else spans all docs.
 	"""
 	filters = _scope(source_document)
-	filters["section_type"] = ["is", "not set"] if section_type == _UNTAGGED else section_type
+	filters["section_type"] = ["is", "not set"] if section_type == UNTAGGED else section_type
 
 	if not source_document:
 		doc_scope = _docs_in_project(project)
diff --git a/wikify/tests/_cleanup.py b/wikify/tests/_cleanup.py
index 440f8e3..64518d2 100644
--- a/wikify/tests/_cleanup.py
+++ b/wikify/tests/_cleanup.py
@@ -66,6 +66,12 @@ def delete_project(name: str) -> None:
 	_finalized(lambda: frappe.db.delete("Wikify Project", {"name": name}))
 
 
+def delete_section_type(type_name: str) -> None:
+	"""Section Type labels are identity — a leaked test type pollutes the real taxonomy,
+	and the agent then offers it to users as a near match."""
+	_finalized(lambda: frappe.db.delete("Section Type", {"name": type_name}))
+
+
 def register_session_sweep(testcase) -> None:
 	"""Snapshot the session table now; at cleanup, delete any sessions created since.
 
diff --git a/wikify/tests/test_agent.py b/wikify/tests/test_agent.py
index c8c7054..e1c8475 100644
--- a/wikify/tests/test_agent.py
+++ b/wikify/tests/test_agent.py
@@ -320,3 +320,127 @@ def test_list_and_new_session(self):
 		frappe.db.set_value("Wikify Agent Session", created["session_id"], "title", "My chat")
 		listed = agent_api.list_sessions()
 		self.assertTrue(any(s["name"] == created["session_id"] for s in listed))
+
+
+class TestSearchSectionsRecall(FrappeTestCase):
+	"""`search_sections` must never let a `query` hint look like an empty section type.
+
+	Live regression: asked "How many job descriptions are in the Demo Corpus project?" the
+	agent reasoned its way to the right type, searched with query="job descriptions", got
+	"No sections of type X match", and told the user there were none — while 15 sections
+	sat under titles reading "Job Description — …".
+	"""
+
+	def setUp(self):
+		self.sd = frappe.get_doc({"doctype": "Source Document", "title": "Recall Test"}).insert(
+			ignore_permissions=True
+		)
+		self.addCleanup(_cleanup.delete_document, self.sd.name)
+		_cleanup.register_session_sweep(self)
+		self.section_type = self._make_type(
+			label=f"Staff Roles {frappe.generate_hash(length=6)}",
+			description="Job descriptions and role profiles — one section per post.",
+		)
+		titles = [
+			"Job Description — Ward Sister",
+			"Job Description — Staff Nurse (Band 5)",
+			"Job Description — Healthcare Assistant",
+		]
+		store.replace_sections(
+			self.sd.name,
+			[_sec("Roles and Responsibilities", 1, ["Roles and Responsibilities"], 1, 1)]
+			+ [
+				_sec(title, 2, ["Roles and Responsibilities", title], page, page)
+				for page, title in enumerate(titles, start=2)
+			],
+		)
+		for row in frappe.get_all(
+			"Source Section", filters={"source_document": self.sd.name, "level": 2}, pluck="name"
+		):
+			frappe.db.set_value("Source Section", row, "section_type", self.section_type.type_name)
+
+	def _make_type(self, **kwargs):
+		type_name = f"t_{frappe.generate_hash(length=6)}"
+		kwargs.setdefault("label", f"Test Type {type_name}")
+		doc = frappe.get_doc({"doctype": "Section Type", "type_name": type_name, **kwargs}).insert(
+			ignore_permissions=True
+		)
+		# The loop test commits mid-turn, so a plain delete would be rolled back with it.
+		self.addCleanup(_cleanup.delete_section_type, type_name)
+		return doc
+
+	def _search(self, **args):
+		return _search_sections(Ctx(session="x", user="Administrator"), args)
+
+	def test_plural_query_matches_singular_titles(self):
+		"""The exact failing shape: the user's plural wording vs singular stored titles."""
+		out = self._search(section_type=self.section_type.type_name, query="job descriptions")
+		self.assertIn("Ward Sister", out)
+		self.assertIn("Staff Nurse", out)
+		self.assertIn("Healthcare Assistant", out)
+		self.assertNotIn("no sections", out.lower())
+
+	def test_unmatched_query_returns_everything_and_says_so(self):
+		out = self._search(section_type=self.section_type.type_name, query="aardvark husbandry")
+		self.assertIn("IGNORED", out)
+		self.assertIn("3 section(s)", out)
+		self.assertIn("Ward Sister", out)
+
+	def test_empty_type_reads_as_empty_not_as_a_filter_miss(self):
+		empty_type = self._make_type()
+		out = self._search(section_type=empty_type.type_name, source_document=self.sd.name)
+		self.assertIn("no sections in", out)
+		self.assertIn(self.section_type.type_name, out)  # names the types that do have content
+
+	def test_project_title_resolves_to_its_id(self):
+		"""The live failure: the user says "the Demo Corpus project" and the model passes
+		that title, which used to scope the query to zero documents."""
+		project = frappe.get_doc(
+			{"doctype": "Wikify Project", "project_name": f"Recall Corpus {frappe.generate_hash(length=6)}"}
+		).insert(ignore_permissions=True)
+		self.addCleanup(_cleanup.delete_project, project.name)
+		frappe.db.set_value("Source Document", self.sd.name, "project", project.name)
+
+		out = self._search(section_type=self.section_type.type_name, project=project.project_name)
+		self.assertIn(project.name, out)
+		self.assertIn("Ward Sister", out)
+
+	def test_unresolvable_project_says_the_scope_was_dropped(self):
+		out = self._search(section_type=self.section_type.type_name, project="No Such Corpus")
+		self.assertIn("matches no project", out)
+		self.assertIn("Ward Sister", out)
+
+	def test_unknown_type_names_near_matches(self):
+		out = self._search(section_type="job_description")
+		self.assertIn("not a Section Type", out)
+		self.assertIn(self.section_type.type_name, out)
+
+	def test_agent_loop_sees_the_count_for_the_failing_question(self):
+		"""End to end through the loop: the tool message the model reads must carry the count."""
+		sess = session.get_or_create(None, user="Administrator", scope="global")
+		session.append_message(
+			sess.name, "user", "How many job descriptions are in this project?", status="done"
+		)
+		session.set_running(sess.name, True)
+		fake = FakeLLM(
+			[
+				[
+					_tool_chunk(
+						0,
+						"call_1",
+						"search_sections",
+						'{"section_type": "%s", "query": "job descriptions"}' % self.section_type.type_name,
+					)
+				],
+				[_text_chunk("There are 3 job descriptions.")],
+			]
+		)
+		with patch("wikify.agent.llm.complete_with_tools", fake):
+			AgentRunner(sess.name, "Administrator").run()
+		tool_result = frappe.get_all(
+			"Wikify Agent Message",
+			filters={"session": sess.name, "role": "tool"},
+			pluck="content",
+		)[0]
+		self.assertIn("3 of 3 section(s)", tool_result)
+		self.assertIn("Ward Sister", tool_result)

From af724a4a9b2c6685120852c6b447500a662f1855 Mon Sep 17 00:00:00 2001
From: rl0007 <rl0007@users.noreply.github.com>
Date: Tue, 11 Aug 2026 23:04:17 +0530
Subject: [PATCH 7/8] perf(rag): pin the OpenRouter provider for routing and
 reranking
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

OpenRouter routes by price by default and re-draws a provider per call, so
concurrent rerank batches were scattering across endpoints of different speed
and the wall clock was set by the slowest draw. Pinning the endpoint removes
the draw.

Measured over 5 batches, 9 runs each: unpinned 7.8s median with 1.9x effective
concurrency and a 24.7s worst case; pinned 2.6s median, 4.2x, 4.1s worst case.

The latency was the smaller half. Six of eighteen unpinned runs returned
truncated JSON, which discards the whole rerank verdict and drops the answer
back to fusion order — against zero of eighteen pinned. That is the most
likely source of the intermittent all-zero rerank we chased earlier, and it
means provider scatter was corrupting results, not just slowing them.

`allow_fallbacks` stays on: a reranker that cannot reach its preferred
provider must degrade to a slower one, never fail.

Retrieval with reranking measures 8.9s to 5.8s end to end. Total ask time is
unchanged in a single sample because the router leg drew slowly on that run;
the router change here is the same one-line pin, so that reading needs
repeating before anything is concluded from it.

Also raises the rerank worker cap now that batches no longer contend for a
provider draw.
---
 wikify/engine/llm.py          |  7 +++++++
 wikify/rag/answer.py          | 13 ++++++++++++-
 wikify/rag/eval.py            |  9 ++++++++-
 wikify/rag/router.py          |  7 +++++++
 wikify/rag/search.py          | 35 ++++++++++++++++++++++++++++-------
 wikify/tests/test_rag_core.py |  2 +-
 6 files changed, 63 insertions(+), 10 deletions(-)

diff --git a/wikify/engine/llm.py b/wikify/engine/llm.py
index 9bd6d63..4f661f6 100644
--- a/wikify/engine/llm.py
+++ b/wikify/engine/llm.py
@@ -50,6 +50,7 @@ def chat_completion(
 	max_tokens: int | None = None,
 	timeout: int = 120,
 	api_key: str = "",
+	provider: dict | None = None,
 ) -> dict:
 	"""POST a chat completion to OpenRouter, recording latency + token cost.
 
@@ -59,6 +60,10 @@ def chat_completion(
 	`api_key` is for callers running this on a worker thread: resolving the key reads
 	`Wikify Settings`, and `frappe.local` is unbound off the request thread, so the caller
 	resolves it once up front and hands it down. Omit it and it is resolved here as usual.
+
+	`provider` is OpenRouter's provider-preference object (`{"sort": "throughput"}`,
+	`{"order": [...], "allow_fallbacks": False}`). Omitted, OpenRouter picks by its
+	price-weighted default, which can land parallel calls on providers of different speed.
 	"""
 	key = api_key or settings.openrouter_key()
 	if not key:
@@ -74,6 +79,8 @@ def chat_completion(
 		body["response_format"] = response_format
 	if max_tokens is not None:
 		body["max_tokens"] = max_tokens
+	if provider:
+		body["provider"] = provider
 
 	t0 = time.monotonic()
 	resp = requests.post(
diff --git a/wikify/rag/answer.py b/wikify/rag/answer.py
index be1eeda..df87098 100644
--- a/wikify/rag/answer.py
+++ b/wikify/rag/answer.py
@@ -193,7 +193,18 @@ def clear_rerank_scores(citations: list[dict]) -> None:
 
 
 def format_context(hits: list) -> str:
-	"""The numbered excerpt block the model cites against — `[n]` is the hit's position."""
+	"""The numbered excerpt block the model cites against — `[n]` is the hit's position.
+
+	Whole sections, not windows around the matched chunk. Windowing is the obvious cost fix
+	and it was measured and rejected: on the six ICAI rate questions of `specs/poc-icai-EVAL.md`,
+	trimming each section to 1,500 chars around its match cut the synthesis prompt 11,002 →
+	3,783 tokens and the spend $0.078 → $0.035 per ask, but dropped the 37% surcharge rate out
+	of G1 and took verified citations 12 → 7. Synthesis is decode-bound (measured: 80% of its
+	wall clock is decode at ~52 tok/s), so the prompt was never buying us speed — only money,
+	and not at the price of a statutory rate.
+	# ponytail: revisit only behind a figure-recall gate over a table-heavy question set, and
+	# with the window sized from the citation quotes rather than a flat character budget.
+	"""
 	blocks = []
 	for position, hit in enumerate(hits, start=1):
 		header = f"[{position}] {rag_search.crumb(hit)} ({rag_search.page_label(hit)})"
diff --git a/wikify/rag/eval.py b/wikify/rag/eval.py
index 885fddf..9b79632 100644
--- a/wikify/rag/eval.py
+++ b/wikify/rag/eval.py
@@ -388,12 +388,18 @@ def run_eval(
 	modes: tuple = MODES,
 	question_ids: list | None = None,
 	check_refusals: bool = True,
+	rerank: bool = False,
 ) -> dict:
 	"""Run every golden question down each mode and score it. JSON-serializable throughout.
 
 	`check_refusals` runs the full `answer()` synthesis for the refusal question only — it
 	is the one question that cannot be scored from retrieval alone, and it is also the only
 	one that costs a generation call.
+
+	`rerank` scores the routed leg AS THE PRODUCT SERVES IT. It defaults off because the
+	scorecard's argument is about routing, and an LLM rerank makes every number non-repeatable
+	— but with it off the harness is blind to `search.RERANK_CANDIDATES` and to the reranker's
+	own recall, which is exactly what a retrieval-tuning run has to see.
 	"""
 	project = project or demo_project()
 	sections = corpus_sections(project)
@@ -428,7 +434,7 @@ def run_eval(
 			naive = rag_answer.naive_retrieve(question["question"], project, rag_search.ALL_PROJECTS, limit=k)
 			row["modes"]["naive"] = score_leg(expected, question["required"], naive)
 		if "routed" in modes:
-			routed = rag_answer.retrieve(decided, project, False, rag_search.ALL_PROJECTS, top_k=k)
+			routed = rag_answer.retrieve(decided, project, rerank, rag_search.ALL_PROJECTS, top_k=k)
 			row["modes"]["routed"] = score_leg(expected, question["required"], routed)
 		if check_refusals and question.get("expect_refusal"):
 			synthesised = rag_answer.answer(
@@ -440,6 +446,7 @@ def run_eval(
 	return {
 		"project": project,
 		"k": k,
+		"rerank": rerank,
 		"modes": list(modes),
 		"generated_at": datetime.now().isoformat(timespec="seconds"),
 		"took_ms": int((time.monotonic() - started) * 1000),
diff --git a/wikify/rag/router.py b/wikify/rag/router.py
index 493d4cc..bc863d6 100644
--- a/wikify/rag/router.py
+++ b/wikify/rag/router.py
@@ -33,6 +33,12 @@
 # "the second one", not enough to blow up the cheap model's context.
 HISTORY_TURNS = 6
 
+# Routing is the first thing an Ask does and nothing else can start until it lands, so the
+# tail matters more than the median. OpenRouter's default price-weighted routing re-draws a
+# provider per call; pinning the one endpoint is what removes the draw. Same preference the
+# reranker uses (`search.RERANK_PROVIDER`), for the same reason.
+PROVIDER = {"order": ["google-ai-studio"], "allow_fallbacks": True}
+
 FALLBACK_REASON = "Routing is unavailable, so I searched both by meaning and by section type."
 
 
@@ -179,6 +185,7 @@ def route(question: str, project: str | None = None, history: list | None = None
 			build_messages(question, project, history),
 			label="rag_route",
 			response_format={"type": "json_object"},
+			provider=PROVIDER,
 		)
 		content = response["choices"][0]["message"]["content"]
 		usage.add(response.get("usage"))
diff --git a/wikify/rag/search.py b/wikify/rag/search.py
index f7cce12..5c01ea4 100644
--- a/wikify/rag/search.py
+++ b/wikify/rag/search.py
@@ -65,9 +65,19 @@ def __repr__(self) -> str:
 # batch of latency rather than all of them. What may NOT cross into a pool thread is frappe:
 # `frappe.local` is unbound there, so the OpenRouter key is resolved on the calling thread and
 # each batch's usage is billed on it too (`usage` is thread-local by design — see `rag.usage`).
-# ponytail: fixed pool width, sized for the ~4 batches a 35-candidate rerank produces; make it
-# proportional to the batch count if RERANK_CANDIDATES ever grows past a couple of hundred.
-RERANK_WORKERS = 4
+# The pool is as wide as there are batches, capped: a width below the batch count costs a whole
+# extra wave, which is what a fixed 4 was doing to the 5 batches of a 50-candidate rerank.
+# ponytail: cap measured against 5 batches at ~2.3s each; revisit if RERANK_CANDIDATES grows
+# past a couple of hundred, where the cap starts serialising again and rate limits come in.
+RERANK_MAX_WORKERS = 8
+# OpenRouter's default routing is price-weighted, so parallel batches are independently sampled
+# from a provider distribution and the wall clock is set by the slowest draw. Pinning them to
+# one endpoint is what makes the pool worth having. Measured on PRJ-2026-00002, 50 candidates
+# in 5 batches, 9 runs each: unpinned 7.8s median (1.9x concurrency, 24.7s worst case), pinned
+# 2.6s (4.2x, 4.1s worst case) — and 6 of 18 unpinned runs came back as truncated JSON that
+# lost the whole verdict, against 0 of 18 pinned. `allow_fallbacks` stays on: a reranker that
+# cannot reach its preferred provider must degrade to a slower one, never fail.
+RERANK_PROVIDER = {"order": ["google-ai-studio"], "allow_fallbacks": True}
 
 RESULT_COLUMNS = [
 	"id",
@@ -344,7 +354,12 @@ def expand_to_sections(
 
 
 def score_batch(
-	query: str, hits: list[Hit], offset: int, model: str, api_key: str = ""
+	query: str,
+	hits: list[Hit],
+	offset: int,
+	model: str,
+	api_key: str = "",
+	provider: dict | None = None,
 ) -> tuple[dict[int, float], dict | None]:
 	"""Score one batch of candidates 0-10, keyed by each one's position in the FULL list.
 
@@ -372,6 +387,7 @@ def score_batch(
 		label="rag_rerank",
 		response_format={"type": "json_object"},
 		api_key=api_key,
+		provider=provider,
 	)
 	parsed = frappe.parse_json(response["choices"][0]["message"]["content"]) or {}
 	scores = {cint(item.get("id")): flt(item.get("score")) for item in parsed.get("scores") or []}
@@ -383,18 +399,23 @@ def rerank_scores(query: str, hits: list[Hit], model: str) -> dict[int, float]:
 	"""Ask the cheap model to score every candidate 0-10 for answering `query`.
 
 	Graded in batches of `RERANK_BATCH_SIZE` — see the constant for the measurement that
-	forced it, and `RERANK_WORKERS` for why the batches run concurrently. The candidate
+	forced it, and `RERANK_MAX_WORKERS` for why the batches run concurrently. The candidate
 	numbers stay global across batches so a score always maps back to the same hit.
 	"""
 	from wikify.engine import settings
 
 	api_key = settings.openrouter_key()
 	offsets = range(0, len(hits), RERANK_BATCH_SIZE)
-	with ThreadPoolExecutor(max_workers=RERANK_WORKERS) as pool:
+	with ThreadPoolExecutor(max_workers=min(len(offsets), RERANK_MAX_WORKERS)) as pool:
 		batches = list(
 			pool.map(
 				lambda offset: score_batch(
-					query, hits[offset : offset + RERANK_BATCH_SIZE], offset, model, api_key
+					query,
+					hits[offset : offset + RERANK_BATCH_SIZE],
+					offset,
+					model,
+					api_key,
+					RERANK_PROVIDER,
 				),
 				offsets,
 			)
diff --git a/wikify/tests/test_rag_core.py b/wikify/tests/test_rag_core.py
index af35c2c..6b8c197 100644
--- a/wikify/tests/test_rag_core.py
+++ b/wikify/tests/test_rag_core.py
@@ -711,7 +711,7 @@ def test_the_rerank_grades_candidates_in_batches(self):
 			reranked = search.rerank_hits("coin", hits)
 
 		self.assertEqual(chat_completion.call_count, 3)
-		# Sorted, because the batches run concurrently (`search.RERANK_WORKERS`) — what must
+		# Sorted, because the batches run concurrently (`search.RERANK_MAX_WORKERS`) — what must
 		# hold is that they partition the candidate list, not the order they come back in.
 		batches = sorted(candidate_ids(call.args[1]) for call in chat_completion.call_args_list)
 		self.assertEqual(batches, [list(range(0, 10)), list(range(10, 20)), list(range(20, 25))])

From 1a82e4421daa5879cf944f7e1ddd3bfacbe43410 Mon Sep 17 00:00:00 2001
From: rl0007 <rl0007@users.noreply.github.com>
Date: Tue, 11 Aug 2026 23:04:40 +0530
Subject: [PATCH 8/8] test(rag): add cost accounting tests and latency
 prior-art research

test_ask_cost pins the per-ask figure against the sum of its individual LLM
calls, and the session total against the sum of its message rows. Without it
the 15x undercount could return silently: litellm reports synthesis price via
a callback rather than on the response, so the leg carrying ~92% of the spend
read as $0.00.

docs/rag-latency-prior-art.md records what production systems do about the
same problems, with sources and dates. Its main finding contradicts the
assumption we started from: synthesis is decode-bound, so trimming the 55k
character prompt is a cost fix rather than a speed fix, and the reranker's
model is the bottleneck rather than its depth.
---
 docs/rag-latency-prior-art.md | 1112 +++++++++++++++++++++++++++++++++
 wikify/tests/test_ask_cost.py |  158 +++++
 2 files changed, 1270 insertions(+)
 create mode 100644 docs/rag-latency-prior-art.md
 create mode 100644 wikify/tests/test_ask_cost.py

diff --git a/docs/rag-latency-prior-art.md b/docs/rag-latency-prior-art.md
new file mode 100644
index 0000000..338fd85
--- /dev/null
+++ b/docs/rag-latency-prior-art.md
@@ -0,0 +1,1112 @@
+# RAG latency & context-size prior art — what production systems actually do
+
+**Date of research:** 2026-08-11. **Scope:** how production RAG/answer systems handle the latency and
+context-size problem we have measured in Wikify's Ask pipeline, and what is genuinely worth adopting.
+
+**Our measured baseline** (the numbers everything below is judged against):
+
+| Stage | Wall clock | Share |
+|---|---|---|
+| Synthesis LLM (claude-sonnet-4.6, ~12k-token prompt) | 13.7s | 55% |
+| Retrieval + rerank (almost entirely the LLM rerank calls) | 8.9s | 36% |
+| Intent router LLM | 2.1s | 8% |
+| Evidence verification | 13ms | ~0% |
+| Vector DB | 12–23ms | ~0% |
+| **Total** | **~25s** | **$0.047/question**, synthesis ≈92% of cost |
+
+Rerank: 50 candidates → 8, LLM reranker (`google/gemini-2.5-flash`), batches of 10, only 1.65× speedup
+from 4-way threading. Synthesis prompt 55,049 chars because parent-document retrieval sends whole
+sections (8 hits × 4,524 chars avg). Corpus: 2 ICAI study PDFs (236pp + 180pp), ~1,150 chunks.
+Constraint: **a wrong statutory rate is disqualifying; slow is merely annoying.**
+
+---
+
+## Reading conventions
+
+Throughout, claims are tagged:
+
+- **[M]** — a number **measured and published in the cited source**.
+- **[V]** — a **vendor marketing** claim, no published methodology.
+- **[I]** — **my inference**, not in any source.
+- **[?]** — source found but I could **not independently verify** the specific figure; treat as weak.
+
+Publication dates are given for everything. This field moves fast: anything older than ~18 months
+(i.e. pre-2025) is flagged where its age might matter.
+
+---
+
+## 0. What we do today vs what production systems do
+
+| Dimension | Wikify today | Production norm | Evidence strength |
+|---|---|---|---|
+| **Rerank model** | LLM (gemini-2.5-flash), listwise, batches of 10 | Dedicated cross-encoder API or self-hosted (Cohere Rerank 4, Voyage rerank-2.5, Jina v3.5, mxbai-rerank-v2) | Strong, multiple independent [M] |
+| **Rerank latency** | **8.9s** | **130ms – 1s** for 50–100 docs | Strong [M] (Jina, Oracle/Cohere, ZeroEntropy, mixedbread) |
+| **Rerank depth** | 50 → 8 | 30–100 → 5–20; depth 50 is fine and well-supported | Strong [M] (Elastic BEIR sweep, Anthropic) |
+| **Retrieved unit sent to LLM** | Whole parent sections, unconditionally, ~1,100 tok each | Capped parents (LangChain `parent_splitter`), gated merging (LlamaIndex ≥n children), or small contextualised chunks (Anthropic) | Strong — **nobody recommends unconditional whole-section expansion** |
+| **Context budget** | ~12k tokens | ~4k–8k total is the convergent recommendation; more, smaller units beats fewer, larger | Medium [M], sources disagree on granularity |
+| **Intent routing** | LLM call, **2.1s** | TF-IDF+SVM (sub-ms), embedding router (single-digit ms), DistilBERT (p50 9ms), or **no router at all** (fuse both modes) | Strong [M] + [?] on 2026 preprints |
+| **Pipeline shape** | 3 sequential LLM calls | Merge prompts, parallelise non-dependent steps, speculatively start retrieval; keep the LLM out of the request path where possible | Strong — OpenAI's own latency guide names our anti-pattern |
+| **Streaming** | None (blocking 25s) | Streaming-first is universal | Strong |
+| **Retrieval latency budget** | 12–23ms (fine) | 200ms–2s published targets (Vespa, Dropbox Dash, Exa) | Our vector store is a non-issue |
+| **Caching** | None | Provider prefix/KV caching yes; semantic caching largely not, for RAG | Medium [M] |
+| **Numeric safety** | Evidence verification (13ms) | No vendor solves exact-figure verification; deterministic renderer-side checking is the published design (PCN) | Weak literature, strong argument |
+
+**The one-line summary:** our vector store is irrelevant, our rerank step is ~15–60× slower than the
+published state of the art at *equal or better quality*, our router should not be an LLM, and our
+synthesis is **decode-bound, not prefill-bound** — which means cutting the 12k-token prompt is a cost
+fix, not a speed fix. That last point inverts the intuition the task brief was built on and is the
+most important finding in this document.
+
+---
+
+## 1. Reranking
+
+### 1.1 Is LLM-as-reranker standard? No — it is the thing vendors and papers argue against
+
+The clearest and most directly comparable study is Voyage AI's **"The Case Against LLMs as Rerankers"**
+(2025-10-22, https://blog.voyageai.com/2025/10/22/the-case-against-llms-as-rerankers/). I fetched and
+verified this myself. Setup: 13 real-world datasets, 8 domains, sliding-window listwise reranking with
+**window size 20**, LLMs prompted "following RankLLM" with structured JSON output — i.e. structurally
+the same architecture as ours.
+
+**[M]** Average NDCG@10:
+
+| System | NDCG@10 |
+|---|---|
+| `rerank-2.5` | **84.32%** |
+| `rerank-2.5-lite` | 83.12% |
+| GPT-5 | ~71.71% (−12.61) |
+| Gemini 2.5 Pro | ~70.89% (−13.43) |
+| Qwen3-32B | ~69.54% (−14.78) |
+
+**[M]** Speed: `rerank-2.5` is *"9x, 36x, and 48x faster than Claude Sonnet 4.5, GPT-5, and Gemini 2.5
+Pro, respectively."* **[M]** Cost: $0.05/MTok (`rerank-2.5`), $0.02 (`lite`), vs $1.25–$3 for the LLMs
+— 25–60× more expensive.
+
+This is a vendor arguing for its own product, so discount the magnitude. But the direction is
+corroborated independently:
+
+- **[M] Set-Encoder** (arXiv:2404.06912, ECIR 2025, rev. 2025-04-05): **RankZephyr is ~110× slower
+  than monoELECTRA-Large at comparable effectiveness**; a 330M Set-Encoder is ~85× faster than
+  windowed RankGPT-4o.
+- **[M] ZeroEntropy** (2025-09-05,
+  https://zeroentropy.dev/articles/should-you-use-llms-for-reranking-a-deep-dive-into-pointwise-listwise-and-cross-encoders/),
+  17 benchmarks, p50 latency on 75kb input — **the cross-encoder was both most accurate AND 5–17×
+  faster than every LLM reranker tested**:
+
+  | Model | NDCG@10 | p50 latency | $/M input tok |
+  |---|---|---|---|
+  | zerank-1 (cross-encoder) | **0.7767** | **129.7ms** | $0.025 |
+  | Cohere Rerank-3.5 | 0.7194 | 198.1ms | $0.050 |
+  | GPT-4.1-mini | 0.7131 | 740ms | $0.80 |
+  | GPT-5-nano | 0.7116 | 1,520ms | $0.050 |
+  | GPT-5-mini | 0.6980 | 2,180ms | $0.250 |
+
+  (ZeroEntropy sells a reranker — self-interested, but the numbers are specific and the setup is
+  disclosed. Their earlier 2025-07-20 post honestly reports the *opposite* quality result in one
+  configuration: listwise Gemini Flash beat BGE-reranker-v2 0.78 vs 0.74 NDCG@10 — **at 420ms vs 12ms
+  and $18 vs $2 per 1k queries.** So: LLM listwise *can* win slightly on quality; it always loses
+  catastrophically on latency and cost.)
+- **[M] Anthropic's own Contextual Retrieval reference pipeline** (2024-09-19,
+  https://www.anthropic.com/news/contextual-retrieval) uses **the Cohere reranker**, not an LLM.
+
+### 1.2 What latency do dedicated rerankers actually achieve?
+
+The most directly comparable figures to our 8.9s:
+
+| Source | Workload | Latency |
+|---|---|---|
+| **Jina** (https://jina.ai/reranker/, jina-reranker-v3 rel. 2025-10-03) **[V, but specific]** | 100 docs × 256 tok, 64-tok query | **~150ms** |
+| Same page | 100 docs × **4096 tok**, 512-tok query | **~7s** — *document length dominates, not count* |
+| **Oracle OCI benchmark of Cohere Rerank 3.5** (https://docs.oracle.com/en-us/iaas/Content/generative-ai/benchmark-cohere-rerank-3-5.htm) **[M, third-party]** | 96 docs × 512 tok | **0.54s** |
+| Same | 96 docs × 256 tok | 0.28s |
+| Same | 96 docs × 2048 tok | 3.77s |
+| **Elastic measuring Jina v3.5 vs v3** (2026-07-27, https://www.elastic.co/search-labs/blog/jina-reranker-35-legal-medical-structured-data) **[M]** | BEIR NQ, 145 tok avg docs | v3 371.1ms → v3.5 305.3ms |
+| Same | AILAcasedocs, 1,904 tok avg docs | v3 16.1s → v3.5 10.3s |
+| **mixedbread** (2025-03-13, https://www.mixedbread.com/blog/mxbai-rerank-v2) **[V, self-benchmark, A100 80GB, NFCorpus]** | doc count not stated | base-v2 (0.5B) **0.67s**; large-v2 (1.5B) 0.89s; **bge-reranker-v2-gemma (2.5B) 7.20s** |
+| **FlashRank on CPU** (https://github.com/clouatre-labs/rag-reranking-benchmarks, 2026) **[M]** | 16 candidates → 8, ms-marco-MiniLM-L-12-v2 (34MB), MacBook Pro CPU | **+31.3ms overhead** (79.1ms vs 47.8ms without), 480 timed queries |
+
+**The load-bearing caveat for us: document length, not document count, is what makes reranking slow.**
+Our chunks average 4,524 chars ≈ 1,100 tokens. Reading Oracle's table, 96 docs at 1024 tok = 1.38s and
+at 2048 tok = 3.77s. **[I]** So 50 docs at ~1,100 tokens with a dedicated reranker is plausibly
+**0.5–1.0s**, not 150ms. That is still a **9–18× improvement** on our 8.9s, but I would not promise
+sub-200ms without measuring on our actual chunk lengths.
+
+**Two negative findings worth internalising:**
+
+1. **"Cross-encoder" does not automatically mean fast.** `bge-reranker-v2-gemma` (2.5B, LLM backbone)
+   measured **7.20s** — as slow as our current setup. The win comes from small encoder backbones
+   (0.5B and below), not from the "cross-encoder" label.
+2. **Cohere publishes no latency figures at all.** The "Rerank 4 Fast is optimized for low latency"
+   framing (https://cohere.com/blog/rerank-4, 2025-12-11) is marketing without numbers. Voyage
+   publishes none either. The only real numbers are third-party (Oracle, Elastic) or from vendors
+   selling *against* the incumbent.
+
+**Current lineup, verified from docs:** Cohere `rerank-v4.0-pro` / `rerank-v4.0-fast` (32k context,
+32,764-tok chunks, max 10,000 docs/request; rerank-3.5 deprecated 2026-07-01 **[?]** — I did not
+confirm the deprecation date on a Cohere page). Voyage `rerank-2.5` / `-lite` (32k context, max 1,000
+docs, 600K total-token budget). Jina `jina-reranker-v3` (0.6B, Qwen3-0.6B backbone, 131k context, up
+to 64 docs/call, 61.94 BEIR nDCG@10 — https://huggingface.co/jinaai/jina-reranker-v3, arXiv 2509.25085).
+
+### 1.3 ColBERT / late interaction — not applicable to us
+
+I found **no rigorous published ColBERT-vs-cross-encoder latency benchmark**. Vespa's
+answerai-colbert-small post (2024-08-14, https://blog.vespa.ai/introducing-answerai-colbert-small/)
+gives 33M params, 96-dim embeddings, 32× storage reduction via binarisation, "enables CPU-based
+serving" — but **no ms figures**. Claims of "within 2–3% of cross-encoders" circulating in secondary
+blogs are not traceable to a primary measurement.
+
+**[I] More importantly, ColBERT is structurally the wrong tool for our problem.** Its speed comes from
+*precomputing document token vectors at index time*; it is a retrieval/indexing technique, not a
+post-hoc reranker over 50 arbitrary candidates. Vespa's own recommended pattern is ColBERT as an
+*intermediate* phase to reduce cross-encoder rerank depth — not to replace the reranker. Adopting it
+means rebuilding ingest. **Skip.**
+
+### 1.4 Rerank depth — 50 is fine; keep it
+
+This is the clearest "don't change it" finding. Elastic's **"Exploring depth in a retrieve-and-rerank
+pipeline"** (2024-12-05, https://www.elastic.co/search-labs/blog/elastic-semantic-reranker-part-3) is
+a BEIR-wide depth sweep to depth 400. I fetched and verified this. **[M]**:
+
+- **72.6%** of dataset/retriever combos show a **Pareto curve** — fast gains, then saturation.
+- **20.2% are unimodal** — nDCG **peaks then declines** with more depth (false positives creep in).
+  Deeper is genuinely not monotonically better.
+- 7.1% degrade vs BM25 outright.
+- *"Obtaining 90% of the maximum gain is feasible at a much smaller depth in all scenarios: on average
+  we have to re-rank 3× fewer pairs."*
+
+**[M]** Budget table:
+
+| Budget | Avg depth | nDCG increase |
+|---|---|---|
+| Small | 32 | 41% |
+| Medium | 100 | 60% |
+| Large | 200 | 66% |
+
+Explicit cost-sensitive recommendation: **rerank the top 30 from BM25**, for ~40% nDCG uplift.
+
+Corroborating evidence on recall lost by reranking fewer candidates — **"Rerank Before You Reason"**
+(Sharifymoghaddam & Lin, arXiv:2601.14224, submitted 2026-01-20, rev. 2026-06-01). I verified title,
+authors and dates on the arXiv abstract page. The depth ablation (retrieve top-100, rerank at
+d ∈ {10, 20, 50}) came from the HTML full text and I could **not** confirm it against the abstract —
+**[?]** treat these specific figures as weaker: truncating to **d=20 keeps ~85% of the rerank lift at
+5.4× fewer listwise windows; d=10 keeps only 58%**; d=50 with a strong LLM reranker moved NDCG@5 from
+19.72 → 46.05. Their headline conclusion **[M, verified in abstract]** is that *"reranking consistently
+improves retrieval and end-to-end accuracy, and that moderate reranking often yields larger gains than
+increasing search-time reasoning"* — and they found cross-encoders **more cost-effective than listwise
+LLM reranking** despite slightly lower peak accuracy.
+
+Vendor guidance: **[M] Anthropic** retrieves **top 150** and reranks to **top 20**; reranked contextual
+embeddings + BM25 cut top-20 retrieval failure **5.7% → 1.9% (67% reduction)**. **Pinecone**'s hosted
+rerank caps at 100 docs and its tutorials use top_k=25 → top_n=3 — but that page is **undated and cites
+2023 material; treat as stale.**
+
+**Conclusion for us:** 50→8 sits between Elastic's "medium budget" and Anthropic's 150→20. **The depth
+is not the problem — the reranker model is.** Cutting depth to 20–25 would halve our LLM rerank cost
+and give up ~15% of the rerank lift; swapping the reranker gives up ~0% and saves ~90% of the latency.
+Do the swap, keep the depth.
+
+### 1.5 The free win to check first
+
+**[I]** Our 8.9s over 5 batches of 10 is ~1.8s per batch, which is exactly the shape of a **sequential**
+loop. Sliding-window listwise reranking *is* inherently sequential (each window's output feeds the
+next), but if our batches are independent pointwise/listwise scores, parallelising them is a ~5× win
+for zero quality change. **Verify whether the batch loop is sequential before doing anything else.**
+(See §3.5 for why the observed 1.65× from 4-way threading is probably a routing artefact, not physics.)
+
+---
+
+## 2. Context size
+
+### 2.1 "Lost in the Middle" — real, but not our problem at 8 documents
+
+**Liu et al., *Lost in the Middle: How Language Models Use Long Contexts*, TACL vol. 12 pp. 157–173,
+2024** (arXiv 2307.03172, July 2023). https://aclanthology.org/2024.tacl-1.9/
+
+**[M]** U-shaped curve: *"Performance is often highest when relevant information occurs at the
+beginning or end of the input context, and significantly degrades when models must access relevant
+information in the middle."* For GPT-3.5-Turbo on multi-document QA the first-vs-middle gap is
+**~20–23 points** (e.g. 20 docs: 75.8% gold-first vs 53.8% gold-middle — *below* the 56.1% closed-book
+score). Doc counts tested: 10, 20, 30.
+
+**⚠️ Confidence note:** two separate fetches of the paper returned different tables for the exact
+digits. The **~20-point first-vs-middle gap is robust across both readings**; do not quote the precise
+percentages without a direct read of Table 1 / Figure 5.
+
+**[M]** The finding that matters most to us: *"model performance saturates long before retriever
+performance saturates."* Going 20 → 50 retrieved documents gained ~1.5% (GPT-3.5) and ~1% (Claude-1.3).
+The paper's own conclusion is that **reranking or truncation beats adding documents.**
+
+**Follow-up work confirms rather than refutes it on modern models:**
+
+- **[M] RULER** (Hsieh et al., NVIDIA, arXiv 2404.06654, Aug 2024): of 17 models claiming ≥32k context,
+  *"only half of them can maintain satisfactory performance at the length of 32K"* — despite near-perfect
+  vanilla needle-in-a-haystack scores. This is the canonical "NIAH is too easy" citation.
+- **[M] NoLiMa** (Modarressi et al., Adobe Research, arXiv 2502.05167, Feb 2025, ICML 2025): needles with
+  *minimal lexical overlap* with the question. Of 13 models claiming ≥128k, **11 drop below 50% of their
+  short-context baseline at 32k**. GPT-4o: 99.3% → **69.7% at 32k**.
+
+**[I] Relevance to Wikify:** at 8 documents and 12k tokens we are nowhere near where these effects bite.
+And our queries frequently *do* share literal tokens with the needle (a section number, a rate) — the
+easy case where models hold up best. NoLiMa's hard case (paraphrased conceptual query, no lexical
+overlap) is the one to worry about, and it's a *retrieval* problem more than a context-length one.
+
+### 2.2 Databricks: quality plateaus long before the context window does
+
+**Blog 2024-08-12** https://www.databricks.com/blog/long-context-rag-performance-llms; **paper** Leng,
+Portes, Havens, Zaharia, Carbin, arXiv 2411.03538 (2024-11-05, NeurIPS 2024 workshop).
+
+**[M]** *"Using longer context does not uniformly increase RAG performance."* Retrieval recall keeps
+climbing; generation quality plateaus or degrades:
+
+| Model | Peak / decline point |
+|---|---|
+| Llama-3.1-405B-instruct | declines after **32k** |
+| GPT-4-0125-preview | declines after **64k** |
+| GPT-4-turbo | peaks at **16k** |
+| Claude-3-sonnet | peaks at **16k** |
+| DBRX-instruct | best at **8k** |
+| Mixtral-8x7b-instruct | optimal at **4k** |
+
+**[M]** Retrieval recall saturation by dataset: Natural Questions 1.0 at **8k**; Databricks DocsQA 0.993
+at 96k; HotPotQA 0.890 at 128k; **FinanceBench 0.916 at 128k** (financial documents genuinely need depth).
+
+**[M]** Failure modes catalogued: repeated content, random/irrelevant content, instruction failures
+(summarising instead of answering; copyright refusals — Claude-3-Sonnet refused **3.7% at 16k → 49.5% at
+64k**; DBRX summarised instead of answering 5.2% → 50.4%), and wrong answers in correct format.
+
+**[I] Conclusion: our 12k is comfortably inside the "everything still works" zone for a frontier model.
+Databricks does not support a claim that 12k is hurting our accuracy. Cut context for cost, not quality.**
+
+### 2.3 Parent-document retrieval — the libraries all cap or gate it; we don't
+
+This is the most actionable finding in this section.
+
+- **LangChain `ParentDocumentRetriever`** — two modes: `child_splitter` only (returns the whole parent
+  document), or **both `parent_splitter` and `child_splitter`**, where documents are first split into
+  *medium-sized* parents. The docs describe mode (b) explicitly as the fix for *"documents [that] are
+  too big"*. **The library's own guidance is to cap parent size, not return whole sections.**
+  (The canonical how-to URL has been redirected in the docs migration; the commonly-cited 2000-parent /
+  400-child example values I could **[?]** not verify in a live fetch.)
+- **[M] LlamaIndex `HierarchicalNodeParser` + `AutoMergingRetriever`**
+  (https://docs.llamaindex.ai/en/latest/examples/retrievers/auto_merging_retriever/): default chunk
+  sizes **2048 / 512 / 128 chars**. Leaf nodes are embedded; parents are pulled from the docstore — and
+  **auto-merging replaces children with the parent only when more than *n* of the top-k retrieved chunks
+  share that parent.**
+
+**[I] Our 4,524-char average parent is ~1,100 tokens — larger than LlamaIndex's *largest* default level
+(2048 chars ≈ 500 tokens) — and we send 8 of them unconditionally. Neither library does this.
+Unconditional whole-section expansion on every hit is not the standard pattern; it is the un-tuned
+version of the standard pattern.**
+
+### 2.4 Sentence-window retrieval — small real gain, mostly a precision win
+
+**[M] `SentenceWindowNodeParser`** (https://docs.llamaindex.ai/en/stable/api_reference/node_parsers/sentence_window/):
+default and near-universal `window_size = 3` sentences either side. Embedding is on the single sentence;
+`MetadataReplacementPostProcessor` swaps in the window at synthesis time.
+
+**[M]** LlamaIndex's own eval (https://developers.llamaindex.ai/python/examples/node_postprocessor/metadatareplacementdemo/):
+
+| Retriever | Correctness | Relevancy | Faithfulness | Semantic sim. |
+|---|---|---|---|---|
+| Sentence Window | **4.37** | **0.93** | 0.93 | 0.96 |
+| Base (naive chunking) | 4.22 | 0.90 | 0.93 | 0.96 |
+
+**[I]** +0.15 correctness is small, and it is primarily a *retrieval precision* win (sentence-level
+embeddings capture technical terms more sharply), not a context-shrinking technique. Its value to us is
+that a ±3-sentence window is far smaller than a whole section while almost always containing the rate —
+statutory numbers live in the sentence or its neighbour. Note results vary meaningfully with window size
+(run-llama/llama_index#14906); it needs tuning, not the default.
+
+### 2.5 Contextual compression (LangChain) — wrong tool for us
+
+Origin: **LangChain blog, 2023-04-20**,
+https://www.langchain.com/blog/improving-document-retrieval-with-contextual-compression. **[M]** Stated
+motivation, verbatim: irrelevant text *"1. might distract the LLM from the relevant information 2. takes
+up precious space."* The 2023 announcement contains **no quantified compression, cost or latency
+numbers** — that is a gap, not a suppressed result. Community measurement **[?]**: ~3× compression
+(3,565 → 1,183 chars) in one LangChain OpenTutorial example, methodology unverified.
+
+**[M] LangChain's own docs say plainly:** *"performing additional LLM calls for each retrieved document
+is costly and slow"*, and position `EmbeddingsFilter` (no LLM — embed docs + query, drop dissimilar) as
+"the cheaper and faster option."
+
+**[I] Verdict: `LLMChainExtractor` is disqualified for us on both axes.** It fires one LLM call *per
+retrieved document* — 8 extra calls to save part of 13.7s — and its failure mode is an extractor LLM
+dropping the sentence containing the rate. That is precisely the failure we cannot tolerate.
+`EmbeddingsFilter` is the variant worth a look: no LLM, drops whole low-similarity hits, ~free.
+
+### 2.6 LLMLingua family — real end-to-end wins, but a bad risk shape for numbers
+
+- **[M] LongLLMLingua** (Jiang et al., arXiv 2310.06839, 2023-10-10 rev. 2024-08-12, **ACL 2024**): on
+  ~10k-token prompts — **2×–6× compression**, up to **+21.4% performance with ~4× fewer tokens** on
+  GPT-3.5-Turbo (NaturalQuestions), **1.4×–2.6× end-to-end latency speedup**, **94.0% cost reduction**
+  on LooGLE.
+- **[M] LLMLingua-2** (Pan et al., arXiv 2403.12968, 2024-03-19, **Findings of ACL 2024**): task-agnostic
+  compression via token classification with a small encoder (XLM-RoBERTa-large / mBERT) distilled from
+  GPT-4. **2×–5× compression**, compressor **3×–6× faster** than prior methods, **1.6×–2.9× end-to-end
+  latency speedup**.
+
+**Does the compressor eat the savings? [M] No** — both papers report *end-to-end* speedups, which are by
+definition net of compressor time. LLMLingua-2 exists precisely because LLMLingua-1's LLaMA-7B perplexity
+scoring was too slow.
+
+**[I] Three reasons to be cautious anyway.** (a) Task-agnostic compression drops tokens by learned
+importance, and a bare figure — "18%", "₹2,50,000" — is short, low-perplexity, and structurally the kind
+of token that compression discards first. (b) It requires a local model in the request path. (c) Verbatim
+citation gets harder: you are citing a compressed prompt, not the source. If pursued, use LongLLMLingua's
+**question-aware** variant and gate it behind a regression suite that specifically checks exact-rate
+recall. **And see §2.9 — compression saves prefill, and prefill is not where our 13.7s goes.**
+
+### 2.7 Anthropic Contextual Retrieval — the strongest alternative to what we do
+
+**2024-09-19**, https://www.anthropic.com/news/contextual-retrieval. **[M]**, verbatim:
+
+- Contextual Embeddings alone: **top-20 retrieval failure 5.7% → 3.7% (−35%)**
+- \+ Contextual BM25: **5.7% → 2.9% (−49%)**
+- \+ reranking: **5.7% → 1.9% (−67%)**
+- Top-k: they evaluated **5, 10 and 20 chunks and found 20 most performant**.
+- Chunk sizing: *"usually no more than a few hundred tokens"*; prepended context **50–100 tokens**/chunk.
+- Cost: **$1.02 per million document tokens**, one-time, using prompt caching to avoid re-reading the
+  source doc per chunk.
+
+**[I] This is the exact inverse of our strategy, and the better one for our corpus.** Rather than
+*enlarging the retrieved unit at query time* to preserve context (paid on every request, in latency and
+tokens), it moves the missing context into the *index* as a 50–100-token generated preamble (paid once,
+offline, ~$1/M tokens). For ICAI study material where sections repeat structure, a preamble like *"This
+chunk is from Chapter 7, Income from House Property, discussing standard deduction"* is exactly what
+makes a 300-token chunk self-sufficient — letting us shrink the retrieved unit without losing the context
+that made us enlarge it.
+
+### 2.8 Is there a sweet spot for passages-per-answer?
+
+No single canonical number — and the disagreement is informative:
+
+| Source | Recommended k | Basis |
+|---|---|---|
+| **Anthropic Contextual Retrieval** (2024-09) | **20** | best of {5, 10, 20}, measured |
+| **Liu et al., Lost in the Middle** (TACL 2024) | ~**20** | reader saturates before retriever; 20→50 gains ~1–1.5% |
+| **Cuconasu et al., The Power of Noise** (SIGIR 2024) | **3–5** | related-but-irrelevant docs actively hurt |
+
+**The Power of Noise: Redefining Retrieval for RAG Systems**, Cuconasu et al., arXiv 2401.14887
+(2024-01-26, v4 May 2024, SIGIR 2024). **[M, from abstract — high confidence]:** *"The retriever's
+highest-scoring documents that are not directly relevant to the query… negatively impact the
+effectiveness of the LLM"*, and counter-intuitively *"adding random documents in the prompt improves the
+LLM accuracy by up to 35%."* **[?, from full-text fetch — verify before quoting]:** recommends retrieving
+**3–5** documents; place the gold document **near the query** (at the end), not in the middle; Llama2-7B
+0.5642 gold-only → 0.4586 with one *related* distractor → 0.5836 with random padding.
+
+**[I] Reconciling 20 vs 3–5:** Anthropic's k=20 is on *small* chunks (few hundred tokens) — 20 × 300 ≈
+**6k tokens**. Cuconasu's 3–5 is on Wikipedia-passage-sized units. **The two agree on total context
+budget (~4k–8k) and disagree only on granularity.** We are at 8 units × ~1,100 tokens = **12k — more
+total context than either recommends, at a granularity coarser than either tested.** The consistent
+signal across all three sources: **more, smaller units beats fewer, larger ones.**
+
+I found no Anthropic/OpenAI cookbook, Vespa or Elastic page stating a hard top-k rule; vendor guidance
+consistently defers to per-corpus evaluation.
+
+### 2.9 ⚠️ The finding that reframes this whole section: our synthesis is decode-bound
+
+**[I] Arithmetic on our own numbers.** Sonnet 4.6 is $3/$15 per MTok. 12k input = $0.036. Synthesis cost
+= 0.92 × $0.047 = $0.0432. Residual $0.0072 ÷ $15/MTok ≈ **~480 output tokens.**
+
+**[M] Artificial Analysis, Claude Sonnet 4.6** (non-reasoning, 10k-input workload, retrieved 2026-08-11,
+https://artificialanalysis.ai/models/claude-sonnet-4-6): **TTFT 1.50s, output speed 42.8 tok/s.**
+
+**[I]** 480 ÷ 42.8 = 11.2s decode + 1.5s TTFT = **12.7s — within 7% of our measured 13.7s.** Our
+synthesis is **~85–90% decode-bound.**
+
+**Two consequences that invert the intuition behind this research brief:**
+
+1. **Cutting 12k → 4k input is a ~50% COST fix and roughly a 3–4% SPEED fix.** Third-party measurement
+   puts 10k-token prefill at ~400–800ms on fast infrastructure **[?]** (https://infercom.ai/blog/llm-inference-speed-explained/
+   measures 388ms TTFT at 10k input for gpt-oss-120b). Dropping to 4k saves perhaps 0.4–0.6s.
+2. **Prompt caching is a rounding error for us.** Our retrieved chunks change every question, so only
+   system + instructions are cacheable. **[I]** If that's ~2k of 12k: 0.9 × 2k × $3/MTok ≈ **$0.0054/question
+   (~11% of synthesis cost)**, near-zero latency benefit, and only within the 5-minute TTL.
+
+**⚠️ Action before acting on this: log actual `output_tokens`.** The 480-token figure is inferred from
+cost arithmetic, not measured. If it's wrong the whole conclusion shifts. This is a five-minute check
+and it should gate every context-reduction decision.
+
+**[M] Prompt caching mechanics, for completeness** (https://platform.claude.com/docs/en/docs/build-with-claude/prompt-caching):
+5-min cache writes 1.25× base input, 1-hour writes 2×, **cache hits 0.1×**; minimum cacheable prefix is
+model-dependent and **not monotonic across generations** — 1,024 tokens on Sonnet 4.6 / Sonnet 5, but
+**4,096 on Haiku 4.5**. Below the minimum, caching **silently no-ops**. Cache key is exact bytes from
+position 0, render order `tools → system → messages`. Anthropic's launch blog **[V]** claims "up to 90%
+cost / up to 85% latency reduction" — but **their own table shows the 85% needs ~100k cached tokens; at
+a realistic ~10k prompt the measured latency win is 31%** (1.6s → 1.1s). **[M]** A genuinely underrated
+benefit: `cache_read_input_tokens` **do not count toward ITPM rate limits** (except Haiku 3.5), which
+raises the effective throughput ceiling.
+
+---
+
+## 3. Pipeline shape and latency
+
+### 3.1 Published latency budgets — retrieval is never the problem
+
+| Source | Number | Type |
+|---|---|---|
+| **Dropbox Dash** (https://dropbox.tech/machine-learning/building-dash-rag-multi-step-ai-agents-business-users) | **"under 2 seconds for over 95% of our queries"**; sub-100ms for feature retrieval | [M] stated as achieved |
+| **Glean Waldo** (2026-04-28, https://www.glean.com/blog/waldo-launch) | **~250ms per LLM call vs ~3s** for GPT-5.4-medium; **~50% lower end-to-end latency, ~25% fewer tokens, no quality regression** | [V] but specific |
+| **Exa Fast** (2025-07-29, https://exa.ai/blog/fastest-search-api) | **p50 < 425ms**; notes Google-wrapping APIs have a **~700ms p50 floor**. Exa Instant (Feb 2026): 100–200ms | [V] |
+| **Vespa** (https://blog.vespa.ai/eliminating-the-precision-latency-trade-off-in-large-scale-rag/) | *"For an interactive RAG pipeline a defensible production target is roughly 200ms of overall retrieval latency"* | guidance |
+
+**Every serious published retrieval budget is 200ms–2s. Our 12–23ms vector DB is not merely acceptable,
+it is excellent. Our 25s is ~100% LLM.**
+
+### 3.2 LinkedIn's honest numbers
+
+**"Musings on Building a Generative AI Product"**, LinkedIn Engineering, Apr 2024,
+https://www.linkedin.com/blog/engineering/generative-ai/musings-on-building-a-generative-ai-product
+
+- **[M]** *"For a 200-token reasoning step, even a 10ms TBT increase means an extra 2s of latency."* —
+  the cleanest statement of why intermediate LLM hops are latency-toxic.
+- **[M]** They reached **80% of the basic experience in the first month**, then spent **four more months**
+  chasing 95%.
+- **[M]** *"2x/3x the TokensPerSecond"* is achievable **by sacrificing both TTFT and TBT**.
+- **[M]** They evaluate up to **500 conversations/day** manually.
+- **Note:** they publish TTFT/TBT as *metrics tracked* but **decline to publish absolute SLA numbers**.
+  Anyone quoting "LinkedIn's 500ms TTFT target" is inventing it.
+
+### 3.3 Parallelism — OpenAI's own guide names our exact anti-pattern
+
+**OpenAI Latency Optimization guide** (https://developers.openai.com/api/docs/guides/latency-optimization):
+
+> **Make fewer requests** — *"If you have sequential steps for the LLM to perform, instead of firing off
+> one request per step consider putting them in a single prompt and getting them all in a single response."*
+> **Parallelize** — *"Execute non-sequential steps simultaneously; use speculative execution for dependent steps."*
+
+Their worked example does precisely what we would need: **merge query-contextualisation + retrieval-check
+into one prompt** (removing a round trip), then **parallelise the retrieval check with the reasoning step**.
+Principle 6 is "Make your users wait less" (streaming, chunking, progress). **Principle 7 is blunt:
+"Don't default to an LLM."** **[M]** They also note that shortening JSON field names removed 19 output
+tokens, *"potentially saving up to a second"* — output-token count is the dominant latency term, which
+matches §2.9.
+
+Other documented parallel architectures:
+
+- **[M] Anthropic, "How we built our multi-agent research system"** (2025-06-13,
+  https://www.anthropic.com/engineering/multi-agent-research-system): lead agent spawns **3–5 subagents
+  simultaneously**, each using **3+ tools in parallel**; *"these changes cut research time by up to 90%
+  for complex queries."* Cost: agents use ~4× the tokens of chat, multi-agent ~15×. Token usage alone
+  explains **80% of performance variance**.
+- **[M] Azure AI Search agentic retrieval** (https://learn.microsoft.com/en-us/azure/search/agentic-retrieval-overview,
+  updated 2026-07-02) — the most directly relevant vendor design: query planning uses an LLM only at
+  `low`/`medium` reasoning effort; **at `minimal` the LLM planning step is skipped entirely** and queries
+  go straight to knowledge sources. All subqueries run **simultaneously**, each semantically reranked.
+  **Microsoft shipped a "skip the router LLM" tier because the hop is expensive.**
+- **[M] DoorDash** (https://careersatdoordash.com/blog/doordash-llms-to-build-content-embeddings-for-search-and-recommendations/):
+  the stated principle is that **the LLM never sits in the request path** — LLMs build content embeddings
+  *offline*; serving is ANN retrieval fused with structured taxonomy retrieval.
+- **[M] Elastic** (2026-06-15, https://www.elastic.co/search-labs/blog/llm-query-routing-elastic-workflows)
+  *does* use an LLM router — but engineered around the latency: the router sees **only search-result
+  metadata** (scores, categories, complexity labels), never document bodies, keeping the prompt to "a few
+  hundred tokens," explicitly because *"a slow router makes every answer slow."*
+
+**[M] ChatGPT search** (https://openai.com/index/introducing-chatgpt-search/, 2024-10-31) is a fine-tuned
+GPT-4o post-trained on synthetic data distilled from o1-preview; it *"rewrites your query into one or more
+targeted queries"* then issues **additional follow-up queries after reviewing initial results.** OpenAI
+publishes **no latency budget** for it.
+
+### 3.4 Streaming and perceived latency — and a result that argues against panic
+
+- **[M] Nielsen, "Response Times: The 3 Important Limits"** (1993, canonical,
+  https://www.nngroup.com/articles/response-times-3-important-limits/): 0.1s direct manipulation; **1.0s
+  uninterrupted flow of thought**; **10s limit of attention** — beyond that users task-switch and need a
+  completion estimate.
+- **[M] Nielsen, "Think-Time UX"** (2025-10-23, https://www.uxtigers.com/post/think-time-ux) revises this
+  for AI with a five-phase "Cognitive Latency Stack": Perception 0–400ms, Comprehension 0.4–2s, Decision
+  2–10s, **Execution 10–60s+**, Recovery minutes-to-hours. Tasks over 10s **must support backgrounding**.
+  He argues AI's **latency variance** is what breaks the old guidance.
+- **[M] CHI 2026, "The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception"**
+  (https://arxiv.org/html/2604.06183v1) **[?] — I did not independently verify this preprint; treat with
+  care.** 240 participants, TTFT manipulated at 2s / 9s / 20s with streaming throughput held constant:
+  responses at 2s were rated **less thoughtful** than at 9s or 20s (2s M=5.76 vs 9s M=6.09, p=.008);
+  **usefulness peaked at 9s** (M=6.44 vs 6.19 at 2s, p=.021); trust, clarity, relevance and NASA-TLX
+  workload showed **no significant latency effect**; latency was consciously detected by 26.6% at 2s,
+  66.3% at 9s, **81.5% at 20s**.
+- **[M] Buell & Norton, "The Labor Illusion"**, *Management Science* 57(9), 2011
+  (https://pubsonline.informs.org/doi/10.1287/mnsc.1110.1376): with operational transparency, people
+  **prefer longer waits to instant identical results.**
+
+**[I] Read:** for an evidence-gathering task like cited QA, a **9–15s visibly-working** wait is plausibly
+near-optimal for perceived quality — but our 25s is past the point where 81% consciously notice, and
+crucially the CHI study manipulated *time to first token with streaming behind it*. **This is not a
+licence to keep a 25s blank screen; it is an argument that streaming + visible progress buys us more
+than shaving the last few seconds.**
+
+**Be careful:** widely-repeated "300ms TTFT is where users stop noticing / 800ms raises abandonment"
+figures circulate on SEO-grade blogs with **no primary study behind them. Do not cite those.** The
+defensible anchors are Nielsen and Buell & Norton.
+
+### 3.5 Why 4-way threading gave us only 1.65× — mostly expected, partly fixable
+
+Four stacked causes:
+
+**(a) Continuous batching merges our requests. [M]** NVIDIA NIM benchmarking docs
+(https://docs.nvidia.com/nim/benchmarking/llm/latest/metrics.html): *"As the number of concurrent requests
+increases, total system TPS increases while TPS per user decreases as latency increases… beyond
+[saturation] TPS can decrease."* Our 4 requests don't get 4 GPUs; they join a shared, memory-bandwidth-bound
+decode batch alongside other tenants.
+
+**(b) The curve is sublinear even on dedicated hardware. [M]** Databricks (2023-10-12,
+https://www.databricks.com/blog/llm-inference-performance-engineering-best-practices), MPT-7B on 1×A100:
+batch 1 → 0.9 req/s; **batch 4 → 3.2 req/s (3.5×)**; batch 64 → 12.5 (14×). **Only 3.5× at 4-way on a
+dedicated, empty GPU.** **[M]** Anyscale (2023-06-22): at QPS=1 continuous batching improves latency at
+all percentiles; **at QPS=4 it degrades.**
+
+**(c) OpenRouter's default routing disperses our threads — this is the fixable part. [M]**
+https://openrouter.ai/docs/features/provider-routing: default routing is **price-weighted with
+inverse-square weighting**. Our 4 parallel requests are **independently sampled from a weighted provider
+distribution**; some land on a fast provider, some slow, and **wall-clock is set by the slowest.**
+Fix: `sort: "throughput"` (or `:nitro`), or an explicit `order` array with `allow_fallbacks: false`.
+Also available: `preferred_max_latency` / `preferred_min_throughput` over rolling 5-min p50/p75/p90/p99
+windows — but these **deprioritise, they do not exclude.**
+
+**(d) Bursts trip acceleration limits. [M]** Anthropic's rate limits use a **token bucket with continuous
+replenishment**, warning *"60 RPM might be enforced as 1 request per second"*; they also document
+**acceleration limits** triggered by sharp usage increases. Neither Anthropic nor OpenAI documents a
+per-key *concurrency* cap — the limits are rate-shaped, not concurrency-shaped. The widely-repeated
+"~50 concurrent requests" OpenRouter figure appears only in third-party guides; **not documented.**
+
+**[I] Verdict: re-run the 4-way test pinned to a single provider before concluding anything.** A
+meaningful chunk of the missing 2.35× is probably routing dispersion, not physics. Then sweep 1/2/4/8/16
+to find our own knee.
+
+**Two gotchas that bite parallel fan-out specifically. [M]** Anthropic: *"Cache entries only become
+available after the first response begins. For parallel requests to hit the same cache, wait for the first
+response before sending subsequent requests."* A 4-way fan-out over a shared prefix produces **4 cache
+writes at 1.25×, not 1 write + 3 reads** — 5× the input cost of the serial-warm case, zero latency
+benefit. **Warm the prefix with one serial call first** (`max_tokens: 0` pre-warms, write charge only).
+Cloudflare AI Gateway carries the same warning.
+
+### 3.6 Caching — prefix yes, semantic no
+
+**Prefix/KV caching (contractual, reliable):** see §2.9 for Anthropic. **[M] OpenAI**
+(https://developers.openai.com/api/docs/guides/prompt-caching): automatic, **1,024-token prefix minimum**,
+writes 1.25× on GPT-5.6+, **30-minute minimum retention**. Critically: *"Requests are routed to a machine
+based on a hash of the initial prefix"* — and they recommend keeping a given `prompt_cache_key` to **~15
+req/min**, past which they spill to more machines and **hit rate degrades**. **[M] Gemini**
+(https://ai.google.dev/gemini-api/docs/caching): implicit caching on by default for 2.5+; cached input at
+**10% of standard price**; minimum 4,096 tokens (Gemini 3.5 Flash) or 2,048 (2.5 Flash); explicit caches
+carry **storage charges of $4.50/M tok/hour (Pro) or $1.00/M tok/hour (Flash)** — at low request rates an
+explicit cache can cost more than it saves.
+
+**Semantic caching — weak, and wrong for us:**
+
+- **[M] "Category-Aware Semantic Caching for Heterogeneous LLM Workloads"** (arXiv 2510.26835, 2025-10-29,
+  IBM Research / Tencent / Red Hat), measured on production at ~100K queries/hour: **head categories hit
+  40–60%** (code gen 55%, API docs 45%); **tail categories hit 5–15%** (medical 6%, legal 10%) and are
+  30–40% of traffic. **False-positive rate 15% at threshold 0.80, 3% at 0.90.** Failure example:
+  `sort_ascending` matching `sort_descending`. Remote vector-DB lookup ~30ms; in-process HNSW 2ms.
+- **[M] vCache** (arXiv 2502.03771, ICLR 2026, Berkeley/TUM incl. Zaharia & Gonzalez): static similarity
+  thresholds — what GPTCache, Portkey and every commercial gateway ship — *"do not give formal correctness
+  guarantees, result in unexpected error rates."* Their learned per-prompt threshold gives **up to 26×
+  lower error at matched latency** and 12.5× more cache hits.
+- **[V] GPTCache** claims 61.6–68.8% hit rate — on *curated benchmark datasets*. **Portkey's own figure
+  for RAG use cases is ~20%.** Helicone and Cloudflare AI Gateway are **exact-match only**.
+
+**[I] Semantic caching is disqualified for cited QA on two independent grounds:** measured RAG hit rates
+of 15–25%, and ICLR-2026 evidence that default static thresholds serve wrong answers at unbounded rates —
+which in a *cited* product means a confidently wrong statutory rate with a real-looking citation attached.
+**Exact-match caching of identical queries is fine and free.**
+
+---
+
+## 4. Cheap alternatives to the LLM router
+
+### 4.1 The ladder, cheapest first
+
+| Option | Measured latency | Source |
+|---|---|---|
+| TF-IDF + linear SVM | sub-ms **[I]** | RAGRouter-Bench (below) |
+| Embedding router, **local** encoder (MiniLM/SetFit) | single-digit ms CPU | OATS |
+| DistilBERT classifier | **p50 9ms, p99 <50ms** (CPU) | Switchcraft / HF Infinity |
+| Small local LLM router (1.5B) | **42ms** | Tiny-Critic RAG |
+| LLM API router | **785ms** (gpt-4o-mini) / **our 2.1s** | Tiny-Critic RAG |
+
+### 4.2 The most on-point evidence
+
+**⚠️ Confidence note:** the three most directly relevant papers below are **2026 arXiv preprints that I
+did not independently verify** — **[?]**. Their *direction* is corroborated by the older, peer-reviewed
+work in §4.3–4.4; treat the precise figures as provisional.
+
+- **[?] RAGRouter-Bench** (arXiv 2604.03455, 2026-04-07) — 7,727 queries, 4 domains, 3 query types
+  (factual / reasoning / summarization). Best config **TF-IDF + SVM: macro-F1 0.928, accuracy 93.2%**,
+  simulating **28.1% token savings**. Striking detail: **lexical TF-IDF beat MiniLM sentence embeddings
+  by 3.1 macro-F1 points** — *"surface keyword patterns are strong predictors of query-type complexity."*
+- **[?] Tiny-Critic RAG** (arXiv 2603.00846, 2026-03-01) — the cleanest analogue of our exact swap:
+
+  | | routing overhead | routing F1 | cost / 10k queries |
+  |---|---|---|---|
+  | Heavy-CRAG (GPT-4o-mini API router) | **785ms** | 0.934 | $3.00 |
+  | Tiny-Critic (Qwen-1.7B, LoRA r=16, constrained decoding) | **42ms** | 0.912 | $0.06 |
+
+  **94.6% latency reduction and 98% cost reduction for 2.2 points of routing F1.**
+
+**[I] Our exhaustive-vs-semantic decision is very likely a lexical-signal problem** — quoted strings,
+section numbers, "list all…" → exhaustive; conceptual phrasing → semantic. A TF-IDF+SVM trained on a few
+hundred labelled queries is the highest-expected-value experiment in this document: **2.1s → sub-millisecond.**
+
+### 4.3 Adaptive-RAG — a weak router still beats no router
+
+**[M] Adaptive-RAG** (arXiv 2403.14403, **NAACL 2024**), FLAN-T5-XL (3B):
+
+| Strategy | steps/query | s/query | avg F1 |
+|---|---|---|---|
+| Single-step | 1.00 | 1.00 | 44.31 |
+| Multi-step | 4.69 | 8.81 | 48.85 |
+| **Adaptive-RAG** | **2.17** | **3.60** | **46.94** |
+| Oracle router | 1.28 | 2.11 | 56.28 |
+
+Two findings that matter most:
+
+- **The classifier is only 54.52% accurate overall** — and routing *still* wins on the efficiency/accuracy
+  frontier. **A weak router beats no router.**
+- **Classifier size barely matters:** T5-Small (60M) 45.83 F1 → Base (223M) 45.97 → Large (770M) 46.94.
+  **~1 F1 point for a 13× parameter increase.** This is direct, peer-reviewed evidence against spending
+  2.1s of frontier-LLM time on the decision.
+
+### 4.4 Small classifiers vs LLMs, generally
+
+- **[M] SetFit** (arXiv 2209.11055, Sep 2022): with **8 labelled examples per class**, comparable to T-Few
+  3B while **27× smaller**; on RAFT, SetFit-RoBERTa-Large (355M) scores **71.3% vs GPT-3 (175B) zero-shot
+  at 62.7%**. Trains in **30 seconds / $0.025** on a V100.
+- **[M] Bucher & Martini** (arXiv 2406.08660, Jun 2024): fine-tuned small models beat zero-shot
+  GPT-3.5/GPT-4/Claude Opus **in all cases** across four classification tasks.
+- **[M] arXiv 2411.05050**: BERT fine-tuned on **200 samples = 71.1%**, already above **GPT zero-shot at
+  70.2%**; GPT few-shot only catches BERT at ~1,000 samples.
+- **[?] Switchcraft** (arXiv 2605.07112, May 2026, Microsoft): DistilBERT router adds **3–17ms**, 722
+  queries/s peak; **82.9% accuracy, 84% inference cost reduction.**
+
+### 4.5 RouteLLM and the honest planning number
+
+**[M] RouteLLM** (Ong et al., arXiv 2406.18665, ICLR 2025; https://lmsys.org/blog/2024-07-01-routellm):
+at **95% of GPT-4 quality retained** — **>85% cost reduction on MT-Bench, 45% on MMLU, 35% on GSM8K.**
+Best router = **matrix factorization** (95% of GPT-4 quality with only **26% of calls to GPT-4**, 14%
+with LLM-judge augmentation). Beat commercial routers Martian and Unify AI with **>40% additional
+savings**; transferred to a new model pair without retraining.
+
+**Two caveats.** (a) The paper reports **cost, not routing latency**; of their four router types only the
+BERT-classifier and matrix-factorization variants are in our latency class — **the causal-LLM-classifier
+router is itself an LLM call and would not help us.** (b) **Note the spread: the easier the benchmark, the
+bigger the saving.** ICAI statutory QA is far closer to MMLU than to MT-Bench — **45% is our honest
+planning number, not 85%.**
+
+### 4.6 semantic-router (Aurelio AI) — verified, with two warnings
+
+- **[V] The "4ms vs 53,860ms" claim on https://www.aurelio.ai/semantic-router is methodologically broken**
+  — their own footnote reveals the "Claude Opus" number is a 3.86s *time-to-first-token* figure inflated
+  to 53,860ms. **No independent latency benchmark of the library exists.**
+- **[M] The local-vs-API encoder split is the real story.** all-MiniLM-L6-v2 (22M) is single-digit-to-low-tens
+  of ms on CPU. But Milvus benchmarked 20+ embedding APIs (2025-05-23,
+  https://milvus.io/blog/we-benchmarked-20-embedding-apis-with-milvus-7-insights-that-will-surprise-you.md)
+  and found API embedding calls take *"hundreds of milliseconds to several seconds"*, with **3–4× inflation**
+  calling NA endpoints from Asia — directly relevant to us. A self-hosted bge-base on **4 CPU cores matched
+  the fastest API provider. Use a local encoder or you give back most of the win.**
+- **⚠️ Security [?]:** versions 0.1.8–0.1.14 are reported yanked on PyPI for CVE-2026-42208 (unbounded
+  `litellm` pin resolving to a compromised wheel). **Pin >=0.1.15** if adopted. I did not verify this on
+  PyPI directly.
+
+### 4.7 The option nobody in our team has considered: delete the router
+
+**[?]** Hybrid BM25 + dense with Reciprocal Rank Fusion is repeatedly claimed to beat either mode alone
+(e.g. 91% recall@10 vs 65–78%), with the argument that *users send both query types in the same session,
+so there is no reliable way to classify which approach each query needs ahead of time* — and RRF fusion
+costs ~6ms. **The specific numbers come from marketing blogs and are unverified. But the architectural
+argument is the strongest case against our router existing at all**, and it is the pattern Dropbox Dash
+and DoorDash both describe. Running both retrieval modes and fusing removes 2.1s *and* removes a
+correctness failure mode (router picks wrong mode → wrong evidence → wrong rate).
+
+---
+
+## 5. Model choice for synthesis
+
+### 5.1 Groundedness and parametric recall are different capabilities — and only one collapses when you shrink
+
+This is the central finding, and the measurement is clean.
+
+**[M] FACTS Benchmark Suite**, Google DeepMind, blog 2025-12-09, paper arXiv 2512.10791:
+
+| Model | Overall | **Grounding** | **Parametric** |
+|---|---|---|---|
+| Gemini 3 Pro | 68.8 | 69.0 | 76.4 |
+| Gemini 2.5 Pro | 62.1 | **74.2** | 63.2 |
+| GPT-5 | 61.8 | 69.6 | 55.8 |
+| **Gemini 2.5 Flash** | 50.4 | **69.9** | **30.7** |
+| GPT-5 mini | 45.9 | 58.3 | 16.0 |
+| GPT o3 | 52.0 | **36.2** | 57.1 |
+
+**Gemini 2.5 Flash beats Gemini 3 Pro and GPT-5 on Grounding while scoring less than half of Gemini 2.5
+Pro on Parametric.** The capability that collapses when you shrink is *recall from weights*; the
+capability that survives is *using facts placed in front of it*. And o3 — a large reasoning model —
+scores 36.2 on grounding: **reasoning and groundedness are distinct and can be anti-correlated.** All 15
+models scored below 70% overall; nobody has solved this.
+
+Same pattern a generation earlier — **[M] FACTS Grounding v1** (arXiv 2501.03200, 2025-01-06): Gemini 1.5
+**Flash 82.9%** vs Gemini 1.5 **Pro 80.0%**, Claude 3.5 Sonnet 79.4%, GPT-4o 78.8%.
+
+**[M] Vectara HHEM leaderboard** (https://github.com/vectara/hallucination-leaderboard, README last
+updated **2026-05-11**, judge HHEM-2.3, 7,700+ articles, temp 0, 118 models). Hallucination rate, lower
+better:
+
+- **OpenAI:** gpt-5.4-nano **3.1%** < gpt-5.4-mini 5.5% < gpt-5.4 **7.0%** < gpt-5.4-pro 8.3%.
+- **Google:** gemini-2.5-flash-lite **3.3%** < gemini-2.5-pro 7.0% < gemini-2.5-flash 7.8%.
+- **Anthropic:** claude-haiku-4-5 **9.8%** — **best in the Claude family**, beating sonnet-4-6 (10.6%)
+  and opus-4-5 (10.9%).
+- **Qwen3:** 4B 5.7% / 8B 4.8% / 14B 5.4% / 32B 5.9% — **flat across an 8× parameter range**.
+- Counterexamples: Phi-4-mini 23.5%, ministral-3-3b 24.2%. **"Small" is not automatically safe.**
+
+**[M] Caveat, quoted from the README:** it measures *summarization* consistency, not QA, and the authors
+concede *"an extractive summarizer model that just copies and pastes… score[s] 100%."* **[I]** Part of
+the small-model advantage is stylistic — terse copy-heavy outputs have less surface to be unfaithful on.
+For our use case that bias is *aligned* with what we want, but the board overstates gap-closure generally.
+
+### 5.2 The countervailing evidence — where small models genuinely fail
+
+- **[M] "Sufficient Context: A New Lens on RAG"** (Joren et al., Google, arXiv 2411.06037, **ICLR 2025**)
+  — the most important negative finding: strong models do well with sufficient context but *"often output
+  incorrect answers instead of abstaining when the context is not"* sufficient — while **small models
+  (Mistral 3, Gemma 2) hallucinate or abstain even when context IS sufficient.** Their selective-generation
+  method improves correct-among-answered by **2–10%**.
+- **[?] "Can Small Language Models Use What They Retrieve?"** (arXiv 2603.11513, 2026-03-12): oracle
+  retrieval, EM% on questions unanswerable from weights — SmolLM2-360M **0.0**, Qwen2.5-1.5B 10.0, 3B 12.8,
+  7B 14.6; 61–100% of oracle failures are "irrelevant generation"; concludes robust context use *"may
+  require models larger than 7B."* **Single-author unreviewed preprint, 4-bit quantized, strict EM, and
+  absolute numbers far below what published RAG work reports with gold passages. Direction only.**
+- **[M] TAT-LLM** (arXiv 2401.13223) error analysis: **48% of total errors are "wrong evidence"** — grabbing
+  the wrong cell/number, not doing the wrong arithmetic. **And it degrades badly when the figure lives in a
+  table rather than a sentence.** ICAI study material is full of rate tables. This is our real risk.
+- **[?] TaxPraBen** (arXiv 2604.08948, 2026-04-22), 19 LLMs / 7,300 instances of Chinese tax practice:
+  closed-source large models top-3 consistently; **open-source 7B models significantly underperform**.
+  **Important scope note: no RAG condition — this is closed-book**, measuring parametric tax knowledge,
+  the axis where small models are known to lose. **It does not refute the grounded-extraction case.**
+
+**The line to hold:** *copying a rate out of a cited span* (extraction — small models near parity) is a
+different task from *computing/comparing/deriving a rate* (derivation — even frontier models fail at rates
+we cannot ship). Route derivation to a deterministic calculator or a large model.
+
+### 5.3 Current pricing and speed (verified from official pages, 2026-08-11)
+
+**[M] Anthropic** (https://platform.claude.com/docs/en/about-claude/pricing):
+
+| Model | API ID | In $/MTok | Out $/MTok | Cache read | AA (non-reasoning, 10k in) |
+|---|---|---|---|---|---|
+| Claude Opus 5 | `claude-opus-5` | 5 | 25 | 0.50 | — |
+| **Claude Sonnet 5** | `claude-sonnet-5` | **2** *(intro → 3 on 2026-09-01)* | **10** *(→ 15)* | 0.20 | **TTFT 1.91s, 62.6 tok/s** |
+| Claude Sonnet 4.6 *(ours)* | `claude-sonnet-4-6` | 3 | 15 | 0.30 | **TTFT 1.50s, 42.8 tok/s** |
+| **Claude Haiku 4.5** | `claude-haiku-4-5-20251001` | **1** | **5** | 0.10 | **TTFT 0.97s, 88.7 tok/s** |
+
+Haiku 4.5: 200k context (not 1M), 64k max output, **min cacheable prefix 4,096 tokens**, no adaptive
+thinking. Anthropic's own reduce-latency page names Haiku 4.5 as the speed pick.
+
+**[M] Google** (https://ai.google.dev/gemini-api/docs/pricing, page updated 2026-08-11):
+`gemini-3.6-flash` $1.50/$7.50 (235.4 tok/s); `gemini-3.5-flash-lite` $0.30/$2.50 (385.7 tok/s);
+`gemini-2.5-flash` $0.30/$2.50; `gemini-2.5-flash-lite` $0.10/$0.40 (**fastest TTFT on the AA
+leaderboard: 0.31s**).
+
+**⚠️ [M] Artificial Analysis caveat:** for reasoning models AA's "time to first token" measures the
+**first reasoning token**, assuming 2,000 reasoning tokens where counts aren't available. That is why
+Gemini 3.5 Flash-Lite shows 11.14s TTFT — that is a thinking-on configuration, not what we'd run.
+
+**[M] Provider spread on identical weights** — Sonnet 4.6, 10k input, 2026-08-11
+(https://artificialanalysis.ai/models/claude-sonnet-4-6/providers):
+
+| Provider | TTFT | Output tok/s |
+|---|---|---|
+| Google Vertex | **1.14s** | 44.2 |
+| Amazon Bedrock | 1.33s | **46.0** |
+| Anthropic direct | 1.50s | 42.8 |
+| Azure/Foundry | 1.93s | 42.6 |
+
+Blended price identical ($2.31/1M) across all four. **[I] ~1s off our wall clock from a routing change
+alone, at zero accuracy risk.**
+
+### 5.4 ⚠️ The documented quick win Anthropic explicitly warns about
+
+**[M]** https://platform.claude.com/docs/en/build-with-claude/effort, verbatim: *"Sonnet 4.6 defaults to
+`high` effort. **Explicitly set effort when using Sonnet 4.6 to avoid unexpected latency.**"* Recommended
+default for 4.6 is **`medium`**; **`low`** is recommended for *"high-volume or latency-sensitive
+workloads… chat and non-coding use cases."* It lives in `output_config.effort`, no beta header, and
+scales *all* output tokens.
+
+**[I]** At ~85–90% decode-bound (§2.9), a 30–40% output-token cut is worth roughly **3–4 seconds**.
+Caveat: changing `effort` between requests invalidates prompt caching (it renders into the prompt).
+
+**[M] Thinking check:** on **Sonnet 4.6, thinking is OFF** until you set `thinking: {type: "adaptive"}` —
+so we are probably clean. But on **Opus 5 / Sonnet 5 / Fable 5, thinking is ON by default with
+`display: "omitted"`** — invisible, billed as output, counts toward `max_tokens`. **This is the trap if
+we upgrade to Sonnet 5.** For scale: **[M]** AA measures Claude Sonnet 5 with adaptive reasoning at max
+effort at **TTFT 182.29s**, vs 1.91s non-reasoning. Same model, two orders of magnitude.
+
+### 5.5 Making a fast model safe for exact figures
+
+**Anthropic Citations API** (https://platform.claude.com/docs/en/build-with-claude/citations, launched
+2025-01-23) is the only mechanism here with a real guarantee. **[M]**, verbatim: *"Because the API parses
+citations into the response formats described in the following sections and extracts `cited_text`
+directly, citations are **guaranteed to contain valid pointers to the provided documents**."* The API
+resolves the pointer and slices text out of our document **server-side** — `cited_text` is a byte-for-byte
+slice of our input, not model-generated prose.
+
+**What it does NOT guarantee: that the claim in the accompanying text is entailed by the cited span.
+Claude can attach a valid citation to a sentence whose number it got wrong.** (Formalised in **[M]**
+"Correctness is not Faithfulness in RAG Attributions", arXiv 2412.18004.) **Citations gives auditability,
+not correctness.**
+
+Mechanics: plain text → sentence-level `char_location`; PDF → `page_location`; `search_result` blocks →
+`search_result_location`. **For numeric span verification we want plain-text-per-chunk (char offsets).**
+Streaming works (`citations_delta`). Cost: document text billed as normal input; **`cited_text` does NOT
+count toward output tokens** — so quote-heavy answers are *cheaper* via Citations than via prompt-based
+"quote your source." No latency figures published.
+
+**Three hard constraints that shape architecture. [M]** (a) **Citations + Structured Outputs returns a
+400** — incompatible "because citations require interleaving citation blocks with text output." We cannot
+have guaranteed-real spans and a guaranteed JSON schema in one call; do free-text cited generation first,
+extract structure second. (b) **Text only — no image citations.** Scanned PDFs without an extractable text
+layer silently fail to be citable. (c) **[?] Whether OpenRouter passes Citations through is undocumented
+and unconfirmed. Test this before it locks in our architecture** — if it doesn't, that decides
+direct-vs-gateway for us.
+
+**[V]** Anthropic's marketing claims for Citations — "up to 15% recall accuracy increase", customer Endex
+"source hallucinations from 10% to 0%" — have **no published methodology. Testimonials, not evidence.**
+
+**The competition:**
+
+- **[M] Vertex AI Check Grounding**
+  (https://docs.cloud.google.com/generative-ai-app-builder/docs/check-grounding) — a standalone verifier
+  and the most useful Google piece here. Input: answer ≤4,096 tokens + ≤200 facts ≤10,000 chars each.
+  Output: **support score 0–1**, cited chunks, **sentence-level claims** with citation indices and
+  per-claim support scores, and a `groundingCheckRequired` flag so boilerplate isn't penalised.
+  **Stated latency target under 500ms.** Semantics: *"Perfect grounding requires that every claim… be
+  wholly entailed by the facts"* and **partially-true claims count as ungrounded** — exactly the right
+  strictness for "12% vs 12.5% GST."
+- **[M] OpenAI file search** `file_citation` annotations give `file_id`, `filename`, and an `index` that
+  is a position **in the output text, not a span in the source. No span guarantee** — materially weaker.
+  **And a `source_quote` field in a Structured Outputs schema is model-generated text: a hallucination
+  surface, not a safeguard. This is the trap to avoid.**
+
+**Prompting techniques with measured numbers:**
+
+- **[M] Chain-of-Verification (CoVe)** (Meta AI, arXiv 2309.11495, Findings of ACL 2024): **FACTSCORE
+  55.9 → 71.4 (+28% relative)**. **The critical mechanism: verification questions are answered
+  *independently, without conditioning on the draft*** — that is what breaks the self-confirmation loop.
+- **[M] "Attribute First, then Generate"** (ACL 2024, aclanthology.org/2024.acl-long.182): select content
+  → plan → generate, so the selected source segments *are* the attribution. More concise citations,
+  quality maintained, **significantly reduced human verification time.**
+- **[M] Anthropic's reduce-hallucinations guidance**: allow "I don't have enough information"; for >20k-token
+  documents, **extract word-for-word quotes first, then answer from only those quotes**; post-hoc, *"for
+  each claim find a direct quote… if you can't find a supporting quote, remove that claim."*
+
+**Constrained decoding — the literature genuinely splits.** Against: **[M]** "Let Me Speak Freely?"
+(EMNLP 2024 Industry) — significant reasoning decline under format restriction. For: **[M]**
+JSONSchemaBench (arXiv 2501.10868, Jan 2025), 10K real schemas / 6 frameworks — *"constrained decoding
+consistently improves… downstream tasks up to 4%."* **[I] Net read: neutral-to-slightly-positive on
+frontier models, plausibly negative on small ones, and dominated by whether the model gets to think in
+free text before the constrained emission.** Latency overhead itself is negligible **[M]** (XGrammar
+<40µs/token, default in vLLM/SGLang/TensorRT-LLM as of March 2026) — but **first-request-per-schema
+grammar compilation adds real latency, so keep schemas stable.**
+
+### 5.6 ⚠️ Cheap NLI verifiers exist, but cannot be trusted as gates
+
+| Verifier | Size | Accuracy | Cost/latency |
+|---|---|---|---|
+| **HHEM-2.1-Open** (Vectara) | 110M | ~71.8–76.6% | **<600MB RAM fp32; ~1.5s for 2k tokens on x86 CPU** — no GPU |
+| **AlignScore-large** | 355M | matches/beats GPT-4-based metrics | GPU-cheap |
+| **MiniCheck-FT5** | 770M | GPT-4-level on LLM-AggreFact | **400× lower cost than GPT-4** |
+| **Bespoke-MiniCheck-7B** | 7B | **77.4%** avg, SOTA on LLM-AggreFact | ~200ms/GPU |
+
+**[M] Tamber et al., EMNLP 2025 Industry** (aclanthology.org/2025.emnlp-industry.54): zero-shot LLM judges
+stay *"below 78% balanced accuracy and F1-macro below 72%"*; on adversarial FaithBench the best is
+**68.8%**; prior work found LLM classifiers near **50% — "negligible ability to identify hallucinated
+responses."** **~77% balanced accuracy means roughly 1 in 4 unsupported claims slips through.**
+
+**[M] FaithJudge** (same paper) is the honest fix: put nine human-annotated peer responses to the *same
+source* in the judge prompt → **84.0% acc / 82.1 F1 on FaithBench**. Judge sensitivity is bought with
+annotated examples, not with a bigger judge — but that costs per-source human annotation.
+
+**[M] On Ragas:** it self-reports 0.95 human agreement on faithfulness (authors' own WikiEval). But FACTS
+v1 measured **+3.23% self-preference bias**, and metric-human correlations are **inflated when the model
+pool spans very strong and very weak systems.** **[I] A Ragas faithfulness of 0.95 has a per-item error
+rate plausibly in the 20–30% range against human labels. It cannot certify a tax rate. Use it as a
+regression signal across releases, not a release gate.**
+
+### 5.7 Cascades — and the honest number
+
+- **[M] FrugalGPT** (arXiv 2305.05176, TMLR): matches GPT-4 with **up to 98% cost reduction**. **⚠️
+  Per-dataset best case on 2023 benchmarks with a GPT-3.5→GPT-4 price gap that no longer exists. Do not
+  budget against 98%.**
+- **[M] "Cost-Saving LLM Cascades with Early Abstention"** (arXiv 2502.09054, 2025-02-13): letting the
+  *small* model abstain early rather than deferring everything gives, averaged across
+  GSM8K/MedMCQA/MMLU/TriviaQA/TruthfulQA/XSum: **−13.0% cost, −5.0% error rate, −2.2% test loss, +4.1%
+  abstention.** Explicitly framed for **risk-sensitive domains (finance, medicine)** — our case. Small
+  effects, right shape.
+- **Products:** OpenRouter's Auto Router is powered by Not Diamond, `cost_quality_tradeoff` 0–10, no
+  surcharge. **I found no independent measured benchmark for any commercial router** — every circulating
+  number traces back to RouteLLM/FrugalGPT. 2025–26 blog posts claiming "40–60% cost-per-task reduction"
+  are content marketing with no methodology. **Do not cite them.**
+
+### 5.8 OpenRouter overhead
+
+**[M] Opper.ai, "LLM Router Latency Benchmark 2026"** (2026-04-21,
+https://opper.ai/blog/llm-router-latency-benchmark-2026), 200 calls/provider on GPT-4.1, 95% CIs:
+
+| | TTFT | tok/s |
+|---|---|---|
+| OpenAI direct | 0.712s | 81.8 |
+| **OpenRouter** | **0.640s** | **73.2** |
+
+OpenRouter was **70ms faster to first token but ~10% slower on throughput**. **[I]** For a decode-bound
+workload that throughput haircut is ~1.1s of our 13.7s — *if* it generalises from OpenAI to Anthropic
+backends, which is unproven. The authors warn results are non-transferable across region/config/time-of-day.
+
+**[V]** OpenRouter's own Latency and Performance doc contains **no ms figures**. It does note cold edge
+caches add latency in a new region for 1–2 min, and that low credit balances trigger aggressive cache
+expiry (keep ≥$10–20).
+
+---
+
+## 6. What we are missing
+
+Specific, and ordered by how badly a naive engineer would miss it.
+
+1. **We are optimising the wrong half of synthesis.** §2.9: ~85–90% of the 13.7s is *decode*, not prefill.
+   Cutting the 12k-token prompt is a **cost** fix. The **speed** fix is fewer output tokens (`effort`,
+   brevity prompting) and a faster-decoding model. **Log `output_tokens` today** — the whole conclusion
+   rests on an inferred 480.
+
+2. **Streaming.** We have none. **[I]** It doesn't reduce generation time; it moves user-visible start
+   from 25s to ~1.5s. Combined with §3.4, this is likely worth more perceived-quality points than any
+   two other items here. Compatible with citation verification: stream prose, hold citation markers back,
+   verify async, patch markers in. **[M]** ZeroEntropy report post-hoc span-pointer extraction at
+   ~sub-100ms per claim, ~400ms for a 4-sentence answer, parallelisable. **[M]** CiteFix (arXiv 2504.15629)
+   is the academic version — citation correction as post-processing.
+
+3. **A deterministic numeric verifier — the only non-probabilistic control available to us.**
+   **[M] Proof-Carrying Numbers (PCN)**, Solatorio (World Bank), arXiv 2509.06902, **2025-09-08** — opens
+   with literally our risk ("reporting 6.0% instead of 5.7%"). Design: numbers emitted as **claim-bound
+   tokens** tied to a structured record; a **verifier in the renderer/presentation layer — deliberately
+   not in the model, so the model cannot spoof a checkmark** — checks each number against a declared policy
+   (exact equality / rounding / aliases / tolerance-with-qualifier); **fail-closed by default**, only
+   claim-verified numbers get a verification mark. Formal proofs of soundness, completeness under honest
+   tokens, fail-closed behaviour. **⚠️ Protocol paper with formal guarantees and no empirical benchmark**
+   — but the architecture transplants directly.
+   **[M]** Supporting: "Real-Time Detection of Hallucinated Entities in Long-Form Generation" (arXiv
+   2509.03531) — **entities (dates, numbers, names, citations) have clean token boundaries and can be
+   verified token-by-token as they stream**, whereas free-form claims need post-hoc extraction that
+   destroys token alignment. This is what makes streaming compatible with verification.
+   **The gap:** I found **no published paper measuring numeric-error reduction from string-matching every
+   number in an answer against retrieved context.** It is well-known folklore, formalised only by PCN,
+   never benchmarked. **[I] It is nonetheless the highest-value control on this list: deterministic, O(1)
+   cost, 100% recall on verbatim mismatch.**
+
+4. **Tables are our real failure mode, not free-form hallucination.** **[M]** TAT-LLM: **48% of errors are
+   wrong-evidence selection**, and degradation is worst when the figure lives in a table. **[M]** FACTS
+   Suite parametric axis (Flash 30.7 vs Pro 63.2) says the other failure is **silently answering from stale
+   weights.** Both are pipeline problems; **neither is fixed by model choice.** An ICAI rate table chunked
+   badly is a correctness bug that no reranker or model upgrade will find.
+
+5. **Batch APIs: confirmed useless for interactive, legitimate for eval. [M]** Anthropic Message Batches:
+   50% discount, "most batches finish in less than 1 hour," results at completion **or 24 hours, whichever
+   first**. Same shape at OpenAI and Google. **But use them for offline eval runs and for pre-generating a
+   known FAQ head** — for an exam-revision product, the head of the query distribution is probably fat.
+
+6. **Speculative decoding is not a knob we control. [M]** Providers including Anthropic run draft-model
+   speculation at the infrastructure level, transparent to API callers. OpenAI's Predicted Outputs is the
+   only exposed variant and only helps with a near-correct draft — irrelevant for open-ended synthesis.
+
+7. **Anthropic Fast mode exists but not for our tier — track it. [M]**
+   https://platform.claude.com/docs/en/build-with-claude/fast-mode: `speed: "fast"`, **up to 2.5× output
+   tok/s, same weights, same quality** — exactly what a decode-bound pipeline wants. **Opus 5 / Opus 4.8
+   only**, research preview, Claude API only, $10/$50 per MTok, explicitly "focused on OTPS, not TTFT."
+   Not on any Sonnet yet.
+
+8. **`max_tokens` is a cap, not a reservation** — no direct latency cost. Real output-length control is
+   brevity prompting plus `effort`.
+
+---
+
+## 7. Ranked recommendations
+
+Scored **L** = latency saved, **C** = cost saved, **R** = risk to answer correctness.
+**⚠️ = could produce a wrong statutory rate — disqualifying unless mitigated.**
+
+| # | Action | L | C | R | Notes |
+|---|---|---|---|---|---|
+| **1** | **Log `output_tokens` and set `output_config.effort` explicitly (`medium`, then trial `low`)** | **~3–4s** | ~15% | **None→Low** | Anthropic's docs literally warn about this for Sonnet 4.6. Free. But `low` effort on a *derivation* question could shorten reasoning — gate behind an eval. |
+| **2** | **Replace the LLM reranker with a dedicated cross-encoder** (Cohere Rerank 4 Fast / Voyage rerank-2.5-lite / Jina v3.5; or self-hosted MiniLM-class on CPU) | **~8s** | small | **None→Improves** | Best-evidenced item here. Voyage [M]: dedicated rerankers *beat* LLMs by 12–15% NDCG@10. Oracle [M]: 96×512tok = 0.54s. **Caveat: our 1,100-token chunks put us in the 0.5–1.0s band, not 150ms — measure.** |
+| **3** | **Turn on streaming** | **~24s perceived** | 0 | **None** | Doesn't reduce compute; moves visible start from 25s to ~1.5s. Nielsen + CHI 2026 + Buell&Norton all point the same way. Stream prose, patch citation markers in after async verification. |
+| **4** | **Replace the LLM intent router with TF-IDF+SVM, or delete it and fuse both modes with RRF** | **~2.1s** | ~5% | **None→Improves** | Adaptive-RAG [M]: a 60M router loses ~1 F1 vs 770M; a *weak* router still beats no router. Fusing removes a failure mode (wrong mode → wrong evidence → ⚠️ wrong rate). Prefer fusion if recall allows. |
+| **5** | **Build the deterministic numeric verifier (PCN shape)** | 0 (+~50ms) | 0 | **Large reduction** | Regex every rate/threshold/date out of the answer; string-match against `cited_text` spans; explicit rounding/alias policy; **fail-closed**. **The only deterministic control in this document.** Worth more than any model choice. |
+| **6** | **Pin the provider / route for throughput** (`sort: "throughput"`, or go direct to Vertex/Bedrock) | **~1s**, plus possibly much of the missing 2.35× on parallel calls | 0 | **None** | [M] Vertex 1.14s TTFT vs Anthropic-direct 1.50s at identical price. [M] OpenRouter defaults to **price-weighted** routing — we are currently optimised for price without meaning to be. |
+| **7** | **Gate parent-section expansion** (LlamaIndex auto-merge: promote to parent only when ≥2 of top-k children share it; otherwise send the child + Anthropic-style contextual preamble) | ~0.5s | **~40–50%** | **Low ⚠️** | Nobody recommends unconditional whole-section expansion. **But this is the item that can drop the sentence containing the rate.** Requires a regression suite checking exact-rate recall before it ships. |
+| **8** | **Adopt Anthropic Contextual Retrieval at index time** ($1.02/M doc tokens, one-off) | indirect | indirect | **Reduces ⚠️** | [M] 49% retrieval-failure reduction, 67% with reranking. Makes #7 safe by making small chunks self-sufficient. **Do #8 before #7.** Our corpus is ~1,150 chunks — this is a cheap one-time job. |
+| **9** | **Reduce rerank depth 50 → 25** | ~0 (after #2) | small | **Low ⚠️** | [M] Elastic: depth 32 gives 41% of nDCG uplift; [?] d=20 keeps ~85% of the rerank lift. **Only worth doing if we stay on an LLM reranker.** After #2 the latency argument evaporates — keep 50. |
+| **10** | **Prompt-cache the system prompt** | ~0 | ~11% of synthesis | **None** | [M] Min prefix 1,024 tok on Sonnet 4.6. Retrieved chunks are volatile and can never be cached. **Budget zero latency benefit.** Also: warm serially before any fan-out or all calls pay a write. |
+| **11** | **Evaluate Sonnet 5 at `medium` effort** (intro pricing ends 2026-09-01) | ~3s | **~33%** | **Low** | [M] $2/$10 intro vs $3/$15; 62.6 vs 42.8 tok/s. **⚠️ Trap: thinking is ON by default on Sonnet 5 with `display: "omitted"` — set `thinking: {type: "disabled"}` or latency explodes.** |
+| **12** | **Evaluate Haiku 4.5 for synthesis, with a hard numeric gate + abstention→escalate** | **~7s** | **~67%** | **Medium ⚠️** | [M] $1/$5, 88.7 tok/s, TTFT 0.97s, **9.8% HHEM — best in the Claude family**. [M] FACTS Suite says groundedness survives shrinking; parametric recall does not. **Only safe behind #5 and a table-heavy eval set.** Watch: 4,096-token min cache prefix. |
+| **13** | **Exact-match query caching** | up to 25s on hits | up to 100% on hits | **None** | Free. Exam-revision traffic likely has a fat head. |
+| **14** | **Add a `groundingCheckRequired`-style verifier pass** (Vertex Check Grounding, or MiniCheck-FT5 self-hosted) | **+0.5s** | small increase | **Reduces, but weakly** | [M] Vertex targets <500ms and treats partially-true claims as ungrounded — right strictness. **But [M] EMNLP 2025: ~77% balanced accuracy means ~1 in 4 unsupported claims slips through. A signal, not a gate.** |
+| — | **Semantic caching** | — | — | **⚠️ Disqualified** | [M] 15–25% RAG hit rates; [M] ICLR 2026 vCache: static thresholds have unbounded error. A near-miss cache hit in a cited product = confidently wrong rate with a real-looking citation. |
+| — | **`LLMChainExtractor` contextual compression** | **negative** | negative | **⚠️ Disqualified** | 8 extra LLM calls; failure mode is dropping the sentence with the number. LangChain's own docs call it "costly and slow." (`EmbeddingsFilter` is fine.) |
+| — | **LLMLingua prompt compression** | ~0.4s | ~50% | **⚠️ High** | [M] Real end-to-end wins (1.4–2.9×) — **but those are prefill wins and we are decode-bound (§2.9), so it buys us almost nothing on latency.** And bare figures are exactly the low-perplexity tokens compression discards. **Cost-only benefit at high correctness risk. Skip.** |
+| — | **ColBERT / late interaction** | — | — | **N/A** | Wrong tool: an indexing technique, not a post-hoc reranker over 50 candidates. Requires rebuilding ingest. |
+
+### Suggested sequencing
+
+**Week 1 (free, no correctness risk, ~15s off the clock):** #1, #3, #6, #13 — plus the two measurements
+that gate everything else: log `output_tokens`, and re-run the 4-way rerank threading test pinned to one
+provider.
+
+**Week 2 (~10s off the clock, still low risk):** #2 (cross-encoder rerank), #4 (kill the LLM router),
+#10.
+
+**Week 3+ (needs an eval set first):** #5 (numeric verifier), then #8 → #7, then #11/#12 behind #5.
+
+**Do not start #7, #11, or #12 without an exam-question regression suite that specifically checks exact
+statutory-rate recall, weighted toward figures that live in tables** — per TAT-LLM, wrong-evidence
+selection from tables is 48% of errors and it is our single most likely path to a wrong rate.
+
+**Realistic destination: ~25s → ~6–8s wall clock, ~1.5s to first token, at roughly a third of current
+cost, with better numeric safety than today.** The bulk of that comes from items #1–#4, none of which
+touch the correctness-critical path.
+
+---
+
+## 8. Confidence register — what I verified myself vs what I did not
+
+**Verified by direct fetch during this research:**
+- Voyage "The Case Against LLMs as Rerankers" — all NDCG, speed, cost and setup figures.
+- Elastic "Exploring depth in a retrieve-and-rerank pipeline" — pattern distribution, budget table,
+  90%-of-gain claim, top-30 recommendation, 2024-12-05 date.
+- arXiv 2601.14224 "Rerank Before You Reason" — title, authors (Sharifymoghaddam & Lin), dates, abstract.
+
+**Well-sourced but not personally re-verified** (fetched by research agents from primary URLs, dates and
+figures internally consistent): Anthropic Contextual Retrieval, Citations, prompt caching, effort and
+pricing docs; Databricks long-context study; Liu et al. TACL 2024; RULER; NoLiMa; LlamaIndex and LangChain
+docs; LLMLingua papers; Adaptive-RAG; RouteLLM; SetFit; FACTS Grounding v1 and FACTS Benchmark Suite;
+Vectara HHEM leaderboard; OpenAI latency and caching guides; OpenRouter provider-routing docs; Oracle
+Cohere benchmark; Jina/Elastic reranker figures; Nielsen; Buell & Norton.
+
+**Weak — cited but flagged [?], do not build on without re-checking:**
+- The 2026 arXiv preprints: RAGRouter-Bench (2604.03455), Tiny-Critic RAG (2603.00846), CHI 2026 latency
+  study (2604.06183), Switchcraft (2605.07112), "Can Small Language Models Use What They Retrieve?"
+  (2603.11513), TaxPraBen (2604.08948). Their *direction* is corroborated by older peer-reviewed work;
+  the precise figures are not confirmed.
+- The d=10/20/50 rerank-depth ablation figures from arXiv 2601.14224 (from HTML full text, not the abstract).
+- The Cuconasu "3–5 documents" recommendation and Llama2-7B figures (full-text fetch, not abstract).
+- Exact percentages in Liu et al. (two fetches disagreed; the ~20-point gap is robust, the digits are not).
+- LangChain `ParentDocumentRetriever` example chunk sizes (docs URL redirected).
+- Cohere rerank-3.5 deprecation date; semantic-router CVE-2026-42208.
+- Whether OpenRouter passes through the Anthropic Citations API — **undocumented, and it is a genuine
+  architectural fork. Test it.**
+
+**Marketing claims cited only as marketing [V]:** Jina's 150ms figure, Cohere's "optimized for low
+latency", Glean Waldo's 50%/25% numbers, Exa's p50s, Anthropic's Citations recall/hallucination
+testimonials, GPTCache hit rates, aurelio.ai's semantic-router benchmark (which is demonstrably
+methodologically broken).
+
+**Explicitly rejected as unsourced:** "300ms TTFT is where users stop noticing"; "800ms raises
+abandonment"; "LinkedIn's 500ms TTFT target"; "LLaMA2-7B + retrieval beats LLaMA2-70B closed-book";
+OpenRouter's "~50 concurrent request" cap; all 2025–26 blog claims of "40–60% cost-per-task reduction"
+from commercial routers.
diff --git a/wikify/tests/test_ask_cost.py b/wikify/tests/test_ask_cost.py
new file mode 100644
index 0000000..ec9b6fa
--- /dev/null
+++ b/wikify/tests/test_ask_cost.py
@@ -0,0 +1,158 @@
+# Copyright (c) 2026, BWH and contributors
+# For license information, please see license.txt
+
+"""What one ask costs, end to end.
+
+The regression this pins: the reported cost of an ask used to omit synthesis entirely —
+the leg that carries ~95% of the spend — because litellm reports a price only to a
+callback, on another thread, after the stream has ended, and the accumulator only read
+what was on the response. The answer therefore said $0.0033 while the logged turn said
+$0.0445 for the same question.
+
+The fakes here reproduce that shape exactly: the two OpenRouter REST legs report their
+cost inline, and the synthesis leg reports its cost the way litellm does — a pre-call on
+the answering thread, a price arriving later from somewhere else. A cost meter that only
+believes the response goes back to reporting the two cheap legs and fails these.
+"""
+
+import threading
+import time
+
+import frappe
+from frappe.tests.utils import FrappeTestCase
+
+from wikify.rag import history, usage
+
+# One ask's three legs, priced as OpenRouter prices them.
+ROUTE_USAGE = {"cost": 0.000021, "prompt_tokens": 900, "completion_tokens": 60}
+RERANK_USAGE = {"cost": 0.002064, "prompt_tokens": 7800, "completion_tokens": 40}
+SYNTHESIS_COST = 0.030516
+SYNTHESIS_TOKENS = {"prompt_tokens": 8187, "completion_tokens": 397}
+ASK_COST = ROUTE_USAGE["cost"] + RERANK_USAGE["cost"] + SYNTHESIS_COST
+
+
+class FakeUsage:
+	"""litellm's streamed usage object: tokens, and no price anywhere on it."""
+
+	def __init__(self, prompt_tokens: int, completion_tokens: int):
+		self.prompt_tokens = prompt_tokens
+		self.completion_tokens = completion_tokens
+
+
+def synthesise(call_id: str, cost: float | None = SYNTHESIS_COST, *, delay: float = 0.05) -> None:
+	"""One synthesis leg, reported the way litellm reports one.
+
+	`log_pre_api_call` runs inline on the answering thread; the price lands afterwards on
+	one of litellm's own threads, which is why the two are correlated by call id rather
+	than by thread.
+	"""
+	spend = usage.LitellmSpend()
+	spend.log_pre_api_call("openrouter/anthropic/claude-sonnet-4.6", [], {"litellm_call_id": call_id})
+	priced = threading.Thread(
+		target=price_later, args=(spend, call_id, cost, delay), name=f"litellm-{call_id}"
+	)
+	priced.start()
+	usage.add(FakeUsage(**SYNTHESIS_TOKENS))
+	priced.join()
+
+
+def price_later(spend, call_id: str, cost: float | None, delay: float) -> None:
+	time.sleep(delay)
+	if cost is None:
+		spend.log_failure_event({"litellm_call_id": call_id}, None, None, None)
+	else:
+		spend.log_success_event({"litellm_call_id": call_id, "response_cost": cost}, None, None, None)
+
+
+def ask_with_known_costs(call_id: str = "call-1") -> dict:
+	"""Run the three legs of one ask against fake responses and return what it reported."""
+	with usage.collect() as spend:
+		usage.add(ROUTE_USAGE)
+		usage.add(RERANK_USAGE)
+		synthesise(call_id)
+		return dict(spend)
+
+
+class TestAskCost(FrappeTestCase):
+	def test_an_ask_costs_the_sum_of_the_calls_it_made(self):
+		spend = ask_with_known_costs()
+
+		self.assertAlmostEqual(spend["cost"], ASK_COST)
+		self.assertEqual(
+			spend["prompt_tokens"],
+			ROUTE_USAGE["prompt_tokens"] + RERANK_USAGE["prompt_tokens"] + SYNTHESIS_TOKENS["prompt_tokens"],
+		)
+
+	def test_a_repeated_price_is_billed_once(self):
+		"""litellm can report the same streamed completion more than once."""
+		spend_logger = usage.LitellmSpend()
+		with usage.collect() as spend:
+			spend_logger.log_pre_api_call("model", [], {"litellm_call_id": "call-repeat"})
+			for _report in range(3):
+				spend_logger.log_success_event(
+					{"litellm_call_id": "call-repeat", "response_cost": SYNTHESIS_COST}, None, None, None
+				)
+			total = dict(spend)
+
+		self.assertAlmostEqual(total["cost"], SYNTHESIS_COST)
+
+	def test_a_failed_synthesis_costs_nothing_and_does_not_hang(self):
+		started = time.monotonic()
+		with usage.collect() as spend:
+			synthesise("call-failed", cost=None)
+			total = dict(spend)
+
+		self.assertEqual(total["cost"], 0.0)
+		self.assertLess(time.monotonic() - started, usage.SPEND_GRACE_SECONDS)
+
+	def test_one_question_is_never_billed_for_another(self):
+		"""The web worker answers on threads, and litellm prices on threads of its own."""
+		reported: dict[str, float] = {}
+
+		def answer(name: str, call_id: str, cost: float):
+			with usage.collect() as spend:
+				synthesise(call_id, cost)
+				reported[name] = spend["cost"]
+
+		threads = [
+			threading.Thread(target=answer, args=("first", "call-first", 0.5)),
+			threading.Thread(target=answer, args=("second", "call-second", 0.25)),
+		]
+		for thread in threads:
+			thread.start()
+		for thread in threads:
+			thread.join()
+
+		self.assertEqual(reported, {"first": 0.5, "second": 0.25})
+
+	def test_a_completion_outside_an_ask_is_billed_to_nobody(self):
+		"""The agent loop shares the process and the callback, but not the meter."""
+		spend_logger = usage.LitellmSpend()
+		spend_logger.log_pre_api_call("model", [], {"litellm_call_id": "call-agent"})
+		spend_logger.log_success_event(
+			{"litellm_call_id": "call-agent", "response_cost": 1.0}, None, None, None
+		)
+
+		with usage.collect() as spend:
+			self.assertEqual(spend, usage.empty())
+
+
+class TestAskCostReachesTheLog(FrappeTestCase):
+	def test_the_session_total_is_the_sum_of_its_turns(self):
+		spend = ask_with_known_costs()
+		result = {
+			"answer": "Health and education cess is 4% [1].",
+			"citations": [],
+			"route": {"intent": "semantic", "query": "health and education cess"},
+			"refused": False,
+			**spend,
+		}
+
+		session = history.record_turn(None, "What is the health and education cess?", result)
+		history.record_turn(session, "How is it computed?", result)
+
+		messages = history.get_session(session)["messages"]
+		conversation = frappe.get_doc("Wikify Ask Session", session)
+		self.assertAlmostEqual(messages[1]["cost"], ASK_COST, places=6)
+		self.assertAlmostEqual(conversation.total_cost, sum(row["cost"] for row in messages))
+		self.assertAlmostEqual(conversation.total_cost, ASK_COST * 2, places=6)