Fix dependency naming and Query Embedding Example

ai/generative-ai-service/a-getting-started-guide/files/N8N-GenAIOCI-Connection.md

@@ -23,8 +23,9 @@ From the repo root:
    ```
    For production (Linux only, with Gunicorn for scaling):
    ```bash
-   gunicorn app:app --workers 16 --worker-class uvicorn.workers.UvicornWorker --timeout 600 --bind 0.0.0.0:8088
+    gunicorn app:app --workers 16 --worker-class uvicorn.workers.UvicornWorker --timeout 600 --bind 0.0.0.0:8088
    ```
+   - **Note**: For Windows, Gunicorn is not natively supported; use Uvicorn directly with a process manager like PM2 (via Node.js) or NSSM for production scaling.
 
 ### Option 2: Run with Podman (Containerized Deployment)
 
@@ -36,9 +37,9 @@ From the repo root:
 3. Build and run:
    ```bash
    podman build -t oci_genai_gateway .
-   podman run -p 8088:8088 \
-     -v ~/.oci:/root/.oci:Z \
-     -it --name oci_genai_gateway oci_genai_gateway
+    podman run -p 8088:8088 \
+    -v ~/.oci:/root/.oci:Z \  # On Linux/macOS; on Windows, use an absolute path like -v C:/Users/<username>/.oci:/root/.oci:Z (ensure the directory is shared/mounted in Podman machine/VM)
+    -it --name oci_genai_gateway oci_genai_gateway
    ```
 4. Verify: Open `http://localhost:8088` in a browser (should show a health check or API docs). Check logs with `podman logs oci_genai_gateway`.
 
@@ -51,7 +52,7 @@ In n8n, use the **OpenAI** node but point it to your local gateway as a custom e
 2. Add an **OpenAI** node (under AI > Chat Models).
 3. Configure credentials:
    * **API Key:** Leave blank or use a dummy value (gateway uses OCI auth).
-   * **Base URL:** `http://host.docker.internal:8088/v1` (for Podman/Docker; use `http://localhost:8088/v1` if running natively).
+   * **Base URL**: `http://host.containers.internal:8088/v1` (preferred for Podman on Windows/macOS; fallback to `http://localhost:8088/v1` if running natively or use the gateway's explicit IP with `--add-host host.containers.internal:<gateway-ip>` in Podman run command).
    * **Model:** Select or enter an OCI model (list available models via gateway docs or OCI Console).
 n8n will now route requests through the gateway to OCI GenAI.
 
@@ -126,4 +127,4 @@ This example creates a workflow that triggers on a webhook (e.g., incoming email
 ## Troubleshooting & Tips
 * **Authentication Errors:** Verify `~/.oci/config` permissions and OCI policies (from prerequisites). Test with `oci` CLI commands like `oci os ns get`.
 * **Connection Issues:** Ensure port 8088 is open (firewall/OCI security lists). For Podman, use `--network=host` if needed.
-* **Model Not Found:** List OCI models in Console under **Analytics & AI > Generative AI**. Ensure your tenancy has the required permissions for `generative-ai-family`.
+* **Model Not Found:** List OCI models in Console under **Analytics & AI > Generative AI**. Ensure your tenancy has the required permissions for `generative-ai-family`.

ai/generative-ai-service/a-getting-started-guide/files/OracleDB-GenAIOCI-Connection.md

@@ -10,7 +10,7 @@
 3. **[AI-enable](https://docs.oracle.com/en/database/oracle/oracle-database/26/aienb/ai-enablement-guide.pdf) the 26ai database schema.**
 4. Install packages:
    ```bash
-   pip install oci python-oracledb
+   pip install oci oracledb
    ```
 
 ## Working Example
@@ -45,22 +45,17 @@ This is the “query vector” used for vector search in the DB.
 
 ```python
 import oci
-from oci.generative_ai_inference.models import EmbedTextDetails, OnDemandServingMode, CohereEmbedTextRequest
-
+from oci.generative_ai_inference.models import EmbedTextDetails, OnDemandServingMode
 def embed_query(genai_client, question: str) -> list[float]:
     embed_details = EmbedTextDetails(
         compartment_id=os.environ["OCI_COMPARTMENT_ID"],
         serving_mode=OnDemandServingMode(model_id=os.environ["EMBED_MODEL_ID"]),
-        embed_text_request=CohereEmbedTextRequest(
-            texts=[question],
-            # Common Cohere v3 pattern:
-            input_type="SEARCH_QUERY",
-        ),
+        inputs=[question],
+        input_type="SEARCH_QUERY",
     )
     resp = genai_client.embed_text(embed_details)
-    # Response parsing can differ slightly across SDK versions; this is the common shape.
-    payload = oci.util.to_dict(resp.data)
-    vector = payload["embeddings"][0]
+    # resp.data is EmbedTextResult with .embeddings attribute
+    vector = resp.data.embeddings[0]
     return vector
 ```
 
@@ -143,4 +138,4 @@ def chat_answer(genai_client, prompt: str) -> str:
 **What changes depending on needs**
 
 * The request object differs if you switch model families/providers (some use a messages array vs a single `message`).
-* generation parameters: `temperature`, `max_tokens`, etc.
+* generation parameters: `temperature`, `max_tokens`, etc.