release/v2.7 -> master#1040
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…1036) The entity URI normalizer stripped every non-ASCII character via the ASCII-only patterns `[^a-z0-9\-.]` and `[^a-z0-9\-]`. For non-Latin entity names (e.g. Chinese), this deleted the entire name, so distinct entities collapsed onto the same URI (".../<ontology>/-"), silently merging unrelated nodes and breaking OntoRAG for non-English content. Switch both filters to `[^\w\-.]` / `[^\w\-]`. In Python 3, `\w` is Unicode-aware for str patterns, so non-ASCII letters (CJK, etc.) are preserved while punctuation and symbols are still removed. ASCII behaviour is unchanged: names are lowercased and underscores converted to hyphens before the filter runs, so for ASCII input the patterns are equivalent to the originals. Adds tests covering ASCII regression guards and non-ASCII names/types, including the core case that two distinct Chinese entities must not collapse onto the same URI. Co-authored-by: arthurxuwei <5179840+arthurxuwei@users.noreply.github.com>
Thread existing JSON schemas from prompt definitions through the text-completion service to LLM backends' native structured output APIs. When a prompt has response-type "json" and a strict-mode compatible schema, the LLM constrains token selection at the logit level to guarantee schema-valid output. Wire-level changes: - Add response_format and schema fields to TextCompletionRequest - Update translator to encode/decode new fields - Pass new fields through LlmService, TextCompletionClient, and PromptManager Runtime schema compatibility checker: - New is_strict_mode_compatible() utility validates schemas against LLM provider constraints (additionalProperties, required fields, no unsupported constraints, no open-ended objects) - Per-prompt eligibility decision: compliant schemas use structured output, non-compliant schemas fall back to free-text + post-hoc validation LLM backend implementations: - OpenAI: response_format with json_schema, variant-aware top-level array rejection (openai variant blocks, llama/vllm variants allow) - New vllm variant for the OpenAI backend - vLLM (dedicated): response_format in raw HTTP body - Ollama: format=<schema> parameter - Claude: tool-use trick (forced tool call with schema as input_schema) - Mistral: native json_schema response_format - Llamafile, LM Studio: OpenAI SDK response_format - Azure OpenAI: AzureOpenAI SDK response_format - Azure serverless: response_format in raw HTTP body - TGI: response_format in raw HTTP body - VertexAI Gemini: response_mime_type + response_schema - VertexAI Claude: tool-use trick - Google AI Studio: response_mime_type + response_schema - Bedrock, Cohere: signature-only (no structured output yet) Post-hoc jsonschema.validate() retained as defence-in-depth. Tech spec added: docs/tech-specs/structured-output.md Update tests
Adds a full-stack image description service: schema, base class,
OpenAI backend, gateway dispatch, client APIs (sync/async REST +
websocket), tg-describe-image CLI, IAM capability, and specs.
Closes #879
Contributor License Agreement ✅All contributors have signed the CLA. Thank you! |
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Keep master sync'd with latest release