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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 |
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| trustgraph/model/text_completion | ||
| pyproject.toml | ||
| README.md | ||