feat(ai): robust response extraction + structured-output foundation
Harden the Anthropic provider and lay the groundwork for schema-constrained JSON, optimizing the existing claude-sonnet-4-6 / claude-haiku-4-5 usage (no model changes). ai_provider.py: - _extract_text_from_response replaces fragile response.content[0].text: skips non-text leading blocks (e.g. thinking), returns the first text block, logs an anthropic.stop_reason warning on max_tokens/refusal (truncation now observable), and raises ValueError on a no-text response. - generate_json gains an optional `schema` param. Anthropic wires it to output_config.format (structured outputs); schema=None preserves the exact prior call for every existing caller. Gemini accepts-and-ignores it. kb_conversion_service.py: - TROUBLESHOOTING_SCHEMA / PROCEDURAL_SCHEMA + _schema_for_target_type(), modelled as a strict superset of every field the prompts emit. - convert_document passes the schema only when the new AI_KB_CONVERT_STRUCTURED_OUTPUT setting is True (default False). The _try_repair_json fallback stays as belt-and-suspenders. Tests: 14 provider + 7 schema, TDD (red-green). Live constrained-decoding smoke-test still required before enabling the flag in production. Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>
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@@ -155,6 +155,12 @@ class Settings(BaseSettings):
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AI_CONVERSATION_TTL_HOURS: int = 24
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AI_MAX_CALLS_PER_FLOW: int = 10
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AI_REQUEST_TIMEOUT_SECONDS: int = 120
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# When True, KB conversion constrains the Anthropic response with a JSON
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# schema (structured outputs) instead of relying on prompt-only JSON +
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# downstream fence-stripping / brace-repair. Default OFF: enable in staging
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# and smoke-test constrained decoding against the live model before turning
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# it on in production. Only affects the Anthropic backend.
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AI_KB_CONVERT_STRUCTURED_OUTPUT: bool = False
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# AI Provider selection
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AI_PROVIDER: str = "anthropic" # "gemini" or "anthropic"
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GOOGLE_AI_API_KEY: Optional[str] = None
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