280 lines
12 KiB
Python
280 lines
12 KiB
Python
from __future__ import annotations
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from .shared import *
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class LLMGenerateDbStructuredMixin:
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async def _generate_structured_with_db(
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self,
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db: AsyncSession,
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prompt: str,
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**kwargs: Any,
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) -> StructuredExtractionResult:
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tenant_id = int(kwargs.get("tenant_id") or 1)
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function_name = str(kwargs.get("function_name") or "llm_generate").strip().lower() or "llm_generate"
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temperature = float(kwargs.get("temperature") or 0.0)
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max_tokens = int(kwargs.get("max_tokens") or 1024)
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use_prompt_profile = bool(kwargs.get("use_prompt_profile", True))
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response_mode = str(kwargs.get("response_mode") or "complete").strip().lower() or "complete"
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prompt_profile = {"role_name": "", "system_prompt": ""}
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prompt_with_context = await self._inject_model_context_bundle_prompt(
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db=db,
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tenant_id=tenant_id,
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function_name=function_name,
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prompt=prompt,
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)
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merged_prompt = prompt_with_context
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provider = ""
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model = ""
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engine_meta: dict[str, Any] = {}
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status = "success"
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response_text = ""
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try:
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if use_prompt_profile:
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prompt_profile = await self._resolve_active_prompt_profile(db, tenant_id=tenant_id)
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merged_prompt = self._merge_prompt_with_profile(
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prompt=prompt_with_context,
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prompt_profile=prompt_profile,
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function_name=function_name,
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)
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engine = await self._resolve_engine_for_call(
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db,
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tenant_id=tenant_id,
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function_name=function_name,
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)
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engine_meta = engine
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provider = str(engine.get("provider") or "")
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model = str(engine.get("model") or "")
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cache_key = ""
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if self._structured_cache_enabled(function_name):
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cache_key = self._structured_cache_key(
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function_name=function_name,
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model=model or "unknown",
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prompt=merged_prompt,
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)
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cached = await self._structured_cache_get(key=cache_key)
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if cached is not None:
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status = "success_cache_hit"
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result = StructuredExtractionResult(
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payload=cached,
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source_field="cache",
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provider=provider,
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model=model,
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raw_text=json.dumps(cached, ensure_ascii=False),
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validation="ok",
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from_cache=True,
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)
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_runtime_log(
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"LLM结构化",
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f"function={function_name}, structured_source=cache, validation=ok, fallback=false",
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)
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return result
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completion = await self._chat_completion_raw(
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provider=provider,
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prompt=prompt_with_context,
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system_prompt=prompt_profile.get("system_prompt") or "",
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api_key=str(engine.get("api_key") or ""),
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base_url=str(engine.get("base_url") or ""),
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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function_name=function_name,
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response_mode=response_mode,
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)
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response_text = completion.text
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extraction = self._extract_structured_object_from_response(
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function_name=function_name,
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response=completion.raw_response,
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fallback_text=completion.text,
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provider=provider,
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model=model,
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)
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_runtime_log(
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"LLM结构化",
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(
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f"function={function_name}, structured_source={extraction.source_field or 'none'}, "
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f"validation={extraction.validation}, fallback=false"
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),
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)
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if extraction.payload is None:
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if self._should_attempt_model_repair(function_name):
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repaired = await self._repair_structured_payload_with_model(
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function_name=function_name,
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provider=provider,
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model=model,
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api_key=str(engine.get("api_key") or ""),
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base_url=str(engine.get("base_url") or ""),
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system_prompt=prompt_profile.get("system_prompt") or "",
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original_prompt=prompt_with_context,
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extraction=extraction,
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raw_response=completion.raw_response,
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fallback_text=completion.text,
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max_tokens=max_tokens,
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)
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if isinstance(repaired, StructuredExtractionResult) and repaired.payload is not None:
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extraction = repaired
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response_text = repaired.raw_text or response_text
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else:
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raise ValueError(str(extraction.error_code or "structured_schema_invalid"))
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else:
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raise ValueError(str(extraction.error_code or "structured_schema_invalid"))
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if extraction.payload is not None and cache_key:
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await self._structured_cache_set(key=cache_key, value=extraction.payload)
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status = extraction.error_code or "success"
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return extraction
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except Exception as exc:
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failover_result, failover_engine = await self._retry_structured_with_high_tier_after_low_model_failure(
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db=db,
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tenant_id=tenant_id,
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function_name=function_name,
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failed_engine=engine_meta,
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prompt=prompt_with_context,
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system_prompt=str(prompt_profile.get("system_prompt") or ""),
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temperature=temperature,
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max_tokens=max_tokens,
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response_mode=response_mode,
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error=exc,
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)
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if isinstance(failover_result, StructuredExtractionResult) and isinstance(failover_engine, dict):
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provider = str(failover_engine.get("provider") or "")
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model = str(failover_engine.get("model") or "")
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response_text = failover_result.raw_text or json.dumps(failover_result.payload or {}, ensure_ascii=False)
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status = failover_result.error_code or "success_failover"
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return failover_result
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error_code = self._classify_structured_exception(exc)
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status = error_code
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reason_text = self._build_model_error_message(
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function_name=function_name,
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provider=provider,
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model=model,
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error=exc,
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)
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response_text = reason_text
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_runtime_log(
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"LLM结构化",
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f"function={function_name}, structured_source=none, validation=invalid, fallback=false",
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)
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raise RuntimeError(reason_text) from exc
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finally:
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try:
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log_payload = response_text or status
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await self._log_ai_call(
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tenant_id=tenant_id,
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function_name=function_name,
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prompt=merged_prompt,
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response=log_payload,
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status=status,
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provider=provider,
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model=model,
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)
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except Exception as log_exc:
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_runtime_log("LLM", f"结构化调用日志写入失败：{log_exc}")
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@staticmethod
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def _build_structured_schema_hint(function_name: str) -> str:
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fn = str(function_name or "").strip().lower()
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schema = STRUCTURED_OBJECT_SCHEMAS.get(fn)
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if not isinstance(schema, dict):
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return "{}"
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schema_hint = {
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"required_keys": list(schema.get("required_keys") or []),
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"type_checks": dict(schema.get("type_checks") or {}),
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"optional_type_checks": dict(schema.get("optional_type_checks") or {}),
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}
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return json.dumps(schema_hint, ensure_ascii=False)
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@staticmethod
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def _should_attempt_model_repair(function_name: str) -> bool:
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fn = str(function_name or "").strip().lower()
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if fn in {"classify_intent_ai_pmo_kernel", "classify_intent_complex"}:
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return _env_bool("LLM_INTENT_STRUCTURED_REPAIR_ENABLED", True)
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return True
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async def _repair_structured_payload_with_model(
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self,
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*,
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function_name: str,
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provider: str,
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model: str,
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api_key: str,
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base_url: str,
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system_prompt: str,
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original_prompt: str,
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extraction: StructuredExtractionResult,
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raw_response: Any,
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fallback_text: str,
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max_tokens: int,
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) -> Optional[StructuredExtractionResult]:
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source_text = str(extraction.raw_text or "").strip()
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if source_text.startswith("[") and source_text.endswith("]"):
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source_text = ""
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if not source_text:
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source_text = str(fallback_text or "").strip()
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if not source_text:
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return None
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schema_hint = self._build_structured_schema_hint(function_name)
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repair_prompt = (
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"首字符必须是 {，末字符必须是 }。\n"
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"你是 JSON 修复器。把模型输出整理为满足目标 schema 的 JSON 对象。\n"
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f"function_name: {function_name}\n"
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f"schema_hint_json: {schema_hint}\n"
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f"original_task_prompt: {str(original_prompt or '')[:2000]}\n"
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f"model_output: {source_text[:2800]}\n"
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"要求：\n"
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"1. 只输出 JSON 对象。\n"
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"2. 必须包含 required_keys。\n"
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"3. 若字段缺失，填 null/空字符串/空数组，并保持类型合法。\n"
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"4. 禁止输出解释、Markdown、代码块。"
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)
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try:
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repair_max_tokens = max(180, min(int(max_tokens or 0), 420))
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if str(function_name or "").strip().lower() in {"classify_intent_ai_pmo_kernel", "classify_intent_complex"}:
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repair_max_tokens = min(repair_max_tokens, 220)
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repair_completion = await self._chat_completion_raw(
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provider=provider,
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prompt=repair_prompt,
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system_prompt=system_prompt,
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api_key=api_key,
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base_url=base_url,
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model=model,
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temperature=0.0,
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max_tokens=repair_max_tokens,
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function_name=function_name,
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response_mode="fast",
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)
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except Exception as exc:
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_runtime_log(
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"LLM结构化",
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(
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"模型结构化修复失败："
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f"function={function_name}, provider={provider}, model={model}, "
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f"error={type(exc).__name__}:{str(exc)[:180]}"
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),
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)
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return None
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repair_extraction = self._extract_structured_object_from_response(
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function_name=function_name,
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response=repair_completion.raw_response,
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fallback_text=repair_completion.text,
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provider=provider,
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model=model,
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)
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if repair_extraction.payload is None:
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_runtime_log(
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"LLM结构化",
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(
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"模型结构化修复未产出有效 JSON："
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f"function={function_name}, source={repair_extraction.source_field or 'none'}, "
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f"error={repair_extraction.error_code or 'unknown'}"
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),
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)
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return None
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_runtime_log(
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"LLM结构化",
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(
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f"function={function_name}, structured_source={repair_extraction.source_field or 'none'}, "
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f"validation={repair_extraction.validation}, fallback=model_repair"
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),
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)
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return repair_extraction
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