167 lines
7.7 KiB
Python
167 lines
7.7 KiB
Python
from __future__ import annotations
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from .shared import *
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class LLMGenerateDbChatMixin:
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async def _generate_with_db(self, db: AsyncSession, prompt: str, **kwargs: Any) -> str:
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started_at = time.perf_counter()
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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")
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temperature = float(kwargs.get("temperature") or 0.2)
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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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routing_meta = _normalize_routing_meta(kwargs.get("routing_meta"))
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response_text = ""
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status = "success"
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routing_decision = "primary_engine"
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engine_meta: dict[str, Any] = {}
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provider_request_payload: Any = None
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provider_response_payload: Any = None
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provider_attempts: Any = None
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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 = 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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def _log_stub_failure(error_obj: Exception, *, used_stub: bool = False) -> None:
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elapsed_ms = (time.perf_counter() - started_at) * 1000
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routing_meta = _read_engine_routing_meta(engine_meta)
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outcome = "兜底 stub" if used_stub else "显式报错"
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logger.warning(
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"[LLM_FAIL] 生成失败 → %s function=%s route=%s error=%r duration_ms=%.0f",
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outcome,
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function_name,
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_routing_meta_log_text(routing_meta) or "unknown",
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error_obj,
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elapsed_ms,
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)
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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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routing_meta=routing_meta,
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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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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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routing_meta=routing_meta,
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)
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response_text = completion.text
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provider_request_payload = completion.raw_request
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provider_response_payload = completion.raw_response
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provider_attempts = completion.provider_attempts
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if not response_text.strip():
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raise ValueError("primary_engine_empty_response")
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if self._call_is_invalid_structured_output(
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function_name=function_name,
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response_text=response_text,
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routing_meta=routing_meta,
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):
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raise ValueError("invalid_structured_output")
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return response_text
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except Exception as exc:
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provider_request_payload = provider_request_payload or getattr(exc, "provider_request_payload", None)
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provider_response_payload = provider_response_payload or getattr(exc, "provider_response_payload", None)
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provider_attempts = provider_attempts or getattr(exc, "provider_attempts", None)
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failover_response, failover_engine = await self._retry_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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routing_meta=routing_meta,
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)
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if failover_response and isinstance(failover_engine, dict):
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routing_decision = "low_to_high_escalated"
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status = "success_failover"
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response_text = failover_response
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engine_meta = failover_engine
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provider_request_payload = failover_engine.get("_provider_request_payload")
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provider_response_payload = failover_engine.get("_provider_response_payload")
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provider_attempts = failover_engine.get("_provider_attempts")
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return failover_response
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status = "failed"
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reason_text = self._build_model_error_message(
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function_name=function_name,
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routing_meta=_read_engine_routing_meta(engine_meta),
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error=exc,
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)
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response_text = reason_text
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_log_stub_failure(exc, used_stub=False)
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raise RuntimeError(reason_text) from exc
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finally:
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try:
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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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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=response_text,
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status=status,
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provider=str(engine_meta.get("provider") or ""),
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model=str(engine_meta.get("model") or ""),
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routing_meta=_merge_call_routing_meta(
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caller_meta=routing_meta,
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engine_meta=_read_engine_routing_meta(engine_meta),
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),
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provider_request_payload=provider_request_payload or engine_meta.get("_provider_request_payload"),
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provider_response_payload=provider_response_payload or engine_meta.get("_provider_response_payload"),
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provider_attempts=provider_attempts or engine_meta.get("_provider_attempts"),
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)
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routing_meta = _merge_call_routing_meta(
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caller_meta=routing_meta,
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engine_meta=_read_engine_routing_meta(engine_meta),
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)
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if (
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routing_meta.get("planned_tier")
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and routing_meta.get("executed_tier")
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and routing_meta.get("planned_tier") != routing_meta.get("executed_tier")
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):
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_runtime_log(
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"LLM路由",
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(
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f"function={function_name}, {_routing_meta_log_text(routing_meta)}"
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),
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)
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except Exception as log_exc:
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_runtime_log("LLM", f"调用日志写入失败：{log_exc}")
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