from __future__ import annotations

from .shared import *

class LLMGenerateDbChatMixin:
    async def _generate_with_db(self, db: AsyncSession, prompt: str, **kwargs: Any) -> str:
        started_at = time.perf_counter()
        tenant_id = int(kwargs.get("tenant_id") or 1)
        function_name = str(kwargs.get("function_name") or "llm_generate")
        temperature = float(kwargs.get("temperature") or 0.2)
        max_tokens = int(kwargs.get("max_tokens") or 1024)
        use_prompt_profile = bool(kwargs.get("use_prompt_profile", True))
        response_mode = str(kwargs.get("response_mode") or "complete").strip().lower() or "complete"
        response_text = ""
        status = "success"
        routing_decision = "primary_engine"
        engine_meta: dict[str, Any] = {}
        prompt_profile = {"role_name": "", "system_prompt": ""}
        prompt_with_context = await self._inject_model_context_bundle_prompt(
            db=db,
            tenant_id=tenant_id,
            function_name=function_name,
            prompt=prompt,
        )
        merged_prompt = self._merge_prompt_with_profile(
            prompt=prompt_with_context,
            prompt_profile=prompt_profile,
            function_name=function_name,
        )

        def _log_stub_failure(error_obj: Exception, *, used_stub: bool = False) -> None:
            elapsed_ms = (time.perf_counter() - started_at) * 1000
            provider = str(engine_meta.get("provider") or "")
            model = str(engine_meta.get("model") or "")
            outcome = "兜底 stub" if used_stub else "显式报错"
            logger.warning(
                "[LLM_FAIL] 生成失败 → %s function=%s provider=%s model=%s error=%r duration_ms=%.0f",
                outcome,
                function_name,
                provider,
                model,
                error_obj,
                elapsed_ms,
            )

        try:
            if use_prompt_profile:
                prompt_profile = await self._resolve_active_prompt_profile(db, tenant_id=tenant_id)
            merged_prompt = self._merge_prompt_with_profile(
                prompt=prompt_with_context,
                prompt_profile=prompt_profile,
                function_name=function_name,
            )
            engine = await self._resolve_engine_for_call(
                db,
                tenant_id=tenant_id,
                function_name=function_name,
            )
            engine_meta = engine
            provider = str(engine.get("provider") or "")
            model = str(engine.get("model") or "")
            response_text = await self._chat_completion(
                provider=provider,
                prompt=prompt_with_context,
                system_prompt=prompt_profile.get("system_prompt") or "",
                api_key=str(engine.get("api_key") or ""),
                base_url=str(engine.get("base_url") or ""),
                model=model,
                temperature=temperature,
                max_tokens=max_tokens,
                function_name=function_name,
                response_mode=response_mode,
            )
            if not response_text.strip():
                raise ValueError("primary_engine_empty_response")

            if self._is_invalid_structured_output(function_name=function_name, response_text=response_text):
                raise ValueError("invalid_structured_output")
            return response_text
        except Exception as exc:
            failover_response, failover_engine = await self._retry_with_high_tier_after_low_model_failure(
                db=db,
                tenant_id=tenant_id,
                function_name=function_name,
                failed_engine=engine_meta,
                prompt=prompt_with_context,
                system_prompt=str(prompt_profile.get("system_prompt") or ""),
                temperature=temperature,
                max_tokens=max_tokens,
                response_mode=response_mode,
                error=exc,
            )
            if failover_response and isinstance(failover_engine, dict):
                routing_decision = "glm47_error_escalate_to_high"
                status = "success_failover"
                response_text = failover_response
                engine_meta = failover_engine
                return failover_response
            status = "failed"
            reason_text = self._build_model_error_message(
                function_name=function_name,
                provider=str(engine_meta.get("provider") or ""),
                model=str(engine_meta.get("model") or ""),
                error=exc,
            )
            response_text = reason_text
            _log_stub_failure(exc, used_stub=False)
            raise RuntimeError(reason_text) from exc
        finally:
            try:
                merged_prompt = self._merge_prompt_with_profile(
                    prompt=prompt_with_context,
                    prompt_profile=prompt_profile,
                    function_name=function_name,
                )
                await self._log_ai_call(
                    tenant_id=tenant_id,
                    function_name=function_name,
                    prompt=merged_prompt,
                    response=response_text,
                    status=status,
                    provider=str(engine_meta.get("provider") or ""),
                    model=str(engine_meta.get("model") or ""),
                )
                if routing_decision != "primary_engine":
                    _runtime_log(
                        "LLM路由",
                        (
                            f"routing_decision={routing_decision}, "
                            f"function={function_name}, provider={engine_meta.get('provider')}, model={engine_meta.get('model')}"
                        ),
                    )
            except Exception as log_exc:
                _runtime_log("LLM", f"调用日志写入失败：{log_exc}")