import asyncio import json import time from collections.abc import AsyncIterator from dataclasses import dataclass import httpx from app.core.config import settings from app.core.exceptions import AppError @dataclass class LLMResponse: """LLM 响应：封装非流式模型输出和耗时指标。""" content: str model: str latency_ms: int token_usage: dict | None = None @dataclass class LLMStreamChunk: """LLM 流式片段：封装 SSE 增量内容和完成状态。""" delta: str done: bool = False first_token_ms: int | None = None total_latency_ms: int | None = None model: str | None = None fallback_used: bool = False class OpenAICompatibleLLMClient: """LLM Adapter：统一封装 OpenAI-compatible 模型的可替换调用。""" def __init__(self) -> None: self.base_url = settings.llm_base_url.rstrip("/") self.api_key = settings.llm_api_key self.timeout = settings.llm_timeout_seconds self.chat_completions_url = self._build_chat_completions_url() @property def is_mock_mode(self) -> bool: """模型模式：没有 API Key 或开启 mock 时使用本地模拟响应。""" return settings.llm_mock_enabled or not self.api_key async def chat( self, messages: list[dict], model: str, *, thinking_enabled: bool | None = None, reasoning_effort: str | None = None, response_format: dict | None = None, max_tokens: int | None = None, ) -> LLMResponse: """非流式调用：向 OpenAI-compatible 接口发送 messages 并返回完整文本。""" start = time.perf_counter() if self.is_mock_mode: content = self._mock_response(messages) return LLMResponse(content=content, model=f"mock-{model}", latency_ms=int((time.perf_counter() - start) * 1000)) try: async with httpx.AsyncClient(timeout=self._http_timeout()) as client: resp = await client.post( self.chat_completions_url, headers={"Authorization": f"Bearer {self.api_key}"}, json=self._build_payload( model=model, messages=messages, stream=False, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, response_format=response_format, max_tokens=max_tokens, ), ) resp.raise_for_status() data = resp.json() content = (data["choices"][0]["message"].get("content") or "").strip() if not content: raise KeyError("empty llm content") return LLMResponse( content=content, model=model, latency_ms=int((time.perf_counter() - start) * 1000), token_usage=data.get("usage"), ) except (httpx.TimeoutException, httpx.HTTPError, KeyError, IndexError, json.JSONDecodeError) as exc: if settings.llm_fallback_to_mock: content = self._mock_response(messages) return LLMResponse( content=content, model=f"mock-fallback-{model}", latency_ms=int((time.perf_counter() - start) * 1000), token_usage={"fallback_reason": exc.__class__.__name__}, ) raise AppError("LLM_CALL_FAILED", "llm service call failed", 502) from exc async def stream_chat( self, messages: list[dict], model: str, *, thinking_enabled: bool | None = None, reasoning_effort: str | None = None, max_tokens: int | None = None, ) -> AsyncIterator[LLMStreamChunk]: """流式调用：以统一 chunk 结构输出 OpenAI-compatible SSE 增量。""" start = time.perf_counter() first_token_ms: int | None = None if self.is_mock_mode: async for chunk in self._mock_stream(messages, model, start, model_label=f"mock-{model}"): yield chunk return try: async with httpx.AsyncClient(timeout=self._http_timeout()) as client: async with client.stream( "POST", self.chat_completions_url, headers={"Authorization": f"Bearer {self.api_key}"}, json=self._build_payload( model=model, messages=messages, stream=True, thinking_enabled=thinking_enabled, reasoning_effort=reasoning_effort, max_tokens=max_tokens, ), ) as resp: resp.raise_for_status() async for line in resp.aiter_lines(): if not line.startswith("data:"): continue payload = line.removeprefix("data:").strip() if payload == "[DONE]": break data = json.loads(payload) delta_obj = data["choices"][0].get("delta", {}) content_delta = delta_obj.get("content") or "" reasoning_delta = delta_obj.get("reasoning_content") or "" if (content_delta or reasoning_delta) and first_token_ms is None: first_token_ms = int((time.perf_counter() - start) * 1000) if content_delta: yield LLMStreamChunk(delta=content_delta, first_token_ms=first_token_ms) except (httpx.TimeoutException, httpx.HTTPError, KeyError, IndexError, json.JSONDecodeError) as exc: if settings.llm_fallback_to_mock: async for chunk in self._mock_stream( messages, model, start, model_label=f"mock-fallback-{model}", fallback_used=True, ): yield chunk return raise AppError("LLM_STREAM_FAILED", "llm stream call failed", 502) from exc yield LLMStreamChunk( delta="", done=True, first_token_ms=first_token_ms, total_latency_ms=int((time.perf_counter() - start) * 1000), model=model, ) async def _mock_stream( self, messages: list[dict], model: str, start: float, model_label: str, fallback_used: bool = False, ) -> AsyncIterator[LLMStreamChunk]: """Mock 流式输出：在模型不可用时保持 Demo 流程可验证。""" first_token_ms: int | None = None content = self._mock_response(messages) for piece in self._split_mock_content(content): await asyncio.sleep(0.02) if first_token_ms is None: first_token_ms = int((time.perf_counter() - start) * 1000) yield LLMStreamChunk(delta=piece, first_token_ms=first_token_ms) yield LLMStreamChunk( delta="", done=True, first_token_ms=first_token_ms, total_latency_ms=int((time.perf_counter() - start) * 1000), model=model_label, fallback_used=fallback_used, ) def _mock_response(self, messages: list[dict]) -> str: """Mock 输出：在没有 DeepSeek Key 时保证 Demo 闭环可运行。""" latest = next((m.get("content", "") for m in reversed(messages) if m.get("role") == "user"), "") prompt_head = " ".join(m.get("content", "").lower() for m in messages[:2]) if "score_type" in prompt_head and "dimension_scores" in prompt_head: return json.dumps( { "score_type": "percentage", "total_score": 82, "dimension_scores": [ {"dimension": "信息获取", "score": 20, "max_score": 25, "comment": "覆盖了发热、咳嗽和喘息，儿科特异性病史仍需加强。"}, {"dimension": "分析推理", "score": 21, "max_score": 25, "comment": "能够识别肺炎方向，鉴别诊断完整性中等。"}, {"dimension": "处置决策", "score": 17, "max_score": 20, "comment": "治疗原则基本合理，风险预案需要更具体。"}, {"dimension": "沟通人文", "score": 12, "max_score": 15, "comment": "有告知意识，家属安抚和健康教育可更系统。"}, {"dimension": "临床整合", "score": 12, "max_score": 15, "comment": "诊疗流程完整，时间分配和整体组织较清晰。"}, ], "errors": [{"title": "儿科特异性病史不足", "description": "疫苗接种、过敏史、既往喘息史追问不足。"}], "improvement_plan": ["补充儿科问诊框架：出生史、接种史、过敏史、既往喘息史。"], "evidence_summary": ["用户完成了核心症状追问、检查申请、诊断和治疗提交。"], "guideline_refs": [], "overall_comment": "本次训练完成主要诊疗流程，诊断方向正确，治疗方案具备基本可执行性。", }, ensure_ascii=False, ) if "体温" in latest or "发热" in latest: return "最高烧到39度多，已经反复四天了，退烧后会好一点，但很快又起来。" if "喘" in latest or "呼吸" in latest: return "昨天开始喘得明显，活动后更明显，晚上咳嗽也更重。" if "精神" in latest or "吃" in latest: return "精神比平时差一些，吃饭少了，但还能喝水，小便比平时略少。" if "既往" in latest or "过敏" in latest: return "以前没有明确哮喘诊断，也没有药物过敏史，小时候感冒时偶尔会咳得久。" return "家长：孩子主要是发热、咳嗽，昨天开始喘，您可以继续问我具体情况。" def _split_mock_content(self, content: str) -> list[str]: """Mock 分片：把本地模拟文本拆成流式输出片段。""" return [content[i : i + 8] for i in range(0, len(content), 8)] def _build_chat_completions_url(self) -> str: """接口地址：兼容 base_url 和完整 chat/completions URL 两种写法。""" if self.base_url.endswith("/chat/completions"): return self.base_url return f"{self.base_url}/chat/completions" def _http_timeout(self) -> httpx.Timeout: """超时策略：限制连接、写入和读取等待，避免前端长时间卡在生成中。""" return httpx.Timeout( timeout=self.timeout, connect=min(8, self.timeout), read=self.timeout, write=min(15, self.timeout), pool=min(8, self.timeout), ) def _build_payload( self, *, model: str, messages: list[dict], stream: bool, thinking_enabled: bool | None = None, reasoning_effort: str | None = None, response_format: dict | None = None, max_tokens: int | None = None, ) -> dict: """请求构造：兼容 DeepSeek V4 thinking、reasoning_effort 和 JSON 输出。""" payload: dict = {"model": model, "messages": messages, "stream": stream} supports_reasoning_options = self._supports_reasoning_options(model) if thinking_enabled is not None and supports_reasoning_options: payload["thinking"] = {"type": "enabled" if thinking_enabled else "disabled"} if reasoning_effort and supports_reasoning_options and thinking_enabled is not False: payload["reasoning_effort"] = reasoning_effort if response_format: payload["response_format"] = response_format if max_tokens: payload["max_tokens"] = max_tokens return payload def _supports_reasoning_options(self, model: str) -> bool: """厂商兼容：只向 DeepSeek 发送 thinking/reasoning_effort 等专有参数。""" base = self.base_url.lower() model_name = model.lower() return "deepseek" in base or model_name.startswith("deepseek") DeepSeekClient = OpenAICompatibleLLMClient