"""LLM 客户端：统一 OpenAI-compatible 接口调用。""" from __future__ import annotations import json import urllib.request import urllib.error from dataclasses import dataclass, field from typing import Any from admin.config import Settings class LLMError(Exception): """LLM 调用失败。""" @dataclass(frozen=True) class LLMResponse: """LLM 响应。""" content: str usage: dict[str, int] = field(default_factory=dict) model: str = "" raw: dict[str, Any] = field(default_factory=dict) class LLMClient: """统一 LLM 客户端，通过 OpenAI-compatible API 调用。 支持: - openai: https://api.openai.com/v1 - dashscope: https://dashscope.aliyuncs.com/compatible-mode/v1 - anthropic: 通过兼容层或直接 API """ def __init__(self, settings: Settings) -> None: self._settings = settings self._timeout = settings.llm_timeout_seconds self._max_tokens = settings.llm_max_tokens # 构建供应商链：主供应商 + fallback 供应商列表 self._providers: list[dict[str, str]] = [] if settings.llm_provider != "none" and settings.llm_api_key: self._providers.append({ "provider": settings.llm_provider, "api_key": settings.llm_api_key, "base_url": settings.llm_base_url.rstrip("/"), "model": settings.llm_model, }) # 解析 fallback 配置 fb_models = [ s.strip() for s in (settings.llm_fallback_models or "").split(",") if s.strip() ] fb_providers = [ s.strip() for s in (settings.llm_fallback_providers or "").split(",") if s.strip() ] fb_keys = [ s.strip() for s in (settings.llm_fallback_api_keys or "").split(",") if s.strip() ] fb_urls = [ s.strip() for s in (settings.llm_fallback_base_urls or "").split(",") if s.strip() ] for i, model in enumerate(fb_models): provider = ( fb_providers[i] if i < len(fb_providers) else (self._providers[0]["provider"] if self._providers else "openai") ) api_key = ( fb_keys[i] if i < len(fb_keys) else (self._providers[0]["api_key"] if self._providers else "") ) base_url = ( fb_urls[i].rstrip("/") if i < len(fb_urls) else ( self._providers[0]["base_url"] if self._providers else "https://api.openai.com/v1" ) ) if api_key: self._providers.append({ "provider": provider, "api_key": api_key, "base_url": base_url, "model": model, }) # 兼容旧接口 self._provider = self._providers[0]["provider"] if self._providers else "none" self._api_key = self._providers[0]["api_key"] if self._providers else "" self._base_url = self._providers[0]["base_url"] if self._providers else "" self._model = self._providers[0]["model"] if self._providers else "" @property def is_configured(self) -> bool: """LLM 是否已配置可用。""" return len(self._providers) > 0 @property def provider_count(self) -> int: """已配置的供应商数量（含主+fallback）。""" return len(self._providers) def chat( self, messages: list[dict[str, str]], *, temperature: float = 0.7, max_tokens: int | None = None, ) -> LLMResponse: """调用 chat completions API，支持多供应商 fallback。 按供应商链顺序依次尝试，第一个成功即返回。 全部失败时抛出最后一个错误。 Args: messages: OpenAI 格式的消息列表。 temperature: 采样温度。 max_tokens: 最大生成 token 数，默认使用 Settings 配置。 Returns: LLMResponse。 Raises: LLMError: 全部供应商都失败。 """ if not self.is_configured: raise LLMError( f"LLM 未配置 (provider={self._provider})。" "请设置 GAOKAO_LLM_PROVIDER 和 GAOKAO_LLM_API_KEY。" ) last_error: LLMError | None = None for idx, prov in enumerate(self._providers): try: return self._call_single_provider( provider=prov, messages=messages, temperature=temperature, max_tokens=max_tokens or self._max_tokens, ) except LLMError as e: last_error = e prov_name = prov["provider"] model_name = prov["model"] # 如果还有下一个供应商，继续尝试 if idx < len(self._providers) - 1: continue # 最后一个也失败了 raise LLMError( f"全部 {len(self._providers)} 个 LLM 供应商均失败。" f"最后错误 ({prov_name}/{model_name}): {e}" ) from e # 理论上不会到达这里 raise last_error or LLMError("未知 LLM 错误") def _call_single_provider( self, *, provider: dict[str, str], messages: list[dict[str, str]], temperature: float, max_tokens: int, ) -> LLMResponse: """调用单个供应商的 API。""" payload: dict[str, Any] = { "model": provider["model"], "messages": messages, "temperature": temperature, "max_tokens": max_tokens, } url = f"{provider['base_url']}/chat/completions" headers = { "Content-Type": "application/json", "Authorization": f"Bearer {provider['api_key']}", } data = json.dumps(payload).encode("utf-8") req = urllib.request.Request(url, data=data, headers=headers, method="POST") try: with urllib.request.urlopen(req, timeout=self._timeout) as resp: body = resp.read().decode("utf-8") result = json.loads(body) except urllib.error.HTTPError as e: raw_body = e.read() error_body = ( raw_body.decode("utf-8", "replace") if isinstance(raw_body, bytes) else str(raw_body) ) raise LLMError(f"LLM API HTTP {e.code}: {error_body[:500]}") from e except urllib.error.URLError as e: raise LLMError(f"LLM API 连接失败: {e}") from e except json.JSONDecodeError as e: raise LLMError(f"LLM API 响应解析失败: {e}") from e choices = result.get("choices", []) if not choices: raise LLMError(f"LLM API 返回空 choices: {result}") content = choices[0].get("message", {}).get("content", "") if not content: raise LLMError(f"LLM API 返回空 content: {result}") usage = result.get("usage", {}) model = result.get("model", provider["model"]) return LLMResponse( content=content, usage=usage, model=model, raw=result, ) def chat_with_system( self, system_prompt: str, user_prompt: str, *, temperature: float = 0.7, max_tokens: int | None = None, ) -> LLMResponse: """便捷方法：system + user 两条消息。""" return self.chat( [ {"role": "system", "content": system_prompt}, {"role": "user", "content": user_prompt}, ], temperature=temperature, max_tokens=max_tokens, )