11fbb591c3
LLMClient 改为供应商链模式: - 主供应商 + GAOKAO_LLM_FALLBACK_MODELS/PROVIDERS/API_KEYS/BASE_URLS - 按顺序尝试, 第一个成功即返回 - 全部失败时抛出聚合错误(含供应商数量和最后错误) 配置层: - Settings 新增 4 个 fallback 字段 - load_settings() 读取对应环境变量 新增测试(4 个): - test_fallback_config_parsed: 验证 fallback 链解析正确 - test_fallback_no_config: 无 fallback 时只有主供应商 - test_fallback_falls_through_to_second_provider: 主失败自动切换 - test_fallback_all_fail_raises: 全部失败抛聚合错误 部署示例: .env.docker.example / .env.payment.example 补充注释掉的 fallback 模板 验证: data/llm/tests 16 passed, admin/tests 40 passed
250 lines
8.0 KiB
Python
250 lines
8.0 KiB
Python
"""LLM 客户端：统一 OpenAI-compatible 接口调用。"""
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from __future__ import annotations
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import json
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import urllib.request
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import urllib.error
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from dataclasses import dataclass, field
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from typing import Any
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from admin.config import Settings
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class LLMError(Exception):
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"""LLM 调用失败。"""
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@dataclass(frozen=True)
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class LLMResponse:
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"""LLM 响应。"""
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content: str
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usage: dict[str, int] = field(default_factory=dict)
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model: str = ""
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raw: dict[str, Any] = field(default_factory=dict)
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class LLMClient:
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"""统一 LLM 客户端，通过 OpenAI-compatible API 调用。
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支持:
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- openai: https://api.openai.com/v1
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- dashscope: https://dashscope.aliyuncs.com/compatible-mode/v1
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- anthropic: 通过兼容层或直接 API
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"""
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def __init__(self, settings: Settings) -> None:
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self._settings = settings
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self._timeout = settings.llm_timeout_seconds
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self._max_tokens = settings.llm_max_tokens
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# 构建供应商链：主供应商 + fallback 供应商列表
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self._providers: list[dict[str, str]] = []
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if settings.llm_provider != "none" and settings.llm_api_key:
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self._providers.append({
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"provider": settings.llm_provider,
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"api_key": settings.llm_api_key,
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"base_url": settings.llm_base_url.rstrip("/"),
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"model": settings.llm_model,
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})
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# 解析 fallback 配置
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fb_models = [
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s.strip()
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for s in (settings.llm_fallback_models or "").split(",")
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if s.strip()
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]
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fb_providers = [
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s.strip()
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for s in (settings.llm_fallback_providers or "").split(",")
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if s.strip()
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]
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fb_keys = [
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s.strip()
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for s in (settings.llm_fallback_api_keys or "").split(",")
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if s.strip()
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]
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fb_urls = [
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s.strip()
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for s in (settings.llm_fallback_base_urls or "").split(",")
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if s.strip()
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]
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for i, model in enumerate(fb_models):
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provider = (
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fb_providers[i]
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if i < len(fb_providers)
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else (self._providers[0]["provider"] if self._providers else "openai")
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)
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api_key = (
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fb_keys[i]
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if i < len(fb_keys)
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else (self._providers[0]["api_key"] if self._providers else "")
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)
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base_url = (
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fb_urls[i].rstrip("/")
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if i < len(fb_urls)
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else (
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self._providers[0]["base_url"]
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if self._providers
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else "https://api.openai.com/v1"
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)
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)
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if api_key:
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self._providers.append({
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"provider": provider,
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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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})
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# 兼容旧接口
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self._provider = self._providers[0]["provider"] if self._providers else "none"
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self._api_key = self._providers[0]["api_key"] if self._providers else ""
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self._base_url = self._providers[0]["base_url"] if self._providers else ""
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self._model = self._providers[0]["model"] if self._providers else ""
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@property
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def is_configured(self) -> bool:
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"""LLM 是否已配置可用。"""
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return len(self._providers) > 0
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@property
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def provider_count(self) -> int:
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"""已配置的供应商数量（含主+fallback）。"""
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return len(self._providers)
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def chat(
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self,
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messages: list[dict[str, str]],
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*,
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temperature: float = 0.7,
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max_tokens: int | None = None,
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) -> LLMResponse:
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"""调用 chat completions API，支持多供应商 fallback。
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按供应商链顺序依次尝试，第一个成功即返回。
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全部失败时抛出最后一个错误。
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Args:
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messages: OpenAI 格式的消息列表。
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temperature: 采样温度。
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max_tokens: 最大生成 token 数，默认使用 Settings 配置。
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Returns:
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LLMResponse。
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Raises:
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LLMError: 全部供应商都失败。
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"""
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if not self.is_configured:
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raise LLMError(
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f"LLM 未配置 (provider={self._provider})。"
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"请设置 GAOKAO_LLM_PROVIDER 和 GAOKAO_LLM_API_KEY。"
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)
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last_error: LLMError | None = None
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for idx, prov in enumerate(self._providers):
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try:
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return self._call_single_provider(
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provider=prov,
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messages=messages,
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temperature=temperature,
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max_tokens=max_tokens or self._max_tokens,
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)
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except LLMError as e:
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last_error = e
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prov_name = prov["provider"]
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model_name = prov["model"]
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# 如果还有下一个供应商，继续尝试
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if idx < len(self._providers) - 1:
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continue
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# 最后一个也失败了
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raise LLMError(
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f"全部 {len(self._providers)} 个 LLM 供应商均失败。"
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f"最后错误 ({prov_name}/{model_name}): {e}"
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) from e
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# 理论上不会到达这里
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raise last_error or LLMError("未知 LLM 错误")
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def _call_single_provider(
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self,
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*,
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provider: dict[str, str],
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messages: list[dict[str, str]],
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temperature: float,
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max_tokens: int,
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) -> LLMResponse:
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"""调用单个供应商的 API。"""
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payload: dict[str, Any] = {
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"model": provider["model"],
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"messages": messages,
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"temperature": temperature,
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"max_tokens": max_tokens,
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}
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url = f"{provider['base_url']}/chat/completions"
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headers = {
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"Content-Type": "application/json",
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"Authorization": f"Bearer {provider['api_key']}",
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}
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data = json.dumps(payload).encode("utf-8")
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req = urllib.request.Request(url, data=data, headers=headers, method="POST")
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try:
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with urllib.request.urlopen(req, timeout=self._timeout) as resp:
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body = resp.read().decode("utf-8")
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result = json.loads(body)
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except urllib.error.HTTPError as e:
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raw_body = e.read()
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error_body = (
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raw_body.decode("utf-8", "replace")
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if isinstance(raw_body, bytes)
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else str(raw_body)
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)
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raise LLMError(f"LLM API HTTP {e.code}: {error_body[:500]}") from e
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except urllib.error.URLError as e:
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raise LLMError(f"LLM API 连接失败: {e}") from e
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except json.JSONDecodeError as e:
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raise LLMError(f"LLM API 响应解析失败: {e}") from e
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choices = result.get("choices", [])
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if not choices:
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raise LLMError(f"LLM API 返回空 choices: {result}")
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content = choices[0].get("message", {}).get("content", "")
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if not content:
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raise LLMError(f"LLM API 返回空 content: {result}")
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usage = result.get("usage", {})
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model = result.get("model", provider["model"])
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return LLMResponse(
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content=content,
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usage=usage,
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model=model,
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raw=result,
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)
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def chat_with_system(
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self,
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system_prompt: str,
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user_prompt: str,
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*,
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temperature: float = 0.7,
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max_tokens: int | None = None,
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) -> LLMResponse:
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"""便捷方法：system + user 两条消息。"""
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return self.chat(
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[
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": user_prompt},
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],
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temperature=temperature,
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max_tokens=max_tokens,
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)
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