"""LLM 客户端适配器 - 支持多种 LLM 提供商""" import os import json from typing import Optional, Dict, Any from abc import ABC, abstractmethod from app.core.logging_config import get_business_logger logger = get_business_logger() class BaseLLMClient(ABC): """LLM 客户端基类""" @abstractmethod async def chat(self, prompt: str, **kwargs) -> str: """发送聊天请求 Args: prompt: 提示词 **kwargs: 其他参数 Returns: LLM 响应文本 """ pass class OpenAIClient(BaseLLMClient): """OpenAI 客户端""" def __init__( self, api_key: Optional[str] = None, model: str = "gpt-3.5-turbo", base_url: Optional[str] = None ): """初始化 OpenAI 客户端 Args: api_key: API 密钥 model: 模型名称 base_url: API 基础 URL（可选，用于兼容其他服务） """ self.api_key = api_key or os.getenv("OPENAI_API_KEY") self.model = model self.base_url = base_url if not self.api_key: raise ValueError("OpenAI API key 未配置") try: from openai import AsyncOpenAI self.client = AsyncOpenAI( api_key=self.api_key, base_url=self.base_url ) except ImportError: raise ImportError("请安装 openai 库: pip install openai") async def chat(self, prompt: str, **kwargs) -> str: """发送聊天请求 Args: prompt: 提示词 **kwargs: 其他参数（temperature, max_tokens 等） Returns: LLM 响应文本 """ try: response = await self.client.chat.completions.create( model=self.model, messages=[{"role": "user", "content": prompt}], temperature=kwargs.get("temperature", 0.3), max_tokens=kwargs.get("max_tokens", 500) ) return response.choices[0].message.content except Exception as e: logger.error(f"OpenAI API 调用失败: {str(e)}") raise class AzureOpenAIClient(BaseLLMClient): """Azure OpenAI 客户端""" def __init__( self, api_key: Optional[str] = None, endpoint: Optional[str] = None, deployment_name: Optional[str] = None, api_version: str = "2024-02-15-preview" ): """初始化 Azure OpenAI 客户端 Args: api_key: API 密钥 endpoint: Azure 端点 deployment_name: 部署名称 api_version: API 版本 """ self.api_key = api_key or os.getenv("AZURE_OPENAI_API_KEY") self.endpoint = endpoint or os.getenv("AZURE_OPENAI_ENDPOINT") self.deployment_name = deployment_name or os.getenv("AZURE_OPENAI_DEPLOYMENT") self.api_version = api_version if not all([self.api_key, self.endpoint, self.deployment_name]): raise ValueError("Azure OpenAI 配置不完整") try: from openai import AsyncAzureOpenAI self.client = AsyncAzureOpenAI( api_key=self.api_key, azure_endpoint=self.endpoint, api_version=self.api_version ) except ImportError: raise ImportError("请安装 openai 库: pip install openai") async def chat(self, prompt: str, **kwargs) -> str: """发送聊天请求""" try: response = await self.client.chat.completions.create( model=self.deployment_name, messages=[{"role": "user", "content": prompt}], temperature=kwargs.get("temperature", 0.3), max_tokens=kwargs.get("max_tokens", 500) ) return response.choices[0].message.content except Exception as e: logger.error(f"Azure OpenAI API 调用失败: {str(e)}") raise class AnthropicClient(BaseLLMClient): """Anthropic Claude 客户端""" def __init__( self, api_key: Optional[str] = None, model: str = "claude-3-sonnet-20240229" ): """初始化 Anthropic 客户端 Args: api_key: API 密钥 model: 模型名称 """ self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY") self.model = model if not self.api_key: raise ValueError("Anthropic API key 未配置") try: from anthropic import AsyncAnthropic self.client = AsyncAnthropic(api_key=self.api_key) except ImportError: raise ImportError("请安装 anthropic 库: pip install anthropic") async def chat(self, prompt: str, **kwargs) -> str: """发送聊天请求""" try: response = await self.client.messages.create( model=self.model, max_tokens=kwargs.get("max_tokens", 500), temperature=kwargs.get("temperature", 0.3), messages=[{"role": "user", "content": prompt}] ) return response.content[0].text except Exception as e: logger.error(f"Anthropic API 调用失败: {str(e)}") raise class LocalLLMClient(BaseLLMClient): """本地 LLM 客户端（通过 HTTP API）""" def __init__( self, base_url: str = "http://localhost:8000", model: str = "local-model" ): """初始化本地 LLM 客户端 Args: base_url: API 基础 URL model: 模型名称 """ self.base_url = base_url self.model = model try: import httpx self.client = httpx.AsyncClient(timeout=30.0) except ImportError: raise ImportError("请安装 httpx 库: pip install httpx") async def chat(self, prompt: str, **kwargs) -> str: """发送聊天请求""" try: response = await self.client.post( f"{self.base_url}/v1/chat/completions", json={ "model": self.model, "messages": [{"role": "user", "content": prompt}], "temperature": kwargs.get("temperature", 0.3), "max_tokens": kwargs.get("max_tokens", 500) } ) response.raise_for_status() data = response.json() return data["choices"][0]["message"]["content"] except Exception as e: logger.error(f"本地 LLM API 调用失败: {str(e)}") raise class MockLLMClient(BaseLLMClient): """模拟 LLM 客户端（用于测试）""" def __init__(self): """初始化模拟客户端""" self.call_count = 0 async def chat(self, prompt: str, **kwargs) -> str: """发送聊天请求（返回模拟结果）""" self.call_count += 1 logger.info(f"模拟 LLM 调用 (第 {self.call_count} 次)") # 简单的规则匹配 prompt_lower = prompt.lower() if "数学" in prompt_lower or "方程" in prompt_lower or "计算" in prompt_lower: return json.dumps({ "agent_id": "math-agent", "confidence": 0.9, "reason": "消息包含数学相关内容" }, ensure_ascii=False) elif "化学" in prompt_lower or "反应" in prompt_lower or "元素" in prompt_lower: return json.dumps({ "agent_id": "chemistry-agent", "confidence": 0.85, "reason": "消息包含化学相关内容" }, ensure_ascii=False) elif "物理" in prompt_lower or "力" in prompt_lower or "速度" in prompt_lower: return json.dumps({ "agent_id": "physics-agent", "confidence": 0.88, "reason": "消息包含物理相关内容" }, ensure_ascii=False) elif "语文" in prompt_lower or "古诗" in prompt_lower or "作文" in prompt_lower: return json.dumps({ "agent_id": "chinese-agent", "confidence": 0.87, "reason": "消息包含语文相关内容" }, ensure_ascii=False) elif "英语" in prompt_lower or "单词" in prompt_lower or "语法" in prompt_lower: return json.dumps({ "agent_id": "english-agent", "confidence": 0.86, "reason": "消息包含英语相关内容" }, ensure_ascii=False) else: return json.dumps({ "agent_id": "math-agent", "confidence": 0.5, "reason": "无法明确判断，使用默认 Agent" }, ensure_ascii=False) class LLMClientFactory: """LLM 客户端工厂""" @staticmethod def create( provider: str = "mock", **kwargs ) -> BaseLLMClient: """创建 LLM 客户端 Args: provider: 提供商名称 (openai, azure, anthropic, local, mock) **kwargs: 客户端配置参数 Returns: LLM 客户端实例 """ provider = provider.lower() if provider == "openai": return OpenAIClient(**kwargs) elif provider == "azure": return AzureOpenAIClient(**kwargs) elif provider == "anthropic": return AnthropicClient(**kwargs) elif provider == "local": return LocalLLMClient(**kwargs) elif provider == "mock": return MockLLMClient() else: raise ValueError(f"不支持的 LLM 提供商: {provider}") @staticmethod def create_from_env() -> BaseLLMClient: """从环境变量创建 LLM 客户端 环境变量： - LLM_PROVIDER: 提供商名称 - OPENAI_API_KEY: OpenAI API 密钥 - AZURE_OPENAI_API_KEY: Azure OpenAI API 密钥 - ANTHROPIC_API_KEY: Anthropic API 密钥 Returns: LLM 客户端实例 """ provider = os.getenv("LLM_PROVIDER", "mock") logger.info(f"从环境变量创建 LLM 客户端: {provider}") return LLMClientFactory.create(provider)