feat: Add base project structure with API and web components
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Ke Sun
340
api/app/services/llm_client.py
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340
api/app/services/llm_client.py
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"""LLM 客户端适配器 - 支持多种 LLM 提供商"""
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import os
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import json
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from typing import Optional, Dict, Any
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from abc import ABC, abstractmethod
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from app.core.logging_config import get_business_logger
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logger = get_business_logger()
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class BaseLLMClient(ABC):
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"""LLM 客户端基类"""
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@abstractmethod
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async def chat(self, prompt: str, **kwargs) -> str:
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"""发送聊天请求
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Args:
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prompt: 提示词
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**kwargs: 其他参数
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Returns:
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LLM 响应文本
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"""
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pass
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class OpenAIClient(BaseLLMClient):
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"""OpenAI 客户端"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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model: str = "gpt-3.5-turbo",
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base_url: Optional[str] = None
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):
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"""初始化 OpenAI 客户端
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Args:
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api_key: API 密钥
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model: 模型名称
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base_url: API 基础 URL（可选，用于兼容其他服务）
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"""
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self.api_key = api_key or os.getenv("OPENAI_API_KEY")
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self.model = model
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self.base_url = base_url
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if not self.api_key:
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raise ValueError("OpenAI API key 未配置")
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try:
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from openai import AsyncOpenAI
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self.client = AsyncOpenAI(
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api_key=self.api_key,
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base_url=self.base_url
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)
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except ImportError:
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raise ImportError("请安装 openai 库: pip install openai")
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async def chat(self, prompt: str, **kwargs) -> str:
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"""发送聊天请求
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Args:
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prompt: 提示词
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**kwargs: 其他参数（temperature, max_tokens 等）
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Returns:
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LLM 响应文本
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"""
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try:
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response = await self.client.chat.completions.create(
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model=self.model,
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messages=[{"role": "user", "content": prompt}],
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temperature=kwargs.get("temperature", 0.3),
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max_tokens=kwargs.get("max_tokens", 500)
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)
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return response.choices[0].message.content
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except Exception as e:
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logger.error(f"OpenAI API 调用失败: {str(e)}")
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raise
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class AzureOpenAIClient(BaseLLMClient):
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"""Azure OpenAI 客户端"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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endpoint: Optional[str] = None,
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deployment_name: Optional[str] = None,
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api_version: str = "2024-02-15-preview"
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):
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"""初始化 Azure OpenAI 客户端
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Args:
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api_key: API 密钥
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endpoint: Azure 端点
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deployment_name: 部署名称
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api_version: API 版本
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"""
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self.api_key = api_key or os.getenv("AZURE_OPENAI_API_KEY")
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self.endpoint = endpoint or os.getenv("AZURE_OPENAI_ENDPOINT")
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self.deployment_name = deployment_name or os.getenv("AZURE_OPENAI_DEPLOYMENT")
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self.api_version = api_version
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if not all([self.api_key, self.endpoint, self.deployment_name]):
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raise ValueError("Azure OpenAI 配置不完整")
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try:
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from openai import AsyncAzureOpenAI
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self.client = AsyncAzureOpenAI(
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api_key=self.api_key,
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azure_endpoint=self.endpoint,
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api_version=self.api_version
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)
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except ImportError:
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raise ImportError("请安装 openai 库: pip install openai")
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async def chat(self, prompt: str, **kwargs) -> str:
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"""发送聊天请求"""
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try:
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response = await self.client.chat.completions.create(
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model=self.deployment_name,
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messages=[{"role": "user", "content": prompt}],
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temperature=kwargs.get("temperature", 0.3),
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max_tokens=kwargs.get("max_tokens", 500)
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)
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return response.choices[0].message.content
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except Exception as e:
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logger.error(f"Azure OpenAI API 调用失败: {str(e)}")
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raise
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class AnthropicClient(BaseLLMClient):
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"""Anthropic Claude 客户端"""
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def __init__(
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self,
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api_key: Optional[str] = None,
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model: str = "claude-3-sonnet-20240229"
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):
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"""初始化 Anthropic 客户端
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Args:
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api_key: API 密钥
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model: 模型名称
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"""
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self.api_key = api_key or os.getenv("ANTHROPIC_API_KEY")
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self.model = model
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if not self.api_key:
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raise ValueError("Anthropic API key 未配置")
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try:
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from anthropic import AsyncAnthropic
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self.client = AsyncAnthropic(api_key=self.api_key)
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except ImportError:
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raise ImportError("请安装 anthropic 库: pip install anthropic")
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async def chat(self, prompt: str, **kwargs) -> str:
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"""发送聊天请求"""
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try:
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response = await self.client.messages.create(
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model=self.model,
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max_tokens=kwargs.get("max_tokens", 500),
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temperature=kwargs.get("temperature", 0.3),
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messages=[{"role": "user", "content": prompt}]
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)
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return response.content[0].text
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except Exception as e:
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logger.error(f"Anthropic API 调用失败: {str(e)}")
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raise
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class LocalLLMClient(BaseLLMClient):
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"""本地 LLM 客户端（通过 HTTP API）"""
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def __init__(
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self,
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base_url: str = "http://localhost:8000",
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model: str = "local-model"
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):
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"""初始化本地 LLM 客户端
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Args:
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base_url: API 基础 URL
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model: 模型名称
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"""
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self.base_url = base_url
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self.model = model
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try:
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import httpx
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self.client = httpx.AsyncClient(timeout=30.0)
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except ImportError:
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raise ImportError("请安装 httpx 库: pip install httpx")
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async def chat(self, prompt: str, **kwargs) -> str:
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"""发送聊天请求"""
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try:
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response = await self.client.post(
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f"{self.base_url}/v1/chat/completions",
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json={
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"model": self.model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": kwargs.get("temperature", 0.3),
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"max_tokens": kwargs.get("max_tokens", 500)
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}
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)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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except Exception as e:
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logger.error(f"本地 LLM API 调用失败: {str(e)}")
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raise
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class MockLLMClient(BaseLLMClient):
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"""模拟 LLM 客户端（用于测试）"""
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def __init__(self):
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"""初始化模拟客户端"""
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self.call_count = 0
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async def chat(self, prompt: str, **kwargs) -> str:
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"""发送聊天请求（返回模拟结果）"""
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self.call_count += 1
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logger.info(f"模拟 LLM 调用 (第 {self.call_count} 次)")
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# 简单的规则匹配
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prompt_lower = prompt.lower()
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if "数学" in prompt_lower or "方程" in prompt_lower or "计算" in prompt_lower:
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return json.dumps({
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"agent_id": "math-agent",
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"confidence": 0.9,
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"reason": "消息包含数学相关内容"
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}, ensure_ascii=False)
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elif "化学" in prompt_lower or "反应" in prompt_lower or "元素" in prompt_lower:
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return json.dumps({
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"agent_id": "chemistry-agent",
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"confidence": 0.85,
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"reason": "消息包含化学相关内容"
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}, ensure_ascii=False)
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elif "物理" in prompt_lower or "力" in prompt_lower or "速度" in prompt_lower:
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return json.dumps({
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"agent_id": "physics-agent",
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"confidence": 0.88,
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"reason": "消息包含物理相关内容"
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}, ensure_ascii=False)
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elif "语文" in prompt_lower or "古诗" in prompt_lower or "作文" in prompt_lower:
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return json.dumps({
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"agent_id": "chinese-agent",
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"confidence": 0.87,
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"reason": "消息包含语文相关内容"
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}, ensure_ascii=False)
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elif "英语" in prompt_lower or "单词" in prompt_lower or "语法" in prompt_lower:
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return json.dumps({
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"agent_id": "english-agent",
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"confidence": 0.86,
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"reason": "消息包含英语相关内容"
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}, ensure_ascii=False)
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else:
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return json.dumps({
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"agent_id": "math-agent",
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"confidence": 0.5,
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"reason": "无法明确判断，使用默认 Agent"
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}, ensure_ascii=False)
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class LLMClientFactory:
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"""LLM 客户端工厂"""
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@staticmethod
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def create(
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provider: str = "mock",
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**kwargs
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) -> BaseLLMClient:
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"""创建 LLM 客户端
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Args:
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provider: 提供商名称 (openai, azure, anthropic, local, mock)
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**kwargs: 客户端配置参数
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Returns:
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LLM 客户端实例
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"""
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provider = provider.lower()
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if provider == "openai":
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return OpenAIClient(**kwargs)
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elif provider == "azure":
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return AzureOpenAIClient(**kwargs)
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elif provider == "anthropic":
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return AnthropicClient(**kwargs)
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elif provider == "local":
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return LocalLLMClient(**kwargs)
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elif provider == "mock":
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return MockLLMClient()
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else:
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raise ValueError(f"不支持的 LLM 提供商: {provider}")
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@staticmethod
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def create_from_env() -> BaseLLMClient:
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"""从环境变量创建 LLM 客户端
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环境变量：
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- LLM_PROVIDER: 提供商名称
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- OPENAI_API_KEY: OpenAI API 密钥
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- AZURE_OPENAI_API_KEY: Azure OpenAI API 密钥
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- ANTHROPIC_API_KEY: Anthropic API 密钥
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Returns:
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LLM 客户端实例
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"""
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provider = os.getenv("LLM_PROVIDER", "mock")
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logger.info(f"从环境变量创建 LLM 客户端: {provider}")
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return LLMClientFactory.create(provider)
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