482 lines
18 KiB
Python
482 lines
18 KiB
Python
"""
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LLM服务 - 处理各种LLM提供商的调用
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"""
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from typing import Dict, Any, Optional, List
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import json
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import asyncio
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import logging
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from openai import AsyncOpenAI
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from app.core.config import settings
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from app.services.tool_registry import tool_registry
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logger = logging.getLogger(__name__)
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class LLMService:
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"""LLM服务类"""
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def __init__(self):
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"""初始化LLM服务"""
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self.openai_client = None
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self.deepseek_client = None
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# 初始化OpenAI客户端
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if settings.OPENAI_API_KEY:
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self.openai_client = AsyncOpenAI(
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api_key=settings.OPENAI_API_KEY,
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base_url=settings.OPENAI_BASE_URL
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)
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# 初始化DeepSeek客户端（兼容OpenAI API）
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if settings.DEEPSEEK_API_KEY:
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self.deepseek_client = AsyncOpenAI(
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api_key=settings.DEEPSEEK_API_KEY,
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base_url=settings.DEEPSEEK_BASE_URL
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)
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async def call_openai(
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self,
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prompt: str,
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model: str = "gpt-3.5-turbo",
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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**kwargs
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) -> str:
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"""
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调用OpenAI API
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Args:
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prompt: 提示词
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model: 模型名称，默认gpt-3.5-turbo
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temperature: 温度参数，默认0.7
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max_tokens: 最大token数
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api_key: API密钥（可选，如果不提供则使用默认配置）
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base_url: API地址（可选，如果不提供则使用默认配置）
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**kwargs: 其他参数
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Returns:
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LLM返回的文本
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"""
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# 如果提供了api_key或base_url，创建临时客户端
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# 注意：api_key 可能是空字符串，需要检查是否为 None
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if api_key is not None or base_url is not None:
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# 如果提供了 api_key，使用它；否则使用系统默认配置
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final_api_key = api_key if api_key else settings.OPENAI_API_KEY
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final_base_url = base_url if base_url else settings.OPENAI_BASE_URL
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if not final_api_key:
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raise ValueError("OpenAI API Key未配置，请在节点配置中设置API Key或在环境变量中设置OPENAI_API_KEY")
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client = AsyncOpenAI(
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api_key=final_api_key,
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base_url=final_base_url
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)
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else:
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# 如果 openai_client 未初始化，尝试从 settings 重新读取并初始化
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if not self.openai_client:
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if settings.OPENAI_API_KEY:
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self.openai_client = AsyncOpenAI(
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api_key=settings.OPENAI_API_KEY,
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base_url=settings.OPENAI_BASE_URL
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)
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else:
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raise ValueError("OpenAI API Key未配置，请在节点配置中设置API Key或在环境变量中设置OPENAI_API_KEY")
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client = self.openai_client
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try:
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response = await client.chat.completions.create(
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model=model,
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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content = response.choices[0].message.content
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if content is None:
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raise Exception("OpenAI API返回的内容为空，请检查API配置和模型名称")
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return content
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except Exception as e:
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raise Exception(f"OpenAI API调用失败: {str(e)}")
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async def call_deepseek(
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self,
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prompt: str,
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model: str = "deepseek-chat",
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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**kwargs
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) -> str:
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"""
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调用DeepSeek API
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Args:
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prompt: 提示词
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model: 模型名称，默认deepseek-chat
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temperature: 温度参数，默认0.7
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max_tokens: 最大token数
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api_key: API密钥（可选，如果不提供则使用默认配置）
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base_url: API地址（可选，如果不提供则使用默认配置）
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**kwargs: 其他参数
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Returns:
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LLM返回的文本
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"""
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# 如果提供了api_key或base_url，创建临时客户端
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# 注意：api_key 可能是空字符串，需要检查是否为 None
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if api_key is not None or base_url is not None:
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# 如果提供了 api_key，使用它；否则使用系统默认配置
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final_api_key = api_key if api_key else settings.DEEPSEEK_API_KEY
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final_base_url = base_url if base_url else settings.DEEPSEEK_BASE_URL
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if not final_api_key:
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raise ValueError("DeepSeek API Key未配置，请在节点配置中设置API Key或在环境变量中设置DEEPSEEK_API_KEY")
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client = AsyncOpenAI(
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api_key=final_api_key,
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base_url=final_base_url
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)
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else:
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# 如果 deepseek_client 未初始化，尝试从 settings 重新读取并初始化
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if not self.deepseek_client:
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if settings.DEEPSEEK_API_KEY:
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self.deepseek_client = AsyncOpenAI(
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api_key=settings.DEEPSEEK_API_KEY,
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base_url=settings.DEEPSEEK_BASE_URL
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)
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else:
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raise ValueError("DeepSeek API Key未配置，请在节点配置中设置API Key或在环境变量中设置DEEPSEEK_API_KEY")
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client = self.deepseek_client
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try:
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# 记录实际发送给LLM的prompt
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import logging
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logger = logging.getLogger(__name__)
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logger.info(f"[rjb] DeepSeek实际发送的prompt前200字符: {prompt[:200] if len(prompt) > 200 else prompt}")
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response = await client.chat.completions.create(
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model=model,
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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content = response.choices[0].message.content
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if content is None:
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raise Exception("DeepSeek API返回的内容为空，请检查API配置和模型名称")
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return content
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except Exception as e:
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raise Exception(f"DeepSeek API调用失败: {str(e)}")
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async def call_llm(
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self,
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prompt: str,
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provider: str = "openai",
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model: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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**kwargs
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) -> str:
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"""
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通用LLM调用接口
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Args:
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prompt: 提示词
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provider: 提供商，支持openai、deepseek
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大token数
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**kwargs: 其他参数
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Returns:
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LLM返回的文本
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"""
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if provider == "openai":
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# 默认模型
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if not model:
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model = "gpt-3.5-turbo"
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return await self.call_openai(
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prompt=prompt,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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elif provider == "deepseek":
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# 默认模型
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if not model:
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model = "deepseek-chat"
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return await self.call_deepseek(
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prompt=prompt,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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else:
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raise ValueError(f"不支持的LLM提供商: {provider}，目前支持: openai, deepseek")
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async def call_openai_with_tools(
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self,
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prompt: str,
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tools: List[Dict[str, Any]],
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model: str = "gpt-3.5-turbo",
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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max_iterations: int = 5
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) -> str:
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"""
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调用OpenAI API，支持工具调用
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Args:
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prompt: 提示词
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tools: 工具定义列表（OpenAI Function格式）
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model: 模型名称
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temperature: 温度参数
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max_tokens: 最大token数
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api_key: API密钥
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base_url: API地址
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max_iterations: 最大工具调用迭代次数
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Returns:
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LLM返回的最终文本
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"""
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# 获取客户端
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if api_key is not None or base_url is not None:
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final_api_key = api_key if api_key else settings.OPENAI_API_KEY
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final_base_url = base_url if base_url else settings.OPENAI_BASE_URL
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if not final_api_key:
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raise ValueError("OpenAI API Key未配置")
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client = AsyncOpenAI(api_key=final_api_key, base_url=final_base_url)
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else:
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if not self.openai_client:
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if settings.OPENAI_API_KEY:
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self.openai_client = AsyncOpenAI(
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api_key=settings.OPENAI_API_KEY,
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base_url=settings.OPENAI_BASE_URL
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)
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else:
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raise ValueError("OpenAI API Key未配置")
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client = self.openai_client
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messages = [{"role": "user", "content": prompt}]
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try:
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for iteration in range(max_iterations):
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# 准备工具参数（只在第一次调用时传递tools）
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create_kwargs = {
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"model": 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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if iteration == 0:
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# 转换工具格式为OpenAI格式
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openai_tools = []
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for tool in tools:
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if isinstance(tool, dict):
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if "type" in tool and tool["type"] == "function":
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openai_tools.append(tool)
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elif "function" in tool:
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openai_tools.append(tool)
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else:
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# 假设是function格式，包装一下
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openai_tools.append({
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"type": "function",
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"function": tool
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})
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create_kwargs["tools"] = openai_tools
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create_kwargs["tool_choice"] = "auto"
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# 调用LLM
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response = await client.chat.completions.create(**create_kwargs)
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message = response.choices[0].message
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# 添加助手回复到消息历史
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messages.append({
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"role": "assistant",
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"content": message.content,
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"tool_calls": [
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{
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"id": tc.id,
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"type": tc.type,
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"function": {
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"name": tc.function.name,
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"arguments": tc.function.arguments
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}
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} for tc in (message.tool_calls or [])
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]
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})
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# 检查是否有工具调用
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if message.tool_calls and len(message.tool_calls) > 0:
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logger.info(f"检测到 {len(message.tool_calls)} 个工具调用")
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# 处理每个工具调用
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for tool_call in message.tool_calls:
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tool_name = tool_call.function.name
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try:
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tool_args = json.loads(tool_call.function.arguments)
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except:
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tool_args = {}
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logger.info(f"执行工具: {tool_name}, 参数: {tool_args}")
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# 执行工具
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tool_result = await self._execute_tool(tool_name, tool_args)
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# 添加工具结果到消息历史
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"content": tool_result
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})
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else:
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# 没有工具调用，返回最终回复
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final_content = message.content or ""
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if final_content:
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logger.info("LLM返回最终回复，工具调用完成")
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return final_content
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# 达到最大迭代次数
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logger.warning(f"达到最大工具调用迭代次数 ({max_iterations})")
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last_message = messages[-1] if messages else {}
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return last_message.get("content", "达到最大工具调用次数")
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except Exception as e:
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logger.error(f"工具调用过程中出错: {str(e)}")
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raise Exception(f"OpenAI工具调用失败: {str(e)}")
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async def call_deepseek_with_tools(
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self,
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prompt: str,
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tools: List[Dict[str, Any]],
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model: str = "deepseek-chat",
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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api_key: Optional[str] = None,
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base_url: Optional[str] = None,
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max_iterations: int = 5
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) -> str:
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"""
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调用DeepSeek API，支持工具调用（DeepSeek兼容OpenAI API格式）
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"""
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# DeepSeek使用相同的实现
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return await self.call_openai_with_tools(
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prompt=prompt,
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tools=tools,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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api_key=api_key or settings.DEEPSEEK_API_KEY,
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base_url=base_url or settings.DEEPSEEK_BASE_URL,
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max_iterations=max_iterations
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)
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async def call_llm_with_tools(
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self,
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prompt: str,
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tools: List[Dict[str, Any]],
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provider: str = "openai",
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model: Optional[str] = None,
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temperature: float = 0.7,
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max_tokens: Optional[int] = None,
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**kwargs
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) -> str:
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"""
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通用LLM调用接口（支持工具）
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Args:
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prompt: 提示词
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tools: 工具定义列表
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provider: 提供商，支持openai、deepseek
|
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model: 模型名称
|
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temperature: 温度参数
|
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max_tokens: 最大token数
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**kwargs: 其他参数
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|
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Returns:
|
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LLM返回的最终文本
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"""
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if provider == "openai":
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if not model:
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model = "gpt-3.5-turbo"
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return await self.call_openai_with_tools(
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prompt=prompt,
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tools=tools,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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elif provider == "deepseek":
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if not model:
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model = "deepseek-chat"
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return await self.call_deepseek_with_tools(
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prompt=prompt,
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tools=tools,
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model=model,
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temperature=temperature,
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max_tokens=max_tokens,
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**kwargs
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)
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else:
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raise ValueError(f"不支持的LLM提供商: {provider}，目前支持: openai, deepseek")
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async def _execute_tool(self, tool_name: str, tool_args: Dict[str, Any]) -> str:
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"""
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执行工具
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Args:
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tool_name: 工具名称
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tool_args: 工具参数
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Returns:
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工具执行结果（JSON字符串）
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"""
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# 从注册表获取工具函数
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tool_func = tool_registry.get_tool_function(tool_name)
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if not tool_func:
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error_msg = f"工具 {tool_name} 未找到"
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logger.error(error_msg)
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return json.dumps({"error": error_msg}, ensure_ascii=False)
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try:
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logger.info(f"执行工具 {tool_name}，参数: {tool_args}")
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# 执行工具（支持异步函数）
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if asyncio.iscoroutinefunction(tool_func):
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result = await tool_func(**tool_args)
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else:
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# 同步函数在事件循环中执行
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result = tool_func(**tool_args)
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# 将结果转换为字符串
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if isinstance(result, (dict, list)):
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result_str = json.dumps(result, ensure_ascii=False)
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else:
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result_str = str(result)
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logger.info(f"工具 {tool_name} 执行成功，结果长度: {len(result_str)}")
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return result_str
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except Exception as e:
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error_msg = f"工具 {tool_name} 执行失败: {str(e)}"
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logger.error(error_msg, exc_info=True)
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return json.dumps({"error": error_msg}, ensure_ascii=False)
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# 全局LLM服务实例
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llm_service = LLMService()
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