6ef72837a5
- 创建IntelligentAgent核心，支持预警处理和知识库置信度处理 - 集成LLM客户端，支持OpenAI、Anthropic和本地LLM - 实现ActionExecutor动作执行器，支持多种动作类型 - 优化TSPAgentAssistant，集成大模型和智能决策能力 - 添加预警自动处理措施，包括重启服务、检查状态、通知等 - 实现知识库置信度处理，自动增强低置信度知识条目 - 支持动作执行历史记录和统计 - 提供LLM使用统计和监控功能
245 lines
8.3 KiB
Python
245 lines
8.3 KiB
Python
#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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大模型客户端 - 统一的LLM接口
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支持多种大模型提供商
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"""
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import logging
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import asyncio
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import json
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from typing import Dict, Any, Optional, List
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from abc import ABC, abstractmethod
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from dataclasses import dataclass
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logger = logging.getLogger(__name__)
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@dataclass
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class LLMConfig:
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"""LLM配置"""
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provider: str # openai, anthropic, local, etc.
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api_key: str
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base_url: Optional[str] = None
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model: str = "gpt-3.5-turbo"
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temperature: float = 0.7
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max_tokens: int = 2000
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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 generate(self, prompt: str, **kwargs) -> str:
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"""生成文本"""
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pass
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@abstractmethod
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async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
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"""对话生成"""
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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__(self, config: LLMConfig):
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self.config = config
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self.client = None
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self._init_client()
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def _init_client(self):
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"""初始化客户端"""
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try:
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import openai
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self.client = openai.AsyncOpenAI(
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api_key=self.config.api_key,
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base_url=self.config.base_url
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)
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except ImportError:
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logger.warning("OpenAI库未安装，将使用模拟客户端")
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self.client = None
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async def generate(self, prompt: str, **kwargs) -> str:
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"""生成文本"""
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if not self.client:
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return self._simulate_response(prompt)
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try:
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response = await self.client.chat.completions.create(
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model=self.config.model,
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messages=[{"role": "user", "content": prompt}],
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temperature=kwargs.get("temperature", self.config.temperature),
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max_tokens=kwargs.get("max_tokens", self.config.max_tokens)
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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: {e}")
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return self._simulate_response(prompt)
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async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
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"""对话生成"""
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if not self.client:
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return self._simulate_chat(messages)
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try:
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response = await self.client.chat.completions.create(
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model=self.config.model,
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messages=messages,
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temperature=kwargs.get("temperature", self.config.temperature),
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max_tokens=kwargs.get("max_tokens", self.config.max_tokens)
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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 Chat API调用失败: {e}")
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return self._simulate_chat(messages)
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def _simulate_response(self, prompt: str) -> str:
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"""模拟响应"""
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return f"模拟LLM响应: {prompt[:100]}..."
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def _simulate_chat(self, messages: List[Dict[str, str]]) -> str:
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"""模拟对话响应"""
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last_message = messages[-1]["content"] if messages else ""
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return f"模拟对话响应: {last_message[:100]}..."
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class AnthropicClient(BaseLLMClient):
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"""Anthropic客户端"""
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def __init__(self, config: LLMConfig):
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self.config = config
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self.client = None
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self._init_client()
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def _init_client(self):
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"""初始化客户端"""
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try:
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import anthropic
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self.client = anthropic.AsyncAnthropic(
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api_key=self.config.api_key
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)
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except ImportError:
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logger.warning("Anthropic库未安装，将使用模拟客户端")
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self.client = None
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async def generate(self, prompt: str, **kwargs) -> str:
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"""生成文本"""
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if not self.client:
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return self._simulate_response(prompt)
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try:
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response = await self.client.messages.create(
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model=self.config.model,
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max_tokens=kwargs.get("max_tokens", self.config.max_tokens),
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temperature=kwargs.get("temperature", self.config.temperature),
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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: {e}")
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return self._simulate_response(prompt)
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async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
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"""对话生成"""
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if not self.client:
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return self._simulate_chat(messages)
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try:
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response = await self.client.messages.create(
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model=self.config.model,
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max_tokens=kwargs.get("max_tokens", self.config.max_tokens),
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temperature=kwargs.get("temperature", self.config.temperature),
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messages=messages
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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 Chat API调用失败: {e}")
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return self._simulate_chat(messages)
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def _simulate_response(self, prompt: str) -> str:
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"""模拟响应"""
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return f"模拟Anthropic响应: {prompt[:100]}..."
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def _simulate_chat(self, messages: List[Dict[str, str]]) -> str:
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"""模拟对话响应"""
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last_message = messages[-1]["content"] if messages else ""
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return f"模拟Anthropic对话: {last_message[:100]}..."
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class LocalLLMClient(BaseLLMClient):
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"""本地LLM客户端"""
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def __init__(self, config: LLMConfig):
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self.config = config
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self.client = None
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self._init_client()
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def _init_client(self):
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"""初始化本地客户端"""
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try:
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# 这里可以集成Ollama、vLLM等本地LLM服务
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logger.info("本地LLM客户端初始化")
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except Exception as e:
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logger.warning(f"本地LLM客户端初始化失败: {e}")
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async def generate(self, prompt: str, **kwargs) -> str:
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"""生成文本"""
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# 实现本地LLM调用
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return f"本地LLM响应: {prompt[:100]}..."
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async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
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"""对话生成"""
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last_message = messages[-1]["content"] if messages else ""
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return f"本地LLM对话: {last_message[:100]}..."
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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_client(config: LLMConfig) -> BaseLLMClient:
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"""创建LLM客户端"""
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if config.provider.lower() == "openai":
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return OpenAIClient(config)
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elif config.provider.lower() == "anthropic":
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return AnthropicClient(config)
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elif config.provider.lower() == "local":
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return LocalLLMClient(config)
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else:
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raise ValueError(f"不支持的LLM提供商: {config.provider}")
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class LLMManager:
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"""LLM管理器"""
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def __init__(self, config: LLMConfig):
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self.config = config
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self.client = LLMClientFactory.create_client(config)
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self.usage_stats = {
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"total_requests": 0,
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"total_tokens": 0,
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"error_count": 0
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}
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async def generate(self, prompt: str, **kwargs) -> str:
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"""生成文本"""
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try:
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self.usage_stats["total_requests"] += 1
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response = await self.client.generate(prompt, **kwargs)
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self.usage_stats["total_tokens"] += len(response)
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return response
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except Exception as e:
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self.usage_stats["error_count"] += 1
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logger.error(f"LLM生成失败: {e}")
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raise
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async def chat(self, messages: List[Dict[str, str]], **kwargs) -> str:
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"""对话生成"""
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try:
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self.usage_stats["total_requests"] += 1
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response = await self.client.chat(messages, **kwargs)
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self.usage_stats["total_tokens"] += len(response)
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return response
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except Exception as e:
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self.usage_stats["error_count"] += 1
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logger.error(f"LLM对话失败: {e}")
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raise
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def get_usage_stats(self) -> Dict[str, Any]:
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"""获取使用统计"""
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return self.usage_stats.copy()
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