172 lines
5.3 KiB
Python
172 lines
5.3 KiB
Python
import logging
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from fastapi import FastAPI, HTTPException
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from fastapi.middleware.cors import CORSMiddleware
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from pydantic import BaseModel
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from typing import List, Optional
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from openai import OpenAI
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import json
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import os
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from rpa_client import rpa_prompt, api_key, base_url
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# 配置日志
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s [%(levelname)s] %(message)s'
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)
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logger = logging.getLogger(__name__)
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# 配置对话日志
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conversation_logger = logging.getLogger('conversation_logger')
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conversation_logger.setLevel(logging.INFO)
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file_handler = logging.FileHandler('rpa_conversation.log')
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file_handler.setLevel(logging.INFO)
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formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')
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file_handler.setFormatter(formatter)
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conversation_logger.addHandler(file_handler)
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# 初始化 FastAPI 应用
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app = FastAPI(title="RPA Chat API")
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# 配置 CORS
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"],
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)
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# 初始化 OpenAI 客户端
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client = OpenAI(api_key=api_key, base_url=base_url)
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# 请求模型
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class ChatRequest(BaseModel):
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message: str
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allowed_paths: Optional[str] = None
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# 响应模型
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class ChatResponse(BaseModel):
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response: str
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commands: List[dict]
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# 存储对话历史的字典
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conversations = {}
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def get_conversation_history(conversation_id: str) -> list:
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"""获取或创建对话历史"""
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if conversation_id not in conversations:
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# 初始化新的对话，包含系统提示
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conversations[conversation_id] = [
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{
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"role": "system",
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"content": rpa_prompt
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}
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]
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return conversations[conversation_id]
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async def interact_with_chatgpt(messages: list) -> str:
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"""与 ChatGPT 交互"""
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try:
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response = client.chat.completions.create(
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model="gpt-4o-mini",
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messages=messages,
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max_tokens=500,
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temperature=0.1,
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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 调用错误: {e}")
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raise
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def parse_chatgpt_response(response_text: str) -> tuple[str, list]:
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"""解析 ChatGPT 的响应"""
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try:
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# 清理响应文本
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response_text = response_text.strip()
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if response_text.startswith("```json"):
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response_text = response_text.replace("```json", "", 1)
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if response_text.endswith("```"):
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response_text = response_text.replace("```", "", 1)
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# 解析 JSON
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data = json.loads(response_text.strip())
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return data["response"], data.get("commands", [])
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except Exception as e:
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logger.error(f"解析响应失败: {e}")
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return "解析响应时出错", []
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@app.post("/chat", response_model=ChatResponse)
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async def chat(request: ChatRequest):
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"""处理聊天请求"""
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try:
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# 生成会话ID (这里简单使用固定ID，您可以根据需要修改)
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conversation_id = "default"
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# 获取对话历史
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messages = get_conversation_history(conversation_id)
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# # 如果提供了新的允许路径，更新系统提示
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# if request.allowed_paths:
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# messages[0]["content"] = rpa_prompt.replace(
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# "{allowed_paths}",
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# request.allowed_paths
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# )
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# 添加用户消息
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messages.append({
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"role": "user",
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"content": request.message
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})
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# 保持对话历史在合理长度
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if len(messages) > 10:
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# 保留系统消息和最近的对话
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messages = [messages[0]] + messages[-9:]
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# 记录对话
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conversation_logger.info(f"User: {request.message}")
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# 调用 ChatGPT
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chatgpt_response = await interact_with_chatgpt(messages)
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# 解析响应
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response_text, commands = parse_chatgpt_response(chatgpt_response)
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# 记录响应
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conversation_logger.info(f"Assistant: {response_text}")
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conversation_logger.info(f"Commands: {commands}")
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# 添加助手响应到对话历史
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messages.append({
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"role": "assistant",
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"content": chatgpt_response
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})
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return ChatResponse(
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response=response_text,
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commands=commands
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)
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except Exception as e:
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logger.error(f"处理请求时出错: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/conversations/{conversation_id}")
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async def get_conversation(conversation_id: str):
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"""获取对话历史"""
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if conversation_id not in conversations:
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raise HTTPException(status_code=404, detail="Conversation not found")
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return conversations[conversation_id]
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@app.delete("/conversations/{conversation_id}")
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async def clear_conversation(conversation_id: str):
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"""清除对话历史"""
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if conversation_id in conversations:
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# 保留系统提示
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system_prompt = conversations[conversation_id][0]
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conversations[conversation_id] = [system_prompt]
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return {"status": "success"}
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=11089) |