148 lines
4.7 KiB
Python
148 lines
4.7 KiB
Python
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"""
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API 路由模块
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定义 FastAPI 接口路由
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"""
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from fastapi import APIRouter, HTTPException
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from pydantic import BaseModel
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from typing import Optional, List, Dict
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from app.services import ai_service
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from app.tools.base import ToolResult
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import json
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from app.tools.registry import ToolRegistry
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router = APIRouter()
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class ChatRequest(BaseModel):
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"""聊天请求模型"""
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message: str
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system_prompt: Optional[str] = None
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history: Optional[List[Dict[str, str]]] = None
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class ChatResponse(BaseModel):
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"""聊天响应模型"""
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success: bool
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message: str
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response: str
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class TimeResponse(BaseModel):
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"""时间响应模型"""
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success: bool
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data: Dict
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@router.post("/chat-with-tools", response_model=ChatResponse)
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async def chat_with_tools(request: ChatRequest):
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"""
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带工具调用的 AI 聊天接口
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AI 可以调用工具（如获取当前时间）来回答问题
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Args:
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request: 聊天请求
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Returns:
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AI 的回复（可能包含工具调用结果）
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"""
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try:
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# 获取所有可用工具的 schema
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tools_instances = ToolRegistry.get_instances()
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tools = [tool.to_openai_format() for tool in tools_instances]
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# 系统提示，告诉 AI 可以使用哪些工具
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system_prompt = """你是一个智能助手，可以使用工具来帮助回答问题"""
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# 第一次调用 AI
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result = await ai_service.chat_with_tools(
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message=request.message,
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tools=tools,
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system_prompt=system_prompt
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)
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message = result["message"]
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# 检查是否有工具调用
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if message.tool_calls:
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# 处理工具调用
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tool_results = []
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for tool_call in message.tool_calls:
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function_name = tool_call.function.name
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function_args = json.loads(tool_call.function.arguments)
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# 从 ToolRegistry 获取并执行工具
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tools_dict = ToolRegistry.get_all()
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if function_name in tools_dict:
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tool_instance = tools_dict[function_name]()
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tool_result = tool_instance.execute(**function_args)
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# 处理 ToolResult 对象
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if isinstance(tool_result, ToolResult):
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result_data = {
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"success": tool_result.success,
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"data": tool_result.data,
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"error": tool_result.error
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}
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else:
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result_data = tool_result
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else:
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result_data = {
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"success": False,
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"error": f"工具 '{function_name}' 未找到"
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}
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tool_results.append({
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"tool_call_id": tool_call.id,
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"role": "tool",
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"name": function_name,
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"content": json.dumps(result_data, ensure_ascii=False)
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})
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# 构建新的消息历史
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messages = [
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{"role": "system", "content": system_prompt},
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{"role": "user", "content": request.message},
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{"role": "assistant", "content": message.content, "tool_calls": [
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{
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"id": tc.id,
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"type": "function",
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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
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]}
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]
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messages.extend(tool_results)
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# 再次调用 AI，让它根据工具结果生成最终回复
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from openai import OpenAI
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from app.config import settings
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client = OpenAI(
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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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final_response = client.chat.completions.create(
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model=settings.openai_model,
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messages=messages
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)
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final_content = final_response.choices[0].message.content
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else:
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# 没有工具调用，直接返回 AI 的回复
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final_content = message.content or "抱歉，我无法理解您的问题。"
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return ChatResponse(
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success=True,
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message="对话成功",
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response=final_content
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)
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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