agent功能开发增加MCP后端
This commit is contained in:
177
mcp_server.py
177
mcp_server.py
@@ -6,9 +6,9 @@ MCP Server for Financial Data Search
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from fastapi import FastAPI, HTTPException, Request
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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from fastapi.responses import JSONResponse, StreamingResponse
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from pydantic import BaseModel, Field
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from typing import List, Dict, Any, Optional, Literal
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from typing import List, Dict, Any, Optional, Literal, AsyncGenerator
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from datetime import datetime, date
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import logging
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import httpx
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@@ -16,6 +16,7 @@ from enum import Enum
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import mcp_database as db
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from openai import OpenAI
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import json
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import asyncio
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# 配置日志
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logging.basicConfig(level=logging.INFO)
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@@ -1327,7 +1328,7 @@ class MCPAgentIntegrated:
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tools: List[dict],
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tool_handlers: Dict[str, Any],
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) -> AgentResponse:
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"""主流程"""
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"""主流程（非流式）"""
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logger.info(f"[Agent] 处理查询: {user_query}")
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try:
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@@ -1368,6 +1369,131 @@ class MCPAgentIntegrated:
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message=f"处理失败: {str(e)}",
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)
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async def process_query_stream(
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self,
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user_query: str,
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tools: List[dict],
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tool_handlers: Dict[str, Any],
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) -> AsyncGenerator[str, None]:
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"""主流程（流式输出）- 逐步返回执行结果"""
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logger.info(f"[Agent Stream] 处理查询: {user_query}")
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try:
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# 发送开始事件
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yield self._format_sse("status", {"stage": "start", "message": "开始处理查询"})
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# 阶段1: Kimi 制定计划
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yield self._format_sse("status", {"stage": "planning", "message": "正在制定执行计划..."})
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plan = await self.create_plan(user_query, tools)
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# 发送计划
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yield self._format_sse("plan", {
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"goal": plan.goal,
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"reasoning": plan.reasoning,
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"steps": [
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{"tool": step.tool, "arguments": step.arguments, "reason": step.reason}
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for step in plan.steps
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],
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})
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# 阶段2: 执行工具（逐步返回）
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yield self._format_sse("status", {"stage": "executing", "message": f"开始执行 {len(plan.steps)} 个步骤"})
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step_results = []
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collected_data = {}
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for i, step in enumerate(plan.steps):
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# 发送步骤开始事件
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yield self._format_sse("step_start", {
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"step_index": i,
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"tool": step.tool,
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"arguments": step.arguments,
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"reason": step.reason,
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})
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start_time = datetime.now()
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try:
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# 替换占位符
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arguments = step.arguments.copy()
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if step.tool == "summarize_news":
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if arguments.get("data") in ["前面的新闻数据", "前面收集的所有数据"]:
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arguments["data"] = json.dumps(collected_data, ensure_ascii=False, indent=2)
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# 执行工具
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result = await self.execute_tool(step.tool, arguments, tool_handlers)
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execution_time = (datetime.now() - start_time).total_seconds()
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step_result = StepResult(
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step_index=i,
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tool=step.tool,
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arguments=arguments,
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status="success",
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result=result,
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execution_time=execution_time,
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)
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step_results.append(step_result)
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collected_data[f"step_{i+1}_{step.tool}"] = result
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# 发送步骤完成事件（包含结果）
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yield self._format_sse("step_complete", {
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"step_index": i,
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"tool": step.tool,
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"status": "success",
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"result": result,
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"execution_time": execution_time,
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})
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except Exception as e:
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execution_time = (datetime.now() - start_time).total_seconds()
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step_result = StepResult(
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step_index=i,
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tool=step.tool,
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arguments=step.arguments,
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status="failed",
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error=str(e),
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execution_time=execution_time,
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)
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step_results.append(step_result)
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# 发送步骤失败事件
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yield self._format_sse("step_complete", {
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"step_index": i,
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"tool": step.tool,
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"status": "failed",
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"error": str(e),
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"execution_time": execution_time,
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})
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# 阶段3: Kimi 生成总结
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yield self._format_sse("status", {"stage": "summarizing", "message": "正在生成最终总结..."})
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final_summary = await self.generate_final_summary(user_query, plan, step_results)
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# 发送最终总结
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yield self._format_sse("summary", {
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"content": final_summary,
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"metadata": {
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"total_steps": len(plan.steps),
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"successful_steps": len([r for r in step_results if r.status == "success"]),
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"failed_steps": len([r for r in step_results if r.status == "failed"]),
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"total_execution_time": sum(r.execution_time for r in step_results),
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},
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})
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# 发送完成事件
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yield self._format_sse("done", {"message": "处理完成"})
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except Exception as e:
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logger.error(f"[Agent Stream] 错误: {str(e)}", exc_info=True)
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yield self._format_sse("error", {"message": f"处理失败: {str(e)}"})
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def _format_sse(self, event: str, data: dict) -> str:
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"""格式化 SSE 消息"""
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return f"event: {event}\ndata: {json.dumps(data, ensure_ascii=False)}\n\n"
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# 创建 Agent 实例（全局）
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agent = MCPAgentIntegrated()
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@@ -1406,7 +1532,7 @@ async def chat(request: ChatRequest):
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@app.post("/agent/chat", response_model=AgentResponse)
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async def agent_chat(request: AgentChatRequest):
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"""智能代理对话端点"""
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"""智能代理对话端点（非流式）"""
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logger.info(f"Agent chat: {request.message}")
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# 获取工具列表
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@@ -1441,6 +1567,49 @@ async def agent_chat(request: AgentChatRequest):
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return response
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@app.post("/agent/chat/stream")
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async def agent_chat_stream(request: AgentChatRequest):
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"""智能代理对话端点（流式 SSE）"""
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logger.info(f"Agent chat stream: {request.message}")
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# 获取工具列表
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tools = [tool.dict() for tool in TOOLS]
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# 添加特殊工具：summarize_news
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tools.append({
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"name": "summarize_news",
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"description": "使用 DeepMoney 模型总结新闻数据，提取关键信息",
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"parameters": {
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"type": "object",
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"properties": {
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"data": {
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"type": "string",
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"description": "要总结的新闻数据（JSON格式）"
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},
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"focus": {
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"type": "string",
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"description": "关注点，例如：'市场影响'、'投资机会'等"
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}
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},
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"required": ["data"]
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}
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})
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# 返回流式响应
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return StreamingResponse(
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agent.process_query_stream(
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user_query=request.message,
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tools=tools,
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tool_handlers=TOOL_HANDLERS,
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),
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media_type="text/event-stream",
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headers={
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"Cache-Control": "no-cache",
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"Connection": "keep-alive",
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"X-Accel-Buffering": "no", # 禁用 Nginx 缓冲
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},
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)
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# ==================== 健康检查 ====================
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@app.get("/health")
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