agent功能开发增加MCP后端
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mcp_agent_system.py
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470
mcp_agent_system.py
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"""
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MCP Agent System - 基于 DeepResearch 逻辑的智能代理系统
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三阶段流程：计划制定 → 工具执行 → 结果总结
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"""
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from pydantic import BaseModel
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from typing import List, Dict, Any, Optional, Literal
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from datetime import datetime
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import json
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import logging
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from openai import OpenAI
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import asyncio
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import os
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logger = logging.getLogger(__name__)
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# ==================== 数据模型 ====================
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class ToolCall(BaseModel):
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"""工具调用"""
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tool: str
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arguments: Dict[str, Any]
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reason: str # 为什么要调用这个工具
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class ExecutionPlan(BaseModel):
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"""执行计划"""
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goal: str # 用户的目标
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steps: List[ToolCall] # 执行步骤
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reasoning: str # 规划reasoning
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class StepResult(BaseModel):
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"""单步执行结果"""
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step_index: int
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tool: str
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arguments: Dict[str, Any]
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status: Literal["success", "failed", "skipped"]
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result: Optional[Any] = None
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error: Optional[str] = None
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execution_time: float = 0
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class AgentResponse(BaseModel):
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"""Agent响应"""
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success: bool
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message: str # 自然语言总结
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plan: Optional[ExecutionPlan] = None # 执行计划
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step_results: List[StepResult] = [] # 每步的结果
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final_summary: Optional[str] = None # 最终总结
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metadata: Optional[Dict[str, Any]] = None
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class ChatRequest(BaseModel):
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"""聊天请求"""
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message: str
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conversation_history: List[Dict[str, str]] = []
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stream: bool = False # 是否流式输出
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# ==================== Agent 系统 ====================
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class MCPAgent:
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"""MCP 智能代理 - 三阶段执行"""
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def __init__(self, provider: str = "qwen"):
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self.provider = provider
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# LLM 配置
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config = {
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"qwen": {
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"api_key": os.getenv("DASHSCOPE_API_KEY", ""),
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"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
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"model": "qwen-plus",
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},
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"deepseek": {
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"api_key": os.getenv("DEEPSEEK_API_KEY", ""),
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"base_url": "https://api.deepseek.com/v1",
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"model": "deepseek-chat",
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},
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"openai": {
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"api_key": os.getenv("OPENAI_API_KEY", ""),
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"base_url": "https://api.openai.com/v1",
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"model": "gpt-4o-mini",
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},
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}.get(provider)
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if not config or not config["api_key"]:
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raise ValueError(f"Provider '{provider}' not configured. Please set API key.")
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self.client = OpenAI(
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api_key=config["api_key"],
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base_url=config["base_url"],
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)
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self.model = config["model"]
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# ==================== 阶段 1: 计划制定 ====================
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def get_planning_prompt(self, tools: List[dict]) -> str:
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"""获取计划制定的系统提示词"""
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tools_desc = "\n\n".join([
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f"**{tool['name']}**\n"
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f"描述：{tool['description']}\n"
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f"参数：{json.dumps(tool['parameters'], ensure_ascii=False, indent=2)}"
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for tool in tools
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])
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return f"""你是一个专业的金融研究助手。你需要根据用户的问题，制定一个详细的执行计划。
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## 可用工具
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{tools_desc}
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## 重要知识
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- 贵州茅台股票代码: 600519
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- 涨停: 股价单日涨幅约10%
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- 概念板块: 相同题材的股票分类
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## 特殊工具说明
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- **summarize_with_llm**: 这是一个特殊工具，用于让你总结和分析收集到的数据
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- 当需要对多个数据源进行综合分析时使用
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- 当需要生成研究报告时使用
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- 参数: {{"data": "要分析的数据", "task": "分析任务描述"}}
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## 任务
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分析用户问题，制定执行计划。返回 JSON 格式：
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```json
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{{
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"goal": "用户的目标（一句话概括）",
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"reasoning": "你的分析思路（为什么这样规划）",
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"steps": [
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{{
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"tool": "工具名称",
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"arguments": {{"参数名": "参数值"}},
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"reason": "为什么要执行这一步"
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}}
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]
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}}
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```
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## 规划原则
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1. **从简到繁**: 先获取基础信息，再深入分析
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2. **数据先行**: 先收集数据，再总结分析
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3. **合理组合**: 可以调用多个工具，但不要超过5个
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4. **包含总结**: 最后一步通常是 summarize_with_llm
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## 示例
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用户："帮我全面分析一下贵州茅台这只股票"
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你的计划：
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```json
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{{
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"goal": "全面分析贵州茅台股票",
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"reasoning": "需要获取基本信息、财务指标、交易数据，然后综合分析",
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"steps": [
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{{
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"tool": "get_stock_basic_info",
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"arguments": {{"seccode": "600519"}},
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"reason": "获取股票基本信息（公司名称、行业、市值等）"
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}},
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{{
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"tool": "get_stock_financial_index",
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"arguments": {{"seccode": "600519", "limit": 5}},
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"reason": "获取最近5期财务指标（营收、利润、ROE等）"
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}},
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{{
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"tool": "get_stock_trade_data",
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"arguments": {{"seccode": "600519", "limit": 30}},
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"reason": "获取最近30天交易数据（价格走势、成交量）"
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}},
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{{
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"tool": "search_china_news",
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"arguments": {{"query": "贵州茅台", "top_k": 5}},
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"reason": "获取最新新闻，了解市场动态"
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}},
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{{
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"tool": "summarize_with_llm",
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"arguments": {{
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"data": "前面收集的所有数据",
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"task": "综合分析贵州茅台的投资价值，包括基本面、财务状况、股价走势、市场情绪"
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}},
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"reason": "综合所有数据，生成投资分析报告"
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}}
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]
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}}
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```
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只返回JSON，不要额外解释。"""
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async def create_plan(self, user_query: str, tools: List[dict]) -> ExecutionPlan:
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"""阶段1: 创建执行计划"""
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logger.info(f"[Planning] Creating plan for: {user_query}")
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messages = [
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{"role": "system", "content": self.get_planning_prompt(tools)},
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{"role": "user", "content": user_query},
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]
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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temperature=0.3,
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max_tokens=1500,
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)
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plan_json = response.choices[0].message.content.strip()
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logger.info(f"[Planning] Raw response: {plan_json}")
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# 清理可能的代码块标记
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if "```json" in plan_json:
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plan_json = plan_json.split("```json")[1].split("```")[0].strip()
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elif "```" in plan_json:
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plan_json = plan_json.split("```")[1].split("```")[0].strip()
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plan_data = json.loads(plan_json)
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plan = ExecutionPlan(
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goal=plan_data["goal"],
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reasoning=plan_data.get("reasoning", ""),
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steps=[
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ToolCall(**step) for step in plan_data["steps"]
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],
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)
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logger.info(f"[Planning] Plan created: {len(plan.steps)} steps")
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return plan
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# ==================== 阶段 2: 工具执行 ====================
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async def execute_tool(
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self,
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tool_name: str,
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arguments: Dict[str, Any],
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tool_handlers: Dict[str, Any],
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) -> Dict[str, Any]:
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"""执行单个工具"""
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# 特殊处理：summarize_with_llm
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if tool_name == "summarize_with_llm":
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return await self.summarize_with_llm(
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data=arguments.get("data", ""),
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task=arguments.get("task", "总结数据"),
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)
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# 调用 MCP 工具
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handler = tool_handlers.get(tool_name)
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if not handler:
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raise ValueError(f"Tool '{tool_name}' not found")
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result = await handler(arguments)
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return result
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async def execute_plan(
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self,
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plan: ExecutionPlan,
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tool_handlers: Dict[str, Any],
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) -> List[StepResult]:
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"""阶段2: 执行计划中的所有步骤"""
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logger.info(f"[Execution] Starting execution: {len(plan.steps)} steps")
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results = []
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collected_data = {} # 收集的数据，供后续步骤使用
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for i, step in enumerate(plan.steps):
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logger.info(f"[Execution] Step {i+1}/{len(plan.steps)}: {step.tool}")
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start_time = datetime.now()
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try:
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# 替换 arguments 中的占位符
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arguments = step.arguments.copy()
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if step.tool == "summarize_with_llm" and arguments.get("data") == "前面收集的所有数据":
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# 将收集的数据传递给总结工具
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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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# 保存结果
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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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results.append(step_result)
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# 收集数据
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collected_data[f"step_{i+1}_{step.tool}"] = result
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logger.info(f"[Execution] Step {i+1} completed in {execution_time:.2f}s")
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except Exception as e:
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logger.error(f"[Execution] Step {i+1} failed: {str(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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results.append(step_result)
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# 根据错误类型决定是否继续
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if "not found" in str(e).lower():
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logger.warning(f"[Execution] Stopping due to critical error")
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break
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else:
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logger.warning(f"[Execution] Continuing despite error")
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continue
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logger.info(f"[Execution] Execution completed: {len(results)} steps")
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return results
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async def summarize_with_llm(self, data: str, task: str) -> str:
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"""特殊工具：使用 LLM 总结数据"""
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logger.info(f"[LLM Summary] Task: {task}")
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的金融分析师。根据提供的数据，完成指定的分析任务。"
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},
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{
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"role": "user",
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"content": f"## 任务\n{task}\n\n## 数据\n{data}\n\n请根据数据完成分析任务，用专业且易懂的语言呈现。"
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},
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]
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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temperature=0.7,
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max_tokens=2000,
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)
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summary = response.choices[0].message.content
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return summary
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# ==================== 阶段 3: 结果总结 ====================
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async def generate_final_summary(
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self,
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user_query: str,
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plan: ExecutionPlan,
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step_results: List[StepResult],
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) -> str:
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"""阶段3: 生成最终总结"""
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logger.info("[Summary] Generating final summary")
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# 收集所有成功的结果
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successful_results = [r for r in step_results if r.status == "success"]
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if not successful_results:
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return "很抱歉，所有步骤都执行失败，无法生成分析报告。"
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# 构建总结提示
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results_text = "\n\n".join([
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f"**步骤 {r.step_index + 1}: {r.tool}**\n"
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f"结果: {json.dumps(r.result, ensure_ascii=False, indent=2)[:1000]}..."
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for r in successful_results
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])
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messages = [
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{
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"role": "system",
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"content": "你是一个专业的金融研究助手。根据执行结果，生成一份简洁清晰的报告。"
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},
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{
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"role": "user",
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"content": f"""
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用户问题：{user_query}
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执行计划：{plan.goal}
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执行结果：
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{results_text}
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请根据以上信息，生成一份专业的分析报告（300字以内）。
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"""
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},
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]
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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temperature=0.7,
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max_tokens=1000,
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)
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summary = response.choices[0].message.content
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logger.info("[Summary] Final summary generated")
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return summary
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# ==================== 主流程 ====================
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async def process_query(
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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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) -> AgentResponse:
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"""主流程：处理用户查询"""
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logger.info(f"[Agent] Processing query: {user_query}")
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try:
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# 阶段 1: 创建计划
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plan = await self.create_plan(user_query, tools)
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# 阶段 2: 执行计划
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step_results = await self.execute_plan(plan, tool_handlers)
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# 阶段 3: 生成总结
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final_summary = await self.generate_final_summary(
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user_query, plan, step_results
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)
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return AgentResponse(
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success=True,
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message=final_summary,
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plan=plan,
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step_results=step_results,
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final_summary=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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except Exception as e:
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logger.error(f"[Agent] Error: {str(e)}", exc_info=True)
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return AgentResponse(
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success=False,
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message=f"处理失败: {str(e)}",
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)
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# ==================== FastAPI 端点 ====================
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"""
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在 mcp_server.py 中添加:
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from mcp_agent_system import MCPAgent, ChatRequest, AgentResponse
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# 创建 Agent 实例
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agent = MCPAgent(provider="qwen")
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@app.post("/agent/chat", response_model=AgentResponse)
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async def agent_chat(request: ChatRequest):
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\"\"\"智能代理对话端点\"\"\"
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logger.info(f"Agent chat: {request.message}")
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# 获取工具列表和处理器
|
||||
tools = [tool.dict() for tool in TOOLS]
|
||||
|
||||
# 处理查询
|
||||
response = await agent.process_query(
|
||||
user_query=request.message,
|
||||
tools=tools,
|
||||
tool_handlers=TOOL_HANDLERS,
|
||||
)
|
||||
|
||||
return response
|
||||
"""
|
||||
Reference in New Issue
Block a user