493 lines
15 KiB
Python
493 lines
15 KiB
Python
"""
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集成到 mcp_server.py 的 Agent 系统
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使用 Kimi (kimi-k2-thinking) 和 DeepMoney 两个模型
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"""
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from openai import OpenAI
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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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logger = logging.getLogger(__name__)
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# ==================== 模型配置 ====================
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# Kimi 配置 - 用于计划制定和深度推理
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KIMI_CONFIG = {
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"api_key": "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5",
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"base_url": "https://api.moonshot.cn/v1",
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"model": "kimi-k2-thinking", # 思考模型
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}
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# DeepMoney 配置 - 用于新闻总结
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DEEPMONEY_CONFIG = {
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"api_key": "", # 空值
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"base_url": "http://111.62.35.50:8000/v1",
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"model": "deepmoney",
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}
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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
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class ExecutionPlan(BaseModel):
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"""执行计划"""
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goal: str
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steps: List[ToolCall]
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reasoning: str
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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
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plan: Optional[ExecutionPlan] = None
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step_results: List[StepResult] = []
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final_summary: Optional[str] = None
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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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# ==================== Agent 系统 ====================
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class MCPAgentIntegrated:
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"""集成版 MCP Agent - 使用 Kimi 和 DeepMoney"""
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def __init__(self):
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# 初始化 Kimi 客户端（计划制定）
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self.kimi_client = OpenAI(
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api_key=KIMI_CONFIG["api_key"],
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base_url=KIMI_CONFIG["base_url"],
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)
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self.kimi_model = KIMI_CONFIG["model"]
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# 初始化 DeepMoney 客户端（新闻总结）
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self.deepmoney_client = OpenAI(
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api_key=DEEPMONEY_CONFIG["api_key"],
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base_url=DEEPMONEY_CONFIG["base_url"],
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)
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self.deepmoney_model = DEEPMONEY_CONFIG["model"]
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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。根据用户问题，制定详细的执行计划。
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## 可用工具
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{tools_desc}
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## 特殊工具
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- **summarize_news**: 使用 DeepMoney 模型总结新闻数据
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- 参数: {{"data": "新闻列表JSON", "focus": "关注点"}}
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- 适用场景: 当需要总结新闻、研报等文本数据时
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## 重要知识
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- 贵州茅台: 600519
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- 涨停: 涨幅约10%
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- 概念板块: 相同题材股票分类
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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：
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```json
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{{
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"goal": "用户目标",
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"reasoning": "分析思路",
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"steps": [
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{{
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"tool": "工具名",
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"arguments": {{"参数": "值"}},
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"reason": "原因"
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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. 先收集数据，再分析总结
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2. 使用 summarize_news 总结新闻类数据
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3. 不超过5个步骤
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4. 最后一步通常是总结
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## 示例
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用户："贵州茅台最近有什么新闻"
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计划：
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```json
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{{
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"goal": "查询并总结贵州茅台最新新闻",
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"reasoning": "先搜索新闻，再用 DeepMoney 总结",
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"steps": [
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{{
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"tool": "search_china_news",
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"arguments": {{"query": "贵州茅台", "top_k": 10}},
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"reason": "搜索贵州茅台相关新闻"
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}},
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{{
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"tool": "summarize_news",
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"arguments": {{
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"data": "前面的新闻数据",
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"focus": "贵州茅台的重要动态和市场影响"
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}},
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"reason": "使用DeepMoney总结新闻要点"
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}}
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]
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}}
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```
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只返回JSON，不要其他内容。"""
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async def create_plan(self, user_query: str, tools: List[dict]) -> ExecutionPlan:
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"""阶段1: 使用 Kimi 创建执行计划（带思考过程）"""
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logger.info(f"[Planning] Kimi开始制定计划: {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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# 使用 Kimi 思考模型
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response = self.kimi_client.chat.completions.create(
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model=self.kimi_model,
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messages=messages,
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temperature=1.0, # Kimi 推荐
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max_tokens=16000, # 足够容纳 reasoning_content
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)
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choice = response.choices[0]
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message = choice.message
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# 提取思考过程
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reasoning_content = ""
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if hasattr(message, "reasoning_content"):
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reasoning_content = getattr(message, "reasoning_content")
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logger.info(f"[Planning] Kimi思考过程: {reasoning_content[:200]}...")
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# 提取计划内容
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plan_json = message.content.strip()
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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", "") + "\n\n" + (reasoning_content[:500] if reasoning_content else ""),
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steps=[ToolCall(**step) for step in plan_data["steps"]],
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)
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logger.info(f"[Planning] 计划制定完成: {len(plan.steps)} 步")
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return plan
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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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# 特殊工具：summarize_news（使用 DeepMoney）
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if tool_name == "summarize_news":
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return await self.summarize_news_with_deepmoney(
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data=arguments.get("data", ""),
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focus=arguments.get("focus", "关键信息"),
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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 summarize_news_with_deepmoney(self, data: str, focus: str) -> str:
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"""使用 DeepMoney 模型总结新闻"""
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logger.info(f"[DeepMoney] 总结新闻，关注点: {focus}")
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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新闻分析师，擅长提取关键信息并进行总结。"
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},
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{
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"role": "user",
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"content": f"请总结以下新闻数据，关注点：{focus}\n\n数据：\n{data[:3000]}"
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},
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]
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try:
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response = self.deepmoney_client.chat.completions.create(
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model=self.deepmoney_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(f"[DeepMoney] 总结完成")
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return summary
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except Exception as e:
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logger.error(f"[DeepMoney] 总结失败: {str(e)}")
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# 降级：返回简化摘要
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return f"新闻总结失败，原始数据：{data[:500]}..."
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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"""
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logger.info(f"[Execution] 开始执行: {len(plan.steps)} 步")
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results = []
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collected_data = {}
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for i, step in enumerate(plan.steps):
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logger.info(f"[Execution] 步骤 {i+1}/{len(plan.steps)}: {step.tool}")
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start_time = datetime.now()
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try:
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# 替换占位符
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arguments = step.arguments.copy()
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# 如果参数值是 "前面的新闻数据" 或 "前面收集的所有数据"
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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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# 将收集的数据传递
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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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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] 步骤 {i+1} 完成: {execution_time:.2f}s")
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except Exception as e:
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logger.error(f"[Execution] 步骤 {i+1} 失败: {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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# 继续执行其他步骤
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continue
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logger.info(f"[Execution] 执行完成")
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return results
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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: 使用 Kimi 生成最终总结"""
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logger.info("[Summary] Kimi生成最终总结")
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# 收集成功的结果
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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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# 构建结果文本（精简版）
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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: {str(r.result)[:800]}..."
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for r in successful_results[:3] # 只取前3个，避免超长
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])
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messages = [
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{
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"role": "system",
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"content": "你是专业的金融研究助手。根据执行结果，生成简洁清晰的报告。"
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},
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{
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"role": "user",
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"content": f"""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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请生成专业的分析报告（300字以内）。"""
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},
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]
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try:
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response = self.kimi_client.chat.completions.create(
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model="kimi-k2-turbpreview", # 使用非思考模型，更快
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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] 总结完成")
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return summary
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except Exception as e:
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logger.error(f"[Summary] 总结失败: {str(e)}")
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# 降级：返回最后一步的结果
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if successful_results:
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last_result = successful_results[-1]
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if isinstance(last_result.result, str):
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return last_result.result
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else:
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return json.dumps(last_result.result, ensure_ascii=False, indent=2)
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return "总结生成失败"
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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"""
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logger.info(f"[Agent] 处理查询: {user_query}")
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try:
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# 阶段1: Kimi 制定计划
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plan = await self.create_plan(user_query, tools)
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# 阶段2: 执行工具
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step_results = await self.execute_plan(plan, tool_handlers)
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# 阶段3: Kimi 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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"model_used": {
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"planning": self.kimi_model,
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"summarization": "kimi-k2-turbpreview",
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"news_summary": self.deepmoney_model,
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},
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},
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)
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except Exception as e:
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logger.error(f"[Agent] 错误: {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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# ==================== 添加到 mcp_server.py ====================
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"""
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在 mcp_server.py 中添加以下代码：
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# 导入 Agent 系统
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from mcp_server_agent_integration import MCPAgentIntegrated, ChatRequest, AgentResponse
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# 创建 Agent 实例（全局）
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agent = MCPAgentIntegrated()
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# 添加端点
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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
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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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# 处理查询
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response = await agent.process_query(
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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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return response
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"""
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