agent功能开发增加MCP后端
This commit is contained in:
@@ -1,361 +0,0 @@
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"""
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Kimi API 集成示例
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演示如何将MCP工具与Kimi大模型结合使用
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"""
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from openai import OpenAI
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import json
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from typing import List, Dict, Any
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from mcp_client_example import MCPClient
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# Kimi API配置
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KIMI_API_KEY = "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5"
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KIMI_BASE_URL = "https://api.moonshot.cn/v1"
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KIMI_MODEL = "kimi-k2-turbpreview"
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# 初始化Kimi客户端
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kimi_client = OpenAI(
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api_key=KIMI_API_KEY,
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base_url=KIMI_BASE_URL,
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)
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# 初始化MCP客户端
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mcp_client = MCPClient()
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def convert_mcp_tools_to_kimi_format() -> tuple[List[Dict], Dict]:
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"""
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将MCP工具转换为Kimi API的tools格式
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Returns:
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tools: Kimi格式的工具列表
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tool_map: 工具名称到执行函数的映射
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"""
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# 获取所有MCP工具
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mcp_tools_response = mcp_client.list_tools()
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mcp_tools = mcp_tools_response["tools"]
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# 转换为Kimi格式
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kimi_tools = []
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tool_map = {}
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for tool in mcp_tools:
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# Kimi工具格式
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kimi_tool = {
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"type": "function",
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"function": {
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"name": tool["name"],
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"description": tool["description"],
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"parameters": tool["parameters"]
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}
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}
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kimi_tools.append(kimi_tool)
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# 创建工具执行函数
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tool_name = tool["name"]
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tool_map[tool_name] = lambda args, name=tool_name: execute_mcp_tool(name, args)
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return kimi_tools, tool_map
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def execute_mcp_tool(tool_name: str, arguments: Dict[str, Any]) -> Dict[str, Any]:
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"""
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执行MCP工具
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Args:
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tool_name: 工具名称
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arguments: 工具参数
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Returns:
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工具执行结果
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"""
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print(f"[工具调用] {tool_name}")
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print(f"[参数] {json.dumps(arguments, ensure_ascii=False, indent=2)}")
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result = mcp_client.call_tool(tool_name, arguments)
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print(f"[结果] 成功: {result.get('success', False)}")
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return result
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def chat_with_kimi(user_message: str, verbose: bool = True) -> str:
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"""
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与Kimi进行对话，支持工具调用
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Args:
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user_message: 用户消息
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verbose: 是否打印详细信息
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Returns:
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Kimi的回复
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"""
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# 获取Kimi格式的工具
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tools, tool_map = convert_mcp_tools_to_kimi_format()
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if verbose:
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print(f"\n{'='*60}")
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print(f"加载了 {len(tools)} 个工具")
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print(f"{'='*60}\n")
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# 初始化对话
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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数据分析助手，由 Moonshot AI 提供支持。
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你可以使用各种工具来帮助用户查询和分析金融数据，包括：
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- 新闻搜索（全球新闻、中国新闻、医疗新闻）
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- 公司研究（路演信息、研究报告）
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- 概念板块分析
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- 股票分析（涨停分析、财务数据、交易数据）
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- 财务报表（资产负债表、现金流量表）
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请根据用户的问题，选择合适的工具来获取信息，并提供专业的分析和建议。"""
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},
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{
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"role": "user",
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"content": user_message
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}
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]
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if verbose:
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print(f"[用户]: {user_message}\n")
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# 对话循环，处理工具调用
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finish_reason = None
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iteration = 0
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max_iterations = 10 # 防止无限循环
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while finish_reason is None or finish_reason == "tool_calls":
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iteration += 1
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if iteration > max_iterations:
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print("[警告] 达到最大迭代次数")
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break
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if verbose and iteration > 1:
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print(f"\n[轮次 {iteration}]")
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# 调用Kimi API
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completion = kimi_client.chat.completions.create(
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model=KIMI_MODEL,
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messages=messages,
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temperature=0.6, # Kimi推荐的temperature值
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tools=tools,
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)
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choice = completion.choices[0]
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finish_reason = choice.finish_reason
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if verbose:
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print(f"[Kimi] finish_reason: {finish_reason}")
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# 处理工具调用
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if finish_reason == "tool_calls":
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# 将Kimi的消息添加到上下文
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messages.append(choice.message)
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# 执行每个工具调用
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for tool_call in choice.message.tool_calls:
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tool_name = tool_call.function.name
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tool_arguments = json.loads(tool_call.function.arguments)
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# 执行工具
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tool_result = tool_map[tool_name](tool_arguments)
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# 将工具结果添加到消息中
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messages.append({
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": tool_name,
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"content": json.dumps(tool_result, ensure_ascii=False),
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})
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if verbose:
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print() # 空行分隔
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# 返回最终回复
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final_response = choice.message.content
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if verbose:
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print(f"\n[Kimi]: {final_response}\n")
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print(f"{'='*60}")
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return final_response
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def demo_simple_query():
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"""演示1: 简单查询"""
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print("\n" + "="*60)
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print("演示1: 简单新闻查询")
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print("="*60)
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response = chat_with_kimi("帮我查找关于人工智能的最新新闻")
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return response
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def demo_stock_analysis():
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"""演示2: 股票分析"""
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print("\n" + "="*60)
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print("演示2: 股票财务分析")
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print("="*60)
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response = chat_with_kimi("帮我分析贵州茅台（600519）的财务状况")
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return response
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def demo_concept_research():
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"""演示3: 概念研究"""
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print("\n" + "="*60)
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print("演示3: 概念板块研究")
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print("="*60)
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response = chat_with_kimi("查找新能源汽车相关的概念板块，并告诉我涨幅最高的是哪些")
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return response
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def demo_industry_comparison():
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"""演示4: 行业对比"""
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print("\n" + "="*60)
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print("演示4: 行业内股票对比")
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print("="*60)
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response = chat_with_kimi("帮我找出半导体行业的龙头股票，并对比它们的财务指标")
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return response
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def demo_comprehensive_analysis():
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"""演示5: 综合分析"""
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print("\n" + "="*60)
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print("演示5: 综合分析")
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print("="*60)
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response = chat_with_kimi("""
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我想投资白酒行业，请帮我：
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1. 搜索白酒行业的主要上市公司
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2. 对比贵州茅台和五粮液的财务数据
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3. 查看最近的行业新闻
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4. 给出投资建议
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""")
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return response
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def interactive_chat():
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"""交互式对话"""
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print("\n" + "="*60)
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print("Kimi 金融助手 - 交互模式")
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print("="*60)
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print("提示：输入 'quit' 或 'exit' 退出")
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print("="*60 + "\n")
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while True:
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try:
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user_input = input("你: ").strip()
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if not user_input:
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continue
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if user_input.lower() in ['quit', 'exit', '退出']:
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print("\n再见！")
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break
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response = chat_with_kimi(user_input)
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except KeyboardInterrupt:
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print("\n\n再见！")
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break
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except Exception as e:
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print(f"\n[错误] {str(e)}\n")
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def test_kimi_connection():
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"""测试Kimi API连接"""
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print("\n" + "="*60)
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print("测试 Kimi API 连接")
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print("="*60 + "\n")
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try:
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# 简单的测试请求
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response = kimi_client.chat.completions.create(
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model=KIMI_MODEL,
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messages=[
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{"role": "user", "content": "你好，请介绍一下你自己"}
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],
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temperature=0.6
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)
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print("[✓] 连接成功!")
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print(f"[✓] 模型: {KIMI_MODEL}")
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print(f"[✓] 回复: {response.choices[0].message.content}\n")
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return True
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except Exception as e:
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print(f"[✗] 连接失败: {str(e)}\n")
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return False
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def show_available_tools():
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"""显示所有可用工具"""
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print("\n" + "="*60)
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print("可用工具列表")
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print("="*60 + "\n")
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tools, _ = convert_mcp_tools_to_kimi_format()
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for i, tool in enumerate(tools, 1):
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func = tool["function"]
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print(f"{i}. {func['name']}")
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print(f" 描述: {func['description'][:80]}...")
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print()
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print(f"总计: {len(tools)} 个工具\n")
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if __name__ == "__main__":
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import sys
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# 首先测试连接
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if not test_kimi_connection():
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print("请检查API Key和网络连接")
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sys.exit(1)
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# 显示可用工具
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show_available_tools()
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# 运行演示
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print("\n选择运行模式:")
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print("1. 简单查询演示")
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print("2. 股票分析演示")
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print("3. 概念研究演示")
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print("4. 行业对比演示")
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print("5. 综合分析演示")
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print("6. 交互式对话")
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print("7. 运行所有演示")
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try:
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choice = input("\n请选择 (1-7): ").strip()
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if choice == "1":
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demo_simple_query()
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elif choice == "2":
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demo_stock_analysis()
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elif choice == "3":
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demo_concept_research()
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elif choice == "4":
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demo_industry_comparison()
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elif choice == "5":
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demo_comprehensive_analysis()
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elif choice == "6":
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interactive_chat()
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elif choice == "7":
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demo_simple_query()
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demo_stock_analysis()
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demo_concept_research()
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demo_industry_comparison()
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demo_comprehensive_analysis()
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else:
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print("无效选择")
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except KeyboardInterrupt:
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print("\n\n程序已退出")
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finally:
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mcp_client.close()
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@@ -1,470 +0,0 @@
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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
|
||||
- **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
|
||||
- 当需要对多个数据源进行综合分析时使用
|
||||
- 当需要生成研究报告时使用
|
||||
- 参数: {{"data": "要分析的数据", "task": "分析任务描述"}}
|
||||
|
||||
## 任务
|
||||
分析用户问题，制定执行计划。返回 JSON 格式：
|
||||
|
||||
```json
|
||||
{{
|
||||
"goal": "用户的目标（一句话概括）",
|
||||
"reasoning": "你的分析思路（为什么这样规划）",
|
||||
"steps": [
|
||||
{{
|
||||
"tool": "工具名称",
|
||||
"arguments": {{"参数名": "参数值"}},
|
||||
"reason": "为什么要执行这一步"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
## 规划原则
|
||||
1. **从简到繁**: 先获取基础信息，再深入分析
|
||||
2. **数据先行**: 先收集数据，再总结分析
|
||||
3. **合理组合**: 可以调用多个工具，但不要超过5个
|
||||
4. **包含总结**: 最后一步通常是 summarize_with_llm
|
||||
|
||||
## 示例
|
||||
|
||||
用户："帮我全面分析一下贵州茅台这只股票"
|
||||
|
||||
你的计划：
|
||||
```json
|
||||
{{
|
||||
"goal": "全面分析贵州茅台股票",
|
||||
"reasoning": "需要获取基本信息、财务指标、交易数据，然后综合分析",
|
||||
"steps": [
|
||||
{{
|
||||
"tool": "get_stock_basic_info",
|
||||
"arguments": {{"seccode": "600519"}},
|
||||
"reason": "获取股票基本信息（公司名称、行业、市值等）"
|
||||
}},
|
||||
{{
|
||||
"tool": "get_stock_financial_index",
|
||||
"arguments": {{"seccode": "600519", "limit": 5}},
|
||||
"reason": "获取最近5期财务指标（营收、利润、ROE等）"
|
||||
}},
|
||||
{{
|
||||
"tool": "get_stock_trade_data",
|
||||
"arguments": {{"seccode": "600519", "limit": 30}},
|
||||
"reason": "获取最近30天交易数据（价格走势、成交量）"
|
||||
}},
|
||||
{{
|
||||
"tool": "search_china_news",
|
||||
"arguments": {{"query": "贵州茅台", "top_k": 5}},
|
||||
"reason": "获取最新新闻，了解市场动态"
|
||||
}},
|
||||
{{
|
||||
"tool": "summarize_with_llm",
|
||||
"arguments": {{
|
||||
"data": "前面收集的所有数据",
|
||||
"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"
|
||||
}},
|
||||
"reason": "综合所有数据，生成投资分析报告"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
只返回JSON，不要额外解释。"""
|
||||
|
||||
async def create_plan(self, user_query: str, tools: List[dict]) -> ExecutionPlan:
|
||||
"""阶段1: 创建执行计划"""
|
||||
logger.info(f"[Planning] Creating plan for: {user_query}")
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": self.get_planning_prompt(tools)},
|
||||
{"role": "user", "content": user_query},
|
||||
]
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.3,
|
||||
max_tokens=1500,
|
||||
)
|
||||
|
||||
plan_json = response.choices[0].message.content.strip()
|
||||
logger.info(f"[Planning] Raw response: {plan_json}")
|
||||
|
||||
# 清理可能的代码块标记
|
||||
if "```json" in plan_json:
|
||||
plan_json = plan_json.split("```json")[1].split("```")[0].strip()
|
||||
elif "```" in plan_json:
|
||||
plan_json = plan_json.split("```")[1].split("```")[0].strip()
|
||||
|
||||
plan_data = json.loads(plan_json)
|
||||
|
||||
plan = ExecutionPlan(
|
||||
goal=plan_data["goal"],
|
||||
reasoning=plan_data.get("reasoning", ""),
|
||||
steps=[
|
||||
ToolCall(**step) for step in plan_data["steps"]
|
||||
],
|
||||
)
|
||||
|
||||
logger.info(f"[Planning] Plan created: {len(plan.steps)} steps")
|
||||
return plan
|
||||
|
||||
# ==================== 阶段 2: 工具执行 ====================
|
||||
|
||||
async def execute_tool(
|
||||
self,
|
||||
tool_name: str,
|
||||
arguments: Dict[str, Any],
|
||||
tool_handlers: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
"""执行单个工具"""
|
||||
|
||||
# 特殊处理：summarize_with_llm
|
||||
if tool_name == "summarize_with_llm":
|
||||
return await self.summarize_with_llm(
|
||||
data=arguments.get("data", ""),
|
||||
task=arguments.get("task", "总结数据"),
|
||||
)
|
||||
|
||||
# 调用 MCP 工具
|
||||
handler = tool_handlers.get(tool_name)
|
||||
if not handler:
|
||||
raise ValueError(f"Tool '{tool_name}' not found")
|
||||
|
||||
result = await handler(arguments)
|
||||
return result
|
||||
|
||||
async def execute_plan(
|
||||
self,
|
||||
plan: ExecutionPlan,
|
||||
tool_handlers: Dict[str, Any],
|
||||
) -> List[StepResult]:
|
||||
"""阶段2: 执行计划中的所有步骤"""
|
||||
logger.info(f"[Execution] Starting execution: {len(plan.steps)} steps")
|
||||
|
||||
results = []
|
||||
collected_data = {} # 收集的数据，供后续步骤使用
|
||||
|
||||
for i, step in enumerate(plan.steps):
|
||||
logger.info(f"[Execution] Step {i+1}/{len(plan.steps)}: {step.tool}")
|
||||
|
||||
start_time = datetime.now()
|
||||
|
||||
try:
|
||||
# 替换 arguments 中的占位符
|
||||
arguments = step.arguments.copy()
|
||||
if step.tool == "summarize_with_llm" and arguments.get("data") == "前面收集的所有数据":
|
||||
# 将收集的数据传递给总结工具
|
||||
arguments["data"] = json.dumps(collected_data, ensure_ascii=False, indent=2)
|
||||
|
||||
# 执行工具
|
||||
result = await self.execute_tool(step.tool, arguments, tool_handlers)
|
||||
|
||||
execution_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
# 保存结果
|
||||
step_result = StepResult(
|
||||
step_index=i,
|
||||
tool=step.tool,
|
||||
arguments=arguments,
|
||||
status="success",
|
||||
result=result,
|
||||
execution_time=execution_time,
|
||||
)
|
||||
results.append(step_result)
|
||||
|
||||
# 收集数据
|
||||
collected_data[f"step_{i+1}_{step.tool}"] = result
|
||||
|
||||
logger.info(f"[Execution] Step {i+1} completed in {execution_time:.2f}s")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Execution] Step {i+1} failed: {str(e)}")
|
||||
|
||||
execution_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
step_result = StepResult(
|
||||
step_index=i,
|
||||
tool=step.tool,
|
||||
arguments=step.arguments,
|
||||
status="failed",
|
||||
error=str(e),
|
||||
execution_time=execution_time,
|
||||
)
|
||||
results.append(step_result)
|
||||
|
||||
# 根据错误类型决定是否继续
|
||||
if "not found" in str(e).lower():
|
||||
logger.warning(f"[Execution] Stopping due to critical error")
|
||||
break
|
||||
else:
|
||||
logger.warning(f"[Execution] Continuing despite error")
|
||||
continue
|
||||
|
||||
logger.info(f"[Execution] Execution completed: {len(results)} steps")
|
||||
return results
|
||||
|
||||
async def summarize_with_llm(self, data: str, task: str) -> str:
|
||||
"""特殊工具：使用 LLM 总结数据"""
|
||||
logger.info(f"[LLM Summary] Task: {task}")
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"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。"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"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。"
|
||||
},
|
||||
]
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.7,
|
||||
max_tokens=2000,
|
||||
)
|
||||
|
||||
summary = response.choices[0].message.content
|
||||
return summary
|
||||
|
||||
# ==================== 阶段 3: 结果总结 ====================
|
||||
|
||||
async def generate_final_summary(
|
||||
self,
|
||||
user_query: str,
|
||||
plan: ExecutionPlan,
|
||||
step_results: List[StepResult],
|
||||
) -> str:
|
||||
"""阶段3: 生成最终总结"""
|
||||
logger.info("[Summary] Generating final summary")
|
||||
|
||||
# 收集所有成功的结果
|
||||
successful_results = [r for r in step_results if r.status == "success"]
|
||||
|
||||
if not successful_results:
|
||||
return "很抱歉，所有步骤都执行失败，无法生成分析报告。"
|
||||
|
||||
# 构建总结提示
|
||||
results_text = "\n\n".join([
|
||||
f"**步骤 {r.step_index + 1}: {r.tool}**\n"
|
||||
f"结果: {json.dumps(r.result, ensure_ascii=False, indent=2)[:1000]}..."
|
||||
for r in successful_results
|
||||
])
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"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。"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"""
|
||||
用户问题：{user_query}
|
||||
|
||||
执行计划：{plan.goal}
|
||||
|
||||
执行结果：
|
||||
{results_text}
|
||||
|
||||
请根据以上信息，生成一份专业的分析报告（300字以内）。
|
||||
"""
|
||||
},
|
||||
]
|
||||
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.7,
|
||||
max_tokens=1000,
|
||||
)
|
||||
|
||||
summary = response.choices[0].message.content
|
||||
logger.info("[Summary] Final summary generated")
|
||||
return summary
|
||||
|
||||
# ==================== 主流程 ====================
|
||||
|
||||
async def process_query(
|
||||
self,
|
||||
user_query: str,
|
||||
tools: List[dict],
|
||||
tool_handlers: Dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""主流程：处理用户查询"""
|
||||
logger.info(f"[Agent] Processing query: {user_query}")
|
||||
|
||||
try:
|
||||
# 阶段 1: 创建计划
|
||||
plan = await self.create_plan(user_query, tools)
|
||||
|
||||
# 阶段 2: 执行计划
|
||||
step_results = await self.execute_plan(plan, tool_handlers)
|
||||
|
||||
# 阶段 3: 生成总结
|
||||
final_summary = await self.generate_final_summary(
|
||||
user_query, plan, step_results
|
||||
)
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
message=final_summary,
|
||||
plan=plan,
|
||||
step_results=step_results,
|
||||
final_summary=final_summary,
|
||||
metadata={
|
||||
"total_steps": len(plan.steps),
|
||||
"successful_steps": len([r for r in step_results if r.status == "success"]),
|
||||
"failed_steps": len([r for r in step_results if r.status == "failed"]),
|
||||
"total_execution_time": sum(r.execution_time for r in step_results),
|
||||
},
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Agent] Error: {str(e)}", exc_info=True)
|
||||
return AgentResponse(
|
||||
success=False,
|
||||
message=f"处理失败: {str(e)}",
|
||||
)
|
||||
|
||||
# ==================== FastAPI 端点 ====================
|
||||
|
||||
"""
|
||||
在 mcp_server.py 中添加:
|
||||
|
||||
from mcp_agent_system import MCPAgent, ChatRequest, AgentResponse
|
||||
|
||||
# 创建 Agent 实例
|
||||
agent = MCPAgent(provider="qwen")
|
||||
|
||||
@app.post("/agent/chat", response_model=AgentResponse)
|
||||
async def agent_chat(request: ChatRequest):
|
||||
\"\"\"智能代理对话端点\"\"\"
|
||||
logger.info(f"Agent chat: {request.message}")
|
||||
|
||||
# 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
|
||||
"""
|
||||
@@ -1,295 +0,0 @@
|
||||
"""
|
||||
MCP Chat Endpoint - 添加到 mcp_server.py
|
||||
集成LLM实现智能对话，自动调用MCP工具并总结结果
|
||||
"""
|
||||
|
||||
from pydantic import BaseModel
|
||||
from typing import List, Dict, Any, Optional
|
||||
import os
|
||||
import json
|
||||
from openai import OpenAI
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ==================== LLM配置 ====================
|
||||
|
||||
# 支持多种LLM提供商
|
||||
LLM_PROVIDERS = {
|
||||
"openai": {
|
||||
"api_key": os.getenv("OPENAI_API_KEY", ""),
|
||||
"base_url": "https://api.openai.com/v1",
|
||||
"model": "gpt-4o-mini", # 便宜且快速
|
||||
},
|
||||
"qwen": {
|
||||
"api_key": os.getenv("DASHSCOPE_API_KEY", ""),
|
||||
"base_url": "https://dashscope.aliyuncs.com/compatible-mode/v1",
|
||||
"model": "qwen-plus",
|
||||
},
|
||||
"deepseek": {
|
||||
"api_key": os.getenv("DEEPSEEK_API_KEY", ""),
|
||||
"base_url": "https://api.deepseek.com/v1",
|
||||
"model": "deepseek-chat",
|
||||
},
|
||||
}
|
||||
|
||||
# 默认使用的LLM提供商
|
||||
DEFAULT_PROVIDER = "qwen" # 推荐使用通义千问，价格便宜
|
||||
|
||||
# ==================== 数据模型 ====================
|
||||
|
||||
class Message(BaseModel):
|
||||
"""消息"""
|
||||
role: str # system, user, assistant
|
||||
content: str
|
||||
|
||||
class ChatRequest(BaseModel):
|
||||
"""聊天请求"""
|
||||
message: str
|
||||
conversation_history: List[Dict[str, str]] = []
|
||||
provider: Optional[str] = DEFAULT_PROVIDER
|
||||
|
||||
class ChatResponse(BaseModel):
|
||||
"""聊天响应"""
|
||||
success: bool
|
||||
message: str
|
||||
tool_used: Optional[str] = None
|
||||
raw_data: Optional[Any] = None
|
||||
error: Optional[str] = None
|
||||
|
||||
# ==================== LLM助手类 ====================
|
||||
|
||||
class MCPChatAssistant:
|
||||
"""MCP聊天助手 - 集成LLM和工具调用"""
|
||||
|
||||
def __init__(self, provider: str = DEFAULT_PROVIDER):
|
||||
self.provider = provider
|
||||
config = LLM_PROVIDERS.get(provider)
|
||||
|
||||
if not config or not config["api_key"]:
|
||||
logger.warning(f"LLM provider '{provider}' not configured, using fallback mode")
|
||||
self.client = None
|
||||
else:
|
||||
self.client = OpenAI(
|
||||
api_key=config["api_key"],
|
||||
base_url=config["base_url"],
|
||||
)
|
||||
self.model = config["model"]
|
||||
|
||||
def get_system_prompt(self, tools: List[dict]) -> str:
|
||||
"""构建系统提示词"""
|
||||
tools_desc = "\n\n".join([
|
||||
f"**{tool['name']}**\n描述：{tool['description']}\n参数：{json.dumps(tool['parameters'], ensure_ascii=False, indent=2)}"
|
||||
for tool in tools
|
||||
])
|
||||
|
||||
return f"""你是一个专业的金融投资助手。你可以使用以下工具来帮助用户查询信息：
|
||||
|
||||
{tools_desc}
|
||||
|
||||
## 工作流程
|
||||
1. **理解用户意图**：分析用户问题，确定需要什么信息
|
||||
2. **选择工具**：从上面的工具中选择最合适的一个或多个
|
||||
3. **提取参数**：从用户输入中提取工具需要的参数
|
||||
4. **返回工具调用指令**（JSON格式）：
|
||||
{{"tool": "工具名", "arguments": {{...}}}}
|
||||
|
||||
## 重要规则
|
||||
- 贵州茅台的股票代码是 **600519**
|
||||
- 如果用户提到股票名称，尝试推断股票代码
|
||||
- 如果不确定需要什么信息，使用 search_china_news 搜索相关新闻
|
||||
- 涨停是指股票当日涨幅达到10%左右
|
||||
- 只返回工具调用指令，不要额外解释
|
||||
|
||||
## 示例
|
||||
用户："查询贵州茅台的股票信息"
|
||||
你：{{"tool": "get_stock_basic_info", "arguments": {{"seccode": "600519"}}}}
|
||||
|
||||
用户："今日涨停的股票有哪些"
|
||||
你：{{"tool": "search_limit_up_stocks", "arguments": {{"query": "", "mode": "hybrid", "page_size": 10}}}}
|
||||
|
||||
用户："新能源概念板块表现如何"
|
||||
你：{{"tool": "search_concepts", "arguments": {{"query": "新能源", "size": 10, "sort_by": "change_pct"}}}}
|
||||
"""
|
||||
|
||||
async def chat(self, user_message: str, conversation_history: List[Dict[str, str]], tools: List[dict]) -> ChatResponse:
|
||||
"""智能对话"""
|
||||
try:
|
||||
if not self.client:
|
||||
# 降级到简单匹配
|
||||
return await self.fallback_chat(user_message)
|
||||
|
||||
# 1. 构建消息历史
|
||||
messages = [
|
||||
{"role": "system", "content": self.get_system_prompt(tools)},
|
||||
]
|
||||
|
||||
# 添加历史对话（最多保留最近10轮）
|
||||
for msg in conversation_history[-20:]:
|
||||
messages.append({
|
||||
"role": "user" if msg.get("isUser") else "assistant",
|
||||
"content": msg.get("content", ""),
|
||||
})
|
||||
|
||||
messages.append({"role": "user", "content": user_message})
|
||||
|
||||
# 2. 调用LLM获取工具调用指令
|
||||
logger.info(f"Calling LLM with {len(messages)} messages")
|
||||
response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=messages,
|
||||
temperature=0.3, # 低温度，更确定性
|
||||
max_tokens=500,
|
||||
)
|
||||
|
||||
tool_call_instruction = response.choices[0].message.content.strip()
|
||||
logger.info(f"LLM response: {tool_call_instruction}")
|
||||
|
||||
# 3. 解析工具调用指令
|
||||
try:
|
||||
tool_call = json.loads(tool_call_instruction)
|
||||
tool_name = tool_call.get("tool")
|
||||
tool_args = tool_call.get("arguments", {})
|
||||
|
||||
if not tool_name:
|
||||
raise ValueError("No tool specified")
|
||||
|
||||
# 4. 调用工具（这里需要导入 mcp_server 的工具处理器）
|
||||
from mcp_server import TOOL_HANDLERS
|
||||
|
||||
handler = TOOL_HANDLERS.get(tool_name)
|
||||
if not handler:
|
||||
raise ValueError(f"Tool '{tool_name}' not found")
|
||||
|
||||
tool_result = await handler(tool_args)
|
||||
|
||||
# 5. 让LLM总结结果
|
||||
summary_messages = messages + [
|
||||
{"role": "assistant", "content": tool_call_instruction},
|
||||
{"role": "system", "content": f"工具 {tool_name} 返回的数据：\n{json.dumps(tool_result, ensure_ascii=False, indent=2)}\n\n请用自然语言总结这些数据，给用户一个简洁清晰的回复（不超过200字）。"}
|
||||
]
|
||||
|
||||
summary_response = self.client.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=summary_messages,
|
||||
temperature=0.7,
|
||||
max_tokens=300,
|
||||
)
|
||||
|
||||
summary = summary_response.choices[0].message.content
|
||||
|
||||
return ChatResponse(
|
||||
success=True,
|
||||
message=summary,
|
||||
tool_used=tool_name,
|
||||
raw_data=tool_result,
|
||||
)
|
||||
|
||||
except json.JSONDecodeError:
|
||||
# LLM没有返回JSON格式，直接返回其回复
|
||||
return ChatResponse(
|
||||
success=True,
|
||||
message=tool_call_instruction,
|
||||
)
|
||||
except Exception as tool_error:
|
||||
logger.error(f"Tool execution error: {str(tool_error)}")
|
||||
return ChatResponse(
|
||||
success=False,
|
||||
message="工具调用失败",
|
||||
error=str(tool_error),
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Chat error: {str(e)}", exc_info=True)
|
||||
return ChatResponse(
|
||||
success=False,
|
||||
message="对话处理失败",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
async def fallback_chat(self, user_message: str) -> ChatResponse:
|
||||
"""降级方案：简单关键词匹配"""
|
||||
from mcp_server import TOOL_HANDLERS
|
||||
|
||||
try:
|
||||
# 茅台特殊处理
|
||||
if "茅台" in user_message or "贵州茅台" in user_message:
|
||||
handler = TOOL_HANDLERS.get("get_stock_basic_info")
|
||||
result = await handler({"seccode": "600519"})
|
||||
return ChatResponse(
|
||||
success=True,
|
||||
message="已为您查询贵州茅台(600519)的股票信息：",
|
||||
tool_used="get_stock_basic_info",
|
||||
raw_data=result,
|
||||
)
|
||||
|
||||
# 涨停分析
|
||||
elif "涨停" in user_message:
|
||||
handler = TOOL_HANDLERS.get("search_limit_up_stocks")
|
||||
query = user_message.replace("涨停", "").strip()
|
||||
result = await handler({"query": query, "mode": "hybrid", "page_size": 10})
|
||||
return ChatResponse(
|
||||
success=True,
|
||||
message="已为您查询涨停股票信息：",
|
||||
tool_used="search_limit_up_stocks",
|
||||
raw_data=result,
|
||||
)
|
||||
|
||||
# 概念板块
|
||||
elif "概念" in user_message or "板块" in user_message:
|
||||
handler = TOOL_HANDLERS.get("search_concepts")
|
||||
query = user_message.replace("概念", "").replace("板块", "").strip()
|
||||
result = await handler({"query": query, "size": 10, "sort_by": "change_pct"})
|
||||
return ChatResponse(
|
||||
success=True,
|
||||
message=f"已为您查询'{query}'相关概念板块：",
|
||||
tool_used="search_concepts",
|
||||
raw_data=result,
|
||||
)
|
||||
|
||||
# 默认：搜索新闻
|
||||
else:
|
||||
handler = TOOL_HANDLERS.get("search_china_news")
|
||||
result = await handler({"query": user_message, "top_k": 5})
|
||||
return ChatResponse(
|
||||
success=True,
|
||||
message="已为您搜索相关新闻：",
|
||||
tool_used="search_china_news",
|
||||
raw_data=result,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Fallback chat error: {str(e)}")
|
||||
return ChatResponse(
|
||||
success=False,
|
||||
message="查询失败",
|
||||
error=str(e),
|
||||
)
|
||||
|
||||
# ==================== FastAPI端点 ====================
|
||||
|
||||
# 在 mcp_server.py 中添加以下代码：
|
||||
|
||||
"""
|
||||
from mcp_chat_endpoint import MCPChatAssistant, ChatRequest, ChatResponse
|
||||
|
||||
# 创建聊天助手实例
|
||||
chat_assistant = MCPChatAssistant(provider="qwen") # 或 "openai", "deepseek"
|
||||
|
||||
@app.post("/chat", response_model=ChatResponse)
|
||||
async def chat_endpoint(request: ChatRequest):
|
||||
\"\"\"智能对话端点 - 使用LLM理解意图并调用工具\"\"\"
|
||||
logger.info(f"Chat request: {request.message}")
|
||||
|
||||
# 获取可用工具列表
|
||||
tools = [tool.dict() for tool in TOOLS]
|
||||
|
||||
# 调用聊天助手
|
||||
response = await chat_assistant.chat(
|
||||
user_message=request.message,
|
||||
conversation_history=request.conversation_history,
|
||||
tools=tools,
|
||||
)
|
||||
|
||||
return response
|
||||
"""
|
||||
@@ -1,248 +0,0 @@
|
||||
"""
|
||||
MCP客户端使用示例
|
||||
演示如何调用MCP服务器的各种工具
|
||||
"""
|
||||
|
||||
import httpx
|
||||
import json
|
||||
from typing import Dict, Any
|
||||
|
||||
|
||||
class MCPClient:
|
||||
"""MCP客户端"""
|
||||
|
||||
def __init__(self, base_url: str = "http://localhost:8900"):
|
||||
self.base_url = base_url
|
||||
self.client = httpx.Client(timeout=60.0)
|
||||
|
||||
def list_tools(self):
|
||||
"""列出所有可用工具"""
|
||||
response = self.client.get(f"{self.base_url}/tools")
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
def get_tool(self, tool_name: str):
|
||||
"""获取特定工具的定义"""
|
||||
response = self.client.get(f"{self.base_url}/tools/{tool_name}")
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
def call_tool(self, tool_name: str, arguments: Dict[str, Any]):
|
||||
"""调用工具"""
|
||||
payload = {
|
||||
"tool": tool_name,
|
||||
"arguments": arguments
|
||||
}
|
||||
response = self.client.post(f"{self.base_url}/tools/call", json=payload)
|
||||
response.raise_for_status()
|
||||
return response.json()
|
||||
|
||||
def close(self):
|
||||
"""关闭客户端"""
|
||||
self.client.close()
|
||||
|
||||
|
||||
def print_result(title: str, result: Dict[str, Any]):
|
||||
"""打印结果"""
|
||||
print(f"\n{'=' * 60}")
|
||||
print(f"{title}")
|
||||
print(f"{'=' * 60}")
|
||||
print(json.dumps(result, ensure_ascii=False, indent=2))
|
||||
|
||||
|
||||
def main():
|
||||
"""主函数 - 演示各种工具的使用"""
|
||||
|
||||
client = MCPClient()
|
||||
|
||||
try:
|
||||
# 1. 列出所有工具
|
||||
print("\n示例1: 列出所有可用工具")
|
||||
tools = client.list_tools()
|
||||
print(f"可用工具数量: {len(tools['tools'])}")
|
||||
for tool in tools['tools']:
|
||||
print(f" - {tool['name']}: {tool['description'][:50]}...")
|
||||
|
||||
# 2. 搜索中国新闻
|
||||
print("\n示例2: 搜索中国新闻（关键词：人工智能）")
|
||||
result = client.call_tool(
|
||||
"search_china_news",
|
||||
{
|
||||
"query": "人工智能",
|
||||
"top_k": 5
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("中国新闻搜索结果", result['data'])
|
||||
|
||||
# 3. 搜索概念板块（按涨跌幅排序）
|
||||
print("\n示例3: 搜索概念板块（关键词：新能源，按涨跌幅排序）")
|
||||
result = client.call_tool(
|
||||
"search_concepts",
|
||||
{
|
||||
"query": "新能源",
|
||||
"size": 5,
|
||||
"sort_by": "change_pct"
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("概念搜索结果", result['data'])
|
||||
|
||||
# 4. 获取股票的相关概念
|
||||
print("\n示例4: 获取股票相关概念（股票代码：600519）")
|
||||
result = client.call_tool(
|
||||
"get_stock_concepts",
|
||||
{
|
||||
"stock_code": "600519",
|
||||
"size": 10
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("股票概念结果", result['data'])
|
||||
|
||||
# 5. 搜索涨停股票
|
||||
print("\n示例5: 搜索涨停股票（关键词：锂电池）")
|
||||
result = client.call_tool(
|
||||
"search_limit_up_stocks",
|
||||
{
|
||||
"query": "锂电池",
|
||||
"mode": "hybrid",
|
||||
"page_size": 5
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("涨停股票搜索结果", result['data'])
|
||||
|
||||
# 6. 搜索研究报告
|
||||
print("\n示例6: 搜索研究报告（关键词：投资策略）")
|
||||
result = client.call_tool(
|
||||
"search_research_reports",
|
||||
{
|
||||
"query": "投资策略",
|
||||
"mode": "hybrid",
|
||||
"size": 3
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("研究报告搜索结果", result['data'])
|
||||
|
||||
# 7. 获取概念统计数据
|
||||
print("\n示例7: 获取概念统计（最近7天）")
|
||||
result = client.call_tool(
|
||||
"get_concept_statistics",
|
||||
{
|
||||
"days": 7,
|
||||
"min_stock_count": 3
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("概念统计结果", result['data'])
|
||||
|
||||
# 8. 搜索路演信息
|
||||
print("\n示例8: 搜索路演信息（关键词：业绩）")
|
||||
result = client.call_tool(
|
||||
"search_roadshows",
|
||||
{
|
||||
"query": "业绩",
|
||||
"size": 3
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("路演搜索结果", result['data'])
|
||||
|
||||
# 9. 获取股票基本信息
|
||||
print("\n示例9: 获取股票基本信息（股票：600519）")
|
||||
result = client.call_tool(
|
||||
"get_stock_basic_info",
|
||||
{
|
||||
"seccode": "600519"
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("股票基本信息", result['data'])
|
||||
|
||||
# 10. 获取股票财务指标
|
||||
print("\n示例10: 获取股票财务指标（股票：600519，最近5期）")
|
||||
result = client.call_tool(
|
||||
"get_stock_financial_index",
|
||||
{
|
||||
"seccode": "600519",
|
||||
"limit": 5
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("财务指标", result['data'])
|
||||
|
||||
# 11. 获取股票交易数据
|
||||
print("\n示例11: 获取股票交易数据（股票：600519，最近10天）")
|
||||
result = client.call_tool(
|
||||
"get_stock_trade_data",
|
||||
{
|
||||
"seccode": "600519",
|
||||
"limit": 10
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("交易数据", result['data'])
|
||||
|
||||
# 12. 按行业搜索股票
|
||||
print("\n示例12: 按行业搜索股票（行业：半导体）")
|
||||
result = client.call_tool(
|
||||
"search_stocks_by_criteria",
|
||||
{
|
||||
"industry": "半导体",
|
||||
"limit": 10
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("行业股票", result['data'])
|
||||
|
||||
# 13. 股票对比分析
|
||||
print("\n示例13: 股票对比分析（600519 vs 000858）")
|
||||
result = client.call_tool(
|
||||
"get_stock_comparison",
|
||||
{
|
||||
"seccodes": ["600519", "000858"],
|
||||
"metric": "financial"
|
||||
}
|
||||
)
|
||||
if result['success']:
|
||||
print_result("股票对比", result['data'])
|
||||
|
||||
except Exception as e:
|
||||
print(f"\n错误: {str(e)}")
|
||||
|
||||
finally:
|
||||
client.close()
|
||||
|
||||
|
||||
def test_single_tool():
|
||||
"""测试单个工具（用于快速测试）"""
|
||||
client = MCPClient()
|
||||
|
||||
try:
|
||||
# 修改这里来测试不同的工具
|
||||
result = client.call_tool(
|
||||
"search_china_news",
|
||||
{
|
||||
"query": "芯片",
|
||||
"exact_match": True,
|
||||
"top_k": 3
|
||||
}
|
||||
)
|
||||
|
||||
print_result("测试结果", result)
|
||||
|
||||
except Exception as e:
|
||||
print(f"错误: {str(e)}")
|
||||
|
||||
finally:
|
||||
client.close()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
# 运行完整示例
|
||||
main()
|
||||
|
||||
# 或者测试单个工具
|
||||
# test_single_tool()
|
||||
@@ -1,492 +0,0 @@
|
||||
"""
|
||||
集成到 mcp_server.py 的 Agent 系统
|
||||
使用 Kimi (kimi-k2-thinking) 和 DeepMoney 两个模型
|
||||
"""
|
||||
|
||||
from openai import OpenAI
|
||||
from pydantic import BaseModel
|
||||
from typing import List, Dict, Any, Optional, Literal
|
||||
from datetime import datetime
|
||||
import json
|
||||
import logging
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
# ==================== 模型配置 ====================
|
||||
|
||||
# Kimi 配置 - 用于计划制定和深度推理
|
||||
KIMI_CONFIG = {
|
||||
"api_key": "sk-TzB4VYJfCoXGcGrGMiewukVRzjuDsbVCkaZXi2LvkS8s60E5",
|
||||
"base_url": "https://api.moonshot.cn/v1",
|
||||
"model": "kimi-k2-thinking", # 思考模型
|
||||
}
|
||||
|
||||
# DeepMoney 配置 - 用于新闻总结
|
||||
DEEPMONEY_CONFIG = {
|
||||
"api_key": "", # 空值
|
||||
"base_url": "http://111.62.35.50:8000/v1",
|
||||
"model": "deepmoney",
|
||||
}
|
||||
|
||||
# ==================== 数据模型 ====================
|
||||
|
||||
class ToolCall(BaseModel):
|
||||
"""工具调用"""
|
||||
tool: str
|
||||
arguments: Dict[str, Any]
|
||||
reason: str
|
||||
|
||||
class ExecutionPlan(BaseModel):
|
||||
"""执行计划"""
|
||||
goal: str
|
||||
steps: List[ToolCall]
|
||||
reasoning: str
|
||||
|
||||
class StepResult(BaseModel):
|
||||
"""单步执行结果"""
|
||||
step_index: int
|
||||
tool: str
|
||||
arguments: Dict[str, Any]
|
||||
status: Literal["success", "failed", "skipped"]
|
||||
result: Optional[Any] = None
|
||||
error: Optional[str] = None
|
||||
execution_time: float = 0
|
||||
|
||||
class AgentResponse(BaseModel):
|
||||
"""Agent响应"""
|
||||
success: bool
|
||||
message: str
|
||||
plan: Optional[ExecutionPlan] = None
|
||||
step_results: List[StepResult] = []
|
||||
final_summary: Optional[str] = None
|
||||
metadata: Optional[Dict[str, Any]] = None
|
||||
|
||||
class ChatRequest(BaseModel):
|
||||
"""聊天请求"""
|
||||
message: str
|
||||
conversation_history: List[Dict[str, str]] = []
|
||||
|
||||
# ==================== Agent 系统 ====================
|
||||
|
||||
class MCPAgentIntegrated:
|
||||
"""集成版 MCP Agent - 使用 Kimi 和 DeepMoney"""
|
||||
|
||||
def __init__(self):
|
||||
# 初始化 Kimi 客户端（计划制定）
|
||||
self.kimi_client = OpenAI(
|
||||
api_key=KIMI_CONFIG["api_key"],
|
||||
base_url=KIMI_CONFIG["base_url"],
|
||||
)
|
||||
self.kimi_model = KIMI_CONFIG["model"]
|
||||
|
||||
# 初始化 DeepMoney 客户端（新闻总结）
|
||||
self.deepmoney_client = OpenAI(
|
||||
api_key=DEEPMONEY_CONFIG["api_key"],
|
||||
base_url=DEEPMONEY_CONFIG["base_url"],
|
||||
)
|
||||
self.deepmoney_model = DEEPMONEY_CONFIG["model"]
|
||||
|
||||
def get_planning_prompt(self, tools: List[dict]) -> str:
|
||||
"""获取计划制定的系统提示词"""
|
||||
tools_desc = "\n\n".join([
|
||||
f"**{tool['name']}**\n"
|
||||
f"描述：{tool['description']}\n"
|
||||
f"参数：{json.dumps(tool['parameters'], ensure_ascii=False, indent=2)}"
|
||||
for tool in tools
|
||||
])
|
||||
|
||||
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。
|
||||
|
||||
## 可用工具
|
||||
|
||||
{tools_desc}
|
||||
|
||||
## 特殊工具
|
||||
- **summarize_news**: 使用 DeepMoney 模型总结新闻数据
|
||||
- 参数: {{"data": "新闻列表JSON", "focus": "关注点"}}
|
||||
- 适用场景: 当需要总结新闻、研报等文本数据时
|
||||
|
||||
## 重要知识
|
||||
- 贵州茅台: 600519
|
||||
- 涨停: 涨幅约10%
|
||||
- 概念板块: 相同题材股票分类
|
||||
|
||||
## 任务
|
||||
分析用户问题，制定执行计划。返回 JSON：
|
||||
|
||||
```json
|
||||
{{
|
||||
"goal": "用户目标",
|
||||
"reasoning": "分析思路",
|
||||
"steps": [
|
||||
{{
|
||||
"tool": "工具名",
|
||||
"arguments": {{"参数": "值"}},
|
||||
"reason": "原因"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
## 规划原则
|
||||
1. 先收集数据，再分析总结
|
||||
2. 使用 summarize_news 总结新闻类数据
|
||||
3. 不超过5个步骤
|
||||
4. 最后一步通常是总结
|
||||
|
||||
## 示例
|
||||
|
||||
用户："贵州茅台最近有什么新闻"
|
||||
|
||||
计划：
|
||||
```json
|
||||
{{
|
||||
"goal": "查询并总结贵州茅台最新新闻",
|
||||
"reasoning": "先搜索新闻，再用 DeepMoney 总结",
|
||||
"steps": [
|
||||
{{
|
||||
"tool": "search_china_news",
|
||||
"arguments": {{"query": "贵州茅台", "top_k": 10}},
|
||||
"reason": "搜索贵州茅台相关新闻"
|
||||
}},
|
||||
{{
|
||||
"tool": "summarize_news",
|
||||
"arguments": {{
|
||||
"data": "前面的新闻数据",
|
||||
"focus": "贵州茅台的重要动态和市场影响"
|
||||
}},
|
||||
"reason": "使用DeepMoney总结新闻要点"
|
||||
}}
|
||||
]
|
||||
}}
|
||||
```
|
||||
|
||||
只返回JSON，不要其他内容。"""
|
||||
|
||||
async def create_plan(self, user_query: str, tools: List[dict]) -> ExecutionPlan:
|
||||
"""阶段1: 使用 Kimi 创建执行计划（带思考过程）"""
|
||||
logger.info(f"[Planning] Kimi开始制定计划: {user_query}")
|
||||
|
||||
messages = [
|
||||
{"role": "system", "content": self.get_planning_prompt(tools)},
|
||||
{"role": "user", "content": user_query},
|
||||
]
|
||||
|
||||
# 使用 Kimi 思考模型
|
||||
response = self.kimi_client.chat.completions.create(
|
||||
model=self.kimi_model,
|
||||
messages=messages,
|
||||
temperature=1.0, # Kimi 推荐
|
||||
max_tokens=16000, # 足够容纳 reasoning_content
|
||||
)
|
||||
|
||||
choice = response.choices[0]
|
||||
message = choice.message
|
||||
|
||||
# 提取思考过程
|
||||
reasoning_content = ""
|
||||
if hasattr(message, "reasoning_content"):
|
||||
reasoning_content = getattr(message, "reasoning_content")
|
||||
logger.info(f"[Planning] Kimi思考过程: {reasoning_content[:200]}...")
|
||||
|
||||
# 提取计划内容
|
||||
plan_json = message.content.strip()
|
||||
|
||||
# 清理可能的代码块标记
|
||||
if "```json" in plan_json:
|
||||
plan_json = plan_json.split("```json")[1].split("```")[0].strip()
|
||||
elif "```" in plan_json:
|
||||
plan_json = plan_json.split("```")[1].split("```")[0].strip()
|
||||
|
||||
plan_data = json.loads(plan_json)
|
||||
|
||||
plan = ExecutionPlan(
|
||||
goal=plan_data["goal"],
|
||||
reasoning=plan_data.get("reasoning", "") + "\n\n" + (reasoning_content[:500] if reasoning_content else ""),
|
||||
steps=[ToolCall(**step) for step in plan_data["steps"]],
|
||||
)
|
||||
|
||||
logger.info(f"[Planning] 计划制定完成: {len(plan.steps)} 步")
|
||||
return plan
|
||||
|
||||
async def execute_tool(
|
||||
self,
|
||||
tool_name: str,
|
||||
arguments: Dict[str, Any],
|
||||
tool_handlers: Dict[str, Any],
|
||||
) -> Dict[str, Any]:
|
||||
"""执行单个工具"""
|
||||
|
||||
# 特殊工具：summarize_news（使用 DeepMoney）
|
||||
if tool_name == "summarize_news":
|
||||
return await self.summarize_news_with_deepmoney(
|
||||
data=arguments.get("data", ""),
|
||||
focus=arguments.get("focus", "关键信息"),
|
||||
)
|
||||
|
||||
# 调用 MCP 工具
|
||||
handler = tool_handlers.get(tool_name)
|
||||
if not handler:
|
||||
raise ValueError(f"Tool '{tool_name}' not found")
|
||||
|
||||
result = await handler(arguments)
|
||||
return result
|
||||
|
||||
async def summarize_news_with_deepmoney(self, data: str, focus: str) -> str:
|
||||
"""使用 DeepMoney 模型总结新闻"""
|
||||
logger.info(f"[DeepMoney] 总结新闻，关注点: {focus}")
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"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。"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"请总结以下新闻数据，关注点：{focus}\n\n数据：\n{data[:3000]}"
|
||||
},
|
||||
]
|
||||
|
||||
try:
|
||||
response = self.deepmoney_client.chat.completions.create(
|
||||
model=self.deepmoney_model,
|
||||
messages=messages,
|
||||
temperature=0.7,
|
||||
max_tokens=1000,
|
||||
)
|
||||
|
||||
summary = response.choices[0].message.content
|
||||
logger.info(f"[DeepMoney] 总结完成")
|
||||
return summary
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[DeepMoney] 总结失败: {str(e)}")
|
||||
# 降级：返回简化摘要
|
||||
return f"新闻总结失败，原始数据：{data[:500]}..."
|
||||
|
||||
async def execute_plan(
|
||||
self,
|
||||
plan: ExecutionPlan,
|
||||
tool_handlers: Dict[str, Any],
|
||||
) -> List[StepResult]:
|
||||
"""阶段2: 执行计划"""
|
||||
logger.info(f"[Execution] 开始执行: {len(plan.steps)} 步")
|
||||
|
||||
results = []
|
||||
collected_data = {}
|
||||
|
||||
for i, step in enumerate(plan.steps):
|
||||
logger.info(f"[Execution] 步骤 {i+1}/{len(plan.steps)}: {step.tool}")
|
||||
|
||||
start_time = datetime.now()
|
||||
|
||||
try:
|
||||
# 替换占位符
|
||||
arguments = step.arguments.copy()
|
||||
|
||||
# 如果参数值是 "前面的新闻数据" 或 "前面收集的所有数据"
|
||||
if step.tool == "summarize_news":
|
||||
if arguments.get("data") in ["前面的新闻数据", "前面收集的所有数据"]:
|
||||
# 将收集的数据传递
|
||||
arguments["data"] = json.dumps(collected_data, ensure_ascii=False, indent=2)
|
||||
|
||||
# 执行工具
|
||||
result = await self.execute_tool(step.tool, arguments, tool_handlers)
|
||||
|
||||
execution_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
step_result = StepResult(
|
||||
step_index=i,
|
||||
tool=step.tool,
|
||||
arguments=arguments,
|
||||
status="success",
|
||||
result=result,
|
||||
execution_time=execution_time,
|
||||
)
|
||||
results.append(step_result)
|
||||
|
||||
# 收集数据
|
||||
collected_data[f"step_{i+1}_{step.tool}"] = result
|
||||
|
||||
logger.info(f"[Execution] 步骤 {i+1} 完成: {execution_time:.2f}s")
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Execution] 步骤 {i+1} 失败: {str(e)}")
|
||||
|
||||
execution_time = (datetime.now() - start_time).total_seconds()
|
||||
|
||||
step_result = StepResult(
|
||||
step_index=i,
|
||||
tool=step.tool,
|
||||
arguments=step.arguments,
|
||||
status="failed",
|
||||
error=str(e),
|
||||
execution_time=execution_time,
|
||||
)
|
||||
results.append(step_result)
|
||||
|
||||
# 继续执行其他步骤
|
||||
continue
|
||||
|
||||
logger.info(f"[Execution] 执行完成")
|
||||
return results
|
||||
|
||||
async def generate_final_summary(
|
||||
self,
|
||||
user_query: str,
|
||||
plan: ExecutionPlan,
|
||||
step_results: List[StepResult],
|
||||
) -> str:
|
||||
"""阶段3: 使用 Kimi 生成最终总结"""
|
||||
logger.info("[Summary] Kimi生成最终总结")
|
||||
|
||||
# 收集成功的结果
|
||||
successful_results = [r for r in step_results if r.status == "success"]
|
||||
|
||||
if not successful_results:
|
||||
return "很抱歉，所有步骤都执行失败，无法生成分析报告。"
|
||||
|
||||
# 构建结果文本（精简版）
|
||||
results_text = "\n\n".join([
|
||||
f"**步骤 {r.step_index + 1}: {r.tool}**\n"
|
||||
f"结果: {str(r.result)[:800]}..."
|
||||
for r in successful_results[:3] # 只取前3个，避免超长
|
||||
])
|
||||
|
||||
messages = [
|
||||
{
|
||||
"role": "system",
|
||||
"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。"
|
||||
},
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"""用户问题：{user_query}
|
||||
|
||||
执行计划：{plan.goal}
|
||||
|
||||
执行结果：
|
||||
{results_text}
|
||||
|
||||
请生成专业的分析报告（300字以内）。"""
|
||||
},
|
||||
]
|
||||
|
||||
try:
|
||||
response = self.kimi_client.chat.completions.create(
|
||||
model="kimi-k2-turbpreview", # 使用非思考模型，更快
|
||||
messages=messages,
|
||||
temperature=0.7,
|
||||
max_tokens=1000,
|
||||
)
|
||||
|
||||
summary = response.choices[0].message.content
|
||||
logger.info("[Summary] 总结完成")
|
||||
return summary
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Summary] 总结失败: {str(e)}")
|
||||
# 降级：返回最后一步的结果
|
||||
if successful_results:
|
||||
last_result = successful_results[-1]
|
||||
if isinstance(last_result.result, str):
|
||||
return last_result.result
|
||||
else:
|
||||
return json.dumps(last_result.result, ensure_ascii=False, indent=2)
|
||||
return "总结生成失败"
|
||||
|
||||
async def process_query(
|
||||
self,
|
||||
user_query: str,
|
||||
tools: List[dict],
|
||||
tool_handlers: Dict[str, Any],
|
||||
) -> AgentResponse:
|
||||
"""主流程"""
|
||||
logger.info(f"[Agent] 处理查询: {user_query}")
|
||||
|
||||
try:
|
||||
# 阶段1: Kimi 制定计划
|
||||
plan = await self.create_plan(user_query, tools)
|
||||
|
||||
# 阶段2: 执行工具
|
||||
step_results = await self.execute_plan(plan, tool_handlers)
|
||||
|
||||
# 阶段3: Kimi 生成总结
|
||||
final_summary = await self.generate_final_summary(
|
||||
user_query, plan, step_results
|
||||
)
|
||||
|
||||
return AgentResponse(
|
||||
success=True,
|
||||
message=final_summary,
|
||||
plan=plan,
|
||||
step_results=step_results,
|
||||
final_summary=final_summary,
|
||||
metadata={
|
||||
"total_steps": len(plan.steps),
|
||||
"successful_steps": len([r for r in step_results if r.status == "success"]),
|
||||
"failed_steps": len([r for r in step_results if r.status == "failed"]),
|
||||
"total_execution_time": sum(r.execution_time for r in step_results),
|
||||
"model_used": {
|
||||
"planning": self.kimi_model,
|
||||
"summarization": "kimi-k2-turbpreview",
|
||||
"news_summary": self.deepmoney_model,
|
||||
},
|
||||
},
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"[Agent] 错误: {str(e)}", exc_info=True)
|
||||
return AgentResponse(
|
||||
success=False,
|
||||
message=f"处理失败: {str(e)}",
|
||||
)
|
||||
|
||||
# ==================== 添加到 mcp_server.py ====================
|
||||
|
||||
"""
|
||||
在 mcp_server.py 中添加以下代码：
|
||||
|
||||
# 导入 Agent 系统
|
||||
from mcp_server_agent_integration import MCPAgentIntegrated, ChatRequest, AgentResponse
|
||||
|
||||
# 创建 Agent 实例（全局）
|
||||
agent = MCPAgentIntegrated()
|
||||
|
||||
# 添加端点
|
||||
@app.post("/agent/chat", response_model=AgentResponse)
|
||||
async def agent_chat(request: ChatRequest):
|
||||
\"\"\"智能代理对话端点\"\"\"
|
||||
logger.info(f"Agent chat: {request.message}")
|
||||
|
||||
# 获取工具列表
|
||||
tools = [tool.dict() for tool in TOOLS]
|
||||
|
||||
# 添加特殊工具：summarize_news
|
||||
tools.append({
|
||||
"name": "summarize_news",
|
||||
"description": "使用 DeepMoney 模型总结新闻数据，提取关键信息",
|
||||
"parameters": {
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"data": {
|
||||
"type": "string",
|
||||
"description": "要总结的新闻数据（JSON格式）"
|
||||
},
|
||||
"focus": {
|
||||
"type": "string",
|
||||
"description": "关注点，例如：'市场影响'、'投资机会'等"
|
||||
}
|
||||
},
|
||||
"required": ["data"]
|
||||
}
|
||||
})
|
||||
|
||||
# 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