362 lines
9.5 KiB
Python
362 lines
9.5 KiB
Python
"""
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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-turbo-preview"
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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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