205 lines
6.5 KiB
Python
205 lines
6.5 KiB
Python
import json
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import httpx
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import openai
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import time
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base_url= "https://api.moonshot.cn/v1"
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api_key= "sk-xW0xjfQM6Mp9ZCWMLlnHiRJcpEOIZPTkXcN0dQ15xpZSuw2y" # 如有需要可放置独立密钥
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class FormulaChatClient:
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def __init__(self, base_url: str, api_key: str):
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"""初始化 Formula 客户端"""
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self.base_url = base_url
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self.api_key = api_key
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self.openai = openai.Client(
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base_url=base_url,
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api_key=api_key,
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)
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self.httpx = httpx.Client(
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base_url=base_url,
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headers={"Authorization": f"Bearer {api_key}"},
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timeout=30.0,
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)
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self.model = "kimi-k2-thinking"
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def get_tools(self, formula_uri: str):
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"""从 Formula API 获取工具定义"""
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response = self.httpx.get(f"/formulas/{formula_uri}/tools")
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response.raise_for_status() # 检查 HTTP 状态码
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try:
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return response.json().get("tools", [])
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except json.JSONDecodeError as e:
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print(f"错误: 无法解析响应为 JSON (状态码: {response.status_code})")
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print(f"响应内容: {response.text[:500]}")
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raise
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def call_tool(self, formula_uri: str, function: str, args: dict):
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"""调用官方工具"""
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response = self.httpx.post(
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f"/formulas/{formula_uri}/fibers",
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json={"name": function, "arguments": json.dumps(args)},
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)
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response.raise_for_status() # 检查 HTTP 状态码
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fiber = response.json()
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if fiber.get("status", "") == "succeeded":
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return fiber["context"].get("output") or fiber["context"].get("encrypted_output")
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if "error" in fiber:
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return f"Error: {fiber['error']}"
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if "error" in fiber.get("context", {}):
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return f"Error: {fiber['context']['error']}"
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return "Error: Unknown error"
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def close(self):
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"""关闭客户端连接"""
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self.httpx.close()
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# 初始化客户端 - 使用硬编码配置
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base_url = "https://api.moonshot.cn/v1"
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api_key = "sk-xW0xjfQM6Mp9ZCWMLlnHiRJcpEOIZPTkXcN0dQ15xpZSuw2y"
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print(f"Base URL: {base_url}")
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print(f"API Key: {api_key[:10]}...{api_key[-10:] if len(api_key) > 20 else api_key}\n")
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# 等待一段时间避免并发限制
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print("等待5秒避免组织并发限制...")
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time.sleep(5)
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client = FormulaChatClient(base_url, api_key)
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# 定义要使用的官方工具 Formula URI
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formula_uris = [
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"moonshot/date:latest",
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"moonshot/web-search:latest"
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]
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# 加载所有工具定义并建立映射
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print("正在加载官方工具...")
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all_tools = []
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tool_to_uri = {} # function.name -> formula_uri 的映射
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for uri in formula_uris:
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try:
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tools = client.get_tools(uri)
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for tool in tools:
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func = tool.get("function")
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if func:
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func_name = func.get("name")
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if func_name:
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tool_to_uri[func_name] = uri
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all_tools.append(tool)
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print(f" 已加载工具: {func_name} from {uri}")
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except Exception as e:
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print(f" 警告: 加载工具 {uri} 失败: {e}")
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continue
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print(f"总共加载 {len(all_tools)} 个工具\n")
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if not all_tools:
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raise ValueError("未能加载任何工具，请检查 API 密钥和网络连接")
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# 初始化消息列表
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messages = [
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{
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"role": "system",
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"content": "你是 Kimi，一个专业的新闻分析师。你擅长收集、分析和整理信息，生成高质量的新闻报告。",
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},
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]
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# 用户请求生成今日新闻报告
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user_request = "请帮我生成一份今日新闻报告，包含重要的科技、经济和社会新闻。"
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messages.append({
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"role": "user",
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"content": user_request
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})
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print(f"用户请求: {user_request}\n")
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max_iterations = 5 # 防止无限循环，减少API调用次数
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for iteration in range(max_iterations):
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# 添加延迟避免速率限制
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print(f"等待3秒避免速率限制...\n")
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time.sleep(3)
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# 调用模型
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try:
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completion = client.openai.chat.completions.create(
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model=client.model,
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messages=messages,
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max_tokens=1024 * 32,
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tools=all_tools,
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temperature=1.0,
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)
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except openai.AuthenticationError as e:
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print(f"认证错误: {e}")
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print("请检查 API key 是否正确，以及 API key 是否有权限访问该端点")
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raise
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except Exception as e:
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print(f"调用模型时发生错误: {e}")
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raise
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# 获取响应
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message = completion.choices[0].message
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# 打印思考过程
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if hasattr(message, "reasoning_content"):
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print(f"=============第 {iteration + 1} 轮思考开始=============")
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reasoning = getattr(message, "reasoning_content")
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if reasoning:
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print(reasoning[:500] + "..." if len(reasoning) > 500 else reasoning)
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print(f"=============第 {iteration + 1} 轮思考结束=============\n")
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# 添加 assistant 消息到上下文（保留 reasoning_content）
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messages.append(message)
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# 如果模型没有调用工具，说明对话结束
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if not message.tool_calls:
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print("=============最终回答=============")
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print(message.content)
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break
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# 处理工具调用
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print(f"模型决定调用 {len(message.tool_calls)} 个工具:\n")
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for tool_call in message.tool_calls:
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func_name = tool_call.function.name
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args = json.loads(tool_call.function.arguments)
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print(f"调用工具: {func_name}")
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print(f"参数: {json.dumps(args, ensure_ascii=False, indent=2)}")
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# 获取对应的 formula_uri
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formula_uri = tool_to_uri.get(func_name)
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if not formula_uri:
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print(f"错误: 找不到工具 {func_name} 对应的 Formula URI")
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continue
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# 调用工具
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result = client.call_tool(formula_uri, func_name, args)
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# 打印结果（截断过长内容）
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if len(str(result)) > 200:
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print(f"工具结果: {str(result)[:200]}...\n")
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else:
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print(f"工具结果: {result}\n")
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# 添加工具结果到消息列表
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tool_message = {
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"role": "tool",
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"tool_call_id": tool_call.id,
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"name": func_name,
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"content": result
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}
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messages.append(tool_message)
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print("\n对话完成！")
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# 清理资源
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client.close() |