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AGENT_SYSTEM_PROMPT = """
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你是一个智能旅行助手。你的任务是分析用户的请求，并使用可用工具一步步地解决问题。
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# 可用工具:
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- `get_weather(city: str)`: 查询指定城市的实时天气。
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- `get_attraction(city: str, weather: str)`: 根据城市和天气搜索推荐的旅游景点。
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# 行动格式:
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你的回答必须严格遵循以下格式。首先是你的思考过程，然后是你要执行的具体行动。
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Thought: [这里是你的思考过程和下一步计划]
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Action: [这里是你要调用的工具，格式为 function_name(arg_name="arg_value")]
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# 任务完成:
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当你收集到足够的信息，能够回答用户的最终问题时，你必须使用 `finish(answer="...")` 来输出最终答案。
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请开始吧！
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"""
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import requests
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import json
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def get_weather(city: str) -> str:
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"""
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通过调用 wttr.in API 查询真实的天气信息。
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"""
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# API端点，我们请求JSON格式的数据
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url = f"https://wttr.in/{city}?format=j1"
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try:
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# 发起网络请求
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response = requests.get(url)
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# 检查响应状态码是否为200 (成功)
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response.raise_for_status()
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# 解析返回的JSON数据
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data = response.json()
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# 提取当前天气状况
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current_condition = data['current_condition'][0]
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weather_desc = current_condition['weatherDesc'][0]['value']
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temp_c = current_condition['temp_C']
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# 格式化成自然语言返回
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return f"{city}当前天气：{weather_desc}，气温{temp_c}摄氏度"
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except requests.exceptions.RequestException as e:
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# 处理网络错误
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return f"错误：查询天气时遇到网络问题 - {e}"
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except (KeyError, IndexError) as e:
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# 处理数据解析错误
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return f"错误：解析天气数据失败，可能是城市名称无效 - {e}"
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import os
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from tavily import TavilyClient
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def get_attraction(city: str, weather: str) -> str:
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"""
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根据城市和天气，使用Tavily Search API搜索并返回优化后的景点推荐。
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"""
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# 从环境变量或主程序配置中获取API密钥
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api_key = os.environ.get("TAVILY_API_KEY") # 推荐方式
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# 或者，我们可以在主循环中传入，如此处代码所示
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if not api_key:
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return "错误：未配置TAVILY_API_KEY。"
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# 2. 初始化Tavily客户端
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tavily = TavilyClient(api_key=api_key)
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# 3. 构造一个精确的查询
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query = f"'{city}' 在'{weather}'天气下最值得去的旅游景点推荐及理由"
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try:
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# 4. 调用API，include_answer=True会返回一个综合性的回答
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response = tavily.search(query=query, search_depth="basic", include_answer=True)
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# 5. Tavily返回的结果已经非常干净，可以直接使用
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# response['answer'] 是一个基于所有搜索结果的总结性回答
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if response.get("answer"):
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return response["answer"]
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# 如果没有综合性回答，则格式化原始结果
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formatted_results = []
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for result in response.get("results", []):
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formatted_results.append(f"- {result['title']}: {result['content']}")
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if not formatted_results:
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return "抱歉，没有找到相关的旅游景点推荐。"
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return "根据搜索，为您找到以下信息：\n" + "\n".join(formatted_results)
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except Exception as e:
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return f"错误：执行Tavily搜索时出现问题 - {e}"
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# 将所有工具函数放入一个字典，方便后续调用
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available_tools = {
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"get_weather": get_weather,
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"get_attraction": get_attraction,
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}
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from openai import OpenAI
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class OpenAICompatibleClient:
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"""
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一个用于调用任何兼容OpenAI接口的LLM服务的客户端。
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"""
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def __init__(self, model: str, api_key: str, base_url: str):
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self.model = model
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self.client = OpenAI(api_key=api_key, base_url=base_url)
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def generate(self, prompt: str, system_prompt: str) -> str:
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"""调用LLM API来生成回应。"""
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print("正在调用大语言模型...")
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try:
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messages = [
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{'role': 'system', 'content': system_prompt},
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{'role': 'user', 'content': prompt}
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]
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response = self.client.chat.completions.create(
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model=self.model,
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messages=messages,
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stream=False
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)
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answer = response.choices[0].message.content
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print("大语言模型响应成功。")
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return answer
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except Exception as e:
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print(f"调用LLM API时发生错误: {e}")
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return "错误：调用语言模型服务时出错。"
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import re
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# --- 1. 配置LLM客户端 ---
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# 请根据您使用的服务，将这里替换成对应的凭证和地址
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API_KEY = "YOUR_API_KEY"
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BASE_URL = "YOUR_BASE_URL"
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MODEL_ID = "YOUR_MODEL_ID"
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os.environ['TAVILY_API_KEY'] = "YOUR_TAVILY_API_KEY"
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llm = OpenAICompatibleClient(
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model=MODEL_ID,
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api_key=API_KEY,
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base_url=BASE_URL
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)
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# --- 2. 初始化 ---
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user_prompt = "你好，请帮我查询一下今天北京的天气，然后根据天气推荐一个合适的旅游景点。"
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prompt_history = [f"用户请求: {user_prompt}"]
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print(f"用户输入: {user_prompt}\n" + "="*40)
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# --- 3. 运行主循环 ---
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for i in range(5): # 设置最大循环次数
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print(f"--- 循环 {i+1} ---\n")
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# 3.1. 构建Prompt
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full_prompt = "\n".join(prompt_history)
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# 3.2. 调用LLM进行思考
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llm_output = llm.generate(full_prompt, system_prompt=AGENT_SYSTEM_PROMPT)
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print(f"模型输出:\n{llm_output}\n")
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prompt_history.append(llm_output)
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# 3.3. 解析并执行行动
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action_match = re.search(r"Action: (.*)", llm_output, re.DOTALL)
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if not action_match:
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print("解析错误：模型输出中未找到 Action。")
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break
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action_str = action_match.group(1).strip()
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if action_str.startswith("finish"):
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final_answer = re.search(r'finish\(answer="(.*)"\)', action_str).group(1)
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print(f"任务完成，最终答案: {final_answer}")
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break
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tool_name = re.search(r"(\w+)\(", action_str).group(1)
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args_str = re.search(r"\((.*)\)", action_str).group(1)
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kwargs = dict(re.findall(r'(\w+)="([^"]*)"', args_str))
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if tool_name in available_tools:
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observation = available_tools[tool_name](**kwargs)
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else:
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observation = f"错误：未定义的工具 '{tool_name}'"
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# 3.4. 记录观察结果
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observation_str = f"Observation: {observation}"
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print(f"{observation_str}\n" + "="*40)
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prompt_history.append(observation_str)
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