GLM 5.2 联网搜索：函数调用实现实时浏览

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_ZHIPU_API_KEY",
    base_url="https://open.bigmodel.cn/api/paas/v4/",
)

response = client.chat.completions.create(
    model="glm-4-plus",
    messages=[
        {
            "role": "user",
            "content": "What are the top AI news stories from the past 24 hours?"
        }
    ],
    tools=[
        {
            "type": "web_search",
            "web_search": {
                "enable": True
            }
        }
    ],
)

print(response.choices[0].message.content)

import json
import requests
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_ZHIPU_API_KEY",
    base_url="https://open.bigmodel.cn/api/paas/v4/",
)

# --- Define the search tool schema ---
tools = [
    {
        "type": "function",
        "function": {
            "name": "web_search",
            "description": (
                "Search the web for current information. "
                "Use this for recent events, live pricing, or the latest documentation."
            ),
            "parameters": {
                "type": "object",
                "properties": {
                    "query": {
                        "type": "string",
                        "description": "The search query to execute."
                    },
                    "num_results": {
                        "type": "integer",
                        "description": "Number of results to return (default 5).",
                        "default": 5
                    }
                },
                "required": ["query"]
            }
        }
    }
]


def run_search(query: str, num_results: int = 5) -> list[dict]:
    """
    Execute a real web search using your preferred provider.
    Replace this stub with a call to SerpAPI, Brave Search, or Tavily.
    """
    # Example stub — replace with your actual search provider:
    # serpapi_key = "YOUR_SERPAPI_KEY"
    # url = "https://serpapi.com/search"
    # params = {"q": query, "num": num_results, "api_key": serpapi_key}
    # data = requests.get(url, params=params).json()
    # return [{"title": r["title"], "snippet": r["snippet"], "url": r["link"]}
    #         for r in data.get("organic_results", [])[:num_results]]

    # Placeholder response for demonstration:
    return [
        {
            "title": f"Search result for: {query}",
            "snippet": "Live snippet from the web would appear here.",
            "url": "https://example.com"
        }
    ]


def chat_with_search(user_message: str) -> str:
    messages = [{"role": "user", "content": user_message}]

    # First turn: model may request a tool call
    response = client.chat.completions.create(
        model="glm-4-plus",
        messages=messages,
        tools=tools,
        tool_choice="auto",
    )

    choice = response.choices[0]

    # If the model wants to search, execute it and send results back
    if choice.finish_reason == "tool_calls":
        tool_call = choice.message.tool_calls[0]
        args = json.loads(tool_call.function.arguments)

        search_results = run_search(
            query=args["query"],
            num_results=args.get("num_results", 5)
        )

        # Append the assistant's tool-call message and our results
        messages.append(choice.message)
        messages.append({
            "role": "tool",
            "tool_call_id": tool_call.id,
            "content": json.dumps(search_results)
        })

        # Second turn: model writes final answer using search results
        final_response = client.chat.completions.create(
            model="glm-4-plus",
            messages=messages,
        )
        return final_response.choices[0].message.content

    # Model answered directly without searching
    return choice.message.content


if __name__ == "__main__":
    answer = chat_with_search(
        "What is the current Python version and when was it released?"
    )
    print(answer)