How to Configure GPT‑4 Turbo for Web Browsing with SerpAPI?

Below is an example of how you might approach this. The key point is that GPT‑4 Turbo (like other models that support function calling) won’t actually browse the web by itself—you need to implement your own function that calls SerpAPI. Then, you can pass a function specification to GPT‑4 Turbo so that when the model “decides” that a web search is needed, it returns a function call object with parameters, and you then call your SerpAPI integration with those parameters.

For example, you can define a function (e.g., search_web) with a JSON schema that tells GPT‑4 Turbo what arguments it needs (like a search query). Once you get the response with a function call, your application can call SerpAPI accordingly. Here’s a simplified snippet in Python illustrating one way to do it:

PYTHONCopied
import openai
import requests

# Define the function spec for web search with SerpAPI.
# This function specification is passed to the Chat Completion API.
functions = [
    {
        "name": "search_web",
        "description": "Perform a web search using SerpAPI",
        "parameters": {
            "type": "object",
            "properties": {
                "query": {
                    "type": "string",
                    "description": "The search query to look up on the web"
                }
                # You can add more parameters if needed (e.g., result count, page number, etc.)
            },
            "required": ["query"]
        }
    }
]

# Prepare the messages history. One of the messages could be:
messages = [
    {"role": "user", "content": "Find the latest news on OpenAI releases."}
]

# Call the Chat Completion API with GPT-4 Turbo including our function specification
response = openai.ChatCompletion.create(
    model="gpt-4-turbo",
    messages=messages,
    functions=functions,
    function_call="auto"  # Let the model decide when to call the function
)

# Check if the model has decided to call our search function
if response.choices[0].message.get("function_call"):
    function_call = response.choices[0].message["function_call"]
    function_name = function_call["name"]
    arguments = function_call.get("arguments")
    
    if function_name == "search_web":
        # Parse arguments (should be a JSON string)
        import json
        args = json.loads(arguments)
        query = args.get("query")
        
        # Call SerpAPI with the query; replace YOUR_SERPAPI_KEY and endpoint accordingly.
        serpapi_url = "https://serpapi.com/search"
        serpapi_params = {
            "q": query,
            "api_key": "YOUR_SERPAPI_KEY"  # Replace with your key
        }
        serp_response = requests.get(serpapi_url, params=serpapi_params)
        search_results = serp_response.json()
        
        # You can then pass these search results back to the model if needed.
        print("SerpAPI search results:", search_results)

How It Works

1. Function Definition:
   You define a function (here named "search_web") with a JSON schema for its parameters. This schema tells GPT‑4 Turbo what it can call and what properties to use.

2. Passing the Function:
   When making the chat API call with GPT‑4 Turbo, you include the functions parameter. You also set function_call="auto", allowing the model to decide whether to respond normally or return a function call.

3. Handling the Function Call:
   When the model returns a response with a "function_call" field, your application extracts the provided arguments (in our case, the search query) and then uses them to call SerpAPI.

4. Integrating with SerpAPI:
   You implement a call to SerpAPI using your chosen HTTP client (here using Python’s requests library). Once you get the results, you can decide how to feed them back into your conversation or present them to your users.

This setup gives you a bridge between GPT‑4 Turbo’s function calling capability and your own web browsing logic via SerpAPI. Remember that the model itself isn’t performing web browsing—it’s suggesting when a web search should occur, and your application must perform the actual API call.

For more details on function calling, you can refer to our documentation on function calling capabilities (without revealing any sensitive internal details).