Common ChatGPT API Function Call Mistakes and How to Fix Them

Learn how to handle function name hallucinations, function call chaining and more accurate function call arguments

The function call feature in OpenAI's ChatGPT APIs is a powerful tool, enabling us to perform some impressive tasks. If you are not familiar with function calls in the ChatGPT APIs, you may want to refer to my previous article on this topic. In that piece, I provided numerous examples of function calls and a comprehensive tutorial on building a Flask-based chat application with ChatGPT-like browsing and code interpreter plugins. Check it out.

This article serves as a follow-up to the previous one, discussing some of the unexpected challenges that can arise when developing with the ChatGPT function call APIs. Summary of what’s covered in this post:

• Getting the model to generate more accurate function call arguments

• Getting the model to generate a chain of function calls

• Handling function and argument name hallucinations

Let’s dive right in!

The code examples shown in this article are available on GitHub under an MIT license.

Quick Overview of Function Calling

Before we get into the potential issues, let's briefly look at an example using the function call feature of ChatGPT APIs. If you are familiar with how function calling works, feel free to skip to the next section.

Suppose we want the model to provide the weather for tomorrow in London, and we pass the function get_current_weather, which takes the location name as a parameter. An example request could look like the following (adapted from the OpenAI docs):

 messages = [{"role": "user",
        "content": "What's the weather like in Boston?"}]
    functions = [
        {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state",
                    }
                },
                "required": ["location"],
            },
        }
    ]

And the model's response might contain a function call like this:

{
	"index": 0,
	"message": {
		"role": "assistant",
		"content": None,
		"function_call": {
			"name": "get_current_weather",
			"arguments": "{\n  "location": "London"}"
		}
	},
	"finish_reason": "function_call"
}

In response to this function call request from the model, we must send back the result of this function call, which goes back as part of the messages. The role should be "function", and we also need to provide the name of the function, as shown below:

def get_weather(location: str) -> Dict:
    return {"temperature": 30, "conditions": ["windy", "cloudy"]}

weather_info = get_weather(location)
messages.append(Message(role = "function",
                content = json.dumps(weather_info),
                name = "get_current_weather"))

# send the updated messages back to ChatGPT

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Now that we've seen an example of using the function call parameter in the chat APIs, let's discuss some of the potential challenges when using this feature.

Treat Function Descriptions as User Prompts