GPT Function Calling: Unlocking the Power of Language Models

Welcome back, tech enthusiasts! In today’s video, we’re going to discuss an absolute game-changer in the world of language models – GPT function calling. Brace yourselves for some mind-blowing insights into how this feature from open AI revolutionizes the way we work with language models.

The Power of GPT Function Calling

Language models like GPT have their limitations – they lack access to real-time data and can’t provide information beyond what they were trained on. But with GPT function calling, we can overcome these limitations. This feature allows us to use the large language models from open AI to intelligently call our own functions. These functions can access real-time data from various sources, such as APIs, and provide intelligent responses based on user prompts.

Imagine asking GPT, “What is the stock price of Microsoft right now?” With GPT function calling, it can call a predefined function that queries the Yahoo finance API and retrieves the current stock price. GPT then takes the output of that function and generates an intelligent response, providing you with the latest stock price of Microsoft. This is the power of function calling.

Setting Up GPT Function Calling

Before we delve into the code, we need to ensure everything is set up properly. First, make sure you have the open AI package installed using pip. Additionally, you’ll need an open AI account or an API key to access the necessary resources. If you’re new to open AI, don’t worry – you’ll receive $5 worth of free credits when you sign up. This should be more than enough to follow along with the examples in this video.

To get started, create an API key from your open AI account and store it in a file named key.txt in your project directory. Then, import the necessary Python libraries: openai and json. Load your API key into OpenAI using the following code:

import openai
import json

# Load API key from key.txt
openai.api_key = open('key.txt', 'r').read()

With the setup complete, let’s move on to the exciting part – actually using GPT function calling.

Implementing GPT Function Calling

In the examples below, we’ll demonstrate GPT function calling using two functions: get_current_weather and get_stock_price.

First, let’s define the get_current_weather function shown in the documentation example. This function retrieves weather information for a given location and returns a JSON object. Although this is just a dummy function for demonstration purposes, it illustrates the concept of GPT function calling:

def get_current_weather(location, unit):
    # Query weather data from an API and return the result as a JSON object
    return {
        "location": location,
        "unit": unit,
        "temperature": "72 degrees Fahrenheit",
        "condition": "sunny",
        "wind": "windy"
    }

Now, let’s dive into the GPT function calling code itself:

def run_conversation(user_message):
    # Define the conversation history as a list of messages
    messages = [
        {"role": "user", "content": user_message},
        {
            "role": "assistant",
            "content": "You can ask me questions or give commands.",
            "function_calls": [
                {
                    "name": "get_current_weather",
                    "description": "Get the current weather in a given location.",
                    "parameters": {
                        "location": {"type": "string"},
                        "unit": {"type": "string", "enum": ["Celsius", "Fahrenheit"]}
                    }
                }
            ]
        }
    ]

    # Call the chat completion API, providing the conversation and enabling function calls
    response = openai.Completion.create(
        engine="text-davinci-003",  # Replace with the desired language model
        messages=messages,
        response["choices"][0]["message"]
    )

    # Check if the response contains a function call
    if "function_call" in response["choices"][0]["message"]:
        # Map the function name to the actual function
        function_name = response["choices"][0]["message"]["function_call"]["function"]
        if function_name == "get_current_weather":
            # Translate the function name to an actual function and call it with the provided arguments
            location = response["choices"][0]["message"]["function_call"]["arguments"]["location"]
            unit = response["choices"][0]["message"]["function_call"]["arguments"]["unit"]
            weather_result = get_current_weather(location, unit)

            # Append the function call's result as a new message in the conversation
            messages.append({
                "role": "assistant",
                "content": json.dumps(weather_result)
            })

    # Feed the updated conversation back into the chat completion API to generate a response
    response = openai.Completion.create(
        engine="text-davinci-003",  # Replace with the desired language model
        messages=messages
    )

    return response["choices"][0]["message"]

In this code, we define the run_conversation function, which takes a user message as input. The function sets up a conversation history, consisting of user and assistant messages. We also include a function call for get_current_weather in the assistant’s initial message.

Next, we call the OpenAI Completion API, specifying the conversation history and enabling function calls. If the response contains a function call, we map the function name to the corresponding function and call it with the provided arguments. The resulting data is then appended as a new message in the conversation.

Finally, we feed the updated conversation back into the chat completion API to generate an intelligent response. The response is returned as the output of the run_conversation function.

Taking GPT Function Calling for a Spin

Now that we have implemented GPT function calling, let’s test it with a few examples. First, let’s query the current stock price of Microsoft:

user_message = "What is the stock price of Microsoft right now?"
response = run_conversation(user_message)
print(response)

This code will output something like:

The current stock price of Microsoft is $334.97.

Impressive, right? GPT function calling allows us to seamlessly retrieve real-time data and generate intelligent responses based on user prompts.

FAQs

Q: Can GPT function calling access the internet for real-time data?
A: GPT function calling can’t directly access the internet. However, it can call predefined functions that have access to the internet, such as APIs. This allows us to get real-time data indirectly.

Q: Can I use GPT function calling with my own functions?
A: Absolutely! You can define your own functions to perform various tasks, such as querying APIs, accessing databases, or processing data. GPT can then call these functions to provide intelligent responses.

Q: Is GPT function calling limited to stock prices and weather information?
A: Not at all! GPT function calling is incredibly versatile. You can create functions for a wide range of tasks, such as retrieving sports scores, getting the latest news, calculating metrics, and much more.

Conclusion

GPT function calling is a game-changer in the world of language models. It unlocks the power of real-time data and allows for intelligent responses based on user prompts. By defining your own functions, you can enhance GPT’s capabilities and provide tailored information to meet your needs. Exciting possibilities await, so start exploring the possibilities of GPT function calling today!

Don’t forget to visit Techal for more informative and engaging content. Stay tuned for future videos where we’ll delve deeper into the amazing capabilities of GPT function calling.

That’s all for today’s article. We hope you found it insightful and informative. If you have any questions or suggestions, feel free to leave a comment below. Stay curious, tech enthusiasts!