What is Function Calling in OpenAI's Language Models?

The language models developed by OpenAI, such as GPT-3.5-turbo-0613 and GPT-4-0613, now feature an innovative capability called Function Calling. This enhancement empowers developers to outline functions to the model, which it then uses to ingeniously generate arguments for those functions. In this blog post, we will unravel the concept of Function Calling, delve into its applications, and walk you through its effective usage.

Understanding Function Calling:

Function Calling in OpenAI's language models is a channel for more reliable and structured connections between the models and external tools and APIs. Developers can define functions with the help of JSON Schema and guide the model to invoke those functions based on user input. The model, in return, creates a JSON object encapsulating the arguments needed for the function call. Developers can leverage this in their code to interact with APIs or external tools.

Applications of Function Calling:

• Creating Chatbots: Developers can architect chatbots that respond to queries by invoking external tools or APIs. A case in point is a chatbot designed to fetch weather information from an external weather API.

• Converting Natural Language into API Calls: Function Calling facilitates developers in translating natural language queries into API calls. This smoothens the integration with various services and databases. An instance of this would be a query like "What are the top ten products sold this month?" that can be converted into an API call to retrieve customer data based on specific criteria.
• Extracting Structured Data: Function Calling can be harnessed to extract structured data from unstructured text. Developers can define functions to pull out specific information from text documents, articles, or web pages.

Step-by-Step Guide to Function Calling:

• Call the model with functions: Provide the user's input and the defined functions to the Chat Completions API. The model will discern if a function call is needed based on the input.
• Handle the function response: If the model generates a function call, retrieve the function name and arguments from the model's response. Use these arguments to call the respective function in your code.
• Send back the response: After executing the function, send the function response as a new message to the model using the Chat Completions API. This allows the model to distill the results, generate a user-friendly response or call another function.

Important Considerations:

• Trustworthy Sources: It is critical to utilize information from trusted tools and APIs to assure the reliability and safety of the application. It is advised to only use functions from trusted sources and authenticate the outputs before performing any actions with real-world impacts.
• User Confirmation Steps: To alleviate potential risks, it is advised to include user confirmation steps before executing actions with significant consequences, such as sending emails, making purchases, or posting online. This helps prevent unintended or undesirable results.
• Cost and Limits: Remember, functions are part of your billing and they count towards the model's limit. So, it's a good idea to keep them short and sweet.

Conclusion:

Function Calling in OpenAI's language models offers developers a robust tool to connect external tools, APIs, and services in a more reliable and structured fashion. Leveraging the capabilities of these models, developers can create intelligent chatbots, convert natural language into API calls, and extract structured data from unstructured text. By following the step-by-step guide and taking into account important considerations, developers can harness the power of Function Calling to enhance their applications with structured and reliable data.

Further Reading:

If you've enjoyed this exploration of Function Calling in OpenAI's language models and are eager to delve deeper into the world of AI, we've got some excellent recommendations for you. Feed your knowledge hunger with these insightful reads:

• AI is Moving Your Cheese: This engaging piece delves into how artificial intelligence is reshaping industries, much like the proverbial 'cheese' in Dr. Spencer Johnson's famous book, "Who Moved My Cheese?". Get a grip on the transformative potential of AI and how it's leading the way for innovation.
• Why it Takes a Village to Build ML Solutions: Just as it takes a village to raise a child, creating successful machine learning solutions isn't a solitary effort. Understand the multi-disciplinary approach necessary for developing effective and sustainable ML solutions in this insightful article.

We hope you enjoy these reads and continue to expand your AI and ML knowledge horizon. Happy reading!

Sources:

GPT - OpenAI API

Function calling and other API updates (openai.com)