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Unlocking the Power of OpenAI's New Function Calling Feature

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Chapter 1: Introduction to Function Calling

OpenAI, the organization behind the influential GPT-4 and GPT-3.5 language models, has recently unveiled a groundbreaking feature known as function calling. This innovation has the potential to transform how developers engage with generative AI.

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Section 1.1: What Is Function Calling?

Function calling refers to a feature that empowers developers to articulate programming functions for GPT-4 and GPT-3.5, enabling the models to produce executable code for these functions. This extension allows AI models to integrate tailored functionalities into their operations.

For instance, developers can construct chatbots that respond to inquiries by invoking external tools, translate natural language into API calls, or extract structured data from text.

This capability is enabled through new API parameters in the Chat Completions endpoint, specifically the "functions" and "function_call" parameters. These allow developers to define functions for the model using JSON Schema and optionally request the model to invoke a specific function.

Subsection 1.1.1: Example Use Case

Example of Function Calling in Action

Section 1.2: How Function Calling Operates

Function calling utilizes the extensive knowledge of language and code that GPT-4 and GPT-3.5 have acquired from vast datasets. These models are finely tuned to identify when a function needs to be invoked based on user input and respond with JSON that conforms to the function's signature.

For example, if a user queries, "What's the weather like in Boston?", the model can determine that a function call to get_current_weather(location: string, unit: 'celsius' | 'fahrenheit') is necessary. It might respond with a JSON object like this:

{

"function_call": {

"function": "get_current_weather",

"args": {

"location": "Boston",

"unit": "fahrenheit"

}

}

}

The developer can then utilize this JSON object to invoke the appropriate external tool or API to retrieve the weather data.

Chapter 2: Practical Applications of Function Calling

Function calling can be leveraged for various tasks that demand advanced reasoning, intricate instruction interpretation, and enhanced creativity. Some potential applications include:

  • Creating chatbots capable of answering questions, executing tasks, or offering recommendations via external APIs or tools. For instance, a chatbot could book a flight, order pizza, or play music by utilizing function calls.
  • Transforming natural language into executable code. For example, a developer could input, "Create a list of numbers from 1 to 10," which would yield a function call like create_list(start: int, end: int, step: int). Alternatively, the input "Draw a circle with radius 5" could result in draw_circle(radius: int).
  • Extracting structured data from text for storage, analysis, or visualization. For instance, a researcher might extract all individuals mentioned in a Wikipedia article through a function call like extract_people_data(people: [{name: string, birthday: string, location: string}]).

The first video explores how OpenAI's new function calling feature represents a significant advancement in AI development.

The second video delves into how function calling is a transformative element in AI applications.

Section 2.1: Why Function Calling Is Revolutionary

Function calling is truly revolutionary as it enhances developers' ability to obtain structured data reliably from the model and connect GPT's functionalities with external APIs and tools. This opens doors for creating innovative applications that harness generative AI.

Moreover, it minimizes the necessity for manual coding or parsing model outputs, which can often lead to errors or be time-consuming. Developers are encouraged to focus on designing the functions that meet their requirements, allowing the model to manage the intricacies.

Currently, function calling is available for ChatGPT Plus subscribers who have access to GPT-4 and GPT-3.5-turbo. OpenAI has also adjusted pricing for certain models and extended the context length for GPT-3.5-turbo, making generative AI more accessible and cost-effective for developers eager to tap into its capabilities.

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