GPT-4 功能调用指南

Table of Contents

1. Introduction
2. Announcement from Open AI
3. Impact on GPT4 and GPT 3.5
4. Changes to GPT 3.5 Turbo
5. Cost Reduction on Tokens for GPT 3.5 Turbo
6. Introduction to Function Calling
7. Use Cases of Function Calling
8. How Function Calling Works
9. Example of Function Calling
10. Custom Example: Stock Information

Introduction

In this article, we will be discussing the recent breaking news announcement and release from Open AI that affects GPT4, GPT 3.5, and several other things. We will Delve into the overlooked aspects of this release and highlight its amazing features. One of the main highlights of this release is function calling, which has a significant impact on various applications. We will explore changes made to GPT4 and GPT 3.5 Turbo, as well as the cost reduction on tokens for GPT 3.5 Turbo. Moreover, we will delve into the concept of function calling, its use cases, and how it works. To illustrate its functionality, we will provide both an example provided by Open AI and a custom example involving stock information using the Alpha Vantage API.

Announcement from Open AI

Open AI has recently made a breaking news announcement and release that introduces exciting features for GPT4, GPT 3.5, and other related aspects. While the main highlight of this release is function calling, there are several other noteworthy changes that should not be overlooked. These changes aim to enhance the functionality of the models and provide more convenience for developers and users.

Impact on GPT4 and GPT 3.5

The release from Open AI includes updates and improvements for both GPT4 and GPT 3.5 models. These updates are designed to enhance the performance and capabilities of the models, ensuring a more efficient and accurate user experience. The release introduces a 16k Context window for GPT 3.5 Turbo, which is four times larger than before. This expanded context window provides users with greater flexibility and enables developers to build chat models or SAS products that utilize chat models more effectively.

Changes to GPT 3.5 Turbo

One significant change introduced in the release is the reduction in cost for tokens used in GPT 3.5 Turbo. Open AI has made GPT 3.5 Turbo even more affordable by reducing the cost of tokens by 25%. This cost reduction makes GPT 3.5 Turbo an even more viable option for various use cases where GPT4 is not required. Developers can benefit from the cost savings while still enjoying the advanced capabilities of GPT 3.5 Turbo.

Cost Reduction on Tokens for GPT 3.5 Turbo

Open AI's cost reduction on tokens for GPT 3.5 Turbo is a game-changer for developers and businesses. With a 25% decrease in token costs, using GPT 3.5 Turbo becomes even more accessible and cost-effective. This reduction in cost opens up new possibilities for developers to utilize GPT 3.5 Turbo in a wide range of applications without breaking the bank.

Introduction to Function Calling

One of the most exciting features introduced in this Open AI release is function calling. Function calling allows developers to describe functions to GPT models and have the model intelligently choose to output a JSON object that contains arguments for those functions. This innovative concept enables developers to leverage the power of GPT models to generate structured inputs for their functions Based on text inputs.

Use Cases of Function Calling

Function calling has a wide range of potential use cases and applications. Developers can Create chatbots that answer questions by calling external tools, convert queries into function calls, and perform various other tasks that involve structured inputs. The versatility of function calling opens up numerous possibilities for enhanced user experiences and automation in different domains.

How Function Calling Works

To understand how function calling works, let's dive into the details. Developers can pass a set of functions to GPT models using the functions parameter. These functions are defined with names, descriptions, and parameters. The model is fine-tuned to detect when a function needs to be called and respond with a JSON object that adheres to the function signature. This JSON object contains the necessary arguments to call the identified functions, allowing developers to seamlessly integrate them into their code.

Example of Function Calling

To illustrate the concept of function calling, let's consider an example provided by Open AI. In this example, a function called "Get Current Weather" is described with two parameters: location and unit. The model intelligently identifies the arguments from the user's input and converts them into structured inputs for the function. This allows the function to retrieve and provide information about the current weather in the specified location.

Custom Example: Stock Information

In addition to the provided example, we can explore custom use cases of function calling. Let's consider an example involving stock information. By utilizing an API like Alpha Vantage, developers can create a function that retrieves real-time stock information based on the user's input. With function calling, the model intelligently processes the user's query, extracts the necessary arguments, and generates a JSON object that can be used to call the function and retrieve the desired stock information.

Highlights

• Open AI's recent release introduces exciting features for GPT4, GPT 3.5, and more.
• The addition of function calling provides developers with the ability to generate structured inputs for their functions based on text inputs.
• GPT 3.5 Turbo now offers a 16k context window, providing greater flexibility for chat models and SAS products.
• The cost reduction on tokens for GPT 3.5 Turbo makes it more affordable and accessible for various use cases.
• Function calling has numerous potential use cases, including the creation of chatbots, conversion of queries into function calls, and more.
• Developers can integrate external tools and processes seamlessly into their code by leveraging the power of function calling.

FAQ

Q: Can function calling be used in chatbots? A: Yes, function calling is a powerful tool for creating chatbots that can answer questions by calling external functions or tools.

Q: Are there any cost benefits to using GPT 3.5 Turbo? A: Absolutely! Open AI has reduced the cost of tokens for GPT 3.5 Turbo, making it a more affordable option for various applications.

Q: Can function calling be used with APIs? A: Yes, function calling can be used to integrate APIs and retrieve real-time information based on user inputs.

Q: What is the benefit of the expanded context window in GPT 3.5 Turbo? A: The expanded context window in GPT 3.5 Turbo allows for more extensive and comprehensive interactions with chat models and SAS products.

Q: Is function calling limited to predefined functions? A: No, function calling allows developers to describe their own functions and have the model intelligently generate arguments based on the user's input.