Build Weather ChatBot in just 9 Minutes with LangChain OpenAI!

Table of Contents

1. Introduction
2. OpenAI's Function Calling Feature
3. Mechanics of the Function Calling Feature
4. Setting Up the Environment
5. Describing Functions for OpenAI
6. Importing Dependencies
7. Creating a Custom Tool
8. Converting Tools to OpenAI Format
9. Calling the Model with User Query
10. Extracting Arguments and Running the Tool
11. Generating the Final Response
12. Using Agents for Simpler Implementation
13. Conclusion

Introduction

In this article, we will explore OpenAI's newly released function calling feature, which allows developers to connect their language models to external and custom APIs and tools. We will Delve into the mechanics of this feature and learn how to utilize it by building a simple weather bot using OpenAI's Langchain and OpenAI API. By the end of this article, You will have a clear understanding of how function calling works and how to incorporate it into your own projects.

1. OpenAI's Function Calling Feature

OpenAI has fine-tuned its GPT-4 and GPT-3.5 turbo 0613 models to detect when a function needs to be called and return the function signature as a JSON object. This feature enables developers to reliably connect their language models to external and custom APIs and tools, providing limitless possibilities for application development.

2. Mechanics of the Function Calling Feature

To make use of OpenAI's function calling feature, the GPT model needs to be aware of the function definition. By providing the function name and its parameters, the GPT model can intelligently infer the arguments Based on user queries. Once the inferred function name and arguments are received, the developer can run the tool with the inferred arguments and obtain the final response in natural language.

3. Setting Up the Environment

Before diving into the code, we need to set up the environment. Make sure you have the latest version of Langchain installed, as it includes support for the function calling feature. Import the necessary dependencies, such as OpenAI Chat, OpenAI Agent, and tool-related modules. Set the OpenAI key and specify the model name, which should be set to "gpt-3.5-turbo-0613" to leverage the function calling feature.

4. Describing Functions for OpenAI

OpenAI expects developers to describe their functions in a specific format. Each function should have a name, description, and parameters. For example, a weather function might have parameters like "location" and "unit". By defining the functions with appropriate descriptions and parameters, the model can understand how to intelligently handle function calls.

5. Importing Dependencies

Import the necessary dependencies, including modules for openAI, tool conversion, and message handling. These dependencies will be used throughout the implementation to Interact with the language model and convert tools into the required format.

6. Creating a Custom Tool

To utilize function calling, we need to Create a custom tool based on the defined function. This tool will allow us to specify the desired response from the language model. Create a class that describes the tool using the function name, description, and argument schema. This tool will be used later in the implementation.

7. Converting Tools to OpenAI Format

To leverage OpenAI's function calling feature, we need to convert the tools into the format expected by the model. Use the "format_tool_to_openAI_function" module to convert the tools. This step ensures that the tools are ready to be used with the language model.

8. Calling the Model with User Query

Now, it's time to call the model with a user query and receive the inferred function name and arguments. Use the "predict_messages" function and pass the user query along with the list of functions. The response will include the function call details, such as the function name and inferred arguments.

9. Extracting Arguments and Running the Tool

After receiving the inferred arguments, we can run the tool with those arguments. This invocation will trigger the execution of the corresponding function. Once the tool is run, the developer can obtain the final response, which contains information generated by the tool.

10. Generating the Final Response

To generate the final response in natural language, call the "predict_messages" function again. Pass the user query, the response from the previous step, the function message, and the list of functions. The model will provide a natural language response based on the input and the execution of the function. The final response can then be used to provide a suitable answer to the user query.

11. Using Agents for Simpler Implementation

Agents provide a simpler way to utilize the function calling feature. Set the agent Type to "openAI_functions" and initialize the agent with the tools and model. This approach eliminates the need to convert tools, run them with inferred arguments, and call the model for the final response. Agents streamline the process and simplify the implementation.

12. Conclusion

OpenAI's function calling feature opens up new possibilities for developers to connect language models with external tools and APIs. By following the steps outlined in this article, you can leverage this feature to build intelligent applications that rely on function executions. Experiment with the tools, tweak the functions, and explore the boundaries of what is possible with OpenAI's function calling feature.

Highlights

• OpenAI's function calling feature allows developers to connect language models with external and custom APIs and tools.
• By providing function definitions, the GPT model can intelligently infer arguments based on user queries.
• Tools can be used to invoke specific functions and obtain the desired responses.
• Agents provide a simpler way to utilize function calling by eliminating the need for explicit tool conversion and function execution.

FAQ

Q: What is OpenAI's function calling feature? A: OpenAI's function calling feature allows developers to connect their language models with external and custom APIs and tools. It enables intelligent function execution and seamless integration with a wide range of applications.

Q: How does function calling work in OpenAI? A: Function calling involves providing the function definition to the GPT model. The model then infers the arguments based on user queries and executes the function using the inferred arguments. The final response is generated and returned in natural language.

Q: Can function calling be used with any type of function? A: Yes, function calling can be used with any function that can be executed by the model. Developers can define custom functions and provide them to the model for intelligent execution.

Q: What are the benefits of using function calling in OpenAI? A: Function calling enables developers to reliably connect their language models with external tools and APIs. It reduces trial and error in prompts and enables better tool selection. Function calling also simplifies the implementation process and enhances the reliability and accuracy of responses.