Create Powerful Chains with LangChain and OpenAI Function Calling

Table of Contents

1. Introduction
2. Installing Packages
3. Loading the API Key
4. Defining the Function
5. Using the Function
6. Making API Calls
7. Working with Language Models
8. Implementing Functions in Slang Chain
9. Creating Custom Tools
10. Using Agents

Article

Introduction

In this article, we will explore the new function calling capabilities of OpenAI's models. We will also learn how to integrate this new feature with a link chain. Function calling provides a Novel way to Connect GPT's capabilities with external tools and APIs, expanding its functionality even further.

Installing Packages

Before we can start using function calling, we need to install some packages. We will need to install LinkChain Python.nf to load the environment file and OpenMyEye to make a connection to the OpenAI API. Make sure You have the latest version of LinkChain (0.0.200 or higher) to support function calling.

Loading the API Key

To make function calling work, we need to have an API key for OpenAI. Store the API key in a .env file with the key name "OPENAI_API_KEY" to securely access it. Load the API key into the OpenAI module as an environment variable.

Defining the Function

To use function calling, we need to define a function. We can mock an API call by creating a function like "get_pizza_info". The function takes a parameter (e.g., the name of the pizza) and returns a fixed price as JSON. We also need to provide a description and information about the function's parameters for the language model to understand its purpose.

Using the Function

Once the function is defined, we can use it in our code. We can Create a Helper function called "chat" that takes a user query as input. Inside the "chat" function, we use the OpenAI API to make a chat completion call. We provide the function's argument as an additional parameter and retrieve the response, which contains the generated message from the AI.

Making API Calls

We can now ask the model various questions and process the responses using function calling. For example, we can ask about the capital of France and receive the answer from the AI. If we ask about the cost of a specific pizza, the language model recognizes that it should use the "get_pizza_info" function and returns the function call information. We can extract the necessary data and make a Second API call to get the actual information.

Working with Language Models

Function calling allows the language model to decide whether to use additional information or external resources to answer a question. This flexibility enhances its capabilities, as it can leverage external tools and APIs to provide more accurate and Relevant responses.

Implementing Functions in Slang Chain

While function calling works with standard language models, it currently requires a workaround when used with Slang Chain. The process involves converting custom classes to openai functions and passing them as additional quarks. The future integration of agents in Slang Chain would simplify this process.

Creating Custom Tools

Tools are classes in Link Chain that provide a standardized way to Interact with the outside world. You can create your own custom tools by inheriting from the base tool class. These tools can be converted to openai functions using the "format_tool" method. You can also use existing tools like the "move_file" tool to perform file operations.

Using Agents

Agents are a specialized form of function calling that aligns with specific chains, such as the LLM Math Chain. Agents can answer questions and perform calculations Based on the provided input. They simplify the process of using external tools and provide a smooth integration within the model.

In conclusion, function calling opens up exciting possibilities for enhancing the capabilities of OpenAI's language models. By integrating with external tools and APIs, these models can provide more sophisticated and accurate responses. With further improvements and the inclusion of agents in Slang Chain, function calling will become even more effortless to use and incorporate into various applications.

Highlights

• Function calling enables the integration of external tools and APIs with OpenAI's language models.
• Install the necessary packages, load the API key, and define functions to use function calling.
• Use the chat function to make API calls and process responses.
• Function calling allows the language model to decide when to use external resources to answer questions.
• Implement custom tools or use existing ones to interact with the outside world.
• Agents offer a streamlined approach to using external tools and providing more specialized responses.

FAQ

Q: Can I use function calling with standard language models? A: Yes, function calling works with standard language models, but it requires an additional conversion process in Slang Chain.

Q: Are there any limitations to function calling? A: Function calling is still being improved and optimized. While it works well with agents, it requires a workaround in standard language models.

Q: Can I create my own custom tools? A: Yes, you can create your own custom tools by inheriting from the base tool class in Link Chain.

Q: Do I need to provide descriptions for my functions? A: Yes, providing descriptions for your functions helps the language model understand their purpose and relevance to user queries.