Build a Powerful LLM App with LangChain & GoLang

Table of Contents

1. Introduction
2. Benefits of Building an LLM Application with Go
3. Step 1: Downloading and Installing Go
4. Step 2: Installing the Official Go Extension for VS Code
5. Step 3: Creating a New Go Project with Go Mods
6. Step 4: Installing Packages with go get
7. Step 5: Creating the main.go File
8. Step 6: Defining the Main Function and Router
9. Step 7: Creating the Generate Completion Function
10. Step 8: Compiling and Running the Application
11. Making a Request to the LLM API Endpoint
12. Conclusion

How to Build an LLM Application with Go

Go is a small and beautiful language that is gaining popularity in the development community. While Python and JavaScript are commonly used for building language models (LMs) like GPT, Go also offers interfacing packages that allow You to Create powerful applications. In this tutorial, we'll walk through the steps to build an LLM application with Go. By the end, you'll have a REST API connected to GPT using the Gin framework and the Link Chain Go Package.

Introduction

Before we dive into coding with Go, let's start by understanding the benefits of building an LLM application with this language.

Benefits of Building an LLM Application with Go

Go offers several advantages for developing language models, including:

1. Performance: Go's statically Typed nature and efficient memory management make it fast and suitable for handling large-Scale applications.
2. Concurrency: Go has strong support for concurrency, allowing you to handle multiple requests simultaneously and efficiently.
3. Simple and Clean Syntax: Go's syntax is designed to be straightforward, making it easier to Read and write code, even for beginners.
4. Extensive Standard Library: Go comes with a rich standard library that offers various packages and functionalities, reducing the need for external dependencies.

Now that we understand the benefits, let's get started with building our LLM application.

Step 1: Downloading and Installing Go

To begin coding in Go, you first need to download and install the Go programming language. Follow these steps:

1. Visit the official Go Website at go.dev.
2. Click on the download button and follow the installation instructions for your operating system.
3. Once installed, you will have access to the Go command-line tools.

Step 2: Installing the Official Go Extension for VS Code

To make Go development easier, it is recommended to install the official Go extension for Visual Studio Code. Follow these steps:

1. Open Visual Studio Code.
2. Search for the "Go" extension in the extensions sidebar.
3. Click on "Install" and then "Enable" to activate the extension.
4. This extension provides syntax highlighting and other helpful features for Go development.

Step 3: Creating a New Go Project with Go Mods

In Go, projects are organized into modules. To start a new project, follow these steps:

1. Open your terminal or command prompt.
2. Navigate to the directory where you want to create your project.
3. Run the command go mod init <module-name>, replacing <module-name> with the desired name for your project. This will create a go.mod file, which manages your project's dependencies.

Step 4: Installing Packages with go get

Once you have a project set up, you can install required packages using the go get command. Let's install the necessary packages for our LLM application:

1. Run the command go get github.com/gin-gonic/gin to install the Gin framework.
2. Run the command go get github.com/OpenAI/api to install the Link Chain Go package.

These packages will be automatically downloaded and added to your project.

Step 5: Creating the main.go File

In the root directory of your project, create a new file called main.go. This file will contain our application's code.

Step 6: Defining the Main Function and Router

Inside the main.go file, start by defining the main function, which will serve as the entry point for our application. Here's an example:

package main

import (
    "context"
    "github.com/gin-gonic/gin"
    "github.com/openai/api"
)

func main() {
    router := gin.Default()
    apiGroup := router.Group("/api/v1")

    apiGroup.POST("/generate", generateCompletion)

    router.Run(":8080")
}

In this code snippet, we import the required packages and create a default Gin router. We define a router group for our API, and within that group, we specify a single endpoint /generate, which will be a POST request.

Step 7: Creating the Generate Completion Function

Next, we need to define the generateCompletion function that will handle the /generate endpoint and Interact with the OpenAI GPT model. Here's an example:

func generateCompletion(c *gin.Context) {
    var data struct {
        Text string `json:"text" binding:"required"`
    }

    if err := c.ShouldBindJSON(&data); err != nil {
        c.JSON(400, gin.H{"error": "Invalid JSON"})
        return
    }

    llm, err := api.New(context.Background())
    if err != nil {
        c.JSON(500, gin.H{"error": "Failed to initialize LLM"})
        return
    }

    completion, err := llm.Call(data.Text)
    if err != nil {
        c.JSON(500, gin.H{"error": "Failed to generate completion"})
        return
    }

    c.JSON(200, gin.H{"completion": completion})
}

In this code snippet, we define a struct that represents the data sent in the request body. We use the bindJSON method to Bind the JSON data to this struct. If the JSON is invalid, we return a 400 error.

Then, we initialize the OpenAI GPT model and call the Call method with the provided text. If there is an error, we return a 500 error. If everything is successful, we return the generated completion as JSON with a 200 response.

Step 8: Compiling and Running the Application

To run the application, use the command go run main.go in your terminal while inside the project directory.

If you want to compile the application into an executable file, use the command go build. This will create an executable file that you can run directly.

Making a Request to the LLM API Endpoint

Once the application is running, you can make requests to the LLM API endpoint to generate completions. Here's an example using Postman:

• Request URL: http://localhost:8080/api/v1/generate
• Request Method: POST
• Request Body: { "text": "Tell me a joke about llms" }

This will send a POST request to the endpoint with the specified prompt in the request body. The response will contain the generated completion.

Conclusion

In this tutorial, we've walked through the steps to build an LLM application with Go. We've covered downloading and installing Go, setting up a new project, installing packages, creating the main function and router, defining the generate completion function, and running the application. With Go's performance and simplicity, you can create powerful language models with ease. Keep exploring the capabilities of Go and experiment with different use cases for LLM applications.

Highlights

• Go is a small and beautiful language for building language models (LMs) like GPT.
• Go offers performance, concurrency, simple syntax, and an extensive standard library.
• To build an LLM application with Go, you need to download and install Go, set up a new project, install packages, create the main function and router, define the generate completion function, and run the application.
• With Go, you can create powerful LLM applications that handle large-scale requests efficiently.

FAQ

Q1: Can I use Go for other types of applications besides LLM? A1: Yes, Go is a versatile language that can be used for various types of applications, including web development, system programming, network programming, and more.

Q2: Can I use a different framework instead of Gin for creating the REST API? A2: Yes, there are other frameworks available in Go for creating REST APIs, such as Echo, Revel, and Iris. You can choose the one that best suits your needs.

Q3: Are there any alternatives to OpenAI's GPT for building LLM applications? A3: Yes, there are other language models available, such as Hugging Face's Transformers library, Microsoft's Text Analytics API, and Google's Cloud Natural Language API. Each has its own features and capabilities.

Q4: Can I deploy my Go application to a cloud platform? A4: Yes, you can deploy your Go application to cloud platforms such as Google Cloud Platform, Amazon Web Services, or Microsoft Azure. You can use tools like Docker and Kubernetes for containerization and orchestration.

Q5: Is it possible to train my own language model using Go? A5: Yes, you can use Go to train your own language model. However, training language models requires extensive computational resources and specialized expertise in natural language processing.