How to Build RAG AI Agents with TypeScript

Building Retrieval Augmented Generation (RAG) AI agents is an exciting project that enables you to enhance the capabilities of your AI by allowing it to fetch external data and integrate that information into its responses. I will take you through the process of building a RAG AI agent using TypeScript. Whether you’re new to the concept of RAG or already familiar with the basics, this guide will walk you through everything you need to know to get started.

Tools and Technologies We’ll Use

Before we start building, let’s review the tools and technologies that will power our RAG AI agent:

TypeScript: This is the programming language we’ll be using to write our code. It’s a superset of JavaScript, offering type safety which helps make the development process smoother and less error-prone.
Node.js: The runtime environment that allows us to execute our TypeScript code and run the application.
External Data Source (API/Database): Our AI agent needs to fetch data from an external source. This could be anything from an API to a database containing relevant information.
AI Library: We’ll be using the Hugging Face Transformers library to integrate natural language understanding and generation models. These models can help us process the data and generate meaningful responses.
Express.js: We’ll use Express.js to set up a simple web server to handle incoming requests to the agent and process them accordingly.

Setting Up the Project

Let’s begin by setting up our project. Open your terminal and follow these steps to get everything up and running:

Initialize the Project

Start by creating a new directory and initializing the Node.js project:

mkdir rag-ai-agent
cd rag-ai-agent
npm init -y

Install Dependencies

Next, install the required dependencies:

npm install typescript ts-node express axios @types/node @types/express

Here’s what each package does:

typescript: Adds TypeScript support.
ts-node: A tool to execute TypeScript code directly.
express: Our web framework for creating the server.
axios: A promise-based HTTP client that will help us make API calls.
@types/node & @types/express: Type definitions for Node.js and Express to help with type safety.

Initialize TypeScript Configuration

Now, set up TypeScript by creating a tsconfig.json file:

npx tsc --init

This will initialize the TypeScript configuration to ensure our TypeScript code gets compiled into JavaScript properly.

Implement the RAG Model

In this step, we’ll implement the core logic that enables the agent to retrieve data and generate a response.

Create the File Structure

Let’s organize our project by creating a few essential files:

src/
├── server.ts
├── agent.ts
└── utils.ts

Writing the Server Code

In server.ts, we’ll set up an Express server that listens for requests and processes them through our AI agent.

import express from 'express';
import { generateResponse } from './agent';

const app = express();
const port = 3000;

app.use(express.json());

app.post('/ask', async (req, res) => {
    const { question } = req.body;
    try {
        const answer = await generateResponse(question);
        res.json({ answer });
    } catch (error) {
        res.status(500).json({ error: 'Something went wrong' });
    }
});

app.listen(port, () => {
    console.log(`Server running on http://localhost:${port}`);
});

Here, we define a POST route /ask that accepts a question and calls the generateResponse function to process the request. The answer is then returned in the response.

Implement the RAG Logic

In agent.ts, we define how the agent retrieves data from an external source (via an API call) and generates a response.

 axios from 'axios';

// Function for retrieving data from an API
async function retrieveData(query: string): Promise<string> {
    const response = await axios.get(`https://api.example.com/data?query=${query}`);
    return response.data;
}

// Function to generate a response using retrieval and augmentation
export async function generateResponse(query: string): Promise<string> {
    const data = await retrieveData(query);
    return `Here is your answer based on the data: ${data}`;
}

In this example, we’re simulating an API call using axios to fetch relevant data based on the user’s query. You can replace https://api.example.com/data?query=${query} with any real API endpoint. After retrieving the data, we generate a response by appending the fetched data to the answer.

Utilities

For now, you can leave the utils.ts file empty, or add any utility functions you may need for data processing. This file is optional for this example but can be expanded as the project grows.

Running the Application

Now that everything is set up, let’s run the application and test it.

Compile and Run

To run the project, use the following command:

npx ts-node src/server.ts

This will start the Express server on http://localhost:3000.

Testing the Agent

Now it’s time to test our RAG agent! You can use Postman or CURL to make a POST request to our /ask route.

Here’s how you can do it using CURL:

curl -X POST http://localhost:3000/ask -H "Content-Type: application/json" -d '{"question": "What is the capital of France?"}'

If everything works correctly, you’ll receive a response like this:

{
  "answer": "Here is your answer based on the data: Paris is the capital of France."
}

Final Thoughts

Building a RAG AI agent with TypeScript is an enjoyable and practical project. By combining the power of data retrieval with AI generation, we’ve created an agent that can pull relevant information from external sources and generate smarter responses. This process opens up a world of possibilities, whether you’re building a customer support bot, a content generator, or even an intelligent personal assistant.