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🛠️ Install and Configure MongoDB-RAG

Step 1: Initialize Your Project​

  1. Create a New Directory: Start by creating a new directory for your project.

    mkdir my-rag-project
    cd my-rag-project

  2. Initialize a Node.js Project: Run the following command to initialize a new Node.js project.

    npm init -y

Step 2: Install MongoDB-RAG​

  1. Install the Library: Use npm to install the mongodb-rag library.

    npm install mongodb-rag

  2. Install Additional Dependencies: Depending on your setup, you might need additional packages like dotenv for environment variables.

    npm install dotenv

Step 3: Configure Environment Variables​

  1. Create a .env File: In the root of your project, create a .env file to store your environment variables.

    MONGODB_URI=your_mongodb_connection_string
    OPENAI_API_KEY=your_openai_api_key

  2. Load Environment Variables: Ensure your application loads these variables. You can do this by requiring dotenv at the start of your main file.

    require('dotenv').config();

Step 4: Create a RAG Application​

  1. Set Up MongoDB Connection: Use the mongodb-rag library to connect to your MongoDB instance.

    const { MongoClient } = require('mongodb');
    const client = new MongoClient(process.env.MONGODB_URI);

    async function connectToDatabase() {
      try {
        await client.connect();
        console.log("Connected to MongoDB");
      } catch (error) {
        console.error("Error connecting to MongoDB:", error);
      }
    }

  2. Initialize RAG Components: Set up the components needed for RAG, such as vector search and augmentation.

    const { RAG } = require('mongodb-rag');

    async function createRAGApp() {
      const rag = new RAG(client);
      // Configure your RAG components here
    }

  3. Run Your Application: Execute your script to ensure everything is set up correctly.

    node index.js

Conclusion​

By following these steps, you will have a basic setup for a RAG application using the mongodb-rag library. This setup will allow you to explore the capabilities of RAG and integrate MongoDB with language models effectively. For more advanced configurations and features, refer to the official documentation.