Lecture-4: RAG for Beginners : Pair Text with Embeddings | Build the Foundation for Semantic Search

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Building the Foundation for Semantic Search: Pairing Text with Embeddings

Welcome to our tutorial series on semantic search! In this post, we will explore how to pair text with embeddings, a crucial step for building a robust recommendation system. Today, we will be focusing on a challenge where we will take real text, generate embeddings for it, and prepare it for storage in a vector database.

Understanding the Concept of Embeddings

Embeddings are numerical representations of text that capture the semantic meaning of words or sentences. By converting text into embeddings, we can perform various operations like searching, clustering, and recommending content based on semantic similarity.

In our case, we will be working with embeddings to enhance a movie recommendation system. The primary goal is to pair each piece of text with its corresponding embedding, which will later facilitate efficient storage and retrieval in a vector database.

Setting Up the Environment

Before we dive into the code, ensure that you have the following set up:

• Node.js installed on your machine
• An API that generates embeddings (we will assume you have this set up already)

For the purpose of this tutorial, we will be using a JavaScript file named 04_pair_text_with_embeddings.js. This file will handle the generation of embeddings and their pairing with the original text.

Step-by-Step Implementation

Step 1: Import Required Functions

At the beginning of your 04_pair_text_with_embeddings.js file, you need to import the function responsible for creating embeddings. Assuming you have previously created an embeddings.js file, you would include the following line:

import { createEmbedding } from './embeddings.js';

Step 2: Define Sample Sentences

Next, we’ll define an array of sample sentences related to movies. These sentences will be the text we want to pair with embeddings.

const sampleSentences = [
    "Inception is a mind-bending thriller.",
    "The Godfather is a classic mafia film.",
    "Toy Story offers a heartwarming story.",
    "The Dark Knight redefined superhero movies.",
    "Pulp Fiction is known for its unique storytelling."
];

Step 3: Generate Embeddings

Now, we will call the createEmbedding function and pass the entire array of sample sentences. This approach is efficient as it sends all texts in a single API request.

const embeddings = await createEmbedding(sampleSentences);

Step 4: Pair Text with Embeddings

After receiving the embeddings, we will create pairs of original text and their corresponding embeddings. We will use the .map() function to iterate through the texts and their embeddings.

const textWithEmbeddings = sampleSentences.map((text, index) => {
    return {
        content: text,
        embedding: embeddings[index] // Assuming embeddings is an array of vectors
    };
});

Step 5: Print the Pairs

To visualize the results, we will loop through the pairs and print them in a clean format. This step is essential for ensuring everything is functioning correctly before moving onto storage.

textWithEmbeddings.forEach((pair, index) => {
    console.log(`Item ${index + 1}:`);
    console.log(`Content: ${pair.content}`);
    console.log(`Embedding: ${JSON.stringify(pair.embedding).substring(0, 50)}...`); // Preview of embedding
});

Step 6: Running the Code

To see everything in action, run the following command in your terminal:

node 04_pair_text_with_embeddings.js

You should see an output that displays each content item along with its corresponding embedding. This confirms that we have successfully created pairs of text and embeddings.

Conclusion

Congratulations on completing today’s challenge! You have successfully generated embeddings for sample movie-related sentences and paired them with the original text. These pairs are now ready to be stored in a vector database, which we will explore in our next tutorial.

If you found this tutorial helpful, please like and subscribe to stay updated with our series on semantic search and recommendation systems. Happy coding!

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