Let's Get Started

Hello Guys! 👋

Excited for the First AI RAG Project ??

Today, we’re kicking it off with our Chat With YouTube project.

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🎯 What are we doing here ?

We are building a tool that helps you chat with any Youtube video. We will be learning on how do you approach this project from a step by step approach and also learn how can we:

• Fetch the transcript of a youtube video from its URL
• Understand Langchain and Document Loaders
• What are embeddings ?
• What do we mean by a Vector Database ?
• What is a Similarity Search ?
• How do we integrate all of it ?

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Important Note

Before starting any project, it is suggested to start with creating the approach of the project first, creating a complete workflow on your approach, making the approach hard as a rock and then start coding.

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🛠️ The Approach?

We use the following approach as given above to start with the project.

🧩 Let’s Understand the approach in detail

1. Get the YouTube Video URL
   We’ll take the user’s input — a YouTube video link.

2. Fetch Transcript from the URL
   Use YouTube’s transcript API or scraping tools to extract the full transcript.

3. Chunk the Transcript
   Split the transcript into manageable parts for efficient storage and retrieval.

4. Convert Chunks into Embeddings
   Use an LLM to convert text chunks into embeddings (vectors).

5. Store in a Vector Database (Supabase)
   Store the embeddings in Supabase with a document ID.

6. Handle User Queries
   Convert user queries into embeddings, perform a similarity search, and retrieve relevant answers.

7. Display Response
   Show the final output on the frontend using streaming.

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Before moving forward, let us understand the term Vector Database

🧠 What is a Vector Database?

Imagine your brain is trying to remember which movie your friend described:

“It’s a sci-fi movie, has robots, and was super emotional.”

Instead of just matching words like “robot” or “sci-fi”, your brain tries to understand the meaning and find the closest match - maybe it thinks: “Sounds like Intersteller or Wall-E!”

That’s what a vector database does — it finds similar things by understanding meaning, not just exact words.

🧮 What’s a “Vector” Anyway?

A vector is just a list of numbers that represent something like:

• a sentence 📝
• an image 🖼️
• a sound 🎵
• or even a product 🎁 For example, the sentence:

“I love ice cream.” 🍦 might be converted into something like: [0.23, -0.88, 1.2, 0.05, 0.77]

This is called embedding — turning things into numbers that machines can understand.

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📦 Tech Stack

Layer	Tech
Frontend	Vite + TailwindCSS
Backend	Node.js
Database	Supabase
LLM	Langchain.

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⚙️ Next Steps

In the next section, we’ll:

• Setup the environment
• Initialise the Backend
• Get the Supabase Credentials from Supabase.

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If you want to know more about this, do checkout our video :

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