Let's Get Started
A complete walkthrough of building a RAG Model that chats with YouTube videos. Learn how to extract transcripts, create embeddings, and query them with AI.
🎯 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.
Fetch Transcript
Extract text from YouTube videos using APIs or scrapers.
Embeddings
Convert text chunks into vector representations using LLMs.
Vector DB
Store and retrieve high-dimensional vectors efficiently.
Similarity Search
Find the most relevant context for user queries.
🛠️ The Approach
Important Note
🧠 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", your brain tries to understand the meaning. That’s what a vector database does — it finds similar things by understanding meaning, not just exact words.
Example: Embeddings
"I love ice cream" might look like:
[0.23, -0.88, 1.2, 0.05, 0.77, ...]Important Note
📦 Tech Stack
Next Steps
In the next section, we’ll:
Setup the environment
Install necessary tools and libraries.
Initialise the Backend
Create the project structure and initial scripts.
Get Supabase Credentials
Connect your project to Supabase.