# Build a RAG System Using Supabase and Lovable

## What is RAG?

RAG stands for **Retrieval-Augmented Generation**.

Instead of hoping your LLM (Large Language Model) remembers everything, RAG retrieves the **most relevant information in real-time** and feeds that context into the model to generate an accurate response.

Think of it as combining **search + AI**, a Google-like brain with human-like understanding.

Here’s how I built a simple chat with pdf app using Lovable and Supabase.

## The Architecture

Here’s the tech stack I used:

* **Frontend**: React (with a streaming chat interface)
    
* **Backend**: Supabase Edge Functions (powered by Deno)
    
* **Database**: PostgreSQL with `pgvector`
    
* **AI**: OpenAI Embeddings + GPT-4o-mini
    

The entire system works in **three stages**:

1. Ingest
    
2. Store
    
3. Retrieve
    

Let’s walk through each of them.

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## Stage 1: Document Processing

Whenever a user uploads a PDF:

1. I extract the text using any text extraction package.
    
2. Then, I split that text into manageable chunks, around **500 characters** per chunk.
    
3. Finally, I generate **embeddings** for each chunk.
    

### What are embeddings?

Embeddings convert text into a vector of numbers that represent its meaning. This lets the system measure similarity between chunks of text, even if they use different words.

I use **OpenAI’s Embedding API** for this step. It’s simple, fast, and highly accurate.

---

## Stage 2: Storing Embeddings

This is where **pgvector** comes in.

Supabase supports the `pgvector` extension, which allows you to store and search high-dimensional vectors right inside your PostgreSQL database.

Each text chunk and its corresponding embedding are stored as a row in the database. This gives you full control over your knowledge base, and there’s no need for external vector DBs.

---

## Stage 3: Smart Retrieval

Now the fun part, asking questions and getting smart answers.

Here’s what happens when a user asks a question:

1. The question is converted into an embedding.
    
2. A **vector similarity search** is run on the database.
    
3. The top 5 most relevant chunks are retrieved.
    
4. These chunks are sent as **context** to the LLM.
    

---

## Stage 4: Response Generation

The retrieved chunks are merged into a single context string.

That context, along with the user’s original question, is sent to **GPT-4o-mini**.

The response is streamed back to the frontend in real time, creating a smooth, chat-like experience.

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## Why This Setup Works

* **No fine-tuning required** — it adapts to any document.
    
* **Highly accurate** — thanks to embedding-based context.
    
* **Real-time streaming** — fast responses, no waiting.
    
* **Scalable and cheap** — built on Supabase + OpenAI.
    
* **Prompt-powered** — easy to evolve using Lovable.dev.
    

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## The Result

What you get is a RAG system that:

* Understands your documents deeply.
    
* Answers questions using real, relevant context.
    
* Streams responses instantly.
    
* Scales effortlessly.
    
* Costs **pennies per query**.
    

I’ll soon be sharing a follow-up on how you can do **all of this** using just **prompts** with Lovable.dev and Supabase, no complex backend required.

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