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:

Ingest
Store
Retrieve

Let’s walk through each of them.

Stage 1: Document Processing

Whenever a user uploads a PDF:

I extract the text using any text extraction package.
Then, I split that text into manageable chunks, around 500 characters per chunk.
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:

The question is converted into an embedding.
A vector similarity search is run on the database.
The top 5 most relevant chunks are retrieved.
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.

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.

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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Build a RAG System Using Supabase and Lovable

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What is RAG?

The Architecture

Stage 1: Document Processing

What are embeddings?

Stage 2: Storing Embeddings

Stage 3: Smart Retrieval

Stage 4: Response Generation

Why This Setup Works

The Result

Command Palette

Comments

More from this blog

What is RAG?

The Architecture

Stage 1: Document Processing

What are embeddings?

Stage 2: Storing Embeddings

Stage 3: Smart Retrieval

Stage 4: Response Generation

Why This Setup Works

The Result