Build a RAG Application with Supabase and pgvector

A chatbot can answer general questions.

But what if you want it to answer questions about your own documents?

That is where Retrieval-Augmented Generation, or RAG, becomes useful.

A RAG system retrieves relevant information from your own data and provides that information to an AI model before generating an answer.

Basic RAG Architecture

A simplified workflow looks like:

Documents
   ↓
Embeddings
   ↓
Supabase + pgvector
   ↓
User Question
   ↓
Similarity Search
   ↓
Relevant Documents
   ↓
AI Model
   ↓
Answer

Step 1: Collect Your Documents

Your source material might include:

• PDFs
• Documentation
• Help articles
• Product information
• Internal knowledge
• Support tickets

The first step is getting that information into your system.

Step 2: Split the Content

Large documents should usually be divided into smaller chunks.

For example:

Large Document
      ↓
Chunk 1
Chunk 2
Chunk 3
Chunk 4

Smaller chunks can make retrieval more focused.

Step 3: Generate Embeddings

Each chunk is converted into an embedding.

Text Chunk
    ↓
Embedding Model
    ↓
Vector

The vectors can then be stored in PostgreSQL using pgvector.

Step 4: Store Metadata

Alongside the vector, store information such as:

document_id
title
chunk
category
source
embedding

This allows your application to identify where retrieved content came from.

Step 5: Search for Relevant Content

When the user asks a question, convert the question into an embedding.

Then perform a similarity search.

For example:

User Question
     ↓
Question Vector
     ↓
Vector Search
     ↓
Top Relevant Chunks

Step 6: Send Context to the AI

The retrieved chunks become context for the AI model.

The model can then generate an answer based on the supplied information.

Why Supabase Works Well Here

A RAG application needs:

• Database
• Vector storage
• User authentication
• Document storage
• APIs

Supabase can provide many of these components within one backend.

Store Documents Separately

The original PDF or image does not need to be stored directly inside PostgreSQL.

You can use object storage for files and PostgreSQL for metadata and embeddings.

For example:

Supabase Storage
      ↓
Original PDF

PostgreSQL
      ↓
Document Metadata + Embeddings

Protect Private Knowledge

If your RAG system contains private company documents, access control becomes important.

A user should only retrieve documents they are authorized to access.

This means authentication and database policies matter.

Monitor Costs

A RAG system may involve several resource-intensive operations:

• Embedding generation
• Vector searches
• AI model requests
• Document processing

Track usage as your application grows.

Final Thoughts

RAG does not require an enormous architecture to get started.

Supabase can provide PostgreSQL, storage, authentication and vector-related database capabilities in one environment.

That makes it a practical backend foundation for building AI applications around your own data.