Practical RAG implementation with Next.js, Supabase pgvector and OpenAI. Index documents, semantic search, augmented answers. Real code, no abstractions.

Index documents, run semantic search, and assemble LLM-augmented answers using Next.js + Supabase pgvector + OpenAI.

Implement RAG in a Next.js SaaS

Enable pgvector in Supabase and create the embeddings table

Turn on the pgvector extension in your Supabase project and create a table with columns for the text chunk, the embedding (vector 1536), and metadata.

Chunk documents and generate embeddings

Split each document into 400-800 token chunks with overlap, generate embeddings using text-embedding-3-small from OpenAI, and insert into Supabase.

Semantic search with cosine similarity

Create a Postgres RPC that, given a query embedding, returns the top-k most similar chunks using pgvector's <=> operator.

Augment the prompt with retrieved context

In the Next.js handler, embed the user question, retrieve top-5 chunks, and inject them into the system prompt before calling the LLM.

Monitor costs and metrics

Track tokens per request, cache hit rate, and latency. Log the augmented prompt for debugging when an answer is bad.

Articles about RAG

RAG step by step for your SaaS: from PDF to chatbot in 2 hours