Advanced · 60 minutes

RAG Chatbot with Supabase pgvector

Chunk docs, generate embeddings, retrieve context, and stream grounded responses in a Next.js app.

Prerequisites

Step 1

Split documentation into semantic chunks, embed each chunk, and store vector + metadata in Supabase with source references.

Step 2

For each user query, embed the query and run similarity search with thresholds. Combine top results and pass citations into model prompt.

Step 3

Use server route streaming with safety instructions: answer only from provided context, cite sections, and refuse unsupported claims.

A trustworthy retrieval-augmented assistant that answers from your knowledge base with citation-aware responses.