Building a RAG Chatbot from Scratch with AI SDK and Supabase
A complete tutorial on building a retrieval-augmented generation chatbot that answers questions about your own documents.
Build a chatbot that knows your data. We'll use AI SDK 6, Supabase with pgvector, and Next.js.
Architecture
1. Upload documents to Supabase Storage
2. Process and chunk text
3. Generate embeddings with OpenAI
4. Store in Supabase with pgvector
5. Build chat interface with AI SDK
Step 1: Database Setup
Create a Supabase project and enable the pgvector extension. Create a table for document chunks with an embedding column.
Step 2: Document Processing
Split documents into overlapping chunks of ~500 tokens. Generate embeddings for each chunk.
Step 3: Chat Interface
Use the AI SDK's useChat hook for a streaming chat interface. On each message, search for relevant chunks and include them in the prompt.
Step 4: Prompt Engineering
Construct a system prompt that instructs the model to answer based only on the provided context, with citations.
Production Tips
Cache embeddings, implement rate limiting, add feedback mechanisms, and monitor answer quality.