Building Custom RAG Systems with Supabase pgvector & Next.js

Retrieval-Augmented Generation (RAG) allows AI models to answer questions using your private business documents and proprietary database records.

Architecture of a Supabase pgvector Setup

1. **Document Chunking & Embeddings**: Split large text documents into semantic chunks and generate vector embeddings using OpenAI `text-embedding-3-small`. 2. **PostgreSQL Vector Storage**: Save embedding vectors inside Supabase using the `pgvector` extension with HNSW indexing. 3. **Similarity Search Function**: Query top-k matching documents using vector cosine distance functions in SQL. 4. **Context Injection**: Feed matching document snippets into OpenAI GPT-4o or Gemini prompt context for accurate response generation.

NextApex Studio designs full-stack SaaS MVPs equipped with custom RAG knowledge systems and automated data pipelines.