The Vector Database Trap: Scaling AI Search with Python & Supabase

The Vector Database Trap: Scaling AI Search with Python, FastAPI, and Supabase pgvector If you've built an AI application in the last year, you've probably implemented Retrieval-Augmented Generation (RAG). The standard tutorial stack is predictable: take some documents, chunk them, embed them with OpenAI, and shove them into a dedicated vector database like Pinecone, Weaviate, or Milvus. It works beautifully for a weekend hackathon. But when you push to production, the cracks start to show. You suddenly have two sources of truth: your primary relational database (PostgreSQL) and your vector database. Keeping them in sync becomes a distributed systems nightmare. When a user deletes their account, you have to ensure their vectors are also purged. When a document is updated, you have to re-embed and upsert. And then there's the cost—dedicated vector databases can get expensive quickly as your data grows. The solution? Stop treating vectors as a special snowflake. They are just data. And