moai-platform-supabase

Vector search with pgvector in Supabase enables semantic search by storing embeddings and querying similarity using HNSW or IVFFlat indexes. Enable the pgvector extension, create a vector column, build an index, and query using distance operators to find semantically related results across your data.

Row-Level Security (RLS) policies in Supabase enforce multi-tenant data isolation by restricting database access at the row level based on authenticated user context. Define policies that filter queries to return only rows belonging to a user's tenant, preventing cross-tenant data leakage.

Supabase real-time subscriptions listen to database changes and push updates to connected clients instantly. Subscribe to table changes, presence events, or specific columns; your application receives notifications when data is inserted, updated, or deleted, enabling live collaboration without polling.

Edge Functions in Supabase run server-side logic at the edge to transform files, validate uploads, or trigger workflows. Pair them with storage hooks to intercept uploads, process images or documents, and generate embeddings or metadata before persisting transformed content to storage.

Yes. Supabase with pgvector supports storing AI embeddings, building efficient indexes for fast similarity search, and querying semantically related content. Store embeddings from language models, use vector indexes for retrieval-augmented generation, and power semantic search features in AI-driven apps.