What is the difference between pgvector similarity search and hybrid search in Supabase?

pgvector is a Postgres extension that adds a vector column type and distance operators (cosine distance, L2, inner product) so you can store embeddings alongside your relational data and query them with SQL, for example: create extension if not exists vector; create table documents (id bigserial primary key, content text, embedding vector(1536));. Pure vector similarity search finds rows whose embeddings are numerically closest to a query embedding, which is powerful for semantic matches but can miss results that share exact keywords without being semantically close in the embedding space, and vice versa.

Hybrid search combines that vector similarity ranking with traditional keyword search (typically BM25-style full-text ranking via Postgres's tsvector) and merges the two rankings, often using a technique like Reciprocal Rank Fusion, into a single result order. This captures cases pure vector search misses — like an exact product code or acronym a user typed — while still surfacing semantically related results that don't share exact wording.

Because both the vector index and the full-text index live in the same Postgres database, a hybrid search can be expressed as a single SQL query joining both ranking signals, without needing to synchronize data between a separate vector database and a separate search engine.