Vectors & AI

A vector database that's also a database.

pgvector in every project. Store embeddings next to relational data, search semantically and build RAG without syncing to a separate vector store.

pgvector 0.8HNSW indexesNo separate vector databaseRAG-ready
search.sql
create extension if not exists vector;

create table documents (
  id bigserial primary key,
  content text,
  embedding vector(1536)
);

create index on documents using hnsw (embedding vector_cosine_ops);

select content from documents
order by embedding <=> $1
limit 5;

Capabilities

What you get with Vector

pgvector and HNSW

HNSW and IVFFlat indexes for fast search across millions of vectors.

Hybrid search

Combine vector similarity with full-text search and SQL filters.

Any model

OpenAI, Anthropic, Cohere, Mistral or open-source models — it's just a vector column.

RLS for embeddings

Users only search their own documents — no extra code.

Automatic embeddings

Triggers and Edge Functions that generate vectors when a row is written.

MCP server

AI agents query your database through the Model Context Protocol.

Use cases

What teams use it for

Chatbots with company knowledge

RAG over docs, policies and your knowledge base.

Semantic search

Results by meaning, not just keywords.

Recommendations

Similar products and content based on embeddings.

Build in a weekend. Scale to millions.

Spin up a free project in 2 minutes. No credit card, no pausing after a week.