Vector

Store, index and search ML model embeddings with pgvector

Total Embeddings

1536

Dimensions

IVFFlat

Index Type

Semantic Search

Enter a query to find semantically similar content

Stored Embeddings

The quick brown fox jumps over the lazy dog

1536 dimensions · 2h ago

ExtraBase is built on PostgreSQL with pgvector

1536 dimensions · 3h ago

Machine learning embeddings for semantic search

1536 dimensions · 1d ago

Setup with pgvector

-- Enable pgvector extension
CREATE EXTENSION IF NOT EXISTS vector;

-- Create a table with embedding column
CREATE TABLE documents (
  id BIGSERIAL PRIMARY KEY,
  content TEXT,
  embedding VECTOR(1536)
);

-- Create an index for fast similarity search
CREATE INDEX ON documents
  USING ivfflat (embedding vector_cosine_ops)
  WITH (lists = 100);

-- Semantic search query
SELECT content, 1 - (embedding <=> $1) AS similarity
FROM documents
ORDER BY embedding <=> $1
LIMIT 5;