zachbg/pgvector-ready repository overview

docker run -d \
  --name pgvector \
  -p 5432:5432 \
  -e POSTGRES_PASSWORD=secret \
  -v pgdata:/var/lib/postgresql/data \
  zachbg/pgvector-ready

services:
  db:
    image: zachbg/pgvector-ready
    environment:
      POSTGRES_PASSWORD: ${DB_PASSWORD}
      POSTGRES_DB: ragapp
    ports:
      - "5432:5432"
    volumes:
      - pgdata:/var/lib/postgresql/data

  ollama:
    image: ollama/ollama
    volumes:
      - ollama:/root/.ollama

volumes:
  pgdata:
  ollama:

INSERT INTO documents (content, metadata, embedding)
VALUES (
  'PostgreSQL is a powerful database',
  '{"source": "docs", "page": 1}',
  '[0.1, 0.2, ...]'::vector
);

-- Find 10 most similar documents
SELECT id, content, 1 - (embedding <=> '[0.1, 0.2, ...]'::vector) AS similarity
FROM documents
ORDER BY embedding <=> '[0.1, 0.2, ...]'::vector
LIMIT 10;

SELECT * FROM match_embeddings(
  '[0.1, 0.2, ...]'::vector,
  'documents',
  'embedding',
  10,     -- top 10 results
  0.8     -- minimum similarity
);

-- For Ollama embeddings (varies by model)
ALTER TABLE documents ALTER COLUMN embedding TYPE vector(4096);

-- Rebuild index
DROP INDEX documents_embedding_idx;
CREATE INDEX documents_embedding_idx ON documents
  USING hnsw (embedding vector_cosine_ops);