example.py

import numpy as np
from pgvector.psycopg import register_vector
import psycopg

# generate random data
rows = 100000
dimensions = 128
embeddings = np.random.rand(rows, dimensions)
categories = np.random.randint(100, size=rows).tolist()
queries = np.random.rand(10, dimensions)

# enable extensions
conn = psycopg.connect(dbname='pgvector_citus', autocommit=True)
conn.execute('CREATE EXTENSION IF NOT EXISTS citus')
conn.execute('CREATE EXTENSION IF NOT EXISTS vector')

# GUC variables set on the session do not propagate to Citus workers
# https://github.com/citusdata/citus/issues/462
# you can either:
# 1. set them on the system, user, or database and reconnect
# 2. set them for a transaction with SET LOCAL
conn.execute("ALTER DATABASE pgvector_citus SET maintenance_work_mem = '512MB'")
conn.execute('ALTER DATABASE pgvector_citus SET hnsw.ef_search = 20')
conn.close()

# reconnect for updated GUC variables to take effect
conn = psycopg.connect(dbname='pgvector_citus', autocommit=True)
register_vector(conn)

print('Creating distributed table')
conn.execute('DROP TABLE IF EXISTS items')
conn.execute('CREATE TABLE items (id bigserial, embedding vector(%d), category_id bigint, PRIMARY KEY (id, category_id))' % dimensions)
conn.execute('SET citus.shard_count = 4')
conn.execute("SELECT create_distributed_table('items', 'category_id')")

print('Loading data in parallel')
with conn.cursor().copy('COPY items (embedding, category_id) FROM STDIN WITH (FORMAT BINARY)') as copy:
    copy.set_types(['vector', 'bigint'])

    for i in range(rows):
        copy.write_row([embeddings[i], categories[i]])

        while conn.pgconn.flush() == 1:
            pass

print('Creating index in parallel')
conn.execute('CREATE INDEX ON items USING hnsw (embedding vector_l2_ops)')

print('Running distributed queries')
for query in queries:
    items = conn.execute('SELECT id FROM items ORDER BY embedding <-> %s LIMIT 10', (query,)).fetchall()
    print([r[0] for r in items])