Citus with pgvecto.rs

citus_pgvectors.py

import numpy as np
from pgvecto_rs.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(
    conninfo="postgres://postgres:123@localhost:5432/postgres",
    dbname="postgres",
    autocommit=True,
)
conn.execute("CREATE EXTENSION IF NOT EXISTS citus")
conn.execute("CREATE EXTENSION IF NOT EXISTS vectors")

conn.execute("ALTER DATABASE postgres SET hnsw.ef_search = 20")
conn.close()

# reconnect for updated GUC variables to take effect
conn = psycopg.connect(
    conninfo="postgres://postgres:123@localhost:5432/postgres",
    dbname="postgres",
    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 vectors (embedding vector_l2_ops) WITH (options = \"[indexing.hnsw]\")")

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])