pgvector-python/examples/loading/example.py at master · pgvector/pgvector-python · GitHub

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import numpy as np

from pgvector import Vector

from pgvector.psycopg import register_vector

import psycopg

# generate random data

rows = 1000000

dimensions = 128

embeddings = np.random.rand(rows, dimensions)

# enable extension

conn = psycopg.connect(dbname='pgvector_example', autocommit=True)

conn.execute('CREATE EXTENSION IF NOT EXISTS vector')

register_vector(conn)

# create table

conn.execute('DROP TABLE IF EXISTS items')

conn.execute(f'CREATE TABLE items (id bigserial, embedding vector({dimensions}))')

# load data

print(f'Loading {len(embeddings)} rows')

cur = conn.cursor()

with cur.copy('COPY items (embedding) FROM STDIN WITH (FORMAT BINARY)') as copy:

# use set_types for binary copy

# https://www.psycopg.org/psycopg3/docs/basic/copy.html#binary-copy

copy.set_types(['vector'])

for i, embedding in enumerate(embeddings):

copy.write_row([Vector(embedding)])

# show progress

if i % 10000 == 0:

print('.', end='', flush=True)

print('\nSuccess!')

# create any indexes *after* loading initial data (skipping for this example)

create_index = False

if create_index:

print('Creating index')

conn.execute("SET maintenance_work_mem = '8GB'")

conn.execute('SET max_parallel_maintenance_workers = 7')

conn.execute('CREATE INDEX ON items USING hnsw (embedding vector_cosine_ops)')

# update planner statistics for good measure

conn.execute('ANALYZE items')

pgvector-python/examples/loading/example.py at master · pgvector/pgvector-python · GitHub

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