connect(dbname='pgvector_example', autocommit=True) conn.execute('CREATE EXTENSION IF NOT EXISTS vector') register_vector(conn) conn.execute('DROP TABLE IF EXISTS images') conn.execute('CREATE TABLE images (id bigserial PRIMARY KEY, hash bit(64))') print('Loading dataset') dataset = load...
from colbert.infra import ColBERTConfig from colbert.modeling.checkpoint import Checkpoint from pgvector.psycopg import register_vector import psycopg conn = psycopg.connect(dbname='pgvector_example', autocommit=True) conn.execute('CREATE EXTENSION IF NOT EXISTS vector') register_vector(conn) conn.execu...
git clone https://github.com/pgvector/pgvector-python.git cd pgvector-python pip install -r requirements.txt createdb pgvector_python_test pytest To run an example: cd examples/loading pip install -r requirements.txt createdb pgvector_example python3 example.pyAbout...
Updated pgvector on CI build #68: Commit 7682bb8 pushed by ankane master July 2, 2023 15:32 1m 0s Added pool example and test for asyncpg - closes #18 build #67: Commit 34db96b pushed by ankane master June 28, 2023 22:30 1m 55s ...
# Example 1: postgres backed application import pgserver pgdata = f'{MY_APP_DIR}/pgdata' db = pgserver.get_server(pgdata) # server ready for connection. print(db.psql('create extension vector')) db_uri = db.get_uri() # use uri with sqlalchemy / psycopg, etc # if no other ...
engine = create_engine('postgresql+psycopg://localhost/pgvector_example') with engine.connect() as conn: conn.execute(text('CREATE EXTENSION IF NOT EXISTS vector')) conn.commit() Base = declarative_base() class User(Base): __tablename__ = 'user' id = mapped_column(Integer, primary_key...
examples citus example.py requirements.txt cohere colbert colpali gensim hybrid_search image_search imagehash implicit lightfm loading openai rag rdkit sentence_transformers sparse_search surprise pgvector tests .gitignore CHANGELOG.md LICENSE.txt Makefile README.md pyproject.toml requirements.txtBreadcrumb...
import numpy as np 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...
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cohere colbert gensim hybrid_search image_search imagehash implicit lightfm loading openai rdkit sentence_transformers sparse_search example.py requirements.txt surprise pgvector tests .gitignore CHANGELOG.md LICENSE.txt Makefile README.md pyproject.toml ...