fetch ( 'SELECT * FROM item ORDER BY factors $1 LIMIT 5', factors ) HistoryĮveryone is encouraged to help improve this project. Get the nearest neighbors to a vector await conn. execute ( 'INSERT INTO item (factors) VALUES ($1)', factors ) Register the vector type with your connection from pgvector.asyncpg import register_vector await register_vector ( conn ) execute ( 'SELECT * FROM item ORDER BY factors %s LIMIT 5', ( factors ,)) cur. Get the nearest neighbors to a vector cur. execute ( 'INSERT INTO item (factors) VALUES ( %s )', ( factors ,)) Register the vector type with your connection or cursor from pgvector.psycopg2 import register_vector register_vector ( conn ) execute ( 'SELECT * FROM item ORDER BY factors %s LIMIT 5', ( factors ,)). Get the nearest neighbors to a vector conn. Register the vector type with your connection from pgvector.psycopg import register_vector register_vector ( conn ) Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance Psycopg 3 factors, postgresql_using = 'ivfflat', postgresql_with = ) index. Make sure that you have the checkbox to save the converted file to your Google Drive. all ()Īlso supports max_inner_product and cosine_distanceĪdd an approximate index index = Index ( 'my_index', Item. Upload your images/icons to Google Drive. Get the nearest neighbors to a vector session. Insert a vector item = Item ( factors = ) session. Use vector_ip_ops for inner product and vector_cosine_ops for cosine distance SQLAlchemyĪdd a vector column from pgvector.sqlalchemy import Vector class Item ( Base ): factors = Column ( Vector ( 3 )) Model ): class Meta : indexes =, lists = 100, opclasses = ) ] order_by ( L2Distance ( 'factors', ))Īlso supports MaxInnerProduct and CosineDistanceĪdd an approximate index from pgvector.django import IvfflatIndex class Item ( models. Get the nearest neighbors to a vector from pgvector.django import L2Distance Item. Insert a vector item = Item ( factors = ) item. Model ): factors = VectorField ( dimensions = 3 ) Migration ): operations = Īdd a vector field from pgvector.django import VectorField class Item ( models. Explicit feedback recommendations with SurpriseĬreate the extension from pgvector.django import VectorExtension class Migration ( migrations.Implicit feedback recommendations with Implicit.Supports Django, SQLAlchemy, Psycopg 3, Psycopg 2, and asyncpgĪnd follow the instructions for your database library:

Import vector data to postview