vecs

Documentation: https://supabase.github.io/vecs/latest/

Source Code: https://github.com/supabase/vecs

Vecs is a Python client library for managing and querying vector stores in PostgreSQL, leveraging the capabilities of the pgvector extension.

Overview

Vector Management: create collections to persist and update vectors in a PostgreSQL database.
Querying: Query vectors efficiently using measures such as cosine distance, l2 distance, l1 distance, or max inner product.
Metadata: Each vector can have associated metadata, which can also be used as filters during queries.
Hybrid Data: vecs creates its own schema and can coexist with your existing relational data

Visit the quickstart guide for how to get started.

TL;DR

Install

pip install vecs

Usage

import vecs

DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"

# create vector store client
vx = vecs.create_client(DB_CONNECTION)

# create a collection of vectors with 3 dimensions
docs = vx.get_or_create_collection(name="docs", dimension=3)

# add records to the *docs* collection
docs.upsert(
    records=[
        (
         "vec0",           # the vector's identifier
         [0.1, 0.2, 0.3],  # the vector. list or np.array
         {"year": 1973}    # associated  metadata
        ),
        (
         "vec1",
         [0.7, 0.8, 0.9],
         {"year": 2012}
        )
    ]
)

# index the collection for fast search performance
docs.create_index()

# query the collection filtering metadata for "year" = 2012
docs.query(
    data=[0.4,0.5,0.6],              # required
    limit=1,                         # number of records to return
    filters={"year": {"$eq": 2012}}, # metadata filters
)

# Returns: ["vec1"]

# Disconnect from the database
vx.disconnect()