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, 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
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()