
vecs
Postgres/pgvector Python Client
Stars: 219

vecs is a Python client for managing and querying vector stores in PostgreSQL with the pgvector extension. It allows users to create collections of vectors with associated metadata, index the collections for fast search performance, and query the collections based on specified filters. The tool simplifies the process of working with vector data in a PostgreSQL database, making it easier to store, retrieve, and analyze vector information.
README:
Documentation: https://supabase.github.io/vecs/latest/
Source Code: https://github.com/supabase/vecs
vecs
is a python client for managing and querying vector stores in PostgreSQL with the pgvector extension. This guide will help you get started with using vecs.
If you don't have a Postgres database with the pgvector ready, see hosting for easy options.
Requires:
- Python 3.7+
You can install vecs using pip:
pip install vecs
Visit the quickstart guide for more complete info.
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"]
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