Documentation: https://supabase.github.io/vecs/
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 vecsVisit 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"]