Documentation: https://supabase.github.io/vecs/api/
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.create_collection(name="docs", dimension=3)
# add records to the *docs* collection
docs.upsert(
vectors=[
(
"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(
query_vector=[0.4,0.5,0.6], # required
limit=1, # number of records to return
filters={"year": {"$eq": 2012}}, # metadata filters
)
# Returns: ["vec1"]