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AI & Vectors

Python client

Manage unstructured vector stores in Postgres.

Supabase provides a Python client called vecs for managing unstructured vector stores. This client provides a set of useful tools for creating and querying collections in Postgres using the pgvector extension.

Quick start#

To see how Vecs works, use a local database. Make sure you have the Supabase CLI installed on your machine.

Initialize your project#

Start a local Postgres instance in any folder using the init and start commands. Make sure you have Docker running!

# Initialize your project
supabase init
# Start Postgres
supabase start

Create a collection#

Inside a Python shell, run the following commands to create a new collection called "docs", with 3 dimensions.

import vecs
# create vector store client
vx = vecs.create_client("postgresql://postgres:postgres@localhost:54322/postgres")
# create a collection of vectors with 3 dimensions
docs = vx.get_or_create_collection(name="docs", dimension=3)

Add embeddings#

Now we can insert some embeddings into our "docs" collection using the upsert() command:

import vecs
# create vector store client
docs = vecs.get_or_create_collection(name="docs", dimension=3)
# a collection of vectors with 3 dimensions
vectors=[
  ("vec0", [0.1, 0.2, 0.3], {"year": 1973}),
  ("vec1", [0.7, 0.8, 0.9], {"year": 2012})
]
# insert our vectors
docs.upsert(vectors=vectors)

Query the collection#

You can now query the collection to retrieve a relevant match:

import vecs
docs = vecs.get_or_create_collection(name="docs", dimension=3)
# 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
)

Deep dive#

For a more in-depth guide on vecs collections, see API.

Resources#