AI & Vectors

Python client

Manage unstructured vector stores in PostgreSQL.

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

Let's see how Vecs works using 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