Google Colab

Use Google Colab to manage your Supabase Vector store.

Google Colab is a hosted Jupyter Notebook service. It provides free access to computing resources, including GPUs and TPUs, and is well-suited to machine learning, data science, and education. We can use Colab to manage collections using Supabase Vecs.

In this tutorial we'll connect to a database running on the Supabase platform. If you don't already have a database, you can create one here: database.new.

Create a new notebook#

Start by visiting colab.research.google.com. There you can create a new notebook.

Install Vecs#

We'll use the Supabase Vector client, Vecs, to manage our collections.

At the top of the notebook add the notebook paste the following code and hit the "execute" button (ctrl+enter):

1pip install vecs

Connect to your database#

On your project dashboard, click Connect. The connection string should look like postgres://postgres.xxxx:password@xxxx.pooler.supabase.com:6543/postgres

Create a new code block below the install block (ctrl+m b) and add the following code using the Postgres URI you copied above:

1import vecs23DB_CONNECTION = "postgres://postgres.xxxx:password@xxxx.pooler.supabase.com:6543/postgres"45# create vector store client6vx = vecs.create_client(DB_CONNECTION)

Execute the code block (ctrl+enter). If no errors were returned then your connection was successful.

Create a collection#

Now we're going to create a new collection and insert some documents.

Create a new code block below the install block (ctrl+m b). Add the following code to the code block and execute it (ctrl+enter):

1collection = vx.get_or_create_collection(name="colab_collection", dimension=3)23collection.upsert(4    vectors=[5        (6         "vec0",           # the vector's identifier7         [0.1, 0.2, 0.3],  # the vector. list or np.array8         {"year": 1973}    # associated  metadata9        ),10        (11         "vec1",12         [0.7, 0.8, 0.9],13         {"year": 2012}14        )15    ]16)

This will create a table inside your database within the vecs schema, called colab_collection. You can view the inserted items in the Table Editor, by selecting the vecs schema from the schema dropdown.

Query your documents#

Now we can search for documents based on their similarity. Create a new code block and execute the following code:

1collection.query(2    query_vector=[0.4,0.5,0.6],  # required3    limit=5,                     # number of records to return4    filters={},                  # metadata filters5    measure="cosine_distance",   # distance measure to use6    include_value=False,         # should distance measure values be returned?7    include_metadata=False,      # should record metadata be returned?8)

You will see that this returns two documents in an array ['vec1', 'vec0']:

It also returns a warning:

1Query does not have a covering index for cosine_distance.

You can lean more about creating indexes in the Vecs documentation.

Resources#

• Vecs API: supabase.github.io/vecs/api