Google Colab

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, paste the following code and click "Execute" (ctrl+enter):

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pip 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:

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import vecs
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DB_CONNECTION = "postgres://postgres.xxxx:password@xxxx.pooler.supabase.com:6543/postgres"
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# create vector store client
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vx = 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):

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collection = vx.get_or_create_collection(name="colab_collection", dimension=3)
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collection.upsert(
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    vectors=[
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        (
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         "vec0",           # the vector's identifier
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         [0.1, 0.2, 0.3],  # the vector. list or np.array
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         {"year": 1973}    # associated  metadata
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        ),
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        (
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         "vec1",
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         [0.7, 0.8, 0.9],
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         {"year": 2012}
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        )
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    ]
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)

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:

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collection.query(
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    query_vector=[0.4,0.5,0.6],  # required
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    limit=5,                     # number of records to return
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    filters={},                  # metadata filters
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    measure="cosine_distance",   # distance measure to use
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    include_value=False,         # should distance measure values be returned?
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    include_metadata=False,      # should record metadata be returned?
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)

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

It also returns a warning:

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Query 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