AI & Vectors

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.

Google Colab 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):


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pip install vecs

Install vecs

Connect to your database

Find the Postgres pooler connection string for your Supabase project in the database settings of the dashboard. Copy the "URI" format, which should look something like postgres://postgres.xxxx:[email protected]: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:[email protected]: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.

Colab documents

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']:

Colab results

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