apps/docs/content/guides/ai/google-colab.mdx
<a className="w-64" href="https://colab.research.google.com/github/supabase/supabase/blob/master/examples/ai/vector_hello_world.ipynb"
</a>
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.
Start by visiting colab.research.google.com. There you can create a new notebook.
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):
pip install vecs
On your project dashboard, click Connect. The connection string should look 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:
import vecs
DB_CONNECTION = "postgres://postgres.xxxx:[email protected]:6543/postgres"
# create vector store client
vx = vecs.create_client(DB_CONNECTION)
Execute the code block (ctrl+enter). If no errors were returned then your connection was successful.
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):
collection = vx.get_or_create_collection(name="colab_collection", dimension=3)
collection.upsert(
vectors=[
(
"vec0", # the vector's identifier
[0.1, 0.2, 0.3], # the vector. list or np.array
{"year": 1973} # associated metadata
),
(
"vec1",
[0.7, 0.8, 0.9],
{"year": 2012}
)
]
)
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.
Now we can search for documents based on their similarity. Create a new code block and execute the following code:
collection.query(
query_vector=[0.4,0.5,0.6], # required
limit=5, # number of records to return
filters={}, # metadata filters
measure="cosine_distance", # distance measure to use
include_value=False, # should distance measure values be returned?
include_metadata=False, # should record metadata be returned?
)
You will see that this returns two documents in an array ['vec1', 'vec0']:
It also returns a warning:
Query does not have a covering index for cosine_distance.
You can lean more about creating indexes in the Vecs documentation.