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
):
_10pip 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: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:
_10import vecs_10_10DB_CONNECTION = "postgres://postgres.xxxx:password@xxxx.pooler.supabase.com:6543/postgres"_10_10# create vector store client_10vx = 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
):
_16collection = vx.get_or_create_collection(name="colab_collection", dimension=3)_16_16collection.upsert(_16 vectors=[_16 (_16 "vec0", # the vector's identifier_16 [0.1, 0.2, 0.3], # the vector. list or np.array_16 {"year": 1973} # associated metadata_16 ),_16 (_16 "vec1",_16 [0.7, 0.8, 0.9],_16 {"year": 2012}_16 )_16 ]_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:
_10collection.query(_10 query_vector=[0.4,0.5,0.6], # required_10 limit=5, # number of records to return_10 filters={}, # metadata filters_10 measure="cosine_distance", # distance measure to use_10 include_value=False, # should distance measure values be returned?_10 include_metadata=False, # should record metadata be returned?_10)
You will see that this returns two documents in an array ['vec1', 'vec0']
:
It also returns a warning:
_10Query 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