id | title | subtitle | breadcrumb |
---|---|---|---|
ai-vecs-python-client |
Creating and managing collections |
Connecting to your database with Colab. |
AI Quickstarts |
This guide will walk you through a basic "Hello World" example using Colab and Supabase Vecs. You'll learn how to:
- Launch a Postgres database that uses pgvector to store embeddings
- Launch a notebook that connects to your database
- Create a vector collection
- Add data to the collection
- Query the collection
Launch our vector_hello_world
notebook in Colab:
<a className="w-64" href="https://colab.research.google.com/github/supabase/supabase/blob/master/examples/ai/vector_hello_world.ipynb"
At the top of the notebook, you'll see a button Copy to Drive
. Click this button to copy the notebook to your Google Drive.
Inside the Notebook, find the cell which specifies the DB_CONNECTION
. It will contain some code like this:
import vecs
DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"
# create vector store client
vx = vecs.create_client(DB_CONNECTION)
Replace the DB_CONNECTION
with your own connection string for your database. You can find the Postgres connection string in the Database Settings of your Supabase project.
SQLAlchemy requires the connection string to start with postgresql://
(instead of postgres://
). Don't forget to rename this after copying the string from the dashboard.
You must use the "connection pooling" string (domain ending in *.pooler.supabase.com
) with Google Colab since Colab does not support IPv6.
Now all that's left is to step through the notebook. You can do this by clicking the "execute" button (ctrl+enter
) at the top left of each code cell. The notebook guides you through the process of creating a collection, adding data to it, and querying it.
You can view the inserted items in the Table Editor, by selecting the vecs
schema from the schema dropdown.
You can now start building your own applications with Vecs. Check our examples for ideas.