47705a8968
* fix: rewrite relative URLs when syncing to GitHub discussion Relative URLs back to supabse.com won't work in GitHub discussions, so rewrite them back to absolute URLs starting with https://supabase.com * fix: replace all supabase urls with relative urls * chore: add linting for relative urls * chore: bump linter version * Prettier --------- Co-authored-by: Chris Chinchilla <chris.ward@supabase.io>
114 lines
3.9 KiB
Plaintext
114 lines
3.9 KiB
Plaintext
---
|
|
id: 'ai-google-colab'
|
|
title: 'Google Colab'
|
|
description: 'Use Google Colab to manage your Supabase Vector store.'
|
|
subtitle: 'Use Google Colab to manage your Supabase Vector store.'
|
|
sidebar_label: 'Google Colab'
|
|
---
|
|
|
|
<a
|
|
className="w-64"
|
|
href="https://colab.research.google.com/github/supabase/supabase/blob/master/examples/ai/vector_hello_world.ipynb"
|
|
>
|
|
<img src="/docs/img/ai/colab-badge.svg" />
|
|
</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](/docs/guides/ai/vecs-python-client).
|
|
|
|
In this tutorial we'll connect to a database running on the Supabase [platform](/dashboard/). If you don't already have a database, you can create one here: [database.new](https://database.new).
|
|
|
|
## Create a new notebook
|
|
|
|
Start by visiting [colab.research.google.com](https://colab.research.google.com/). There you can create a new notebook.
|
|
|
|
|
|
|
|
## Install Vecs
|
|
|
|
We'll use the Supabase Vector client, [Vecs](/docs/guides/ai/vecs-python-client), to manage our collections.
|
|
|
|
At the top of the notebook add the notebook paste the following code and hit the "execute" button (`ctrl+enter`):
|
|
|
|
```py
|
|
pip install vecs
|
|
```
|
|
|
|
|
|
|
|
## Connect to your database
|
|
|
|
On your project dashboard, click [Connect](/dashboard/project/_?showConnect=true). 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:
|
|
|
|
```py
|
|
import vecs
|
|
|
|
DB_CONNECTION = "postgres://postgres.xxxx:password@xxxx.pooler.supabase.com: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.
|
|
|
|
## 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`):
|
|
|
|
```py
|
|
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](/dashboard/project/_/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:
|
|
|
|
```py
|
|
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](https://supabase.github.io/vecs/api/#create-an-index).
|
|
|
|
## Resources
|
|
|
|
- Vecs API: [supabase.github.io/vecs/api](https://supabase.github.io/vecs/api)
|