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Anthony Lio
3de0e3a614
## What kind of change does this PR introduce? a11y fix ## What is the current behavior? Colab badge image is missing alt attribute leaving both image and the link unnamed _ screen reader users have no way to tell what the link does ## What is the new behavior? - adds `alt="Open in Colab"`, matching the text rendered inside the SVG so voice control users can activate it by its visible label ## Test 1. visit [/docs/guides/ai/google-colab](https://supabase.com/docs/guides/ai/google-colab) <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **Documentation** - Improved accessibility across AI guides and quickstarts by adding descriptive alternative text to “Open in Colab” badge images. - Updated Google Colab, LlamaIndex, face similarity, hello world, and text deduplication documentation. <!-- end of auto-generated comment: release notes by coderabbit.ai -->
114 lines
3.9 KiB
Plaintext
114 lines
3.9 KiB
Plaintext
---
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id: 'ai-google-colab'
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title: 'Google Colab'
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description: 'Use Google Colab to manage your Supabase Vector store.'
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subtitle: 'Use Google Colab to manage your Supabase Vector store.'
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sidebar_label: 'Google Colab'
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---
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<a
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className="w-64"
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href="https://colab.research.google.com/github/supabase/supabase/blob/master/examples/ai/vector_hello_world.ipynb"
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>
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<img src="/docs/img/ai/colab-badge.svg" alt="Open in Colab" />
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</a>
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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).
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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).
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## Create a new notebook
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Start by visiting [colab.research.google.com](https://colab.research.google.com/). There you can create a new notebook.
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## Install Vecs
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We'll use the Supabase Vector client, [Vecs](/docs/guides/ai/vecs-python-client), to manage our collections.
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At the top of the notebook, paste the following code and click "Execute" (`ctrl+enter`):
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```py
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pip install vecs
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```
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## Connect to your database
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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`
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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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```py
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import vecs
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DB_CONNECTION = "postgres://postgres.xxxx:password@xxxx.pooler.supabase.com:6543/postgres"
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# create vector store client
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vx = vecs.create_client(DB_CONNECTION)
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```
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Execute the code block (`ctrl+enter`). If no errors were returned then your connection was successful.
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## Create a collection
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Now we're going to create a new collection and insert some documents.
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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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```py
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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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)
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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](/dashboard/project/_/editor/), by selecting the `vecs` schema from the schema dropdown.
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## Query your documents
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Now we can search for documents based on their similarity. Create a new code block and execute the following code:
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```py
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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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)
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```
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You will see that this returns two documents in an array `['vec1', 'vec0']`:
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It also returns a warning:
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```
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Query does not have a covering index for cosine_distance.
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```
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You can lean more about creating indexes in the [Vecs documentation](https://supabase.github.io/vecs/api/#create-an-index).
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## Resources
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- Vecs API: [supabase.github.io/vecs/api](https://supabase.github.io/vecs/api)
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