Learn how to integrate Supabase with LlamaIndex, a data framework for your LLM applications.

This guide will walk you through a basic example using the LlamaIndex SupabaseVectorStore.

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Launching a notebook

Launch our LlamaIndex notebook in Colab:

<a className="w-64" href="https://colab.research.google.com/github/supabase/supabase/blob/master/examples/ai/llamaindex/llamaindex.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.

Fill in your OpenAI credentials

Inside the Notebook, add your OPENAI_API_KEY key. Find the cell which contains this code:

import os
os.environ['OPENAI_API_KEY'] = "[your_openai_api_key]"

Connecting to your database

Inside the Notebook, find the cell which specifies the DB_CONNECTION. It will contain some code like this:

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. You can find the connection string on your project dashboard by clicking Connect.

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.

Stepping through the notebook

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.

Resources

• Visit the LlamaIndex + SupabaseVectorStore docs
• Visit the official LlamaIndex repo