Learn how to integrate Supabase with LlamaIndex, a data framework for your LLM applications.
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.
Project setup#
Let's create a new Postgres database. This is as simple as starting a new Project in Supabase:
- Create a new project in the Supabase dashboard.
- Enter your project details. Remember to store your password somewhere safe.
Your database will be available in less than a minute.
Finding your credentials:
You can find your project credentials inside the project settings, including:
- Database credentials: connection strings and connection pooler details.
- API credentials: your serverless API URL and
anon/service_rolekeys.
Launching a notebook#
Launch our LlamaIndex notebook in Colab:
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:
_10import os_10os.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:
_10DB_CONNECTION = "postgresql://<user>:<password>@<host>:<port>/<db_name>"_10_10# create vector store client_10vx = vecs.create_client(DB_CONNECTION)
Replace the DB_CONNECTION with your own connection string for your database, which you set up in first step of this guide.
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.
