Storing and Retrieving Vectors in Supabase using LlamaIndex

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Welcome all, in this blog we explore storing and retrieving vectors in Supabase using LlamaIndex!

This blog is about how to use LlamaIndex, a vector search library, to store and retrieve vectors in Supabase, a popular backend-as-a-service platform.

This blog also contains examples of how to use LlamaIndex to store and retrieve vectors in Supabase.

What is LlamaIndex?

LlamaIndex is a data framework for large language model (LLM) applications. It provides tools to help developers ingest, structure, and query data to improve the performance and accuracy of their LLMs.

LlamaIndex offers the following features:

Data connectors: LlamaIndex provides a variety of data connectors that allow you to ingest data from a variety of sources, including APIs, PDFs, documents, and SQL databases.
Data indexing: LlamaIndex indexes your data so that it can be easily used with LLMs. This can include creating vector representations of your data or storing it in a graph database.
Query interface: LlamaIndex provides a query interface that allows you to retrieve context and knowledge-augmented output from your data using any LLM input prompt.

LlamaIndex is designed to be easy to use for both beginner and advanced users. The high-level API allows beginner users to use LlamaIndex to ingest and query their data in just a few lines of code. Advanced users can customize and extend any module in LlamaIndex to meet their specific needs.

What is Supabase?

Supabase is an open-source backend platform that provides a full suite of features for building web and mobile applications, including:

Postgres database: Supabase provides a fully-managed Postgres database for every project, with built-in support for row-level security, real-time database changes, and extensions.
Authentication: Supabase provides a suite of authentication features, including email and password, passwordless, OAuth, and mobile logins.
Storage: Supabase provides secure and scalable object storage for your application’s files.
Realtime: Supabase provides real-time subscriptions, allowing you to listen for and broadcast changes to your data to connected clients.
Edge Functions: Supabase provides globally distributed, serverless functions that you can use to execute your code close to your users for the lowest latency.

Supabase is a popular choice for developers who want to build secure, scalable, and high-performance applications without having to worry about managing the underlying infrastructure. It is also a good choice for developers who want to avoid the vendor lock-in associated with proprietary backend platforms.

Supabase is available as a fully-managed service, or you can self-host it on your own infrastructure. It also offers a free tier, so you can get started without any upfront costs.

Creating a Supabase Project

Go to the Supabase website and create an account.
Once you are logged in, click on the “Create new project” button.
Enter a name for your project, database password and region
Click on the “Create project” button.

Supabase will create a new project for you and provision all of the necessary infrastructure. This process may take a few minutes.

Once your project is created, you will be able to access the Supabase dashboard. This is where you can manage your project’s settings, database, storage, and other features.

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In this tutorial, we explored a practical approach to efficiently extract and index headings from documents using LlamaIndex and Supabase. We’ve seen how LlamaIndex, a versatile data framework, can be harnessed to enhance the performance and accuracy of large language models (LLMs) by indexing and querying data. Additionally, Supabase, a robust backend platform, provides us with the necessary infrastructure to store and manage our data seamlessly.

We began by creating a Supabase project and setting up a storage bucket for our documents. Then, we extracted headings from web pages and built an index of these headings in our Supabase database. This indexing process enables us to quickly locate relevant information within documents.

By focusing on indexing headings, we’ve provided a practical solution for users interested in searching, categorizing, and retrieving specific content within their documents. Whether you’re working with research papers, articles, or any textual content with structured headings, this approach can significantly streamline your data management and retrieval processes.

As you continue to explore the potential of LlamaIndex and Supabase, remember that customization is key. You can adapt these techniques to your specific use cases and requirements, making them a valuable addition to your data management toolkit.

We hope this tutorial has been a helpful guide in your journey to effectively work with specific data elements, and we encourage you to further explore the capabilities of LlamaIndex and Supabase to enhance your data-related projects. Thank you for joining us on this data indexing adventure, and we look forward to seeing what you create with these powerful tools