Crash Course on Creating a Free Vector Store with Supabase

Overview

• Learn to create a free vector store in Supabase to save data and embeddings.
• Use RAG (retrieval augmented generation) to chat with your data.
• Build a full-stack application to save and interact with emails.
• Free source code available via the link in the description.

Setting Up Supabase

1. Create a Supabase Project

   • Free to create; utilize Supabase for database storage.
   • Use Supabase Postgres to store emails and embeddings.
   • Embedding: A numerical text representation for easy querying.

2. Database Setup

   • Create tables for storing email data.
   • Use vector stores for saving embeddings.
   • Importance of embeddings for querying similar data.

3. Next.js Project Setup

   • Create a Next.js project to send emails to a database.
   • Backend function will handle text saving and embedding.
   • Embedding involves breaking up emails into smaller pieces.

Working with Vectors and Embeddings

• Purpose of Embeddings: Facilitate numerical comparisons in databases.
• Querying with Embeddings: Use vectors to perform similarity searches.

Creating a Supabase Function

1. Vector Store Indexes

   • Use cosine distance for semantic search.
   • Create an index for comparing embeddings using vector cosine operator.

2. SQL Editor Usage

   • Write SQL commands to create necessary tables and indexes.
   • Utilize Supabase features for enhanced querying capabilities.

Building Full Stack Application

• Form Creation: Use Next.js with Tailwind CSS for UI.
• Backend API Setup
  • Utilize Zod for validating incoming data.
  • OpenAI for embeddings and database interaction.

Handling Data and Requests

• Email Processing: Store raw emails, split into sections, embed, and save.
• API Routes: Use Next.js API routes for handling data submissions.
• Supabase Client: Connect and manage database operations programmatically.

Querying with RAG

1. Function Creation for Queries

   • Postgres function to combine SQL and vector queries.
   • Retrieve relevant email sections based on query embeddings.

2. Front-end Interaction

   • Setup UI for querying data using a question-answer format.
   • Send and receive data through API endpoints to/from the vector store.

Conclusion

• Full-cycle understanding from email data storage to interactive querying.
• Supabase and Next.js provide a robust platform for building AI-driven applications.
• Additional resources and support available through linked communities.