Embedding Soup

This project demonstrates how to use Convex's vector search capabilities in a simple web application. It provides a practical example of semantic search using vector embeddings.

Features

• Add words/phrases to a collection that will be vectorized
• View all added words/phrases in a list
• Remove words/phrases from the collection
• Perform semantic searches using vector similarity
• Real-time updates of search results

How it Works

1. Users can input words or phrases into a text area
2. Each entry is stored in Convex and converted into a vector embedding
3. Users can view and manage their collection of entries
4. A separate search input allows users to find semantically similar entries
5. Results are displayed in real-time, ordered by similarity

Technical Stack

• Frontend: React + TypeScript
• Backend: Convex
• Vector Embeddings: Convex built-in embeddings

Getting Started

1. Clone this repository
2. Install dependencies with bun install
3. Start the development server with bun run dev
4. Open your browser to the displayed URL

Project Structure

• /convex - Backend Convex functions and schema
• /src - Frontend React application
• /components - React components