Quest 3 - Implementing Text-to-Image Vector Search

In this quest, you'll take your vector search skills to the next level by integrating a user-friendly interface using Gradio, a Python library for building machine learning and data science demos. This quest will guide you through building an interactive text-to-image search application, where users can enter a natural language description and retrieve the most similar image from a collection. You’ll continue using the CLIP model for generating embeddings and ChromaDB for storing and searching those embeddings, but this time with an easy-to-use interface that allows real-time interaction.

By the end of this quest, you'll be able to deploy a full-fledged application that demonstrates the power of vector search with a modern UI. This quest will give you practical experience in combining powerful machine learning models with intuitive interfaces, enabling you to build solutions that are not only functional but also accessible to users.

For technical help on the StackUp platform & quest-related questions, join our Discord, head to the quest-helpdesk channel and look for the correct thread to ask your question.