Build A Text-Image Search Engine in 10 Minutes| CLIP + Chroma DB Tutorial
Science & Technology
Introduction
Hello everyone, my name is Naita and welcome back to another video. In this video, I will show you how to build an image search engine from scratch with just a few lines of code. By the end of this video, you'll be able to search any database of images based on the content inside them. Here's a quick demo: you can search for something like "ambulance parked in a garage," and it will find the closest matches in your image database, even if the images are not labeled.
The three main tools we will use are:
- CLIP Model by OpenAI: This model understands both images and texts. It uses both an image encoder and a text encoder to understand visual content and natural language together. The model has been trained on 400 million images.
- Chroma Database: This is an open-source vector database by Google. It is designed to store feature vectors from images for quick access.
- Streamlit: We will use this tool to build the user interface for our application.
Step-by-Step Guide
Installing Libraries
First, import all the necessary libraries and install them using pip. Make sure to install the specific versions to avoid dependency issues.