Redis Tutorial: Building a Text to Image AI Assistant with Redis Search

Introduction

In recent months, both the text-to-image and Vector Database model markets have grown significantly. These two technologies are powerful on their own, but when combined, they can yield even greater results! In this tutorial, I will guide you through building a simple application that aids in finding similar prompts and images for text-to-image models. Join the lablab.ai community to learn more about leveraging Redis during our upcoming Hackathon in artificial intelligence!

What is RediSearch?

RediSearch is a powerful Redis Database module that enables querying and indexing data. In this tutorial, we will utilize RediSearch to index data and enable vector similarity searches for finding similar prompts and images.

Understanding CLIP

CLIP, or Contrastive Language-Image Pre-training, is a neural network trained using natural language supervision. It learns visual concepts from pairs of images and text. CLIP is capable of predicting the most relevant image for a given text description or vice versa, making it essential for our application in finding similar prompts and images.

Application Structure

Our application will consist of two primary components:

1. API
2. Streamlit Application (UI)

Setting Up the Redis Database

First, we need to set up the Redis Database. You can use Redis Cloud or simply a Docker image for local development. Starting with Redis is free, and it offers ample opportunities for learning.

Data Sources

For this project, we will utilize the popular Flickr8k dataset. This dataset is widely available for download from platforms like Kaggle.

Installing Dependencies

To kickstart our project, we need to establish a proper file structure. Here's how you can do it:

mkdir text-to-image-app
cd text-to-image-app
python3 -m venv venv
source venv/bin/activate

Create a 'requirements.txt' file to list all the dependencies needed for our app. This will include libraries such as streamlit, redis, and more.

pip install -r requirements.txt

Model Preparation

We will start by preparing our model for processing images and captions. This will be implemented in the src/model/clip.py file. We will import necessary dependencies and create a class for our model, incorporating methods for simpler functionality.

Utility Functions

Next, we will develop utility functions for indexing our data in Redis. In this phase, we will import the required dependencies and define a constant value, EMBEDDING_DIM, to specify the size of the vectors used for indexing data.

API Development

We will implement the API in the src/main.py file to create two endpoints: one for image-based search and one for description-based search. The API will facilitate querying similar images or prompts based on user inputs.

Building the User Interface

The UI will be created using Streamlit, featuring text input, file input for images, and a submission button. The objective is to make the interface intuitive and user-friendly.

Testing the Application

Once the app is ready, we can run it and test its functionalities by entering descriptions or uploading images to see how well it performs in fetching similar results.

Conclusion

Our finished application demonstrates impressive results in finding relevant prompts and images, showcasing the integration of AI technologies like CLIP and Redis. If you've followed along, congratulations on building your own project! I encourage you to explore further technologies, perhaps a GPT-3 application or building on tools like Cohere — the possibilities with AI are limitless!

Project Repository

For the complete code and more details, visit our project repository here.