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

Introduction

In recent months, the fields of text-to-image models and Vector Database applications have witnessed remarkable growth. Both of these technologies are incredibly powerful in their own right, but their integration can yield even more transformative results. This tutorial aims to guide you through the process of building a straightforward application that streamlines the discovery of similar prompts and images utilizing text-to-image models. Join lablab.ai's community to learn more about leveraging Redis during our upcoming Hackathon focused on artificial intelligence!

What is RediSearch?

RediSearch is a module for Redis databases that empowers users to perform efficient querying and indexing of data. Its versatility allows for various applications, and in this tutorial, we'll utilize it to index data and conduct similarity searches for prompts and images using vector similarity.

Understanding CLIP

CLIP (Contrastive Language-Image Pre-Training) is an advanced neural network that learns visual concepts through natural language supervision. Trained on a multitude of image-text pairs, it can predict the most relevant image based on a given text description or vice versa. For our project, we will harness CLIP to discover similar prompts and images based on user-inputted descriptions or uploaded images.

Starting the Project

Setting Up the Environment

We'll structure the application into two primary components:

API
Streamlit Application (UI)

First, let's get started with the Redis Database. You have the option to use

Redis Cloud or, for local development, you can run a Docker image. You can even start using Redis for free!

Data Acquisition

For our application, we will leverage the well-known Flickr8k dataset, which can easily be downloaded from platforms like Kaggle.

Installing Dependencies

Before we dive into coding, it's crucial to set up a proper file structure. Begin by creating a main project directory, then initiate a virtual environment and install the necessary dependencies. You can create a requirements.txt file containing all required libraries.

Coding the Application

Model Preparation

We'll start modeling our image processing and captioning functionalities in a new file located at src/model/clip.py. Import all the necessary libraries at the top, then define a class for our model. This class will encapsulate methods that simplify our interactions with CLIP, utilizing LAION AI’s implementation available on Hugging Face.

Utility Functions

Next, we will develop utility functions to facilitate indexing our data in the Redis database. Define a constant value EMBEDDING_DIM to establish the size of the vector used for indexing (this size corresponds to the output from the CLIP model).

We will need a function to embed our descriptions and another to index the data in Redis.

Building the API

Now let's focus on creating the API, which will be implemented in the src/main.py file. We will establish two endpoints: one for image-based searches and another for description-based searches. Start by importing the required dependencies.

Next, initialize both the model and Redis client, and index your data as needed. Finally, you'll need a function to query images.

The API will feature two vital endpoints:

One for processing input images
One for processing text descriptions

UI Implementation

The last segment of our application involves the UI, built using Streamlit. The interface will comprise text input, file input for images, and a submission button.

Now that we're prepared, let's run our fully functional application!

Conclusion

Finally, let's observe how our application operates by entering a description or uploading an image. The results generated are quite impressive!

If you made it this far, congratulations! You've acquired valuable knowledge. Feel free to explore additional technologies. Perhaps you'll be inspired to create a GPT-3 application or enhance your existing project? The possibilities with AI are limitless!

Project Repository

For complete source code and additional resources, visit the project repository.