Build a Practical AI App with Python, Redis, and OpenAI

Build a Practical AI App with Python, Redis, and OpenAI

Table of Contents:

1. Introduction
2. The Reality of AI
3. Building an AI-Based App
4. The Concept of Embeddings
5. Practical Steps for Building an AI-based App
6. Using Redis as a Vector Database
7. Integrating Azure Open AI Service
8. Creating a User Interface with Streamlit
9. Deploying the Application to Azure
10. Other Use Cases for AI Applications
11. Next Steps and Additional Resources

Introduction

In this article, we will explore the practical side of AI and how it can be applied to real-world applications. While AI has gained a lot of Attention and hype, it's important to understand how it can benefit us in the present moment. We will focus on building an AI-based movie recommendation app using Python, Azure Cache for Redis, and Azure Open AI Service. By the end of this article, You will have a clear understanding of the tools and steps required to Create your own AI-based application.

The Reality of AI

AI has become a buzzword in recent years, with many people touting its potential to change the world. However, amidst the hype, it's important to separate reality from fiction. While AI has incredible potential, it's unlikely to cure cancer or solve all of humanity's problems in the next five years. In this section, we will Delve into what AI truly means for businesses and applications right now.

Building an AI-based App

To truly understand AI and its practical applications, we need to dive into the process of building an AI-based app. We will explore the toolkit and stack required for building such an app, focusing on Python libraries and practical steps for deployment on Azure. Our goal is to create a movie recommendation app that takes user input, such as keywords or a description, and provides Relevant movie recommendations along with a matching score. This app will utilize vector similarity search, a powerful use case for AI.

The Concept of Embeddings

To understand the inner workings of our movie recommendation app, we need to grasp the concept of embeddings. Embeddings are an output of machine learning models that transform input data, such as text or media, into a vector representation. These vectors capture various Dimensions and magnitudes, allowing us to compare different pieces of text or media without a defined schema. We will explore the theory behind embeddings and their practical application in building our movie recommendation app.

Practical Steps for Building an AI-based App

With a solid understanding of embeddings, we can now delve into the practical steps required to build our movie recommendation app. We will start by acquiring a suitable dataset from Kaggle, ingesting and cleaning the data using Python's Pandas framework. Next, we will set up the necessary tools and services, including Azure Open AI Service for generating embeddings, Azure Cache for Redis as our vector database, and Streamlit for creating the user interface. We will guide you through each step, providing code examples and explanations along the way.

Using Redis as a Vector Database

Redis is a popular open-source in-memory data store and cache. Azure Cache for Redis, a managed version of Redis on Azure, offers advanced features such as vector search. In this section, we will explore the advantages of using Redis as a vector database and its integration with Lanechain. We will also discuss different indexing methods and distance metrics for comparing vectors.

Integrating Azure Open AI Service

Azure Open AI Service provides powerful text-based AI models that can generate embeddings. We will explore how to set up and integrate Azure Open AI Service into our movie recommendation app. Using Python and Langchain, we will connect to the service, pass in our data, and generate embeddings. This step is crucial in storing and retrieving vectors for our movie search application.

Creating a User Interface with Streamlit

To make our movie recommendation app more user-friendly, we will utilize Streamlit, a Python framework for creating rich web applications. Streamlit allows us to build interactive user interfaces without extensive knowledge of front-end technologies. We will walk through the process of creating a Streamlit app that takes user input, queries the Redis vector database, and displays relevant movie recommendations. Through code examples and explanations, you will learn how to create a seamless user experience.

Deploying the Application to Azure

With our movie recommendation app now fully functional, We Are ready to deploy it to the Azure cloud. We will guide you through the steps of containerizing the app and deploying it to Azure Container Apps. Azure Container Apps simplifies the deployment process and allows for easy scaling and management. By the end of this section, you will have a live application accessible on the internet.

Other Use Cases for AI Applications

While our focus has been on movie recommendations, the concepts and tools covered in this article can be applied to various other AI applications. We will briefly discuss some additional use cases, including visual search, semantic search, custom data sources, and recommendation systems. This will provide you with a broader understanding of how AI can be leveraged in different domains.

Next Steps and Additional Resources

To Continue exploring the world of AI and gain hands-on experience, we have provided additional resources and next steps. A repository containing the code and Jupyter notebook for our movie recommendation app is available, along with other example applications and a detailed blog post. We encourage you to dive deeper into the world of vectors, embeddings, and using Redis as a vector data store. Additionally, we invite you to join the Azure Cache for Redis community standup for more insights and discussions on Redis integration.

Highlights:

1. Understand the practical reality of AI and its Current applications.
2. Build an AI-based movie recommendation app using Python, Azure Cache for Redis, and Azure Open AI Service.
3. Explore the concept of embeddings and their role in comparing text and media.
4. Learn the practical steps involved in building an AI-based app, from data ingestion to deployment on Azure.
5. Utilize Redis as a vector database and understand its advantages for AI applications.
6. Integrate Azure Open AI Service to generate embeddings for our movie recommendation app.
7. Create a user-friendly interface using Streamlit framework.
8. Deploy the application to Azure Container Apps for easy scaling and management.
9. Discover other use cases for AI applications, such as visual search and recommendation systems.
10. Access additional resources and explore next steps in AI development.