Supercharge Your OpenAI Apps on Azure with Custom Data

Table of Contents

1. Introduction

2. Building Intelligent Applications with Azure AI

2.1 Overview of Azure Dev Tools

2.2 Building Intelligent Applications on Azure

2.3 Introducing the Retrieval Augmented Generation Pattern

2.4 Use Cases of the RAG Pattern

2.5 Building an Application with the RAG Pattern

3. Getting Started with Azure Open AI

3.1 What is Azure Open AI?

3.2 SDK Libraries and Reference Applications

3.3 Playwright and Python Documentation

4. Building an Application with Playwright

4.1 Understanding Playwright Framework

4.2 Building a Chat Experience

4.3 Retrieving Information from Playwright Docs

4.4 Generating Python Code Snippets

5. Application Architecture

5.1 Overview of the Application Architecture

5.2 Front-end Components in TypeScript

5.3 Backend Components in Python

6. Deploying the Application to Azure

6.1 Introduction to Azure Developer CLI

6.2 Configuring Azure Developer CLI

6.3 Deploying the Application to Azure App Service

6.4 Managing Azure Resources with Azure Tools for VS Code

7. Configuring Continuous Integration and Pipelines

7.1 Overview of Continuous Integration and Pipelines

7.2 Setting up Azure DevOps Pipeline

7.3 Testing and Monitoring with Azure Developer CLI

7.4 Monitoring Application Performance and Reliability

Building Intelligent Applications with Azure AI

In today's digital world, developers are constantly seeking ways to build intelligent applications that leverage the capabilities of Cloud services like Azure. With the advent of Azure Open AI, developers now have access to powerful tools and libraries that facilitate the development of intelligent applications. In this article, we will explore the process of building such applications using Azure Dev Tools and the retrieval augmented generation (RAG) pattern.

Introduction

Azure Dev Tools is a team dedicated to making developers' lives easier. Their goal is to provide tools and libraries that assist developers in working with Azure and building successful applications, regardless of the programming language used. One area of focus for the Azure Dev Tools team is building intelligent applications that utilize the capabilities of Azure Open AI. These applications can be used in various industries, including retail and healthcare, to help teams and individuals access information quickly and effectively.

Building Intelligent Applications on Azure

Azure Open AI provides developers with a range of SDK libraries, reference applications, and tools to build intelligent applications that leverage the capabilities of Azure. The SDK libraries are available in various programming languages, including Python, JavaScript, Java, and .NET, making it easier for developers to incorporate Azure services into their applications. These libraries include authentication, security, and retries, allowing developers to integrate Azure services seamlessly.

One popular pattern for building intelligent applications is the retrieval augmented generation (RAG) pattern. This pattern is used to add interactive experiences to applications across different use cases and industries. The RAG pattern involves using a combination of retrieval and generation models to provide contextually Relevant responses to user queries. This pattern has proven to be effective in scenarios where users need to access information quickly and effectively.

Use Cases of the RAG Pattern

The RAG pattern is applicable in various industries and use cases. For example, in retail, the RAG pattern can be used to build chatbots that help customers navigate through product catalogs and provide personalized recommendations. In the healthcare sector, the RAG pattern can be utilized to build intelligent applications that assist healthcare professionals in accessing relevant medical information and providing accurate diagnoses. The RAG pattern can also be employed in enterprises to facilitate effective information retrieval for employees, boosting productivity and efficiency.

Building an Application with the RAG Pattern

In this article, we will walk through the process of building an application that utilizes the RAG pattern. Specifically, we will focus on building an application that helps developers ramp up on a new technology called Playwright. Playwright is an open-source framework for reliable end-to-end testing of modern web applications. The application we build will provide developers with a chat experience where they can ask questions about the Playwright documentation and get relevant information and code snippets in response.

To build this application, we will use the Playwright Dev Python docs as the data source for our chat experience. We will follow the RAG pattern, combining retrieval and generation models to provide contextually relevant responses to user queries. The application will leverage the capabilities of Azure Open AI, including SDK libraries and reference applications, to facilitate the implementation of the RAG pattern.

Throughout the article, we will provide step-by-step instructions on how to develop the application, deploy it to Azure, and manage and test it. We will also highlight the resources available on GitHub that can assist developers in building and customizing their own intelligent applications. Let's get started and explore the architecture of the application!

Application Architecture

The architecture of the application plays a crucial role in its functionality and performance. In this section, we will examine the components of the application architecture and how they work together.

The front-end of the application is built using TypeScript and utilizes React components. When a user submits a question, the front-end combines the question with their previous messages and any additional settings. The front-end then sends this combined information as a post request to the back-end. The front-end also provides various settings for the user to customize their chat experience, such as suggesting follow-up questions and adjusting the search method (hybrid search or vector search).

The back-end of the application is built in Python using asynchronous frameworks like Flask or FastAPI. It receives the post request from the front-end and determines the appropriate RAG approach. In this case, we will use the Chat, Read, Retrieve approach, which combines the Azure Search SDK and the Azure Open AI SDK. The back-end processes the user's questions and searches the Azure Cognitive Search index for relevant documents. It then retrieves the appropriate chunks of text and vectors from the search results and passes them to the Azure Open AI model for generating a response. The response, along with any citations or replies, is sent back to the front-end for display to the user.

The application architecture follows best practices for cloud deployment and utilizes Azure services like Azure App Service, Azure Cognitive Search, and Azure Blob Storage. The resources are managed using the Azure Developer CLI, which simplifies the process of configuring and deploying the application to Azure. The CLI uses a declarative paradigm, allowing developers to focus on writing code rather than dealing with low-level operations on cloud resources. The Azure Developer CLI also provides integration with GitHub Actions, enabling developers to set up continuous integration and deployment pipelines with ease.

In the next section of this article, we will Delve into the process of deploying the application to Azure using the Azure Developer CLI. We will explore how to configure the CLI, provision the necessary infrastructure, and deploy the application code. Stay tuned for more exciting steps in building your intelligent application on Azure!