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GenAI RAG Chat Solution with .NET, Semantic Kernel Function, Python, and React JS

Overview

This repository provides a comprehensive guide to deploying a Generative AI (GenAI) solution on Azure utilizing a Retrieval-Augmented Generation (RAG) architecture. The solution features a RAG Chat API with a web front-end that seamlessly integrates CosmosDB data and technical HTML files, enabling customer support teams to resolve issues more efficiently.

Built on a .NET-based API backend, the solution leverages Azure AI Search for vector storage and Azure OpenAI for model inference. It is further enhanced with Semantic Kernel functions, Python scripts, and a React JS front-end, ensuring a robust and scalable framework for delivering accurate and context-aware customer support.

For a detailed understanding of the project, please refer to the article: Enhance Customer Support with Semantic Kernel and Azure OpenAI

Key Features

• RAG Architecture: Implements a Retrieval-Augmented Generation workflow, combining generative AI with vectorized retrieval for contextually enriched responses.
• Azure AI Search Vector Store: Leverages Azure AI Search for efficient embedding storage, enabling fast retrieval of relevant data to enhance responses.
• Azure Cosmos DB Document Store: Utilizes Azure Cosmos DB to store documents and provide additional context for the GenAI chat application.
• Azure OpenAI Integration: Incorporates OpenAI models for generating high-quality responses and insights.
• LangChain Integration: Utilizes LangChain with Azure OpenAI to streamline and optimize AI-driven interactions.
• Prompty Integration: Enhances prompt engineering for dynamic and adaptive AI behavior, improving user query handling.
• Semantic Kernel Integration: Employs Semantic Kernel to orchestrate workflows, ensuring efficient, context-aware AI responses.
• .NET Backend: Handles API orchestration and server-side processing, seamlessly integrating Azure services.
• Event-Driven Document Processing: Uses Azure Functions to create vector embeddings in Azure Cosmos DB for MongoDB VCore, triggered by document uploads.
• Infrastructure as Code (IaC): Deploys resources using Bicep and Azure CLI to ensure consistent, scalable, and secure infrastructure management.

Architecture

Components Overview

• Backend:

  • Built with .NET, using a controller-based API for efficient AI logic handling and data processing.
  • Azure Functions manage event-driven tasks for scalable, responsive execution.

• Database:

  • Azure Cosmos DB serves as the primary database, supporting document storage and context enrichment.
  • Azure AI Search provides vector search capabilities, ensuring fast and relevant data retrieval.

• AI Models:

  • Powered by Azure OpenAI Service, featuring advanced generative text and embedding models.
  • Enhances user interactions with high-quality, context-aware responses based on input queries.

Requirements

• Azure subscription for deploying Azure GenAI RAG Application.
• Azure CLI (Command Line Interface)
• .NET 8.0 is installed and properly configured in the development environment.
• Python 3.11.4 installed on development environment.
• Node.js 20.11.1 installed in your development environment.
  • Please follow the steps outlined here to download and install Node.JS.
• An IDE for Development, such as VS Code

Usage

Follow these steps to set up and deploy the solution:

1. Clone the Repository from GitHub:

Begin by cloning the repository to your local machine using the following command:

git clone https://github.com/jonathanscholtes/Azure-AI-RAG-CSharp-Semantic-Kernel-Functions.git
cd Azure-AI-RAG-CSharp-Semantic-Kernel-Functions

2. Deploy the Solution Using Bicep:

Navigate to the deployment directory:

cd infra

Then, use the following PowerShell command to deploy the solution. Make sure to replace the placeholders with your actual subscription name, Azure Region (location)

PowerShell

.\deploy.ps1 -Subscription '[Subscription Name]' -Location 'southcentralus'

This script will provision the necessary resources in your Azure subscription according to the specified parameters. The deployment may take upto 20 minutes to provision all Azure resources.

3. Upload Documents for Azure AI Search Indexing:

Upload the HTML files from the documents directory to the load container of the Azure Storage Account.

4. Validate Embedding Load in Azure AI Search

The Loader Azure Function will trigger and upload the index as the documents are added to the Azure Storage Account. We can view the index and number of documents in the index from our Azure AI Search Service.

Grant Access to Query Index From Portal

If you received an error attempting to query the index from the portal you will need to set the API Access control to both.

Validate Index Creation

You can quickly verify that you have the index azure-support with the indexed documents by viewing indexes.

5. Setting Up the Environment for the API

The Azure Cosmos DB connection string is not incorporated into the API environment variables during the deployment process, so it will need to be added manually. Through the Azure portal, we can modify the Environment variables of our newly created API.

Azure Cosmos DB Connection String

Access the connection string by selecting Keys under Settings. Copy the string value.

Adding Environment Variables

In-order for our Semantic Kernel function to be able to access product information we will need to add the Azure Cosmos DB connection string to the Environment variables.

• Select Environment variables under Settings
• Ensure the App settings tab is selected
• Add the connection string value to CosmosDb_ConnectionString
• Click Apply

6. Validate GenAI Application

Access the React Web Application using the default domain (api-chatapi-demo-[random created].azurewebsites.net) on the Azure Web App (api-chatapi-demo-[random created]) by navigating to its assigned domain. Once the application loads, test its functionality by submitting the default question, "How do I fix Access Denied with Azure Blob Storage?".

Clean-Up

After completing testing, ensure to delete any unused Azure resources or remove the entire Resource Group to avoid incurring additional charges.

Resource Purge for Redeployment

If you plan to delete and redeploy this solution, you may need to manually purge Azure OpenAI.

Azure OpenAI Purge

To permanently delete an Azure OpenAI or other Azure AI service, follow these steps:

1. Navigate to the Azure AI Service Hub:
   Go to the hub of the Azure AI service associated with the deleted resource. For instance, if you need to purge an "Anomaly Detector" resource, search for "Anomaly Detector" in the Azure portal search bar, select the service, and then choose Manage Deleted Resources from the menu.

2. Select the Subscription and Deleted Resource:
   In the Manage Deleted Resources section, choose the relevant subscription from the dropdown list, then locate and select the deleted resource(s) you wish to purge.

3. Purge the Resource:
   Click Purge to permanently delete the selected resource(s). Be aware that purging is irreversible and removes all associated configurations and data for the resource.

License

This project is licensed under the MIT License, granting permission for commercial and non-commercial use with proper attribution.

Support

For any questions or issues, please open an issue on GitHub or reach out to the project maintainers.

Disclaimer

This demo application is intended solely for educational and demonstration purposes. It is provided "as-is" without any warranties, and users assume all responsibility for its use.