/azure-search-comparison-tool

A demo app showcasing Vector Search using Azure Cognitive Search, Azure OpenAI for text embeddings, and Azure AI Vision for image embeddings.

Primary LanguagePythonMIT LicenseMIT

Azure Search Comparison Tool

This repository contains a React application that demonstrates the Azure Search Comparison Tool. This tool provides a web interface for visualizing different retrieval modes available in Azure Cognitive Search. Additionally, the tool supports image search using text-to-image and image-to-image search functionalities. It leverages OpenAI for text embeddings and Azure AI Vision API for image embeddings.

You can find a live demo at aka.ms/VectorSearchDemo

Vector Search Video

Features

  • Generate text embeddings using Azure OpenAI and insert them into a vector store in Azure Cognitive Search.
  • Perform vector search queries on text data, including vector searches with metadata filtering and hybrid (text + vectors) search.
  • Generate image embeddings using Azure AI Vision API.
  • Perform text-to-image and image-to-image vector searches.

Prerequisites

  • An Azure subscription with access to Azure Cognitive Search and Azure AI Services.
  • Access to Azure OpenAI for generating text embeddings.
  • Access to Azure AI Vision for generating image embeddings.

To run this demo locally, you will need the following:

  • Azure Developer CLI
  • Python 3.9+
    • Important: Python and the pip package manager must be in the path in Windows for the setup scripts to work.
    • Important: Ensure you can run python --version from console. On Ubuntu, you might need to run sudo apt install python-is-python3 to link python to python3.
  • Node.js 14+
  • Git
  • Powershell 7+ (pwsh) - For Windows users only.
    • Important: Ensure you can run pwsh.exe from a PowerShell command. If this fails, you likely need to upgrade PowerShell.

Setup

Starting from scratch

Execute the following commands, if you don't have any pre-existing Azure services and want to start from a fresh deployment.

  1. Clone this repository to your local machine.

  2. Run azd auth login

  3. Run azd up - This will provision Azure resources and deploy this demo to those resources

  4. After the application has been successfully deployed you will see a URL printed to the console. Click that URL to interact with the application in your browser.

    NOTE: It may take a minute for the application to be fully deployed. If you see a "Python Developer" welcome screen, then wait a minute and refresh the page.

Using existing resources

  1. Run azd env set AZURE_OPENAI_SERVICE {Name of existing Azure OpenAI service}
  2. Run azd env set AZURE_OPENAI_DEPLOYMENT_NAME {Name of existing embedding model deployment}. Only needed if your deployment is not the default 'embeddings'. Typically this'll be a text-embedding-ada-002 model.
  3. Run azd env set AZURE_SEARCH_SERVICE_ENDPOINT {Endpoint of existing Azure Cognitive Search service}
  4. Run azd env set AZURE_SEARCH_TEXT_INDEX_NAME {Name of existing Azure Cognitive Search index with text}
  5. Run azd env set AZURE_SEARCH_IMAGE_INDEX_NAME {Name of existing Azure Cognitive Search index with images}
  6. Run azd env set AZURE_VISIONAI_ENDPOINT {Endpoint of existing Azure AI Vision}
  7. Run azd env set AZURE_VISIONAI_KEY {Key of existing Azure AI Vision}
  8. Run azd up

Deploying again

If you've only changed the backend/frontend code in the app folder, then you don't need to re-provision the Azure resources. You can just run:

azd deploy

If you've changed the infrastructure files (infra folder or azure.yaml), then you'll need to re-provision the Azure resources. You can do that by running:

azd up

Running locally

  1. Run azd auth login
  2. Change dir to app
  3. Run ./start.ps1 or ./start.sh or run the "VS Code Task: Start App" to start the project locally.

Running locally with hot reloading

This is recommended only for development, and provides hot reloading of both backend and frontend:

In one terminal run the backend:

  1. Run azd auth login
  2. Change dir to app
  3. Run ./start.ps1 or ./start.sh

In a second terminal run the frontend:

  1. Change dir to app/frontend
  2. Run npm start
  3. Use website from https://localhost:5173/

Usage

  • In Azure: navigate to the Azure WebApp deployed by azd. The URL is printed out when azd completes (as "Endpoint"), or you can find it in the Azure portal.
  • Running locally: navigate to 127.0.0.1:50505

  1. The Azure Search Comparison Tool allows you to search for text queries by entering them in the search bar and pressing 'Enter'. The application will generate text embeddings using Azure OpenAI and perform vector searches on the data stored in Azure Cognitive Search.

  2. The search results will be displayed as cards. Feel free to click on the settings icon to explore the different query approaches such as Hybrid Search and Hybrid Search with Semantic Ranking, Captions, and Highlights powered by Microsoft Bing. Note that you will need to enroll in a Semantic Plan in your Azure Cognitive Search service to use this feature. See Semantic search.

Conclusion

We hope you find this repository useful for demoing Vector search and exploring the different features Azure Cognitive Search has to offer. Feel free to explore and customize the code to meet your specific requirements.
If you have any questions or suggestions, please feel free to open an issue and we'll be happy to help.

Happy searching!

References