azure search openai demo
2024-11-19b: Turkish translations
This solution creates a ChatGPT-like frontend experience over your own documents using RAG (Retrieval Augmented Generation). It uses Azure OpenAI Service to access GPT models, and Azure AI Search for data indexing and retrieval.
This solution's backend is written in Python. There are also JavaScript, .NET, and Java samples based on this one. Learn more about developing AI apps using Azure AI Services.
Watch a video overview of the app.
This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access a GPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.
The repo includes sample data so it's ready to try end to end. In this sample application we use a fictitious company called Contoso Electronics, and the experience allows its employees to ask questions about the benefits, internal policies, as well as job descriptions and roles.
IMPORTANT: In order to deploy and run this example, you'll need:
Microsoft.Authorization/roleAssignments/write permissions, such as Role Based Access Control Administrator, User Access Administrator, or Owner. If you don't have subscription-level permissions, you must be granted RBAC for an existing resource group and deploy to that existing group.Microsoft.Resources/deployments/write permissions on the subscription level.Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. However, you can try the Azure pricing calculator for the resources below.
To reduce costs, you can switch to free SKUs for various services, but those SKUs have limitations. See this guide on deploying with minimal costs for more details.
azd down.
You have a few options for setting up this project. The easiest way to get started is GitHub Codespaces, since it will setup all the tools for you, but you can also set it up locally if desired.
You can run this repo virtually by using GitHub Codespaces, which will open a web-based VS Code in your browser:
Once the codespace opens (this may take several minutes), open a terminal window.
A related option is VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:
Start Docker Desktop (install it if not already installed)
Open the project:
In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
Install the required tools:
python --version from console. On Ubuntu, you might need to run sudo apt install python-is-python3 to link python to python3.pwsh.exe from a PowerShell terminal. If this fails, you likely need to upgrade PowerShell.Create a new folder and switch to it in the terminal.
Run this command to download the project code:
azd init -t azure-search-openai-demoNote that this command will initialize a git repository, so you do not need to clone this repository.
The steps below will provision Azure resources and deploy the application code to Azure Container Apps. To deploy to Azure App Service instead, follow the app service deployment guide.
Login to your Azure account:
azd auth loginFor GitHub Codespaces users, if the previous command fails, try:
azd auth login --use-device-codeCreate a new azd environment:
azd env newEnter a name that will be used for the resource group.
This will create a new folder in the .azure folder, and set it as the active environment for any calls to azd going forward.
(Optional) This is the point where you can customize the deployment by setting environment variables, in order to use existing resources, enable optional features (such as auth or vision), or deploy to free tiers.
Run azd up - This will provision Azure resources and deploy this sample to those resources, including building the search index based on the files found in the ./data folder.
azd down or delete the resources manually to avoid unnecessary spending.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. It will look like the following:
NOTE: It may take 5-10 minutes after you see 'SUCCESS' for the application to be fully deployed. If you see a "Python Developer" welcome screen or an error page, then wait a bit and refresh the page.
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 deployIf 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 upTo give someone else access to a completely deployed and existing environment, either you or they can follow these steps:
azd init -t azure-search-openai-demo or clone this repository.azd env refresh -e {environment name}
They will need the azd environment name, subscription ID, and location to run this command. You can find those values in your .azure/{env name}/.env file. This will populate their azd environment's .env file with all the settings needed to run the app locally.AZURE_PRINCIPAL_ID either in that .env file or in the active shell to their Azure ID, which they can get with az ad signed-in-user show../scripts/roles.ps1 or .scripts/roles.sh to assign all of the necessary roles to the user. If they do not have the necessary permission to create roles in the subscription, then you may need to run this script for them. Once the script runs, they should be able to run the app locally.You can only run locally after having successfully run the azd up command. If you haven't yet, follow the deploying steps above.
azd auth login
app
./start.ps1 or ./start.sh or run the "VS Code Task: Start App" to start the project locally.See more tips in the local development guide.
Once in the web app:
To clean up all the resources created by this sample:
azd down
y
y
The resource group and all the resources will be deleted.
Besides the tips below, you can find extensive documentation in the docs folder.
Once you successfully deploy the app, you can start customizing it for your needs: changing the text, tweaking the prompts, and replacing the data. Consult the app customization guide as well as the data ingestion guide for more details.
By default, deployed apps use Application Insights for the tracing of each request, along with the logging of errors.
To see the performance data, go to the Application Insights resource in your resource group, click on the "Investigate -> Performance" blade and navigate to any HTTP request to see the timing data. To inspect the performance of chat requests, use the "Drill into Samples" button to see end-to-end traces of all the API calls made for any chat request:
To see any exceptions and server errors, navigate to the "Investigate -> Failures" blade and use the filtering tools to locate a specific exception. You can see Python stack traces on the right-hand side.
You can also see chart summaries on a dashboard by running the following command:
azd monitorThis sample is designed to be a starting point for your own production application, but you should do a thorough review of the security and performance before deploying to production. Read through our productionizing guide for more details.
Here are the most common failure scenarios and solutions:
The subscription (AZURE_SUBSCRIPTION_ID) doesn't have access to the Azure OpenAI service. Please ensure AZURE_SUBSCRIPTION_ID matches the ID specified in the OpenAI access request process.
You're attempting to create resources in regions not enabled for Azure OpenAI (e.g. East US 2 instead of East US), or where the model you're trying to use isn't enabled. See this matrix of model availability.
You've exceeded a quota, most often number of resources per region. See this article on quotas and limits.
You're getting "same resource name not allowed" conflicts. That's likely because you've run the sample multiple times and deleted the resources you've been creating each time, but are forgetting to purge them. Azure keeps resources for 48 hours unless you purge from soft delete. See this article on purging resources.
You see CERTIFICATE_VERIFY_FAILED when the prepdocs.py script runs. That's typically due to incorrect SSL certificates setup on your machine. Try the suggestions in this StackOverflow answer.
After running azd up and visiting the website, you see a '404 Not Found' in the browser. Wait 10 minutes and try again, as it might be still starting up. Then try running azd deploy and wait again. If you still encounter errors with the deployed app and are deploying to App Service, consult the guide on debugging App Service deployments. Please file an issue if the logs don't help you resolve the error.
This is a sample built to demonstrate the capabilities of modern Generative AI apps and how they can be built in Azure. For help with deploying this sample, please post in GitHub Issues. If you're a Microsoft employee, you can also post in our Teams channel.
This repository is supported by the maintainers, not by Microsoft Support, so please use the support mechanisms described above, and we will do our best to help you out.
Note: The PDF documents used in this demo contain information generated using a language model (Azure OpenAI Service). The information contained in these documents is only for demonstration purposes and does not reflect the opinions or beliefs of Microsoft. Microsoft makes no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability or availability with respect to the information contained in this document. All rights reserved to Microsoft.
