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Productionizing the Chat App

This 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. Here are some things to consider:

Azure resource configuration

  • OpenAI Capacity: The default TPM (tokens per minute) is set to 30K. That is equivalent to approximately 30 conversations per minute (assuming 1K per user message/response). You can increase the capacity by changing the chatGptDeploymentCapacity and embeddingDeploymentCapacity parameters in infra/main.bicep to your account's maximum capacity. You can also view the Quotas tab in Azure OpenAI studio to understand how much capacity you have.
  • Azure Storage: The default storage account uses the Standard_LRS SKU. To improve your resiliency, we recommend using Standard_ZRS for production deployments, which you can specify using the sku property under the storage module in infra/main.bicep.
  • Azure Cognitive Search: The default search service uses the Standard SKU with the free semantic search option, which gives you 1000 free queries a month. Assuming your app will experience more than 1000 questions, you should either change semanticSearch to "standard" or disable semantic search entirely in the /app/backend/approaches files. If you see errors about search service capacity being exceeded, you may find it helpful to increase the number of replicas by changing replicaCount in infra/core/search/search-services.bicep or manually scaling it from the Azure Portal.
  • Azure App Service: The default app service plan uses the Basic SKU with 1 CPU core and 1.75 GB RAM. We recommend using a Premium level SKU, starting with 1 CPU core. You can use auto-scaling rules or scheduled scaling rules, and scale up the maximum/minimum based on load.

Additional security measures

  • Authentication: By default, the deployed app is publicly accessible. We recommend restricting access to authenticated users. See Enabling authentication to learn how to enable authentication.
  • Networking: We recommend deploying inside a Virtual Network. If the app is only for internal enterprise use, use a private DNS zone. Also consider using Azure API Management (APIM) for firewalls and other forms of protection. For more details, read Azure OpenAI Landing Zone reference architecture.

Load testing

We recommend running a loadtest for your expected number of users. You can use the locust tool with the locustfile.py in this sample or set up a loadtest with Azure Load Testing.

To use locust, first install the dev requirements that includes locust:

python3 -m pip install -r requirements-dev.txt

Or manually install locust:

python3 -m pip install locust

Then run the locust command:

locust

Open the locust UI at http://localhost:8089/, the URI displayed in the terminal.

Start a new test with the URI of your website, e.g. https://my-chat-app.azurewebsites.net. Do not end the URI with a slash. You can start by pointing at your localhost if you're concerned more about load on OpenAI/Cognitive Search than the host platform.

For the number of users and spawn rate, we recommend starting with 20 users and 1 users/second. From there, you can keep increasing the number of users to simulate your expected load.

Here's an example loadtest for 50 users and a spawn rate of 1 per second:

Screenshot of Locust charts showing 5 requests per second

After each test, check the local or App Service logs to see if there are any errors.