productionizing.md

RAG chat: Productionizing the 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
• Additional security measures
• Load testing
• Evaluation

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.

If the maximum TPM isn't enough for your expected load, you have a few options:

• Use a backoff mechanism to retry the request. This is helpful if you're running into a short-term quota due to bursts of activity but aren't over long-term quota. The tenacity library is a good option for this, and this pull request shows how to apply it to this app.

• If you are consistently going over the TPM, then consider implementing a load balancer between OpenAI instances. Most developers implement that using Azure API Management or container-based load balancers. A native Python approach that integrates with the OpenAI Python API Library is also possible. For integration instructions with this sample, please check:

  • Scale Azure OpenAI for Python with Azure API Management
  • Scale Azure OpenAI for Python chat using RAG with Azure Container Apps
  • Pull request: Scale Azure OpenAI for Python with the Python openai-priority-loadbalancer

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 AI Search

The default search service uses the "Basic" SKU with the free semantic ranker option, which gives you 1000 free queries a month. After 1000 queries, you will get an error message about exceeding the semantic ranker free capacity.

• Assuming your app will experience more than 1000 questions per month, you should upgrade the semantic ranker SKU from "free" to "standard" SKU:

  azd env set AZURE_SEARCH_SEMANTIC_RANKER standard

  Or disable semantic search entirely:

  azd env set AZURE_SEARCH_SEMANTIC_RANKER disabled

• The search service can handle fairly large indexes, but it does have per-SKU limits on storage sizes, maximum vector dimensions, etc. You may want to upgrade the SKU to either a Standard or Storage Optimized SKU, depending on your expected load. However, you cannot change the SKU of an existing search service, so you will need to re-index the data or manually copy it over. You can change the SKU by setting the AZURE_SEARCH_SERVICE_SKU azd environment variable to an allowed SKU.

  azd env set AZURE_SEARCH_SERVICE_SKU standard

  See the Azure AI Search service limits documentation for more details.

• 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:

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

Or manually install locust:

python -m pip install locust

Then run the locust command, specifying the name of the User class to use from locustfile.py. We've provided a ChatUser class that simulates a user asking questions and receiving answers, as well as a ChatVisionUser to simulate a user asking questions with the GPT-4 vision mode enabled.

locust ChatUser

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/AI 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:

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

Evaluation

Before you make your chat app available to users, you'll want to rigorously evaluate the answer quality. You can use tools in the AI RAG Chat evaluator repository to run evaluations, review results, and compare answers across runs.