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2.3 Validate Your Setup

THIS SECTION IS OPTIONAL!. It is meant to help you understand your infrastructure setup and troubleshoot any issues that occur in setup. Move directly to IDEATE if time is limited.

LET'S REVIEW: We have completed the Provision & Setup stages

Dev Workflow

At this time, we should have done the following:

  • We launched GitHub Codespaces and forked the sample repo
  • We have provisioned infrastructure resources on Azure
  • We authenticated with Azure and refreshed our local env vars
  • We completed post-provisioning tasks to populate data

3.1 Visit Azure AI Portal

The Azure Portal helps us view the resources provisioned on Azure and check that they are setup correctly

Here's a reminder of the Azure Application Architecture - let's check our provisioned Resource Group to make sure these resources were created.

ACA Architecture

  1. Open a new browser tab and navigate to the link below. You may be prompted to login. 

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    https://portal.azure.com/#browse/resourcegroups

  2. Sign in → If prompted, use the Username and Password from the "Azure Credentials" section in your Skillable Lab instructions panel.

  3. You may be presented with a "Welcome to Microsoft Azure" screen. Click Cancel (to dismiss it) or click Get Started (to take an introductory tour of the Azure Portal).
  4. You should be taken directly to the Resource Groups page for your subscription.
    • You should see an rg-AITOUR resource. This was created for you and contains all the resources needed to build and deploy your RAG-based copilot.
  5. Click rg-AITOUR to visit the Resource Group overview page.
    • Check: Deployments (look under "Essentials") - You should see: 35 succeeded.
    • Check: Resources (in Overview) - You should see: 15 resources.

Leave the Azure Portal open on this tab. We'll revisit it later.

3.2 Visit Azure AI Foundry

The Azure AI Studio portal lets us view and manage the Azure AI project for our app.

  1. Open a new browser tab and navigate to this page: 

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    https://ai.azure.com

  2. Click Sign in → you will auto-login with the Azure credentials used to sign into the portal.

    • Check: You should see a Hub resource (with a name like ai-hub-XXXXXXXX)

    • Check: You should see a Project resource (with a name like ai-project-XXXXXXXX)

      The Azure AI hub collects AI resources that can be shared between AI projects. The Azure AI project helps you organize your work when building applications.

    • Click the Project link. You will be taken to a Project details page.

    • Click "Connected Resources" in the left pane,
    • Check: You should see Hub connections to OpenAI endpoints, Azure AI Search & Storage.
    • Click "Models + endpoints" in the left pane.

    • Check: You should see deployed models for this project.

      For this application, we will use the chat completion models gpt-4 and gpt-35-turbo, and the embedding model text-embedding-ada-002.

3.3. Check Azure Cosmos DB

The Azure CosmosDB resource holds the customer data for our application. It is a noSQL database that contains JSON data for each customer, and the prior purchases they made.

  1. Switch to the Azure Portal tab and display the rg-AITOUR resource group Overview
  2. Click the Azure Cosmos DB account resource name to visit its details page
  3. Click Data Explorer in the top-nav menu
    • dismiss the popup dialog to skip the movie
    • see: contoso-outdoor container with customers database
    • click customers, then select Items
    • you should see: 12 data items in database

The Azure AI Search resources contains the product index for our retailer's product catalog. It is the information retrieval service for RAG solutions, using sentence similarity and semantic ranking to return the most relevant results for a given customer query.

  1. Switch to the Azure Portal tab and display the rg-AITOUR resource group Overview
  2. Click the Search service resource name to visit its details page
  3. Click Search Explorer in the top-nav menu
    • see Search explorer with default index contoso-products
    • click "Search" with no other input
    • you will see: Results dialog filled with index data for the entire product database.
  4. Enter sleeping bag in the text box, and click Search
    • Verify that the first result returned relates to a sleeping bag from the catalog

    Check the 'value' section of the response for semantically-ranked results.

  5. Enter something to make food with in the text box, and click Search
    • Verify that the first result returned relates to a camping stove

3.5. Check Azure Container App

The Azure Container App provides the hosting environment for our copilot (API endpoint)

Azure Container Apps hosts the API endpoint for the Contoso Chat application that remote clients talk to. We should have deployed an initial version of Contoso Chat at this point.

  1. Switch to the Azure Portal tab and display the rg-AITOUR resource group Overview
  2. Click the Container App resource to display the Overview page
  3. Look for Application Url (at top right), and click it to launch in new tab
  4. You should see: A Hello World message on the screen (confirming app was deployed)
Azure Container Apps (ACA) using a FastAPI-based application server to expose a /create_request API endpoint to make requests to our copilot. (click to expand for details). The server has a Swagger API endpoint that we can use for manual testing. Let's do that next.

Our chat application works by sending chat messages to a custom endpoint hosted as an Azure Container App.

  • The inputs to the endpoint are
    • question → the customer's chat message),
    • customer_id → the logged-in customer's ID number)
    • chat_history → the prior conversation, for context.
  • The response from the endpoint is the response returned by the chatbot.

When iterating on a prototype application, we start with manual testing, using a single "test prompt" to validate our scenario. We then move to automated evaluations with larger test datasets.

  1. Return to the deployed Azure Container App tab
  2. Add a /docs suffix to the URL and browse to that path - you will see: FastAPI page
  3. Expand the POST section by clicking the arrow
    • click Try it out to make inputs editable
    • enter Tell me about your tents for question
    • enter 2 for customer_id
    • enter [] for chat_history
    • enter Execute to run the endpoint with the provided parameters.

You will get a response body with question, answer and context components.

  • Check that the question reflects the one you entered above
  • Check that the answer provides a relevant response to the question
  • Check that the context shows the retrieved knowledge used to ground the response.
    • This includes information about products relevant to the customer question.
    • The products selected may depend on customer_id and the associated order history.

Exercise → Repeat exercise with a different customer ID (between 1 and 12). How did the response change?

(OPTIONAL) CONNECT THE DOTS - Click to expand the section to learn how this works

"Recall that the Retrieval Augmented Generation works by retrieving relevant knowledge from your data stores, and augmenting the user query with it to create an enhanced prompt - which generates the final response."

To implement this RAG pattern, we need to execute three steps:

  1. Setup data sources and populate them with our data (product catalog, customer orders)
  2. Create indexes for efficient information retrieval by LLMs (e.g., find matching products)
  3. Connect our Azure AI project to access data/indexes code-first, for use in processing steps.

In the previous section we setup the data sources (provisioning infra) and populated them with data (post-provisioning scripts) as follows:

  1. Azure CosmosDB - loaded 12 records from data/customer_info, got customers database.
  2. Azure AI Search - loaded 20 records from data/product_info, got contoso-products index.

This checks off the first two idents from our RAG checklist above. Now, let's see how we can achieve the thirst ep with a code-first approach that makes use of the Azure AI Search, Azure CosmosDB and Azure OpenAI services through their Azure SDKs.

CONGRATULATIONS. You validated your setup! It's time to IDEATE!