CosmicWorks has big plans for their retail site. They're eager to launch a POC of a simple chat interface where users can interact with a virtual agent to find product and account information.

The scenario centers around a consumer retail "Intelligent Agent" that allows users to ask questions on vectorized product, customer and sales order data stored in a database. The data in this solution is the Cosmic Works sample for Azure Cosmos DB. This data is an adapted subset of the Adventure Works 2017 dataset for a retail Bike Shop that sells bicycles, biking accessories, components and clothing.

BUT you can bring your own data instead.

This hackathon will challenge you and your team to launch a POC of a chat interface where users can interact with a virtual agent to find product and account information. Through the course of the hackathon, you will modify an existing application to do the following:

• Store the chat messages in an Azure Cosmos DB database, grouped by chat sessions
• Use Azure OpenAI Service to create vector embeddings and chat completions
• Use Azure Cognitive Search as a vector database to search for product and account information by the vector embeddings
• Load up existing product and account information into Azure Cosmos DB and the Azure Cognitive Search vector index
• Create a process that manages the conversation flow, vectorization, search, data handling, and response generation
• Externally manage system prompts

• Azure Subscription
• Visual Studio
• .NET 7 SDK
• Docker Desktop
• Azure CLI 2.49.0
• Helm v3.11.1 or greater - https://helm.sh/ (for AKS)
• Subscription with access to the Azure OpenAI Service. Start here to Request Access to Azure OpenAI Service

The following steps will guide you through the process needed to being the hackathon.

Clone this repository and change to the main branch

git clone https://github.com/Azure/Build-Modern-AI-Apps-Hackathon
cd .\Build-Modern-AI-Apps-Hackathon\
git checkout main

1. Open the PowerShell command line and navigate to the directory where you cloned the repo.
2. Navigate into the starter-artifacts\code\VectorSearchAiAssistant folder.
3. Run the following PowerShell script to provision the infrastructure and deploy the API and frontend. Provide the name of a NEW resource group that will be created. This will provision all of the required infrastructure, deploy the API and web app services into Azure Kubernetes Service (AKS) if using the deployAks flag below or Azure Container Apps (ACA), and import data into Cosmos DB.

./scripts/Starter-Deploy.ps1  -resourceGroup <resource-group-name> -location <location> -subscription <subscription-id> -deployAks 1

NOTE:

If <resource-group-name> already exists, your user must have Owner permissions on the resource group. If <resource-group-name> does not exist exists, the deployment script will create it. In this case, your user must have Owner permissions on the subscription in which the resource group will be created.

NOTE:

By default, the deployment script will attempt to create new Azure Open AI model deployments for the gpt-35-turbo and text-embedding-ada-002 models. If you already have deployments for these models, you can skip the deployment by passing the following parameters to the script:

-openAiName <open-ai-name> -openAiRg <open-ai-resource-group> -openAiCompletionsDeployment <completions-deployment-name> -openAiEmbeddingsDeployment <embeddings-deployment-name> -stepDeployOpenAi $false

In case you will defer the Open AI deployment to the script, make sure have enough Tokens Per Minute (TPM) in thousands quota available in your subscription. By default, the script will attempt to set a value of 120K for each deployment. In case you need to change this value, you can edit lines 22 and 29 in the starter-artifacts\code\VectorSearchAiAssistant\scripts\Deploy-OpenAi.ps1 file.

If using your own Azure OpenAI account, it will be necessary to update the appsettings.json file in the ChatServiceWebApi project to use the model deployment names used in your existing Azure OpenAI account. See Configure Local Settings section below for more details.

The deployment script supports two types of containerization:

• Azure Kubernetes Service - AKS - this option allows you to deploy the application into an AKS cluster. This option is more complex to set up, but it provides more flexibility and control over the deployment. To use AKS, pass the following parameter to the deployment script:

  -deployAks $true

• Azure Container Apps - ACA - this option allows you to deploy containerized applications without having to manage the underlying infrastructure and thus is an easier option to get started with.

For the purpose of this hackathon, you can whatever your preference is, either AKS or ACA.

NOTE:

For the reminder of this hackathon, please interpret any documentation references to AKS as ACA if you chose to use Azure Container Apps (and vice-versa).

1. After the command completes, navigate to resource group and obtain the name of the AKS service.

2. Execute the following command to obtain the website's endpoint:

   For ACA:

   az deployment group show -g <resource-group-name> -n cosmosdb-openai-azuredeploy -o json --query properties.outputs.webFqdn.value

   For AKS:

   az aks show -n <aks-name> -g <resource-group-name> -o tsv --query addonProfiles.httpApplicationRouting.config.HTTPApplicationRoutingZoneName

3. Browse to the website with the returned hostname.

If the website loads, you are ready to continue with the hackathon challenges. Don't worry if the website is not fully operational yet - you will get it there!

After the deployment is complete the following Azure services will be deployed.

• Azure OpenAI Service
• Azure Cosmos DB
• Azure Cognitive Search
• Azure Container Apps (or AKS)
• Azure Storage
• Azure Networking (not pictured)

You can run the website and the REST API that supports it locally. You need to first update your local configuration and then you can run the solution in the debugger using Visual Studio.

• In the ChatServiceWebApi project, review the content of the appsettings.json file.

NOTE:

If you deploy using a pre-existing Azure OpenAI account. You will need to update CompletionsDeployment and EmbeddingsDeployment values to match the names used for these models in your Azure OpenAI account.

```json
{
    "Logging": {
        "LogLevel": {
            "Default": "Information",
            "Microsoft.AspNetCore": "Warning",
            "Microsoft.SemanticKernel": "Error"
        },
        "ApplicationInsights": {
            "LogLevel": {
                "Default": "Information",
                "Microsoft.AspNetCore": "Warning",
                "Microsoft.SemanticKernel": "Error"
            }
        }
    },
    "AllowedHosts": "*",
    "MSCosmosDBOpenAI": {
        "CognitiveSearch": {
            "IndexName": "vector-index",
            "MaxVectorSearchResults": 10
        },
        "OpenAI": {
            "CompletionsDeployment": "completions",
            "CompletionsDeploymentMaxTokens": 8096,
            "EmbeddingsDeployment": "embeddings",
            "EmbeddingsDeploymentMaxTokens": 8191,
            "ChatCompletionPromptName": "RetailAssistant.Default",
            "ShortSummaryPromptName": "Summarizer.TwoWords",
            "PromptOptimization": {
                "CompletionsMinTokens": 50,
                "CompletionsMaxTokens": 300,
                "SystemMaxTokens": 1500,
                "MemoryMinTokens": 1500,
                "MemoryMaxTokens": 7000,
                "MessagesMinTokens": 100,
                "MessagesMaxTokens": 200
            }
        },
        "CosmosDB": {
            "Containers": "completions, customer, product",
            "MonitoredContainers": "customer, product",
            "Database": "database",
            "ChangeFeedLeaseContainer": "leases"
        },
        "DurableSystemPrompt": {
            "BlobStorageContainer": "system-prompt"
        },
        "CognitiveSearchMemorySource": {
            "IndexName": "vector-index",
            "ConfigBlobStorageContainer": "memory-source",
            "ConfigFilePath": "ACSMemorySourceConfig.json"
        },
        "BlobStorageMemorySource": {
            "ConfigBlobStorageContainer": "memory-source",
            "ConfigFilePath": "BlobMemorySourceConfig.json"
        }
    }
}
```

• In the ChatServiceWebApi project, create an appsettings.Development.json file with the following content (replace all <...> placeholders with the values from your deployment):

  {
      "MSCosmosDBOpenAI": {
          "CognitiveSearch": {
              "Endpoint": "https://<...>.search.windows.net",
              "Key": "<...>"
          },
          "OpenAI": {
              "Endpoint": "https://<...>.openai.azure.com/",
              "Key": "<...>"
          },
          "CosmosDB": {
              "Endpoint": "https://<...>.documents.azure.com:443/",
              "Key": "<...>"
          },
          "DurableSystemPrompt": {
              "BlobStorageConnection": "<...>"
          },
          "BlobStorageMemorySource": {
              "ConfigBlobStorageConnection": "<...>"
          },
          "CognitiveSearchMemorySource": {
              "Endpoint": "https://<...>.search.windows.net",
              "Key": "<...>",
              "ConfigBlobStorageConnection": "<...>"
          }
      }
  }

  NOTE: THe BlobStorageConnection value can be found in the Azure Portal by navigating to the Storage Account created by the deployment (the one that has a container named system-prompt) and selecting the Access keys blade. The value is the Connection string for the key1 key.

To run locally and debug using Visual Studio, open the solution file to load the projects and prepare for debugging.

Before you can start debugging, you need to set the startup projects. To do this, right-click on the solution in the Solution Explorer and select Set Startup Projects.... In the dialog that opens, select Multiple startup projects and set the Action for the ChatServiceWebApi and Search projects to Start.

Also, make sure the newly created appsettings.Development.json file is copied to the output directory. To do this, right-click on the file in the Solution Explorer and select Properties. In the properties window, set the Copy to Output Directory property to Copy always..

You are now ready to start debugging the solution locally. To do this, press F5 or select Debug > Start Debugging from the menu.

NOTE: With Visual Studio, you can also use alternate ways to manage the secrets and configuration. For example, you can use the Manage User Secrets option from the context menu of the ChatWebServiceApi project to open the secrets.json file and add the configuration values there.

When you have finished with the hackathon, simply delete the resource group that was created.