Indexing Large Datasets for AI Search Capabilities on Azure

Question

Pulumi · Accepted Answer

To index large datasets for AI Search capabilities on Azure, we will make use of Azure Cognitive Search, a service that provides AI-powered search capabilities for a variety of content. Cognitive Search can index content from various data sources, allowing the creation of search indices that enable rich search experiences in custom apps.

The main resources we will work with are:

- **Azure Resource Group**: This logical container holds related Azure resources. In our case, it will contain our Azure Cognitive Search service.
- **Azure Cognitive Search Service**: The search service resource where indexers, indexes, and other search components live.

We will create an Azure Resource Group and then provision an Azure Cognitive Search service within this resource group using Pulumi's Azure Native provider.

The following program shows how to do this in Python using Pulumi:

```python
import pulumi
import pulumi_azure_native as azure_native

# Create an Azure Resource Group, which will contain the Cognitive Search Service
resource_group = azure_native.resources.ResourceGroup("my-search-rg")

# Provision an Azure Cognitive Search Service with a basic (free) SKU
search_service = azure_native.search.Service("my-search-service",
    resource_group_name=resource_group.name,
    location=resource_group.location,
    # Sku can be adjusted based on the expected workload and capacity (basic, free, standard, storage_optimized_l1/2, etc.)
    sku=azure_native.search.SkuArgs(
        name="basic"
    ),
    # Replica and partition counts can be adjusted for performance and scale
    replica_count=1,
    partition_count=1,
    # Identity is required for encryption and for access to encryption keys stored in Azure Key Vault
    identity=azure_native.search.IdentityArgs(
        type="SystemAssigned"
    )
)

# Export the primary key of the search service, which will be used to authenticate applications to the search service
primary_key = pulumi.Output.secret(search_service.get_primary_key(args=azure_native.search.GetServicePrimaryKeyArgs(
    resource_group_name=resource_group.name,
    search_service_name=search_service.name
)))

pulumi.export('search_service_primary_key', primary_key)
```

In the above program, we first create a resource group `my-search-rg`, which is a prerequisite for creating most Azure resources. Next, we provision an Azure Cognitive Search service named `my-search-service`.

The `sku` argument specifies the pricing tier of the search service. Azure Cognitive Search offers several `Sku` options, and here we're using the 'basic' tier as an example. Depending on the scale and performance requirements, you can choose from various pricing tiers, including free, standard, and storage optimized levels.

We set the `replica_count` and `partition_count` to one, which is suitable for development purposes or small workloads. For production workloads or large datasets, you may need to increase these numbers to ensure better performance and throughput.

We also include an `identity` argument of type `SystemAssigned`, which is required for using encryption and Azure Key Vault.

Finally, we retrieve and export the primary key for the search service. This key will be used for authentication when interacting with the service.

To run the above program, save it to a file `__main__.py`, and run `pulumi up`. Pulumi will automatically perform the deployment, and you will be provided with the primary key as output, which should be kept secret as it allows access to your search service.

After provisioning the service, you can start creating indexes, indexers, and datasources on the Azure portal or programmatically. Then, you can use the Azure SDKs in your preferred programming language to query your data within these indexes.