1. Custom AI Indexing Pipelines on Azure Cognitive Search.

    Python

    To create a custom AI indexing pipeline using Azure Cognitive Search, you will need to set up Azure Cognitive Search and integrate it with various components that allow for data ingestion, enrichment, and indexing. The goal is to ingest data from a source, apply custom AI to process that data, and then store the processed data in an index for searching.

    Here's an outline of the process you'll follow with Pulumi:

    1. Set up an Azure Cognitive Search service: This service provides the search capabilities needed to index and query your data.
    2. Configure Indexes: Define how the data should be structured within the Azure Cognitive Search service.
    3. Create Data Sources: Establish connections to your data that need to be ingested, which can be from various sources like Azure Blob Storage or Azure SQL Database.
    4. Set up Indexers: Define how the data from the data sources are ingested and processed by the Azure Cognitive Search service.
    5. Integrate custom AI enrichments: Use Azure Cognitive Services to add AI capabilities to your indexing pipeline, enriching the data which can be in the form of natural language processing, image analysis, etc.
    6. Manage resources with Pulumi code: Use Pulumi's infrastructure as code to automate setup and deployment.

    Below, you'll find a Pulumi program in Python to set up an Azure Cognitive Search service and the initial configuration you'll need for a custom AI indexing pipeline:

    import pulumi import pulumi_azure_native as azure_native # Replace these variable placeholders with your actual values resource_group_name = 'my-resource-group' search_service_name = 'my-search-service' # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup(resource_group_name) # Create Azure Cognitive Search service search_service = azure_native.search.Service( "searchService", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.search.SkuArgs( name="basic", # Choose the appropriate service tier (free, basic, standard, etc.) ), # Identity property is required if you want to use Managed Identity to securely connect to your data source identity=azure_native.search.IdentityArgs( type="SystemAssigned", ), public_network_access="Enabled", # Determines network access to the search service ) # Define a search index to structure the data search_index = azure_native.search.Index( "searchIndex", resource_group_name=resource_group.name, search_service_name=search_service.name, fields=[ azure_native.search.FieldArgs( name="id", type="Edm.String", key=True, searchable=False ), # Add other fields that will match the structure of your data # ... ], ) # TODO: Create and link data sources, indexers, and AI enrichments as necessary # When data sources and indexers are defined, set up the custom AI part integrating with Azure Cognitive Services # and set up skillsets if needed to define AI tasks in the indexing pipeline. # Export any important URLs or identifiers for your resources pulumi.export("search_service_name", search_service.name) pulumi.export("search_service_primary_key", search_service.properties.primary_key.apply(lambda key: key))

    In the code above:

    • We define a ResourceGroup that will contain all our Azure resources.
    • We set up an azure_native.search.Service which is our Azure Cognitive Search service, specifying the SKU and identity details. Modify the sku.name as per your requirement; for production, you might choose a standard SKU.
    • We define an azure_native.search.Index, which is the structure that will hold our data within the search service. The fields you define will depend on the data you're working with.
    • You would continue to define Data Source, Indexer, and possibly Skillset resources afterward to complete the ingestion pipeline and AI enrichment, based on your specific requirements.
    • At the end, we export the search service name and primary key, which might be used to connect with the search service from other applications.

    This program sets up the foundational search service. The creation of the actual pipeline with data sources, indexers, and AI enrichment steps depend on your specific data needs and processing requirements, and involves a more in-depth setup.

    Remember to replace placeholders like my-resource-group and my-search-service with your actual resource names.

    This Pulumi program leverages the azure-native Pulumi provider to provision resources in Azure. Execute this program using Pulumi CLI commands pulumi up for deploying the stack and pulumi destroy for removal.


  2. type Index doest not

    Python

    It seems like there is a typo in your query. I believe you want help with creating an Azure Cognitive Search Index using Pulumi. I'll help you define the Azure Cognitive Search Service along with an Index.

    Below is a Pulumi program in Python to accomplish this, including the creation of an Azure resource group, Cognitive Search service, and a search index:

    import pulumi import pulumi_azure_native as azure_native # Replace these variable placeholders with your actual values resource_group_name = 'my-resource-group' search_service_name = 'my-search-service' # Create an Azure resource group resource_group = azure_native.resources.ResourceGroup(resource_group_name) # Create Azure Cognitive Search service search_service = azure_native.search.Service( "searchService", resource_group_name=resource_group.name, location=resource_group.location, sku=azure_native.search.SkuArgs( name="basic", # Choose the appropriate service tier (free, basic, standard, etc.) ), # Identity property is required if you want to use Managed Identity to securely connect to your data source identity=azure_native.search.IdentityArgs( type="SystemAssigned", ), public_network_access="Enabled", # Determines network access to the search service ) # Define a search index to structure the data search_index = azure_native.search.Index( "searchIndex", resource_group_name=resource_group.name, search_service_name=search_service.name, fields=[ azure_native.search.FieldArgs( name="id", type="Edm.String", key=True, searchable=False ), # Add other fields that will match the structure of your data azure_native.search.FieldArgs( name="description", type="Edm.String", searchable=True ), azure_native.search.FieldArgs( name="title", type="Edm.String", searchable=True ), # Add more fields as needed ], ) # Export important URLs or identifiers for your resources pulumi.export("search_service_name", search_service.name) pulumi.export("search_service_primary_key", search_service.primary_key)

    This program will set up a search service and index with basic field definitions. You can adapt the fields section to include any fields your data structure requires.


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