Azure AI Search (formerly Azure Cognitive Search) is a cloud-based search service that enables developers to build sophisticated search experiences over content. Provisioning and creating indexes are fundamental steps in implementing knowledge mining solutions.
**Provisioning Azure AI Search:**
T…Azure AI Search (formerly Azure Cognitive Search) is a cloud-based search service that enables developers to build sophisticated search experiences over content. Provisioning and creating indexes are fundamental steps in implementing knowledge mining solutions.
**Provisioning Azure AI Search:**
To provision Azure AI Search, navigate to the Azure Portal and create a new Search service resource. You must specify the subscription, resource group, service name (which becomes part of your endpoint URL), location, and pricing tier. Pricing tiers range from Free (for development) to Basic, Standard, and Storage Optimized tiers for production workloads. Each tier offers different capacities for partitions, replicas, and storage. After deployment, you receive an endpoint URL and admin/query API keys for authentication.
**Creating Indexes:**
An index is the primary data structure in Azure AI Search, containing searchable content. Creating an index involves defining a schema that specifies:
1. **Fields**: Each field has a name, data type (string, integer, boolean, datetime, geographic coordinates, collections), and attributes determining its behavior.
2. **Field Attributes**: These include Searchable (full-text search enabled), Filterable (used in filter expressions), Sortable (enables ordering results), Facetable (enables faceted navigation), and Retrievable (returned in search results).
3. **Analyzers**: Language analyzers process text for tokenization and normalization during indexing and querying.
4. **Suggesters**: Enable autocomplete and search-as-you-type functionality.
5. **Scoring Profiles**: Customize relevance ranking based on field values.
Indexes can be created using the Azure Portal, REST API, or SDKs (.NET, Python, Java, JavaScript). After index creation, you populate it with documents through indexing operations, either by pushing data or configuring indexers to pull data from supported data sources like Azure Blob Storage, SQL Database, or Cosmos DB.
Proper index design is crucial for search performance and user experience in knowledge mining solutions.
Provisioning Azure AI Search Indexes - Complete Guide
Why is Provisioning Azure AI Search Important?
Azure AI Search (formerly Azure Cognitive Search) is a critical component for building intelligent search experiences in applications. Provisioning and creating indexes correctly ensures that your knowledge mining solutions can efficiently search, filter, and retrieve relevant information from large datasets. For the AI-102 exam, understanding this topic demonstrates your ability to implement scalable search solutions that leverage AI capabilities.
What is Azure AI Search?
Azure AI Search is a cloud search service that provides developers with APIs and tools for building rich search experiences over private, heterogeneous content. Key components include:
• Search Service: The Azure resource that hosts your indexes and handles search queries • Index: A schema that defines the structure of searchable content • Indexer: A crawler that extracts searchable data from data sources • Skillset: A collection of AI skills that enrich content during indexing • Data Source: The connection to your source data (Blob Storage, SQL, Cosmos DB, etc.)
How Does Provisioning Work?
Step 1: Create a Search Service • Choose a pricing tier (Free, Basic, Standard, Storage Optimized) • Select a region close to your data and users • Configure replicas (for high availability) and partitions (for storage/throughput)
Step 2: Define an Index Schema • Specify fields with names and data types (Edm.String, Edm.Int32, Collection(Edm.String), etc.) • Set field attributes: searchable, filterable, sortable, facetable, retrievable • Define a key field (unique identifier for each document) • Configure analyzers for language-specific search
Step 3: Create Data Source Connection • Provide connection strings to supported data sources • Configure authentication credentials
Step 4: Configure Indexer • Map source fields to index fields • Set scheduling for incremental updates • Attach skillsets for AI enrichment
Pricing Tiers and Their Characteristics:
• Free: Limited to 50 MB storage, 3 indexes - suitable for learning • Basic: Up to 2 GB storage, 15 indexes - small production workloads • Standard (S1, S2, S3): Scalable storage and partitions - enterprise workloads • Storage Optimized (L1, L2): Large storage capacity - big data scenarios
Key Field Attributes Explained:
• Searchable: Field is full-text searchable • Filterable: Field can be used in $filter expressions • Sortable: Results can be ordered by this field • Facetable: Field can be used for faceted navigation • Retrievable: Field is returned in search results
Exam Tips: Answering Questions on Provisioning Azure AI Search
Tip 1: Remember that the key field must be of type Edm.String and must be unique for each document.
Tip 2: Know the difference between replicas (for query load and high availability) and partitions (for storage and indexing throughput).
Tip 3: Understand that Collection(Edm.String) is used for arrays of values, commonly used with faceting.
Tip 4: For questions about performance, remember that increasing replicas improves query throughput, while partitions improve indexing speed.
Tip 5: When asked about supported data sources, recall: Azure Blob Storage, Azure Table Storage, Azure Cosmos DB, Azure SQL Database, and SQL Server on Azure VMs.
Tip 6: The Import Data wizard in the Azure portal can create all components (data source, index, skillset, indexer) in one workflow.
Tip 7: Pay attention to questions about analyzers - standard.lucene is the default, but language-specific analyzers exist for better linguistic processing.
Tip 8: Remember that changing certain index properties after creation requires rebuilding the entire index - plan your schema carefully.
Tip 9: For high availability requirements, you need at least 2 replicas for read availability and 3 replicas for read/write availability during updates.