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.

Creating data sources and indexers is fundamental to implementing knowledge mining solutions in Azure Cognitive Search. A data source defines the connection to your content repository, while an indexer automates the process of extracting and indexing that content.

**Data Sources**

A data source in Azure Cognitive Search represents a connection to external data that you want to index. Supported data sources include Azure Blob Storage, Azure SQL Database, Azure Cosmos DB, Azure Table Storage, and Azure Data Lake Storage Gen2. When creating a data source, you must specify the connection string, container or table name, and credentials for authentication. You can use managed identities for secure, passwordless connections to Azure resources.

To create a data source, you can use the Azure portal, REST API, or Azure SDKs. The configuration includes specifying the data source type, name, connection details, and optionally a query to filter which data to extract.

**Indexers**

An indexer automates the data extraction process by connecting to your data source, reading content, serializing it into JSON documents, and populating your search index. Indexers can run on-demand or on a scheduled basis for incremental updates.

Key indexer configurations include field mappings that define how source fields map to index fields, output field mappings for skillset outputs, and change detection policies for efficient updates. Indexers support parameters like batch size and maximum items per execution.

**Skillsets Integration**

Indexers can optionally include skillsets that apply AI enrichment during indexing. This enables cognitive processing such as entity recognition, key phrase extraction, image analysis, and custom skills.

**Best Practices**

Implement change tracking to enable incremental indexing. Configure appropriate schedules based on data freshness requirements. Monitor indexer status and handle failures through the Azure portal or programmatic APIs. Use field mappings to transform data during ingestion.

Custom skills in Azure Cognitive Search extend the built-in cognitive capabilities by allowing you to integrate your own processing logic into the enrichment pipeline. These skills enable you to perform specialized transformations, entity extraction, or analysis that the default skillset does not provide.

To implement custom skills, you typically create an Azure Function or a web API endpoint that accepts JSON input and returns JSON output in a specific format. The custom skill must conform to the Web API custom skill interface, which requires handling batch requests containing records with unique identifiers and data payloads.

The input schema includes a 'values' array containing records, each with a 'recordId' and 'data' object. Your custom logic processes this data and returns results in the same structure, including any warnings or errors encountered during processing.

When including custom skills in your skillset definition, you specify the skill type as 'WebApiSkill' and configure several properties: the 'uri' pointing to your endpoint, 'httpMethod' (usually POST), 'timeout' for request duration, 'batchSize' for processing efficiency, and 'degreeOfParallelism' for concurrent requests.

You must define 'inputs' that map enrichment tree nodes to your skill's expected parameters and 'outputs' that specify where processed results should be stored in the enrichment tree. These mappings use the '/document' path notation to reference specific fields.

Authentication options include API keys passed via headers or Azure Active Directory tokens for enhanced security. You can also configure retry policies for resilience.

Common use cases for custom skills include proprietary entity recognition, sentiment analysis in specific domains, language translation using external services, document classification using trained machine learning models, and data validation or transformation logic unique to your business requirements.

Proper error handling ensures the indexer continues processing even when individual records fail, maintaining pipeline reliability.

Creating and running indexers in Azure Cognitive Search is a fundamental component of implementing knowledge mining and information extraction solutions. An indexer is an automated crawler that extracts searchable content from various data sources and populates a search index.

To create an indexer, you first need to establish three key components: a data source connection, a target search index, and optionally a skillset for AI enrichment. The data source defines where your content resides, such as Azure Blob Storage, Azure SQL Database, Cosmos DB, or Azure Table Storage.

You can create indexers through multiple methods: the Azure portal, REST API, or Azure SDKs (.NET, Python, Java). When using the REST API, you submit a POST request to the indexers endpoint with a JSON definition specifying the data source name, target index name, and scheduling parameters.

Key configuration options include field mappings, which define how source fields map to index fields, and output field mappings for skillset-enriched content. You can also configure change detection policies to enable incremental indexing, processing only new or modified documents.

Running indexers can be done on-demand or scheduled. On-demand execution triggers a single indexing run, useful for testing or initial data loads. Scheduled execution allows indexers to run at defined intervals, keeping your index synchronized with source data changes.

Monitoring indexer status is essential for maintaining healthy search solutions. Azure provides execution history, showing success counts, failure details, and processing times. You can track document-level errors and warnings through the indexer status API.

Best practices include setting appropriate batch sizes based on document complexity, implementing retry policies for transient failures, and using reset operations when reprocessing is necessary. For large datasets, consider partitioning strategies and parallel indexer runs to optimize throughput and ensure efficient knowledge extraction from your data sources.

Querying indexes in Azure Cognitive Search involves using a powerful query syntax to retrieve relevant documents from your search index. The query language supports various operators and expressions to refine your search results effectively.

**Basic Query Syntax:**
Azure Cognitive Search uses OData syntax for queries. The primary parameter is 'search' which accepts simple or full Lucene query syntax. Simple syntax supports basic operators like AND, OR, and NOT, while full Lucene enables wildcards, fuzzy matching, proximity searches, and field-scoped queries.

Example: `search=azure AND cloud` finds documents containing both terms.

**Filtering:**
Filters use the '$filter' parameter with OData expressions to narrow results based on field values. Filters operate on filterable fields and support comparison operators (eq, ne, gt, lt, ge, le), logical operators (and, or, not), and functions.

Example: `$filter=category eq 'Technology' and rating gt 4`

Filters are evaluated before query execution, reducing the document set that needs scoring, which improves performance significantly.

**Sorting:**
The '$orderby' parameter controls result ordering. You can sort by one or multiple fields in ascending (asc) or descending (desc) order. Only sortable fields can be used.

Example: `$orderby=publishDate desc, title asc`

By default, results are ordered by relevance score when no orderby is specified.

**Additional Parameters:**
- '$select': Specifies which fields to return
- '$top': Limits the number of results
- '$skip': Enables pagination
- '$count': Returns total matching document count
- 'facets': Returns aggregated category counts

**Combined Example:**

search=machine learning
&$filter=year ge 2020 and category eq 'AI'
&$orderby=relevanceScore desc
&$select=title,author,summary
&$top=10

Understanding these query capabilities allows you to build sophisticated search experiences that return precisely the information users need from your knowledge mining solutions.

Knowledge Store projections in Azure Cognitive Search allow you to persist enriched data from your AI enrichment pipeline into durable storage for further analysis and downstream processing. Managing these projections effectively is crucial for building robust knowledge mining solutions.

There are three types of projections you can configure:

1. **Table Projections**: Store structured data in Azure Table Storage. These are ideal for tabular data that you want to query using Power BI or other analytical tools. Each table projection maps enriched fields to table columns.

2. **Object Projections**: Save complex JSON objects to Azure Blob Storage. These preserve hierarchical data structures and are useful when you need to maintain nested relationships between enriched content.

3. **File Projections**: Store binary content like images or normalized documents in Blob Storage. This is particularly useful for preserving extracted images from documents.

When managing projections, you define them within a skillset using the knowledgeStore property. Key configuration elements include:

- **storageConnectionString**: The connection to your Azure Storage account
- **projections**: An array containing your projection definitions
- **generatedKeyName**: A unique identifier for tracking documents

Best practices for managing projections include:

- Use the Shaper skill to create well-structured data shapes before projecting
- Group related projections together to maintain referential integrity
- Plan your table schemas carefully to support your reporting needs
- Consider data volume and storage costs when designing your projection strategy

You can update projections by modifying the skillset definition and rerunning the indexer. The indexer will repopulate the knowledge store with the new projection configuration.

Monitoring projection health involves checking indexer execution status, reviewing any warnings or errors, and validating that data appears correctly in your storage containers. Using Azure Portal or REST APIs, you can inspect and troubleshoot projection issues to ensure your knowledge mining solution functions as expected.

Implementing semantic and vector store solutions in Azure involves leveraging advanced AI capabilities to enhance search and information retrieval beyond traditional keyword matching. Vector stores enable storage of numerical representations (embeddings) of text, images, or other data types, allowing for similarity-based searches that understand meaning rather than just matching exact terms.

Azure AI Search provides built-in vector search capabilities, allowing you to index and query vector embeddings alongside traditional text content. To implement this, you first generate embeddings using models like Azure OpenAI's text-embedding-ada-002. These embeddings convert content into high-dimensional numerical arrays that capture semantic meaning.

When configuring your search index, you define vector fields with specific dimensions matching your embedding model output. The index schema includes properties like vectorSearchProfile and algorithm configurations such as HNSW (Hierarchical Navigable Small World) or exhaustive KNN for similarity calculations.

Semantic ranking enhances search results by applying machine learning models to re-rank results based on semantic relevance. You enable semantic configuration in your index, specifying which fields contain title content, keywords, and body text. This feature analyzes query intent and document context to surface the most relevant results.

Hybrid search combines vector similarity with traditional full-text search, providing comprehensive results that leverage both approaches. You can configure fusion methods like Reciprocal Rank Fusion (RRF) to merge results from different search techniques.

The implementation workflow typically includes: creating an Azure AI Search resource, generating embeddings for your documents using Azure OpenAI or other embedding services, defining an index schema with vector fields, uploading documents with their corresponding embeddings, and configuring search queries to utilize vector or hybrid search modes.

These solutions are particularly valuable for knowledge mining scenarios where understanding document semantics, finding similar content, and extracting meaningful insights from unstructured data are essential requirements for building intelligent applications.

Provisioning Document Intelligence resources in Azure involves setting up the Azure AI Document Intelligence service (formerly known as Form Recognizer) to extract information from documents automatically. This service enables organizations to build intelligent document processing solutions that can analyze invoices, receipts, identity documents, and custom forms.

To provision Document Intelligence resources, you first need an active Azure subscription. Navigate to the Azure portal and select 'Create a resource,' then search for 'Document Intelligence' or 'Form Recognizer.' You will need to configure several settings during the provisioning process.

The key configuration options include selecting the appropriate subscription, choosing or creating a resource group for organizational purposes, selecting a region closest to your users for optimal performance, and providing a unique name for your resource. You must also select a pricing tier - the Free tier (F0) allows limited transactions for testing, while the Standard tier (S0) supports production workloads with higher throughput.

Once provisioned, the resource provides two essential pieces of information: an endpoint URL and API keys. The endpoint serves as the base URL for all API calls, while the keys authenticate your applications when accessing the service. Azure provides two keys for rotation purposes, allowing you to regenerate one while using the other.

For enterprise scenarios, you can configure additional settings such as managed identities for secure authentication, virtual network integration for network isolation, and customer-managed keys for encryption. Private endpoints can be established to ensure traffic remains within your Azure virtual network.

You can also provision Document Intelligence resources using Azure CLI, PowerShell, ARM templates, or Bicep for infrastructure-as-code approaches. This enables repeatable deployments across different environments. After provisioning, you can access the Document Intelligence Studio, a web-based interface for testing models, labeling training data, and building custom extraction models tailored to your specific document types.

Prebuilt models in Azure AI Document Intelligence provide ready-to-use machine learning models that can extract structured data from common document types, eliminating the need to train custom models from scratch. These models are designed to handle specific document formats and extract relevant information with high accuracy.

Azure offers several prebuilt models including Invoice, Receipt, ID Document, Business Card, W-2 tax forms, and Health Insurance Cards. Each model is optimized for its specific document type and understands the layout, fields, and data patterns typical of that format.

To use prebuilt models, you first create a Document Intelligence resource in Azure. Then you can submit documents through the REST API or SDK by specifying the model ID and providing the document as a URL or base64-encoded content. The service analyzes the document and returns structured JSON containing extracted fields, confidence scores, and bounding box coordinates.

For example, the Invoice model extracts vendor information, invoice numbers, dates, line items, totals, and payment terms. The Receipt model captures merchant details, transaction dates, item lists, subtotals, taxes, and total amounts. The ID Document model reads passports and driver licenses to extract names, dates of birth, document numbers, and expiration dates.

Key benefits include reduced development time since no training data collection is required, consistent accuracy across standard document formats, and automatic updates as Microsoft improves the underlying models. The models support multiple languages and handle variations in document layouts.

Implementation typically involves calling the analyze endpoint with your chosen model, polling for results, and then parsing the returned JSON to integrate extracted data into your applications. Confidence scores help you determine when manual review might be necessary for uncertain extractions. This approach enables efficient document processing workflows for invoice automation, expense management, identity verification, and various business processes requiring structured data extraction from unstructured documents.

Implementing custom document intelligence models in Azure involves creating tailored solutions for extracting information from documents that standard prebuilt models cannot handle effectively. Azure AI Document Intelligence (formerly Form Recognizer) provides the capability to train custom models on your specific document types.

To implement custom models, you first need to gather a representative dataset of your documents. Azure requires a minimum of 5 sample documents for training, though more samples typically yield better accuracy. These documents should represent the variety of layouts and formats you expect to process.

The implementation process begins with creating an Azure AI Document Intelligence resource in your Azure subscription. Next, you use Azure AI Studio or the REST API to create a custom model project. You upload your training documents to Azure Blob Storage and use the labeling tool to annotate fields you want to extract.

Azure supports two types of custom models: template models work best with fixed-layout documents where fields appear in consistent locations, while neural models handle documents with varying structures more effectively. Neural models use deep learning and require more training data but offer greater flexibility.

After labeling your documents, you initiate the training process through the API or studio interface. Azure analyzes the labeled examples and builds a model that understands your document structure. Training typically completes within minutes for template models.

Once trained, you evaluate model performance using test documents and review confidence scores. You can retrain with additional samples to improve accuracy. The trained model receives a unique model ID for integration into your applications.

For production deployment, you call the analyze endpoint with your model ID, submit documents for processing, and receive structured JSON responses containing extracted field values with confidence scores. You can also compose multiple custom models together to handle different document types within a single solution.

Training and publishing custom document models in Azure AI Document Intelligence allows you to create specialized models tailored to your specific document types and business needs. This process involves several key steps that enable accurate extraction of information from your unique documents.

First, you need to gather a representative sample of your documents. Azure recommends at least five sample documents of the same type, though more samples typically improve model accuracy. These documents should represent the variety of layouts and content you expect to process.

Next, you create a project in Document Intelligence Studio or use the REST API/SDKs. Within this project, you label your documents by identifying and tagging the fields you want to extract. For example, in an invoice, you might label vendor name, invoice date, total amount, and line items. The labeling process teaches the model what information matters for your use case.

Once labeling is complete, you train the model using Azure's machine learning capabilities. The training process analyzes the labeled documents and learns patterns to recognize similar fields in new documents. Training typically takes a few minutes depending on document complexity and quantity.

After training, you should evaluate the model's performance using the confidence scores and accuracy metrics provided. If results are unsatisfactory, you can add more labeled samples and retrain to improve accuracy.

When satisfied with performance, you publish the model to make it available for production use. Publishing creates an endpoint that applications can call to analyze documents. You can integrate this endpoint into your workflows using REST APIs, SDKs for various programming languages, or Azure Logic Apps.

Custom models can be composed into larger models, allowing you to handle multiple document types with a single API call. This approach provides flexibility while maintaining accuracy across diverse document processing scenarios in your knowledge mining solutions.

Composed document intelligence models in Azure AI Document Intelligence allow you to combine multiple custom models into a single unified model that can analyze different document types. This powerful feature enables organizations to handle diverse document processing scenarios with a single API call.

When creating composed models, you first build individual custom models trained on specific document types. For example, you might have separate models for invoices, receipts, and purchase orders. Each custom model learns the unique structure and fields of its respective document type.

To create a composed model, you use the Azure AI Document Intelligence Studio or the REST API. The process involves selecting existing custom models and combining them into a new composed model. The composed model can contain up to 200 individual custom models, providing extensive flexibility for complex document processing workflows.

When a document is submitted to a composed model for analysis, the service automatically determines which component model best matches the input document. This classification happens based on the document structure and content patterns. The appropriate model then extracts the relevant fields and returns the results along with a confidence score indicating which model was used.

Key benefits of composed models include simplified client applications since only one model ID needs to be managed, reduced API complexity, and streamlined document processing pipelines. Organizations can add new document types by training additional custom models and incorporating them into the existing composed model.

Best practices include ensuring each component model is well-trained with sufficient sample documents, testing the composed model with various document types to verify correct model selection, and monitoring confidence scores to identify potential classification issues. You should also consider organizing related document types together and maintaining clear naming conventions for component models to facilitate management and troubleshooting of your composed document intelligence solutions.

OCR (Optical Character Recognition) pipelines in Azure enable automated text extraction from images and documents, forming a crucial component of knowledge mining solutions. Azure Cognitive Services provides powerful OCR capabilities through the Computer Vision API and Form Recognizer service.

To create an effective OCR pipeline, you typically start by ingesting documents into Azure Blob Storage. These documents can include scanned PDFs, images, photographs of text, or handwritten notes. The pipeline then processes these files through several stages.

The first stage involves preprocessing, where images may be enhanced for better recognition accuracy. This includes adjusting contrast, removing noise, and correcting skew angles. Azure's built-in capabilities handle many preprocessing tasks automatically.

Next, the OCR engine analyzes the document structure. The Read API in Computer Vision excels at extracting printed and handwritten text from complex documents. It returns text organized by pages, lines, and words, along with bounding box coordinates for each element.

For structured documents like invoices, receipts, or forms, Form Recognizer provides specialized models that extract both text and key-value pairs. Custom models can be trained on your specific document types to improve extraction accuracy.

Integration with Azure Cognitive Search enhances the pipeline by indexing extracted text, making it searchable across large document repositories. Custom skills can be added to the indexing pipeline to perform additional processing like entity recognition or translation.

The pipeline architecture typically uses Azure Functions or Logic Apps for orchestration, triggering processing when new documents arrive. Results can be stored in Cosmos DB or Azure SQL for downstream applications.

Best practices include implementing error handling for unreadable documents, using confidence scores to flag low-quality extractions for human review, and batching requests to optimize costs. Monitoring through Application Insights helps track pipeline performance and identify bottlenecks in your text extraction workflow.

Summarizing and classifying documents are essential capabilities within Azure AI's knowledge mining and information extraction solutions. These features leverage Azure Cognitive Services and Azure AI Search to transform unstructured data into actionable insights.

Document summarization involves extracting key information from lengthy documents to create concise representations. Azure AI Language service provides extractive summarization, which identifies and extracts the most important sentences from source documents, and abstractive summarization, which generates new sentences that capture the main ideas. This is particularly valuable when processing large volumes of documents in knowledge mining pipelines, enabling users to quickly understand document content.

Document classification categorizes documents into predefined or custom categories based on their content. Azure AI offers both single-label and multi-label classification capabilities. Single-label classification assigns one category per document, while multi-label allows multiple categories. You can use pre-built models for common classification tasks or train custom models using Azure AI Language Studio with your own labeled training data.

In Azure AI Search implementations, these capabilities integrate through skillsets in the enrichment pipeline. The summarization skill can be added to extract key points during indexing, making search results more informative. Classification skills help organize content into taxonomies, improving search relevance and enabling faceted navigation.

To implement these features, you typically create an Azure AI Language resource, define your classification schema or summarization parameters, and integrate them into your Azure AI Search indexer pipeline. The enriched data is then stored in the search index for querying.

Key considerations include selecting appropriate model types based on your use case, providing quality training data for custom classifiers, and optimizing pipeline performance for large document volumes. These AI-powered capabilities significantly enhance knowledge mining solutions by making vast document repositories more accessible and organized for end users.

Extracting entities, tables, and images from documents is a crucial capability in Azure AI's knowledge mining and information extraction solutions. This process leverages Azure Cognitive Services, particularly Azure Form Recognizer and Azure Cognitive Search, to transform unstructured documents into structured, searchable data.

**Entity Extraction** involves identifying and classifying key information within documents such as names, dates, locations, organizations, and custom entities specific to your domain. Azure Form Recognizer uses pre-built and custom models to detect these entities. The service applies machine learning algorithms to recognize patterns and extract relevant data points from invoices, receipts, business cards, and other document types.

**Table Extraction** enables the identification and parsing of tabular data embedded within documents. Azure Form Recognizer can detect table boundaries, recognize row and column structures, and extract cell contents while maintaining their relationships. This is particularly valuable for processing financial statements, reports, and forms containing structured data layouts.

**Image Extraction** focuses on identifying and extracting visual elements from documents. This includes photographs, diagrams, charts, logos, and signatures. Azure services can extract these images as separate assets, apply OCR to extract text within images, and use Computer Vision capabilities to analyze and describe image contents.

**Implementation Approach:**
1. Use Azure Form Recognizer's Layout API to analyze document structure
2. Deploy pre-built models for common document types or train custom models
3. Integrate with Azure Cognitive Search to create searchable indexes
4. Configure skillsets to enrich extracted data with additional AI capabilities

The extracted information can be stored in Azure Blob Storage and indexed using Azure Cognitive Search, creating a powerful knowledge mining solution. This enables organizations to unlock insights from large document repositories, automate data entry processes, and build intelligent search applications that understand document content at a granular level.

Processing documents, images, videos, and audio in Azure AI involves leveraging Azure Cognitive Services and Azure AI Search to extract valuable insights from unstructured data sources. This knowledge mining approach transforms raw content into searchable, structured information.

For document processing, Azure Form Recognizer and Azure AI Document Intelligence extract text, key-value pairs, tables, and structured data from PDFs, invoices, receipts, and business documents. These services use pre-built and custom models to understand document layouts and semantically interpret content.

Image processing utilizes Azure Computer Vision to analyze visual content. This includes optical character recognition (OCR) for text extraction, object detection, image classification, facial recognition, and generating descriptive captions. The service can identify brands, landmarks, and inappropriate content while extracting rich metadata.

Video analysis through Azure Video Indexer provides comprehensive insights including speech-to-text transcription, face identification, emotion detection, scene segmentation, and keyword extraction. It identifies speakers, detects visual text, and recognizes objects and actions throughout video content. The service creates searchable indexes enabling users to find specific moments within extensive video libraries.

Audio processing primarily involves Azure Speech Services for speech-to-text conversion, speaker recognition, and language identification. Real-time and batch transcription capabilities convert spoken content into searchable text while preserving speaker attribution and timestamps.

Azure AI Search serves as the central indexing and search platform that aggregates processed content from all these sources. Through skillsets and enrichment pipelines, raw data flows through cognitive skills that extract entities, detect language, analyze sentiment, and perform custom processing. The resulting enriched content populates search indexes enabling powerful full-text search, faceted navigation, and semantic ranking across your entire knowledge base.

These capabilities combine to create comprehensive knowledge mining solutions that unlock insights hidden within enterprise content repositories.