Cognitive Search Fundamentals

Introduction

Azure AI Search (formerly Cognitive Search) is a managed search service that adds AI enrichment to search experiences. It indexes your content and can apply skills like OCR, entity extraction, and vector embeddings to make it more searchable. It is increasingly used to power retrieval for generative AI applications.

Definition

Cognitive Search uses AI to extract information from unstructured content and make it searchable.

Types

Content Extraction

Extract text and structure from documents

Entity Recognition

Identify people, organizations, and locations

Key Phrase Extraction

Extract important phrases from content

Language Detection

Automatically detect document language

Image Analysis

Extract information from images and photos

Use Cases

• Enterprise document search
• E-commerce product search
• Knowledge management systems
• Compliance and legal document search
• Media content discovery

Implementation

Cognitive Search uses AI skills to process content during indexing, making it searchable and discoverable.

In Practice

An enrichment pipeline pulls content from sources, applies cognitive skills to extract and structure information, and builds a searchable index. With vector search and semantic ranking, Azure AI Search underpins retrieval-augmented generation, grounding language models in your own data.

Key Points

• Combines search with AI capabilities
• Supports various content types
• Scalable indexing and search
• Custom skills for specific domains

References

• Cognitive Search Documentation — Complete guide to Azure Cognitive Search