Implementing Intelligent Search
On this page (13sections)
Introduction
Implementing search with Azure AI Search involves defining indexes, indexers, and the queries your application runs. You model your data into an index schema, connect data sources, and expose search, filtering, faceting, and ranking to users. Thoughtful implementation makes content fast and easy to find.
Definition
Intelligent search combines traditional search with AI to provide more relevant and contextual results.
Types
Semantic Search
Search that understands meaning and context
Faceted Search
Search with filtering and refinement options
Autocomplete
Real-time search suggestions
Synonyms and Query Expansion
Search that understands related terms
Use Cases
- Improving search relevance
- Reducing search time
- Enhancing user experience
- Handling complex queries
- Supporting multiple languages
Implementation
Search implementation involves configuring indexes, defining search skills, and optimizing for performance and relevance.
In Practice
Implementation steps include designing the index fields and analyzers, configuring indexers to pull from sources like Blob Storage or SQL, and tuning scoring profiles, synonyms, and semantic or vector search. Monitoring query patterns guides ongoing relevance improvements.
Key Points
- Design for user experience
- Optimize for performance
- Consider multilingual support
- Monitor search analytics
References
- Search Implementation Guide — Guide to implementing Azure Search