Define search indexes with fields, analyzers, and scoring profiles.
Last verified: May 2026
Define search indexes with fields, analyzers, and scoring profiles.
Required Fields
namefieldsfields[0]fields[0].namescoringProfilesOutput will appear here...The Azure AI Search Index Builder helps you define index schemas for Azure AI Search (formerly Cognitive Search). An index schema specifies fields, data types, analyzers, scoring profiles, and suggesters that determine how your data is indexed and queried. This tool provides a visual schema designer that generates the JSON index definition, making it easy to build well-structured search indexes without hand-crafting complex JSON.
Your team is building product search for an e-commerce site with 500K SKUs. The naive approach: mark every field Searchable+Filterable+Sortable+Facetable. The builder helps you classify each field intentionally: name = Searchable+Retrievable, price = Filterable+Sortable+Retrievable, category = Filterable+Facetable+Retrievable, description = Searchable only. Index size drops 60% vs. all-attributes, query latency drops 40%, and full reindex completes in 2 hours instead of 8.
Field attributes have storage and CPU costs. Marking every field as Searchable + Filterable + Sortable + Facetable doubles index size and significantly slows reindexing. Be deliberate: most fields only need 1-2 attributes. Categorize fields before designing the schema.
You can ADD new fields to an index after creation but not REMOVE or MODIFY them. If a field's data type changes (e.g., string → integer), you must rebuild the index. Plan carefully and prefer string fields when uncertain — they accept more value types and can be filtered with appropriate analyzers.
Custom analyzers are powerful but expensive. Each custom analyzer roughly doubles indexing time for affected fields. Use the built-in language analyzers (en.microsoft, de.microsoft, etc.) when possible — they're optimized and cover most language-specific tokenization needs.
The builder generates an Azure AI Search index definition JSON with fields (name, type, attribute flags, analyzer, synonyms, vector dimension), suggesters, scoring profiles, and CORS settings. It validates that the schema is consistent (e.g., facetable requires retrievable, vector fields have positive dimension) and outputs the JSON ready for the Azure AI Search REST API or `az search index create`.
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