Normalization and semantic processing instructions (for alphanumeric fields or facets
only). These instructions are contained in semantic processors that are automatically
configured and added to the analysis pipeline.
New dedicated index fields and category facets mapping document metas in the index
schema.
Storage settings of index fields in the index schema.
Instructions saying whether to allow full-text search on index fields and search with
prefix handlers. For example, the query title: Recipes with the
title: prefix handler search for documents having
Recipes in their titles).
The corresponding hit meta and hit facets to display in the search results, and prefix
handlers in the search logic.
Typical Data Model Expansion
For example, creating an alphanumeric property called name in a
customer class expands into the following:
Phase
generates...
in...
Data Processing
A customer_name output context that you can manage to
transform/normalize your data before indexation.
Data Processing > Document Processors/ Semantic Processors
Mapping
A mapping from the customer_name output context to the
customer_name field.
If sales is not the default class, the
output context name would instead be customer_name.
If the
customer_name property is to be both an index field and a facet,
the data model creates two customer_nameoutput contexts, with one
mapped to the category, the other mapped to the index field.
Data Processing > Mappings
Index schema
An index field of type text called customer_name.
Data model > Advanced Schema
Search Query
A prefix handler called customer_name that targets the
customer_name index field.
Search Logics > Query Language
Search Results
A facet named customer_name.
Search Logics > Facets
A hit meta named customer_name. At search time, this meta is
automatically renamed to name so the class name
(customer) does not appear in the hit content.
