[Search] Improve and normalizes the search data model

Things to keep in mind:

• Normalize text inputs fields: text, inputs, words must be normalized and use a common pattern for all tasks
• Several es analyzers for text fields: standard and whitespace(?) for fine tuning searches. Default as standard
• What about text fields in metadata ? For now, only terms queries are supported. It’s mean that metadata fields with large content are not enabled to be queries as full text search.
• Created indices should contain mapping info only for its fields. A text classification index should not include mapping info for tokens or text predicted (text2text).
• Review filter fields and align with UI names (if any)
• What about nested fields? like token or metrics info for token classification, or label and its score for text classification. As default, query string dsl does not support nested queries, but it could be nice include some minimal support for that kind of queries.

@dvsrepo @dcfidalgo Anything to include here?

Tasks

To achieve to do the work, we need tackle following tasks (that will be created as separated issues and linked here)

1. [Datasets] Avoid using global template for all indices
2. [Datasets] Dataset migration mechanisms for each release
3. [Datasets] New es document model per task with backward compatibility fields
4. [Datasets] Apply migration to new es doc model
5. [Datasets] Build searches and aggregations using new doc model

[frascuchon]

Not, really. The only “problem” is that you cannot select with predicted sentence you use. It will search in all of them. But i think we can assume that

[frascuchon]

Note: PR recognai/rubrix#1018 introduces breaking changes to version <0.9.0. So we cannot include those changes until v0.11.0 in order to keep compatibility at lease 2 version prior to release