**Changes:**
The Documents changes now take a selector closure instead of a list of field to match the field to extract.
The seek_leaf_values_in_object function now uses a selector closure of a list of field to match the field to extract
The facet database extraction is now relying on the FilterableAttributesRule to match the field to extract.
The facet-search database extraction is now relying on the FieldIdMapWithMetadata to select the field to index.
The facet level database extraction is now relying on the FieldIdMapWithMetadata to select the field to index.
**Important:**
Because the filterable attributes are patterns now,
the fieldIdMap will only register the fields that exists in at least one document.
if a field doesn't exist in any document, it will not be registered even if it has been specified in the filterable fields.
**Impact:**
- Document Addition/modification facet indexing
- Document deletion facet indexing
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5288: Improve AI logging r=dureuill a=Kerollmops
This PR fixes#5285 and brings the changes from #5233 to simplify debugging indexation and search performance issues related to AI. The following texts can be found in the logs to debug and understand performance issues:
- `embed_one: search` represents the time we spent waiting for the embedding generation, i.e., OpenAI, local HuggingFace, Ollama.
- `filtered_universe: search::universe` the time spent filtering the documents.
- ~`next_bucket: search::vector_sort` is the time spent finding the nearest neighbors (ANNs) in the vector store (arroy), locally~ was being triggered too many times.
- `indexing::vectors` is the time arroy spends indexing the new vectors for a batch.
- `documents::extract vectors` and `documents::merge vectors` to see the time spent generating and writing the embeddings.
Co-authored-by: Kerollmops <clement@meilisearch.com>
