**Changes:**
The transform structure is now relying on FieldIdMapWithMetadata and AttributePatterns to prepare
the obkv documents during a settings reindexing.
The InnerIndexSettingsDiff and InnerIndexSettings structs are now relying on FieldIdMapWithMetadata, FilterableAttributesRule and AttributePatterns to define the field and the databases that should be reindexed.
The faceted_fields_ids, localized_searchable_fields_ids and localized_faceted_fields_ids have been removed in favor of the FieldIdMapWithMetadata.
We are now relying on the FieldIdMapWithMetadata to retain vectors_fids from the facets and the searchables.
The searchable database computing is now relying on the FieldIdMapWithMetadata to know if a field is searchable and retrieve the locales.
The facet database computing is now relying on the FieldIdMapWithMetadata to compute the facet databases, the facet-search and retrieve the locales.
The facet level database computing is now relying on the FieldIdMapWithMetadata and the facet level database are cleared depending on the settings differences (clear_facet_levels_based_on_settings_diff).
The vector point extraction uses the FieldIdMapWithMetadata instead of FieldsIdsMapWithMetadata.
**Impact:**
- Dump import
- Settings update
**Changes:**
The building of the Attributes ranking rule graph was comparing fieldids with weights
which doesn't make sense and may be bug prone, we are now comparing fieldids with fieldids.
**Impact:**
- search: Attribute ranking rule
**Changes:**
The FieldIdMapWithMetadata structure now stores more information about fields.
The metadata_for_field function computes all the needed information relying on the user provided data instead of the enriched data (searchable/sortable)
which may solve an indexing bug on sortable attributes that was not matching the nested fields.
The FieldIdMapWithMetadata structure was duplicated in the embeddings as FieldsIdsMapWithMetadata,
so the FieldsIdsMapWithMetadata has been removed in favor of FieldIdMapWithMetadata.
The Facet distribution is now relying on the FieldIdMapWithMetadata with metadata to match is a field can be faceted.
**Impact:**
- searchable attributes matching
- searchable attributes weight computation
- sortable attributes matching
- faceted fields matching
- prompt computing
- facet distribution
**Changes:**
The search filters are now using the FilterableAttributesFeatures from the FilterableAttributesRules to know if a field is filterable.
Moreover, the FilterableAttributesFeatures is more precise and an error will be returned if an operator is used on a field that doesn't have the related feature.
The facet-search is now checking if the feature is allowed in the FilterableAttributesFeatures and an error will be returned if the field doesn't have the related feature.
**Impact:**
- facet-search is now relying on AttributePatterns to match the locales
- search using filters is now relying on FilterableAttributesFeatures
- distinct attribute is now relying on FilterableAttributesRules
**Changes:**
The filterableAttributes type has been changed from a `BTreeSet<String>` to a `Vec<FilterableAttributesRule>`,
Which is a list of rules defining patterns to match the documents' fields and a set of feature to apply on the matching fields.
The rule order given by the user is now an important information, the features applied on a filterable field will be chosen based on the rule order as we do for the LocalizedAttributesRules.
This means that the list will not be reordered anymore and will keep the user defined order,
moreover, if there are any duplicates, they will not be de-duplicated anymore.
**Impact:**
- Settings API
- the database format of the filterable attributes changed
- may impact the LocalizedAttributesRules due to the AttributePatterns factorization
- OpenAPI generator
5355: Support fetching the pooling method from the model configuration r=Kerollmops a=dureuill
# Pull Request
## Related issue
Fixes#5354
## What does this PR do?
- Fetches the pooling configuration from the model repository
- Use a pooling method that depends on the pooling configuration of that model.
- Allow overriding the pooling method with a new huggingFace embedder parameter `pooling`
- for backward-compatibility with Meilisearch v1.13
- for compatibility with embedders that exhibit the same behavior as Meilisearch v1.13
- Handle the default value of that new parameter
- for compatibility, when importing a db/a dump, it should be set to `forceMean`
- when (re)set from the settings for an embedder, it should be set to `useModel`
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
5351: Bring back v1.13.0 changes into main r=irevoire a=Kerollmops
This PR brings back the changes made in v1.13 into the main branch.
Co-authored-by: ManyTheFish <many@meilisearch.com>
Co-authored-by: Kerollmops <clement@meilisearch.com>
Co-authored-by: Louis Dureuil <louis@meilisearch.com>
Co-authored-by: Clémentine <clementine@meilisearch.com>
Co-authored-by: meili-bors[bot] <89034592+meili-bors[bot]@users.noreply.github.com>
Co-authored-by: Tamo <tamo@meilisearch.com>
Co-authored-by: Clément Renault <clement@meilisearch.com>
5316: Fix the dumpless upgrade corruption r=dureuill a=irevoire
# Pull Request
## Related issue
Fixes https://github.com/meilisearch/meilisearch/issues/5280
## What does this PR do?
- Add a test that ensure we write the version in the index-scheduler even if we have a bug while writing the VERSION file
- Do what was described in the issue
Co-authored-by: Tamo <tamo@meilisearch.com>
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>
