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https://github.com/meilisearch/MeiliSearch
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Reindex embedders
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parent
03fda78901
commit
21296190a3
@ -1238,7 +1238,7 @@ impl InnerIndexSettingsDiff {
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old_settings: InnerIndexSettings,
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new_settings: InnerIndexSettings,
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primary_key_id: Option<FieldId>,
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embedding_config_updates: BTreeMap<String, EmbedderAction>,
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mut embedding_config_updates: BTreeMap<String, EmbedderAction>,
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settings_update_only: bool,
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) -> Self {
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let only_additional_fields = match (
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@ -1273,6 +1273,32 @@ impl InnerIndexSettingsDiff {
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let cache_user_defined_searchables = old_settings.user_defined_searchable_fields
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!= new_settings.user_defined_searchable_fields;
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// if the user-defined searchables changed, then we need to reindex prompts.
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if cache_user_defined_searchables {
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for (embedder_name, (config, _)) in new_settings.embedding_configs.inner_as_ref() {
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// skip embedders that don't use document templates
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if !config.uses_document_template() {
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continue;
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}
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// note: this could currently be entry.or_insert(..), but we're future-proofing with an explicit match
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// this always makes the code clearer by explicitly handling the cases
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match embedding_config_updates.entry(embedder_name.clone()) {
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std::collections::btree_map::Entry::Vacant(entry) => {
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entry.insert(EmbedderAction::Reindex(ReindexAction::RegeneratePrompts));
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}
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std::collections::btree_map::Entry::Occupied(entry) => match entry.get() {
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EmbedderAction::WriteBackToDocuments(_) => { /* we are deleting this embedder, so no point in regeneration */
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}
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EmbedderAction::Reindex(ReindexAction::FullReindex) => { /* we are already fully reindexing */
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}
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EmbedderAction::Reindex(ReindexAction::RegeneratePrompts) => { /* we are already regenerating prompts */
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}
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},
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};
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}
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}
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InnerIndexSettingsDiff {
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old: old_settings,
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new: new_settings,
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@ -305,6 +305,16 @@ impl Embedder {
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Embedder::Rest(embedder) => embedder.distribution(),
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}
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}
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pub fn uses_document_template(&self) -> bool {
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match self {
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Embedder::HuggingFace(_)
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| Embedder::OpenAi(_)
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| Embedder::Ollama(_)
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| Embedder::Rest(_) => true,
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Embedder::UserProvided(_) => false,
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}
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}
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}
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/// Describes the mean and sigma of distribution of embedding similarity in the embedding space.
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