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https://github.com/meilisearch/meilisearch.git
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Merge branch 'request-fragments-test' into fragment-filters
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@ -101,6 +101,10 @@ impl<T> Setting<T> {
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matches!(self, Self::NotSet)
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}
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pub const fn is_reset(&self) -> bool {
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matches!(self, Self::Reset)
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}
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/// If `Self` is `Reset`, then map self to `Set` with the provided `val`.
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pub fn or_reset(self, val: T) -> Self {
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match self {
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@ -1213,6 +1217,10 @@ impl<'a, 't, 'i> Settings<'a, 't, 'i> {
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// new config
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EitherOrBoth::Right((name, mut setting)) => {
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tracing::debug!(embedder = name, "new embedder");
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// if we are asked to reset an embedder that doesn't exist, just ignore it
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if setting.is_reset() {
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continue;
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}
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// apply the default source in case the source was not set so that it gets validated
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crate::vector::settings::EmbeddingSettings::apply_default_source(&mut setting);
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crate::vector::settings::EmbeddingSettings::apply_default_openai_model(
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@ -59,12 +59,24 @@ pub struct EmbedderOptions {
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impl Embedder {
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pub fn new(
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EmbedderOptions { search, index }: EmbedderOptions,
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EmbedderOptions { search: search_options, index: index_options }: EmbedderOptions,
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cache_cap: usize,
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) -> Result<Self, NewEmbedderError> {
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let search = SubEmbedder::new(search, cache_cap)?;
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// don't check similarity if one child is a rest embedder with fragments
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// FIXME: skipping the check isn't ideal but we are unsure how to handle fragments in this context
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let mut skip_similarity_check = false;
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for options in [&search_options, &index_options] {
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if let SubEmbedderOptions::Rest(options) = &options {
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if !options.search_fragments.is_empty() || !options.indexing_fragments.is_empty() {
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skip_similarity_check = true;
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break;
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}
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}
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}
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let search = SubEmbedder::new(search_options, cache_cap)?;
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// cache is only used at search
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let index = SubEmbedder::new(index, 0)?;
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let index = SubEmbedder::new(index_options, 0)?;
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// check dimensions
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if search.dimensions() != index.dimensions() {
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@ -73,7 +85,12 @@ impl Embedder {
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index.dimensions(),
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));
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}
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// check similarity
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if skip_similarity_check {
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return Ok(Self { search, index });
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}
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let search_embeddings = search
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.embed(
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vec![
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