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https://github.com/meilisearch/MeiliSearch
synced 2024-11-26 14:54:27 +01:00
Normalize the vectors during indexation and search
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@ -285,6 +285,18 @@ pub fn normalize_facet(original: &str) -> String {
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CompatibilityDecompositionNormalizer.normalize_str(original.trim()).to_lowercase()
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}
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/// Normalize a vector by dividing the dimensions by the lenght of it.
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pub fn normalize_vector(mut vector: Vec<f32>) -> Vec<f32> {
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let squared: f32 = vector.iter().map(|x| x * x).sum();
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let length = squared.sqrt();
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if length <= f32::EPSILON {
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vector
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} else {
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vector.iter_mut().for_each(|x| *x = *x / length);
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vector
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}
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}
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#[cfg(test)]
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mod tests {
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use serde_json::json;
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@ -49,7 +49,8 @@ use self::interner::Interned;
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use crate::score_details::{ScoreDetails, ScoringStrategy};
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use crate::search::new::distinct::apply_distinct_rule;
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use crate::{
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AscDesc, DocumentId, Filter, Index, Member, Result, TermsMatchingStrategy, UserError, BEU32,
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normalize_vector, AscDesc, DocumentId, Filter, Index, Member, Result, TermsMatchingStrategy,
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UserError, BEU32,
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};
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/// A structure used throughout the execution of a search query.
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@ -454,7 +455,8 @@ pub fn execute_search(
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let hnsw = ctx.index.vector_hnsw(ctx.txn)?.unwrap_or_default();
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let ef = hnsw.len().min(100);
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let mut dest = vec![Neighbor { index: 0, distance: 0 }; ef];
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let neighbors = hnsw.nearest(vector, ef, &mut searcher, &mut dest[..]);
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let vector = normalize_vector(vector.clone());
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let neighbors = hnsw.nearest(&vector, ef, &mut searcher, &mut dest[..]);
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let mut docids = Vec::new();
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for Neighbor { index, distance: _ } in neighbors.iter() {
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@ -283,7 +283,7 @@ fn send_and_extract_flattened_documents_data(
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faceted_fields: &HashSet<FieldId>,
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primary_key_id: FieldId,
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geo_fields_ids: Option<(FieldId, FieldId)>,
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vector_field_id: Option<FieldId>,
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vectors_field_id: Option<FieldId>,
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stop_words: &Option<fst::Set<&[u8]>>,
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max_positions_per_attributes: Option<u32>,
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) -> Result<(
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@ -312,11 +312,11 @@ fn send_and_extract_flattened_documents_data(
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});
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}
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if let Some(vector_field_id) = vector_field_id {
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if let Some(vectors_field_id) = vectors_field_id {
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let documents_chunk_cloned = flattened_documents_chunk.clone();
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let lmdb_writer_sx_cloned = lmdb_writer_sx.clone();
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rayon::spawn(move || {
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let result = extract_vector_points(documents_chunk_cloned, indexer, vector_field_id);
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let result = extract_vector_points(documents_chunk_cloned, indexer, vectors_field_id);
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let _ = match result {
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Ok(vector_points) => {
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lmdb_writer_sx_cloned.send(Ok(TypedChunk::VectorPoints(vector_points)))
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@ -19,6 +19,7 @@ use super::helpers::{
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use super::{ClonableMmap, MergeFn};
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use crate::error::UserError;
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use crate::facet::FacetType;
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use crate::normalize_vector;
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use crate::update::facet::FacetsUpdate;
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use crate::update::index_documents::helpers::{as_cloneable_grenad, try_split_array_at};
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use crate::{lat_lng_to_xyz, CboRoaringBitmapCodec, DocumentId, GeoPoint, Index, Result, BEU32};
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@ -253,6 +254,7 @@ pub(crate) fn write_typed_chunk_into_index(
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return Err(UserError::InvalidVectorDimensions { expected, found })?;
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}
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let vector = normalize_vector(vector);
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let vector_id = hnsw.insert(vector, &mut searcher) as u32;
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index.vector_id_docid.put(wtxn, &BEU32::new(vector_id), &BEU32::new(docid))?;
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}
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