Various changes

- fixed seed for arroy
- check vector dimensions as soon as it is provided to search
- don't embed whitespace

meilisearch/src/routes/indexes/search.rs

@@ -9,6 +9,7 @@ use meilisearch_types::error::deserr_codes::*;
 use meilisearch_types::error::ResponseError;
 use meilisearch_types::index_uid::IndexUid;
 use meilisearch_types::milli;
+use meilisearch_types::milli::vector::DistributionShift;
 use meilisearch_types::serde_cs::vec::CS;
 use serde_json::Value;
 
@@ -200,10 +201,11 @@ pub async fn search_with_url_query(
     let index = index_scheduler.index(&index_uid)?;
     let features = index_scheduler.features();
 
-    embed(&mut query, index_scheduler.get_ref(), &index).await?;
+    let distribution = embed(&mut query, index_scheduler.get_ref(), &index).await?;
 
     let search_result =
-        tokio::task::spawn_blocking(move || perform_search(&index, query, features)).await?;
+        tokio::task::spawn_blocking(move || perform_search(&index, query, features, distribution))
+            .await?;
     if let Ok(ref search_result) = search_result {
         aggregate.succeed(search_result);
     }
@@ -238,10 +240,11 @@ pub async fn search_with_post(
 
     let features = index_scheduler.features();
 
-    embed(&mut query, index_scheduler.get_ref(), &index).await?;
+    let distribution = embed(&mut query, index_scheduler.get_ref(), &index).await?;
 
     let search_result =
-        tokio::task::spawn_blocking(move || perform_search(&index, query, features)).await?;
+        tokio::task::spawn_blocking(move || perform_search(&index, query, features, distribution))
+            .await?;
     if let Ok(ref search_result) = search_result {
         aggregate.succeed(search_result);
     }
@@ -257,39 +260,74 @@ pub async fn embed(
     query: &mut SearchQuery,
     index_scheduler: &IndexScheduler,
     index: &milli::Index,
-) -> Result<(), ResponseError> {
-    if let (None, Some(q), Some(HybridQuery { semantic_ratio: _, embedder })) =
-        (&query.vector, &query.q, &query.hybrid)
-    {
-        let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
-        let embedders = index_scheduler.embedders(embedder_configs)?;
+) -> Result<Option<DistributionShift>, ResponseError> {
+    match (&query.hybrid, &query.vector, &query.q) {
+        (Some(HybridQuery { semantic_ratio: _, embedder }), None, Some(q))
+            if !q.trim().is_empty() =>
+        {
+            let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
+            let embedders = index_scheduler.embedders(embedder_configs)?;
 
-        let embedder = if let Some(embedder_name) = embedder {
-            embedders.get(embedder_name)
-        } else {
-            embedders.get_default()
-        };
+            let embedder = if let Some(embedder_name) = embedder {
+                embedders.get(embedder_name)
+            } else {
+                embedders.get_default()
+            };
 
-        let embedder = embedder
-            .ok_or(milli::UserError::InvalidEmbedder("default".to_owned()))
-            .map_err(milli::Error::from)?
-            .0;
-        let embeddings = embedder
-            .embed(vec![q.to_owned()])
-            .await
-            .map_err(milli::vector::Error::from)
-            .map_err(milli::Error::from)?
-            .pop()
-            .expect("No vector returned from embedding");
+            let embedder = embedder
+                .ok_or(milli::UserError::InvalidEmbedder("default".to_owned()))
+                .map_err(milli::Error::from)?
+                .0;
 
-        if embeddings.iter().nth(1).is_some() {
-            warn!("Ignoring embeddings past the first one in long search query");
-            query.vector = Some(embeddings.iter().next().unwrap().to_vec());
-        } else {
-            query.vector = Some(embeddings.into_inner());
+            let distribution = embedder.distribution();
+
+            let embeddings = embedder
+                .embed(vec![q.to_owned()])
+                .await
+                .map_err(milli::vector::Error::from)
+                .map_err(milli::Error::from)?
+                .pop()
+                .expect("No vector returned from embedding");
+
+            if embeddings.iter().nth(1).is_some() {
+                warn!("Ignoring embeddings past the first one in long search query");
+                query.vector = Some(embeddings.iter().next().unwrap().to_vec());
+            } else {
+                query.vector = Some(embeddings.into_inner());
+            }
+            Ok(distribution)
         }
+        (Some(hybrid), vector, _) => {
+            let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
+            let embedders = index_scheduler.embedders(embedder_configs)?;
+
+            let embedder = if let Some(embedder_name) = &hybrid.embedder {
+                embedders.get(embedder_name)
+            } else {
+                embedders.get_default()
+            };
+
+            let embedder = embedder
+                .ok_or(milli::UserError::InvalidEmbedder("default".to_owned()))
+                .map_err(milli::Error::from)?
+                .0;
+
+            if let Some(vector) = vector {
+                if vector.len() != embedder.dimensions() {
+                    return Err(meilisearch_types::milli::Error::UserError(
+                        meilisearch_types::milli::UserError::InvalidVectorDimensions {
+                            expected: embedder.dimensions(),
+                            found: vector.len(),
+                        },
+                    )
+                    .into());
+                }
+            }
+
+            Ok(embedder.distribution())
+        }
+        _ => Ok(None),
     }
-    Ok(())
 }
 
 #[cfg(test)]

meilisearch/src/routes/multi_search.rs

@@ -75,12 +75,15 @@ pub async fn multi_search_with_post(
                 })
                 .with_index(query_index)?;
 
-            embed(&mut query, index_scheduler.get_ref(), &index).await.with_index(query_index)?;
+            let distribution = embed(&mut query, index_scheduler.get_ref(), &index)
+                .await
+                .with_index(query_index)?;
 
-            let search_result =
-                tokio::task::spawn_blocking(move || perform_search(&index, query, features))
-                    .await
-                    .with_index(query_index)?;
+            let search_result = tokio::task::spawn_blocking(move || {
+                perform_search(&index, query, features, distribution)
+            })
+            .await
+            .with_index(query_index)?;
 
             search_results.push(SearchResultWithIndex {
                 index_uid: index_uid.into_inner(),

meilisearch/src/search.rs

@@ -13,6 +13,7 @@ use meilisearch_types::error::deserr_codes::*;
 use meilisearch_types::heed::RoTxn;
 use meilisearch_types::index_uid::IndexUid;
 use meilisearch_types::milli::score_details::{self, ScoreDetails, ScoringStrategy};
+use meilisearch_types::milli::vector::DistributionShift;
 use meilisearch_types::milli::{FacetValueHit, OrderBy, SearchForFacetValues};
 use meilisearch_types::settings::DEFAULT_PAGINATION_MAX_TOTAL_HITS;
 use meilisearch_types::{milli, Document};
@@ -90,16 +91,22 @@ pub struct SearchQuery {
 #[deserr(error = DeserrJsonError<InvalidHybridQuery>, rename_all = camelCase, deny_unknown_fields)]
 pub struct HybridQuery {
     /// TODO validate that sementic ratio is between 0.0 and 1,0
-    #[deserr(default, error = DeserrJsonError<InvalidSearchSemanticRatio>)]
+    #[deserr(default, error = DeserrJsonError<InvalidSearchSemanticRatio>, default)]
     pub semantic_ratio: SemanticRatio,
     #[deserr(default, error = DeserrJsonError<InvalidEmbedder>, default)]
     pub embedder: Option<String>,
 }
 
-#[derive(Debug, Clone, Copy, Default, PartialEq, Deserr)]
+#[derive(Debug, Clone, Copy, PartialEq, Deserr)]
 #[deserr(try_from(f32) = TryFrom::try_from -> InvalidSearchSemanticRatio)]
 pub struct SemanticRatio(f32);
 
+impl Default for SemanticRatio {
+    fn default() -> Self {
+        DEFAULT_SEMANTIC_RATIO()
+    }
+}
+
 impl std::convert::TryFrom<f32> for SemanticRatio {
     type Error = InvalidSearchSemanticRatio;
 
@@ -374,6 +381,7 @@ fn prepare_search<'t>(
     rtxn: &'t RoTxn,
     query: &'t SearchQuery,
     features: RoFeatures,
+    distribution: Option<DistributionShift>,
 ) -> Result<(milli::Search<'t>, bool, usize, usize), MeilisearchHttpError> {
     let mut search = index.search(rtxn);
 
@@ -389,6 +397,8 @@ fn prepare_search<'t>(
         return Err(MeilisearchHttpError::MissingSearchHybrid);
     }
 
+    search.distribution_shift(distribution);
+
     if let Some(ref vector) = query.vector {
         match &query.hybrid {
             // If semantic ratio is 0.0, only the query search will impact the search results,
@@ -482,12 +492,13 @@ pub fn perform_search(
     index: &Index,
     query: SearchQuery,
     features: RoFeatures,
+    distribution: Option<DistributionShift>,
 ) -> Result<SearchResult, MeilisearchHttpError> {
     let before_search = Instant::now();
     let rtxn = index.read_txn()?;
 
     let (search, is_finite_pagination, max_total_hits, offset) =
-        prepare_search(index, &rtxn, &query, features)?;
+        prepare_search(index, &rtxn, &query, features, distribution)?;
 
     let milli::SearchResult { documents_ids, matching_words, candidates, document_scores, .. } =
         match &query.hybrid {
@@ -718,8 +729,9 @@ pub fn perform_facet_search(
     let before_search = Instant::now();
     let rtxn = index.read_txn()?;
 
-    let (search, _, _, _) = prepare_search(index, &rtxn, &search_query, features)?;
-    let mut facet_search = SearchForFacetValues::new(facet_name, search);
+    let (search, _, _, _) = prepare_search(index, &rtxn, &search_query, features, None)?;
+    let mut facet_search =
+        SearchForFacetValues::new(facet_name, search, search_query.hybrid.is_some());
     if let Some(facet_query) = &facet_query {
         facet_search.query(facet_query);
     }

meilisearch/tests/search/hybrid.rs

@@ -27,11 +27,11 @@ async fn index_with_documents<'a>(server: &'a Server, documents: &Value) -> Inde
         )
         .await;
     assert_eq!(202, code, "{:?}", response);
-    index.wait_task(0).await;
+    index.wait_task(response.uid()).await;
 
     let (response, code) = index.add_documents(documents.clone(), None).await;
     assert_eq!(202, code, "{:?}", response);
-    index.wait_task(1).await;
+    index.wait_task(response.uid()).await;
     index
 }
 
@@ -68,7 +68,7 @@ async fn simple_search() {
         )
         .await;
     snapshot!(code, @"200 OK");
-    snapshot!(response["hits"], @r###"[{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2","_vectors":{"default":[1.0,2.0]},"_semanticScore":0.97434163},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3","_vectors":{"default":[2.0,3.0]},"_semanticScore":0.99029034},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1","_vectors":{"default":[1.0,3.0]},"_semanticScore":0.9472136}]"###);
+    snapshot!(response["hits"], @r###"[{"title":"Captain Planet","desc":"He's not part of the Marvel Cinematic Universe","id":"2","_vectors":{"default":[1.0,2.0]}},{"title":"Captain Marvel","desc":"a Shazam ersatz","id":"3","_vectors":{"default":[2.0,3.0]}},{"title":"Shazam!","desc":"a Captain Marvel ersatz","id":"1","_vectors":{"default":[1.0,3.0]}}]"###);
 
     let (response, code) = index
         .search_post(

milli/src/search/mod.rs

@@ -154,6 +154,15 @@ impl<'a> Search<'a> {
         self
     }
 
+    pub fn execute_for_candidates(&self, has_vector_search: bool) -> Result<RoaringBitmap> {
+        if has_vector_search {
+            let ctx = SearchContext::new(self.index, self.rtxn);
+            filtered_universe(&ctx, &self.filter)
+        } else {
+            Ok(self.execute()?.candidates)
+        }
+    }
+
     pub fn execute(&self) -> Result<SearchResult> {
         let embedder_name;
         let embedder_name = match &self.embedder_name {
@@ -297,11 +306,16 @@ pub struct SearchForFacetValues<'a> {
     query: Option<String>,
     facet: String,
     search_query: Search<'a>,
+    is_hybrid: bool,
 }
 
 impl<'a> SearchForFacetValues<'a> {
-    pub fn new(facet: String, search_query: Search<'a>) -> SearchForFacetValues<'a> {
-        SearchForFacetValues { query: None, facet, search_query }
+    pub fn new(
+        facet: String,
+        search_query: Search<'a>,
+        is_hybrid: bool,
+    ) -> SearchForFacetValues<'a> {
+        SearchForFacetValues { query: None, facet, search_query, is_hybrid }
     }
 
     pub fn query(&mut self, query: impl Into<String>) -> &mut Self {
@@ -351,7 +365,9 @@ impl<'a> SearchForFacetValues<'a> {
             None => return Ok(vec![]),
         };
 
-        let search_candidates = self.search_query.execute()?.candidates;
+        let search_candidates = self
+            .search_query
+            .execute_for_candidates(self.is_hybrid || self.search_query.vector.is_some())?;
 
         match self.query.as_ref() {
             Some(query) => {

milli/src/update/index_documents/mod.rs

@@ -509,7 +509,7 @@ where
         // We write the primary key field id into the main database
         self.index.put_primary_key(self.wtxn, &primary_key)?;
         let number_of_documents = self.index.number_of_documents(self.wtxn)?;
-        let mut rng = rand::rngs::StdRng::from_entropy();
+        let mut rng = rand::rngs::StdRng::seed_from_u64(42);
 
         for (embedder_name, dimension) in dimension {
             let wtxn = &mut *self.wtxn;

milli/src/vector/hf.rs

@@ -7,7 +7,7 @@ use hf_hub::{Repo, RepoType};
 use tokenizers::{PaddingParams, Tokenizer};
 
 pub use super::error::{EmbedError, Error, NewEmbedderError};
-use super::{Embedding, Embeddings};
+use super::{DistributionShift, Embedding, Embeddings};
 
 #[derive(
     Debug,
@@ -184,4 +184,12 @@ impl Embedder {
     pub fn dimensions(&self) -> usize {
         self.dimensions
     }
+
+    pub fn distribution(&self) -> Option<DistributionShift> {
+        if self.options.model == "BAAI/bge-base-en-v1.5" {
+            Some(DistributionShift { current_mean: 0.85, current_sigma: 0.1 })
+        } else {
+            None
+        }
+    }
 }

milli/src/vector/mod.rs

@@ -202,6 +202,14 @@ impl Embedder {
             Embedder::UserProvided(embedder) => embedder.dimensions(),
         }
     }
+
+    pub fn distribution(&self) -> Option<DistributionShift> {
+        match self {
+            Embedder::HuggingFace(embedder) => embedder.distribution(),
+            Embedder::OpenAi(embedder) => embedder.distribution(),
+            Embedder::UserProvided(_embedder) => None,
+        }
+    }
 }
 
 #[derive(Debug, Clone, Copy)]

milli/src/vector/openai.rs

@@ -4,7 +4,7 @@ use reqwest::StatusCode;
 use serde::{Deserialize, Serialize};
 
 use super::error::{EmbedError, NewEmbedderError};
-use super::{Embedding, Embeddings};
+use super::{DistributionShift, Embedding, Embeddings};
 
 #[derive(Debug)]
 pub struct Embedder {
@@ -65,6 +65,14 @@ impl EmbeddingModel {
             _ => None,
         }
     }
+
+    fn distribution(&self) -> Option<DistributionShift> {
+        match self {
+            EmbeddingModel::TextEmbeddingAda002 => {
+                Some(DistributionShift { current_mean: 0.90, current_sigma: 0.08 })
+            }
+        }
+    }
 }
 
 pub const OPENAI_EMBEDDINGS_URL: &str = "https://api.openai.com/v1/embeddings";
@@ -326,6 +334,10 @@ impl Embedder {
     pub fn dimensions(&self) -> usize {
         self.options.embedding_model.dimensions()
     }
+
+    pub fn distribution(&self) -> Option<DistributionShift> {
+        self.options.embedding_model.distribution()
+    }
 }
 
 // retrying in case of failure