hybrid search uses semantic ratio, error handling

meilisearch-types/src/error.rs

@@ -299,6 +299,7 @@ MissingFacetSearchFacetName           , InvalidRequest       , BAD_REQUEST ;
 MissingIndexUid                       , InvalidRequest       , BAD_REQUEST ;
 MissingMasterKey                      , Auth                 , UNAUTHORIZED ;
 MissingPayload                        , InvalidRequest       , BAD_REQUEST ;
+MissingSearchHybrid                   , InvalidRequest       , BAD_REQUEST ;
 MissingSwapIndexes                    , InvalidRequest       , BAD_REQUEST ;
 MissingTaskFilters                    , InvalidRequest       , BAD_REQUEST ;
 NoSpaceLeftOnDevice                   , System               , UNPROCESSABLE_ENTITY;

meilisearch/src/analytics/segment_analytics.rs

@@ -692,7 +692,7 @@ impl SearchAggregator {
             ret.max_terms_number = q.split_whitespace().count();
         }
 
-        if let Some(meilisearch_types::milli::VectorQuery::Vector(ref vector)) = vector {
+        if let Some(ref vector) = vector {
             ret.max_vector_size = vector.len();
         }
 

meilisearch/src/error.rs

@@ -51,6 +51,8 @@ pub enum MeilisearchHttpError {
     DocumentFormat(#[from] DocumentFormatError),
     #[error(transparent)]
     Join(#[from] JoinError),
+    #[error("Invalid request: missing `hybrid` parameter when both `q` and `vector` are present.")]
+    MissingSearchHybrid,
 }
 
 impl ErrorCode for MeilisearchHttpError {
@@ -74,6 +76,7 @@ impl ErrorCode for MeilisearchHttpError {
             MeilisearchHttpError::FileStore(_) => Code::Internal,
             MeilisearchHttpError::DocumentFormat(e) => e.error_code(),
             MeilisearchHttpError::Join(_) => Code::Internal,
+            MeilisearchHttpError::MissingSearchHybrid => Code::MissingSearchHybrid,
         }
     }
 }

meilisearch/src/routes/indexes/facet_search.rs

@@ -7,7 +7,6 @@ use meilisearch_types::deserr::DeserrJsonError;
 use meilisearch_types::error::deserr_codes::*;
 use meilisearch_types::error::ResponseError;
 use meilisearch_types::index_uid::IndexUid;
-use meilisearch_types::milli::VectorQuery;
 use serde_json::Value;
 
 use crate::analytics::{Analytics, FacetSearchAggregator};
@@ -121,7 +120,7 @@ impl From<FacetSearchQuery> for SearchQuery {
             highlight_post_tag: DEFAULT_HIGHLIGHT_POST_TAG(),
             crop_marker: DEFAULT_CROP_MARKER(),
             matching_strategy,
-            vector: vector.map(VectorQuery::Vector),
+            vector,
             attributes_to_search_on,
             hybrid,
         }

meilisearch/src/routes/indexes/search.rs

@@ -8,7 +8,7 @@ use meilisearch_types::deserr::{DeserrJsonError, DeserrQueryParamError};
 use meilisearch_types::error::deserr_codes::*;
 use meilisearch_types::error::ResponseError;
 use meilisearch_types::index_uid::IndexUid;
-use meilisearch_types::milli::{self, VectorQuery};
+use meilisearch_types::milli;
 use meilisearch_types::serde_cs::vec::CS;
 use serde_json::Value;
 
@@ -128,7 +128,7 @@ impl From<SearchQueryGet> for SearchQuery {
 
         Self {
             q: other.q,
-            vector: other.vector.map(CS::into_inner).map(VectorQuery::Vector),
+            vector: other.vector.map(CS::into_inner),
             offset: other.offset.0,
             limit: other.limit.0,
             page: other.page.as_deref().copied(),
@@ -258,21 +258,13 @@ pub async fn embed(
     index_scheduler: &IndexScheduler,
     index: &milli::Index,
 ) -> Result<(), ResponseError> {
-    match query.vector.take() {
+    if let (None, Some(q), Some(HybridQuery { semantic_ratio: _, embedder })) =
-        Some(VectorQuery::String(prompt)) => {
+        (&query.vector, &query.q, &query.hybrid)
+    {
         let embedder_configs = index.embedding_configs(&index.read_txn()?)?;
         let embedders = index_scheduler.embedders(embedder_configs)?;
 
-            let embedder_name =
+        let embedder = if let Some(embedder_name) = embedder {
-                if let Some(HybridQuery { semantic_ratio: _, embedder: Some(embedder) }) =
-                    &query.hybrid
-                {
-                    Some(embedder)
-                } else {
-                    None
-                };
-
-            let embedder = if let Some(embedder_name) = embedder_name {
             embedders.get(embedder_name)
         } else {
             embedders.get_default()
@@ -283,7 +275,7 @@ pub async fn embed(
             .map_err(milli::Error::from)?
             .0;
         let embeddings = embedder
-                .embed(vec![prompt])
+            .embed(vec![q.to_owned()])
             .await
             .map_err(milli::vector::Error::from)
             .map_err(milli::Error::from)?
@@ -292,15 +284,11 @@ pub async fn embed(
 
         if embeddings.iter().nth(1).is_some() {
             warn!("Ignoring embeddings past the first one in long search query");
-                query.vector =
+            query.vector = Some(embeddings.iter().next().unwrap().to_vec());
-                    Some(VectorQuery::Vector(embeddings.iter().next().unwrap().to_vec()));
         } else {
-                query.vector = Some(VectorQuery::Vector(embeddings.into_inner()));
+            query.vector = Some(embeddings.into_inner());
         }
     }
-        Some(vector) => query.vector = Some(vector),
-        None => {}
-    };
     Ok(())
 }
 

meilisearch/src/search.rs

@@ -7,14 +7,13 @@ use deserr::Deserr;
 use either::Either;
 use index_scheduler::RoFeatures;
 use indexmap::IndexMap;
-use log::warn;
 use meilisearch_auth::IndexSearchRules;
 use meilisearch_types::deserr::DeserrJsonError;
 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::{FacetValueHit, OrderBy, SearchForFacetValues, VectorQuery};
+use meilisearch_types::milli::{FacetValueHit, OrderBy, SearchForFacetValues};
 use meilisearch_types::settings::DEFAULT_PAGINATION_MAX_TOTAL_HITS;
 use meilisearch_types::{milli, Document};
 use milli::tokenizer::TokenizerBuilder;
@@ -44,7 +43,7 @@ pub struct SearchQuery {
     #[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
     pub q: Option<String>,
     #[deserr(default, error = DeserrJsonError<InvalidSearchVector>)]
-    pub vector: Option<milli::VectorQuery>,
+    pub vector: Option<Vec<f32>>,
     #[deserr(default, error = DeserrJsonError<InvalidHybridQuery>)]
     pub hybrid: Option<HybridQuery>,
     #[deserr(default = DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSearchOffset>)]
@@ -105,6 +104,8 @@ impl std::convert::TryFrom<f32> for SemanticRatio {
     type Error = InvalidSearchSemanticRatio;
 
     fn try_from(f: f32) -> Result<Self, Self::Error> {
+        // the suggested "fix" is: `!(0.0..=1.0).contains(&f)`` which is allegedly less readable
+        #[allow(clippy::manual_range_contains)]
         if f > 1.0 || f < 0.0 {
             Err(InvalidSearchSemanticRatio)
         } else {
@@ -139,7 +140,7 @@ pub struct SearchQueryWithIndex {
     #[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
     pub q: Option<String>,
     #[deserr(default, error = DeserrJsonError<InvalidSearchQ>)]
-    pub vector: Option<VectorQuery>,
+    pub vector: Option<Vec<f32>>,
     #[deserr(default, error = DeserrJsonError<InvalidHybridQuery>)]
     pub hybrid: Option<HybridQuery>,
     #[deserr(default = DEFAULT_SEARCH_OFFSET(), error = DeserrJsonError<InvalidSearchOffset>)]
@@ -376,8 +377,16 @@ fn prepare_search<'t>(
 ) -> Result<(milli::Search<'t>, bool, usize, usize), MeilisearchHttpError> {
     let mut search = index.search(rtxn);
 
-    if query.vector.is_some() && query.q.is_some() {
+    if query.vector.is_some() {
-        warn!("Attempting hybrid search");
+        features.check_vector("Passing `vector` as a query parameter")?;
+    }
+
+    if query.hybrid.is_some() {
+        features.check_vector("Passing `hybrid` as a query parameter")?;
+    }
+
+    if query.hybrid.is_none() && query.q.is_some() && query.vector.is_some() {
+        return Err(MeilisearchHttpError::MissingSearchHybrid);
     }
 
     if let Some(ref vector) = query.vector {
@@ -385,14 +394,9 @@ fn prepare_search<'t>(
             // If semantic ratio is 0.0, only the query search will impact the search results,
             // skip the vector
             Some(hybrid) if *hybrid.semantic_ratio == 0.0 => (),
-            _otherwise => match vector {
+            _otherwise => {
-                VectorQuery::Vector(vector) => {
                 search.vector(vector.clone());
             }
-                VectorQuery::String(_) => {
-                    panic!("Failed while preparing search; caller did not generate embedding for query")
-                }
-            },
         }
     }
 
@@ -431,10 +435,6 @@ fn prepare_search<'t>(
         features.check_score_details()?;
     }
 
-    if query.vector.is_some() {
-        features.check_vector("Passing `vector` as a query parameter")?;
-    }
-
     if let Some(HybridQuery { embedder: Some(embedder), .. }) = &query.hybrid {
         search.embedder_name(embedder);
     }
@@ -492,7 +492,7 @@ pub fn perform_search(
     let milli::SearchResult { documents_ids, matching_words, candidates, document_scores, .. } =
         match &query.hybrid {
             Some(hybrid) => match *hybrid.semantic_ratio {
-                0.0 | 1.0 => search.execute()?,
+                ratio if ratio == 0.0 || ratio == 1.0 => search.execute()?,
                 ratio => search.execute_hybrid(ratio)?,
             },
             None => search.execute()?,
@@ -700,10 +700,7 @@ pub fn perform_search(
         hits: documents,
         hits_info,
         query: query.q.unwrap_or_default(),
-        vector: match query.vector {
+        vector: query.vector,
-            Some(VectorQuery::Vector(vector)) => Some(vector),
-            _ => None,
-        },
         processing_time_ms: before_search.elapsed().as_millis(),
         facet_distribution,
         facet_stats,

meilisearch/tests/search/hybrid.rs

@@ -1,4 +1,4 @@
-use meili_snap::{json_string, snapshot};
+use meili_snap::snapshot;
 use once_cell::sync::Lazy;
 
 use crate::common::index::Index;

milli/src/lib.rs

@@ -59,7 +59,7 @@ pub use self::index::Index;
 pub use self::search::{
     FacetDistribution, FacetValueHit, Filter, FormatOptions, MatchBounds, MatcherBuilder,
     MatchingWords, OrderBy, Search, SearchForFacetValues, SearchResult, TermsMatchingStrategy,
-    VectorQuery, DEFAULT_VALUES_PER_FACET,
+    DEFAULT_VALUES_PER_FACET,
 };
 
 pub type Result<T> = std::result::Result<T, error::Error>;

milli/src/search/hybrid.rs

@@ -1,49 +1,37 @@
 use std::cmp::Ordering;
-use std::collections::HashMap;
 
 use itertools::Itertools;
 use roaring::RoaringBitmap;
 
-use super::new::{execute_vector_search, PartialSearchResult};
 use crate::score_details::{ScoreDetails, ScoreValue, ScoringStrategy};
-use crate::{
+use crate::{MatchingWords, Result, Search, SearchResult};
-    execute_search, DefaultSearchLogger, MatchingWords, Result, Search, SearchContext, SearchResult,
-};
 
-struct CombinedSearchResult {
+struct ScoreWithRatioResult {
     matching_words: MatchingWords,
     candidates: RoaringBitmap,
-    document_scores: Vec<(u32, CombinedScore)>,
+    document_scores: Vec<(u32, ScoreWithRatio)>,
 }
 
-type CombinedScore = (Vec<ScoreDetails>, Option<Vec<ScoreDetails>>);
+type ScoreWithRatio = (Vec<ScoreDetails>, f32);
 
-fn compare_scores(left: &CombinedScore, right: &CombinedScore) -> Ordering {
+fn compare_scores(
-    let mut left_main_it = ScoreDetails::score_values(left.0.iter());
+    &(ref left_scores, left_ratio): &ScoreWithRatio,
-    let mut left_sub_it =
+    &(ref right_scores, right_ratio): &ScoreWithRatio,
-        ScoreDetails::score_values(left.1.as_ref().map(|x| x.iter()).into_iter().flatten());
+) -> Ordering {
-
+    let mut left_it = ScoreDetails::score_values(left_scores.iter());
-    let mut right_main_it = ScoreDetails::score_values(right.0.iter());
+    let mut right_it = ScoreDetails::score_values(right_scores.iter());
-    let mut right_sub_it =
-        ScoreDetails::score_values(right.1.as_ref().map(|x| x.iter()).into_iter().flatten());
-
-    let mut left_main = left_main_it.next();
-    let mut left_sub = left_sub_it.next();
-    let mut right_main = right_main_it.next();
-    let mut right_sub = right_sub_it.next();
 
     loop {
-        let left =
+        let left = left_it.next();
-            take_best_score(&mut left_main, &mut left_sub, &mut left_main_it, &mut left_sub_it);
+        let right = right_it.next();
-
-        let right =
-            take_best_score(&mut right_main, &mut right_sub, &mut right_main_it, &mut right_sub_it);
 
         match (left, right) {
             (None, None) => return Ordering::Equal,
             (None, Some(_)) => return Ordering::Less,
             (Some(_), None) => return Ordering::Greater,
             (Some(ScoreValue::Score(left)), Some(ScoreValue::Score(right))) => {
+                let left = left * left_ratio as f64;
+                let right = right * right_ratio as f64;
                 if (left - right).abs() <= f64::EPSILON {
                     continue;
                 }
@@ -72,94 +60,17 @@ fn compare_scores(left: &CombinedScore, right: &CombinedScore) -> Ordering {
     }
 }
 
-fn take_best_score<'a>(
+impl ScoreWithRatioResult {
-    main_score: &mut Option<ScoreValue<'a>>,
+    fn new(results: SearchResult, ratio: f32) -> Self {
-    sub_score: &mut Option<ScoreValue<'a>>,
+        let document_scores = results
-    main_it: &mut impl Iterator<Item = ScoreValue<'a>>,
-    sub_it: &mut impl Iterator<Item = ScoreValue<'a>>,
-) -> Option<ScoreValue<'a>> {
-    match (*main_score, *sub_score) {
-        (Some(main), None) => {
-            *main_score = main_it.next();
-            Some(main)
-        }
-        (None, Some(sub)) => {
-            *sub_score = sub_it.next();
-            Some(sub)
-        }
-        (main @ Some(ScoreValue::Score(main_f)), sub @ Some(ScoreValue::Score(sub_v))) => {
-            // take max, both advance
-            *main_score = main_it.next();
-            *sub_score = sub_it.next();
-            if main_f >= sub_v {
-                main
-            } else {
-                sub
-            }
-        }
-        (main @ Some(ScoreValue::Score(_)), _) => {
-            *main_score = main_it.next();
-            main
-        }
-        (_, sub @ Some(ScoreValue::Score(_))) => {
-            *sub_score = sub_it.next();
-            sub
-        }
-        (main @ Some(ScoreValue::GeoSort(main_geo)), sub @ Some(ScoreValue::GeoSort(sub_geo))) => {
-            // take best advance both
-            *main_score = main_it.next();
-            *sub_score = sub_it.next();
-            if main_geo >= sub_geo {
-                main
-            } else {
-                sub
-            }
-        }
-        (main @ Some(ScoreValue::Sort(main_sort)), sub @ Some(ScoreValue::Sort(sub_sort))) => {
-            // take best advance both
-            *main_score = main_it.next();
-            *sub_score = sub_it.next();
-            if main_sort >= sub_sort {
-                main
-            } else {
-                sub
-            }
-        }
-        (
-            Some(ScoreValue::GeoSort(_) | ScoreValue::Sort(_)),
-            Some(ScoreValue::GeoSort(_) | ScoreValue::Sort(_)),
-        ) => None,
-
-        (None, None) => None,
-    }
-}
-
-impl CombinedSearchResult {
-    fn new(main_results: SearchResult, ancillary_results: PartialSearchResult) -> Self {
-        let mut docid_scores = HashMap::new();
-        for (docid, score) in
-            main_results.documents_ids.iter().zip(main_results.document_scores.into_iter())
-        {
-            docid_scores.insert(*docid, (score, None));
-        }
-
-        for (docid, score) in ancillary_results
             .documents_ids
-            .iter()
+            .into_iter()
-            .zip(ancillary_results.document_scores.into_iter())
+            .zip(results.document_scores.into_iter().map(|scores| (scores, ratio)))
-        {
+            .collect();
-            docid_scores
-                .entry(*docid)
-                .and_modify(|(_main_score, ancillary_score)| *ancillary_score = Some(score));
-        }
-
-        let mut document_scores: Vec<_> = docid_scores.into_iter().collect();
-
-        document_scores.sort_by(|(_, left), (_, right)| compare_scores(left, right).reverse());
 
         Self {
-            matching_words: main_results.matching_words,
+            matching_words: results.matching_words,
-            candidates: main_results.candidates,
+            candidates: results.candidates,
             document_scores,
         }
     }
@@ -200,7 +111,7 @@ impl CombinedSearchResult {
 }
 
 impl<'a> Search<'a> {
-    pub fn execute_hybrid(&self) -> Result<SearchResult> {
+    pub fn execute_hybrid(&self, semantic_ratio: f32) -> Result<SearchResult> {
         // TODO: find classier way to achieve that than to reset vector and query params
         // create separate keyword and semantic searches
         let mut search = Search {
@@ -223,8 +134,6 @@ impl<'a> Search<'a> {
         };
 
         let vector_query = search.vector.take();
-        let keyword_query = self.query.as_deref();
-
         let keyword_results = search.execute()?;
 
         // skip semantic search if we don't have a vector query (placeholder search)
@@ -233,7 +142,7 @@ impl<'a> Search<'a> {
         };
 
         // completely skip semantic search if the results of the keyword search are good enough
-        if self.results_good_enough(&keyword_results) {
+        if self.results_good_enough(&keyword_results, semantic_ratio) {
             return Ok(keyword_results);
         }
 
@@ -243,94 +152,18 @@ impl<'a> Search<'a> {
         // TODO: would be better to have two distinct functions at this point
         let vector_results = search.execute()?;
 
-        // Compute keyword scores for vector_results
+        let keyword_results = ScoreWithRatioResult::new(keyword_results, 1.0 - semantic_ratio);
-        let keyword_results_for_vector =
+        let vector_results = ScoreWithRatioResult::new(vector_results, semantic_ratio);
-            self.keyword_results_for_vector(keyword_query, &vector_results)?;
-
-        // compute vector scores for keyword_results
-        let vector_results_for_keyword =
-            // can unwrap because we returned already if there was no vector query
-            self.vector_results_for_keyword(search.vector.as_ref().unwrap(), &keyword_results)?;
-
-        /// TODO apply sementic ratio
-        let keyword_results =
-            CombinedSearchResult::new(keyword_results, vector_results_for_keyword);
-        let vector_results = CombinedSearchResult::new(vector_results, keyword_results_for_vector);
 
         let merge_results =
-            CombinedSearchResult::merge(vector_results, keyword_results, self.offset, self.limit);
+            ScoreWithRatioResult::merge(vector_results, keyword_results, self.offset, self.limit);
         assert!(merge_results.documents_ids.len() <= self.limit);
         Ok(merge_results)
     }
 
-    fn vector_results_for_keyword(
+    fn results_good_enough(&self, keyword_results: &SearchResult, semantic_ratio: f32) -> bool {
-        &self,
+        // A result is good enough if its keyword score is > 0.9 with a semantic ratio of 0.5 => 0.9 * 0.5
-        vector: &[f32],
+        const GOOD_ENOUGH_SCORE: f64 = 0.45;
-        keyword_results: &SearchResult,
-    ) -> Result<PartialSearchResult> {
-        let embedder_name;
-        let embedder_name = match &self.embedder_name {
-            Some(embedder_name) => embedder_name,
-            None => {
-                embedder_name = self.index.default_embedding_name(self.rtxn)?;
-                &embedder_name
-            }
-        };
-
-        let mut ctx = SearchContext::new(self.index, self.rtxn);
-
-        if let Some(searchable_attributes) = self.searchable_attributes {
-            ctx.searchable_attributes(searchable_attributes)?;
-        }
-
-        let universe = keyword_results.documents_ids.iter().collect();
-
-        execute_vector_search(
-            &mut ctx,
-            vector,
-            ScoringStrategy::Detailed,
-            universe,
-            &self.sort_criteria,
-            self.geo_strategy,
-            0,
-            self.limit + self.offset,
-            self.distribution_shift,
-            embedder_name,
-        )
-    }
-
-    fn keyword_results_for_vector(
-        &self,
-        query: Option<&str>,
-        vector_results: &SearchResult,
-    ) -> Result<PartialSearchResult> {
-        let mut ctx = SearchContext::new(self.index, self.rtxn);
-
-        if let Some(searchable_attributes) = self.searchable_attributes {
-            ctx.searchable_attributes(searchable_attributes)?;
-        }
-
-        let universe = vector_results.documents_ids.iter().collect();
-
-        execute_search(
-            &mut ctx,
-            query,
-            self.terms_matching_strategy,
-            ScoringStrategy::Detailed,
-            self.exhaustive_number_hits,
-            universe,
-            &self.sort_criteria,
-            self.geo_strategy,
-            0,
-            self.limit + self.offset,
-            Some(self.words_limit),
-            &mut DefaultSearchLogger,
-            &mut DefaultSearchLogger,
-        )
-    }
-
-    fn results_good_enough(&self, keyword_results: &SearchResult) -> bool {
-        const GOOD_ENOUGH_SCORE: f64 = 0.9;
 
         // 1. we check that we got a sufficient number of results
         if keyword_results.document_scores.len() < self.limit + self.offset {
@@ -341,7 +174,7 @@ impl<'a> Search<'a> {
         // we need to check all results because due to sort like rules, they're not necessarily in relevancy order
         for score in &keyword_results.document_scores {
             let score = ScoreDetails::global_score(score.iter());
-            if score < GOOD_ENOUGH_SCORE {
+            if score * ((1.0 - semantic_ratio) as f64) < GOOD_ENOUGH_SCORE {
                 return false;
             }
         }

milli/src/search/mod.rs

@@ -3,7 +3,6 @@ use std::ops::ControlFlow;
 
 use charabia::normalizer::NormalizerOption;
 use charabia::Normalize;
-use deserr::{DeserializeError, Deserr, Sequence};
 use fst::automaton::{Automaton, Str};
 use fst::{IntoStreamer, Streamer};
 use levenshtein_automata::{LevenshteinAutomatonBuilder as LevBuilder, DFA};
@@ -57,53 +56,6 @@ pub struct Search<'a> {
     embedder_name: Option<String>,
 }
 
-#[derive(Debug, Clone, PartialEq)]
-pub enum VectorQuery {
-    Vector(Vec<f32>),
-    String(String),
-}
-
-impl<E> Deserr<E> for VectorQuery
-where
-    E: DeserializeError,
-{
-    fn deserialize_from_value<V: deserr::IntoValue>(
-        value: deserr::Value<V>,
-        location: deserr::ValuePointerRef,
-    ) -> std::result::Result<Self, E> {
-        match value {
-            deserr::Value::String(s) => Ok(VectorQuery::String(s)),
-            deserr::Value::Sequence(seq) => {
-                let v: std::result::Result<Vec<f32>, _> = seq
-                    .into_iter()
-                    .enumerate()
-                    .map(|(index, v)| match v.into_value() {
-                        deserr::Value::Float(f) => Ok(f as f32),
-                        deserr::Value::Integer(i) => Ok(i as f32),
-                        v => Err(deserr::take_cf_content(E::error::<V>(
-                            None,
-                            deserr::ErrorKind::IncorrectValueKind {
-                                actual: v,
-                                accepted: &[deserr::ValueKind::Float, deserr::ValueKind::Integer],
-                            },
-                            location.push_index(index),
-                        ))),
-                    })
-                    .collect();
-                Ok(VectorQuery::Vector(v?))
-            }
-            _ => Err(deserr::take_cf_content(E::error::<V>(
-                None,
-                deserr::ErrorKind::IncorrectValueKind {
-                    actual: value,
-                    accepted: &[deserr::ValueKind::String, deserr::ValueKind::Sequence],
-                },
-                location,
-            ))),
-        }
-    }
-}
-
 impl<'a> Search<'a> {
     pub fn new(rtxn: &'a heed::RoTxn, index: &'a Index) -> Search<'a> {
         Search {