diff --git a/meilisearch/src/search.rs b/meilisearch/src/search.rs
index c0d707657..f7cfe99f9 100644
--- a/meilisearch/src/search.rs
+++ b/meilisearch/src/search.rs
@@ -17,7 +17,7 @@ use meilisearch_types::{milli, Document};
 use milli::tokenizer::TokenizerBuilder;
 use milli::{
     AscDesc, FieldId, FieldsIdsMap, Filter, FormatOptions, Index, MatchBounds, MatcherBuilder,
-    SortError, TermsMatchingStrategy, DEFAULT_VALUES_PER_FACET,
+    SortError, TermsMatchingStrategy, VectorOrArrayOfVectors, DEFAULT_VALUES_PER_FACET,
 };
 use ordered_float::OrderedFloat;
 use regex::Regex;
@@ -432,7 +432,6 @@ pub fn perform_search(
     formatter_builder.highlight_suffix(query.highlight_post_tag);
 
     let mut documents = Vec::new();
-
     let documents_iter = index.documents(&rtxn, documents_ids)?;
 
     for ((_id, obkv), score) in documents_iter.into_iter().zip(document_scores.into_iter()) {
@@ -460,7 +459,9 @@ pub fn perform_search(
         }
 
         if let Some(vector) = query.vector.as_ref() {
-            insert_semantic_similarity(&vector, &mut document);
+            if let Some(vectors) = extract_field("_vectors", &fields_ids_map, obkv)? {
+                insert_semantic_similarity(vector, vectors, &mut document);
+            }
         }
 
         let ranking_score =
@@ -548,20 +549,18 @@ fn insert_geo_distance(sorts: &[String], document: &mut Document) {
     }
 }
 
-fn insert_semantic_similarity(query: &[f32], document: &mut Document) {
-    if let Some(value) = document.get("_vectors") {
-        let vectors: Vec<Vec<f32>> = match serde_json::from_value(value.clone()) {
-            Ok(Either::Left(vector)) => vec![vector],
-            Ok(Either::Right(vectors)) => vectors,
+fn insert_semantic_similarity(query: &[f32], vectors: Value, document: &mut Document) {
+    let vectors =
+        match serde_json::from_value(vectors).map(VectorOrArrayOfVectors::into_array_of_vectors) {
+            Ok(vectors) => vectors,
             Err(_) => return,
         };
-        let similarity = vectors
-            .into_iter()
-            .map(|v| OrderedFloat(dot_product_similarity(query, &v)))
-            .max()
-            .map(OrderedFloat::into_inner);
-        document.insert("_semanticSimilarity".to_string(), json!(similarity));
-    }
+    let similarity = vectors
+        .into_iter()
+        .map(|v| OrderedFloat(dot_product_similarity(query, &v)))
+        .max()
+        .map(OrderedFloat::into_inner);
+    document.insert("_semanticSimilarity".to_string(), json!(similarity));
 }
 
 fn compute_formatted_options(
@@ -691,6 +690,22 @@ fn make_document(
     Ok(document)
 }
 
+/// Extract the JSON value under the field name specified
+/// but doesn't support nested objects.
+fn extract_field(
+    field_name: &str,
+    field_ids_map: &FieldsIdsMap,
+    obkv: obkv::KvReaderU16,
+) -> Result<Option<serde_json::Value>, MeilisearchHttpError> {
+    match field_ids_map.id(field_name) {
+        Some(fid) => match obkv.get(fid) {
+            Some(value) => Ok(serde_json::from_slice(value).map(Some)?),
+            None => Ok(None),
+        },
+        None => Ok(None),
+    }
+}
+
 fn format_fields<A: AsRef<[u8]>>(
     document: &Document,
     field_ids_map: &FieldsIdsMap,
diff --git a/milli/src/lib.rs b/milli/src/lib.rs
index c93bf88ff..63cf6f397 100644
--- a/milli/src/lib.rs
+++ b/milli/src/lib.rs
@@ -286,6 +286,23 @@ pub fn normalize_facet(original: &str) -> String {
     CompatibilityDecompositionNormalizer.normalize_str(original.trim()).to_lowercase()
 }
 
+/// Represents either a vector or an array of multiple vectors.
+#[derive(serde::Serialize, serde::Deserialize, Debug)]
+#[serde(transparent)]
+pub struct VectorOrArrayOfVectors {
+    #[serde(with = "either::serde_untagged")]
+    inner: either::Either<Vec<f32>, Vec<Vec<f32>>>,
+}
+
+impl VectorOrArrayOfVectors {
+    pub fn into_array_of_vectors(self) -> Vec<Vec<f32>> {
+        match self.inner {
+            either::Either::Left(vector) => vec![vector],
+            either::Either::Right(vectors) => vectors,
+        }
+    }
+}
+
 /// Normalize a vector by dividing the dimensions by the lenght of it.
 pub fn normalize_vector(mut vector: Vec<f32>) -> Vec<f32> {
     let squared: f32 = vector.iter().map(|x| x * x).sum();
diff --git a/milli/src/update/index_documents/extract/extract_vector_points.rs b/milli/src/update/index_documents/extract/extract_vector_points.rs
index 7e2bd25c5..c2a08b320 100644
--- a/milli/src/update/index_documents/extract/extract_vector_points.rs
+++ b/milli/src/update/index_documents/extract/extract_vector_points.rs
@@ -3,11 +3,10 @@ use std::fs::File;
 use std::io;
 
 use bytemuck::cast_slice;
-use either::Either;
 use serde_json::from_slice;
 
 use super::helpers::{create_writer, writer_into_reader, GrenadParameters};
-use crate::{FieldId, InternalError, Result};
+use crate::{FieldId, InternalError, Result, VectorOrArrayOfVectors};
 
 /// Extracts the embedding vector contained in each document under the `_vectors` field.
 ///
@@ -31,9 +30,11 @@ pub fn extract_vector_points<R: io::Read + io::Seek>(
         // first we retrieve the _vectors field
         if let Some(vectors) = obkv.get(vectors_fid) {
             // extract the vectors
-            let vectors: Either<Vec<Vec<f32>>, Vec<f32>> =
-                from_slice(vectors).map_err(InternalError::SerdeJson).unwrap();
-            let vectors = vectors.map_right(|v| vec![v]).into_inner();
+            // TODO return a user error before unwrapping
+            let vectors = from_slice(vectors)
+                .map_err(InternalError::SerdeJson)
+                .map(VectorOrArrayOfVectors::into_array_of_vectors)
+                .unwrap();
 
             for (i, vector) in vectors.into_iter().enumerate() {
                 match u16::try_from(i) {