diff --git a/docs/_src/api/api/document_store.md b/docs/_src/api/api/document_store.md
index 85f7895d63..61b69bf7d8 100644
--- a/docs/_src/api/api/document_store.md
+++ b/docs/_src/api/api/document_store.md
@@ -212,8 +212,6 @@ TODO drop params
 #### normalize\_embedding
 
 ```python
-@staticmethod
-@njit
 def normalize_embedding(emb: np.ndarray) -> None
 ```
 
diff --git a/haystack/document_stores/base.py b/haystack/document_stores/base.py
index 6b6f810e5c..f66e9c3791 100644
--- a/haystack/document_stores/base.py
+++ b/haystack/document_stores/base.py
@@ -341,9 +341,7 @@ def get_document_count(
     ) -> int:
         pass
 
-    @staticmethod
-    @njit  # (fastmath=True)
-    def normalize_embedding(emb: np.ndarray) -> None:
+    def normalize_embedding(self, emb: np.ndarray) -> None:
         """
         Performs L2 normalization of embeddings vector inplace. Input can be a single vector (1D array) or a matrix
         (2D array).
@@ -352,16 +350,26 @@ def normalize_embedding(emb: np.ndarray) -> None:
 
         # Single vec
         if len(emb.shape) == 1:
-            norm = np.sqrt(emb.dot(emb))  # faster than np.linalg.norm()
-            if norm != 0.0:
-                emb /= norm
+            self._normalize_embedding_1D(emb)
         # 2D matrix
         else:
-            for vec in emb:
-                vec = np.ascontiguousarray(vec)
-                norm = np.sqrt(vec.dot(vec))
-                if norm != 0.0:
-                    vec /= norm
+            self._normalize_embedding_2D(emb)
+
+    @staticmethod
+    @njit  # (fastmath=True)
+    def _normalize_embedding_1D(emb: np.ndarray) -> None:
+        norm = np.sqrt(emb.dot(emb))  # faster than np.linalg.norm()
+        if norm != 0.0:
+            emb /= norm
+
+    @staticmethod
+    @njit  # (fastmath=True)
+    def _normalize_embedding_2D(emb: np.ndarray) -> None:
+        for vec in emb:
+            vec = np.ascontiguousarray(vec)
+            norm = np.sqrt(vec.dot(vec))
+            if norm != 0.0:
+                vec /= norm
 
     def finalize_raw_score(self, raw_score: float, similarity: Optional[str]) -> float:
         if similarity == "cosine":