diff --git a/libs/community/langchain_community/embeddings/bedrock.py b/libs/community/langchain_community/embeddings/bedrock.py
index 529809fb911..7ab94df4dcb 100644
--- a/libs/community/langchain_community/embeddings/bedrock.py
+++ b/libs/community/langchain_community/embeddings/bedrock.py
@@ -3,6 +3,7 @@ import json
 import os
 from typing import Any, Dict, List, Optional
 
+import numpy as np
 from langchain_core.embeddings import Embeddings
 from langchain_core.pydantic_v1 import BaseModel, Extra, root_validator
 from langchain_core.runnables.config import run_in_executor
@@ -64,6 +65,9 @@ class BedrockEmbeddings(BaseModel, Embeddings):
     endpoint_url: Optional[str] = None
     """Needed if you don't want to default to us-east-1 endpoint"""
 
+    normalize: bool = False
+    """Whether the embeddings should be normalized to unit vectors"""
+
     class Config:
         """Configuration for this pydantic object."""
 
@@ -145,6 +149,12 @@ class BedrockEmbeddings(BaseModel, Embeddings):
         except Exception as e:
             raise ValueError(f"Error raised by inference endpoint: {e}")
 
+    def _normalize_vector(self, embeddings: List[float]) -> List[float]:
+        """Normalize the embedding to a unit vector."""
+        emb = np.array(embeddings)
+        norm_emb = emb / np.linalg.norm(emb)
+        return norm_emb.tolist()
+
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         """Compute doc embeddings using a Bedrock model.
 
@@ -157,7 +167,12 @@ class BedrockEmbeddings(BaseModel, Embeddings):
         results = []
         for text in texts:
             response = self._embedding_func(text)
+
+            if self.normalize:
+                response = self._normalize_vector(response)
+
             results.append(response)
+
         return results
 
     def embed_query(self, text: str) -> List[float]:
@@ -169,7 +184,12 @@ class BedrockEmbeddings(BaseModel, Embeddings):
         Returns:
             Embeddings for the text.
         """
-        return self._embedding_func(text)
+        embedding = self._embedding_func(text)
+
+        if self.normalize:
+            return self._normalize_vector(embedding)
+
+        return embedding
 
     async def aembed_query(self, text: str) -> List[float]:
         """Asynchronous compute query embeddings using a Bedrock model.