diff --git a/semantic_router/encoders/bedrock.py b/semantic_router/encoders/bedrock.py
index ff4c0d0c2832c83d592faa4a3ff9a5f228bde290..7adf60c717252b71da0d9f604aec43d2dc5226b9 100644
--- a/semantic_router/encoders/bedrock.py
+++ b/semantic_router/encoders/bedrock.py
@@ -138,7 +138,9 @@ class BedrockEncoder(DenseEncoder):
             ) from err
         return bedrock_client
 
-    def __call__(self, docs: List[Union[str, Dict]], model_kwargs: Optional[Dict] = None) -> List[List[float]]:
+    def __call__(
+        self, docs: List[Union[str, Dict]], model_kwargs: Optional[Dict] = None
+    ) -> List[List[float]]:
         """Generates embeddings for the given documents.
 
         Args:
@@ -169,27 +171,29 @@ class BedrockEncoder(DenseEncoder):
                 embeddings = []
                 if self.name and "amazon" in self.name:
                     for doc in docs:
-                        
+
                         embedding_body = {}
 
                         if isinstance(doc, dict):
-                            embedding_body['inputText'] = doc.get('text')
-                            embedding_body['inputImage'] = doc.get('image')  # expects a base64-encoded image
+                            embedding_body["inputText"] = doc.get("text")
+                            embedding_body["inputImage"] = doc.get(
+                                "image"
+                            )  # expects a base64-encoded image
                         else:
-                            embedding_body['inputText'] = doc
+                            embedding_body["inputText"] = doc
 
                         # Add model-specific inference parameters
                         if model_kwargs:
                             embedding_body = embedding_body | model_kwargs
-                        
+
                         # Clean up null values
                         embedding_body = {k: v for k, v in embedding_body.items() if v}
-                        
+
                         # Format payload
-                        embedding_body = json.dumps(embedding_body)
+                        embedding_body_payload: str = json.dumps(embedding_body)
 
                         response = self.client.invoke_model(
-                            body=embedding_body,
+                            body=embedding_body_payload,
                             modelId=self.name,
                             accept="application/json",
                             contentType="application/json",
@@ -199,10 +203,7 @@ class BedrockEncoder(DenseEncoder):
                 elif self.name and "cohere" in self.name:
                     chunked_docs = self.chunk_strings(docs)
                     for chunk in chunked_docs:
-                        chunk = {
-                            'texts': chunk,
-                            'input_type': self.input_type
-                        }
+                        chunk = {"texts": chunk, "input_type": self.input_type}
 
                         # Add model-specific inference parameters
                         # Note: if specified, input_type will be overwritten by model_kwargs