diff --git a/src/backend/langflow/components/llms/AmazonBedrock.py b/src/backend/langflow/components/llms/AmazonBedrock.py
index 04785db63..306f84e30 100644
--- a/src/backend/langflow/components/llms/AmazonBedrock.py
+++ b/src/backend/langflow/components/llms/AmazonBedrock.py
@@ -1,7 +1,9 @@
 from typing import Optional
-from langflow import CustomComponent
-from langchain.llms.bedrock import Bedrock
+
 from langchain.llms.base import BaseLLM
+from langchain.llms.bedrock import Bedrock
+
+from langflow import CustomComponent
 
 
 class AmazonBedrockComponent(CustomComponent):
@@ -27,18 +29,32 @@ class AmazonBedrockComponent(CustomComponent):
             },
             "credentials_profile_name": {"display_name": "Credentials Profile Name"},
             "streaming": {"display_name": "Streaming", "field_type": "bool"},
-            "code": {"show": False},
+            "endpoint_url": {"display_name": "Endpoint URL"},
+            "region_name": {"display_name": "Region Name"},
+            "model_kwargs": {"display_name": "Model Kwargs"},
+            "cache": {"display_name": "Cache"},
+            "code": {"advanced": True},
         }
 
     def build(
         self,
         model_id: str = "anthropic.claude-instant-v1",
         credentials_profile_name: Optional[str] = None,
+        region_name: Optional[str] = None,
+        model_kwargs: Optional[dict] = None,
+        endpoint_url: Optional[str] = None,
+        streaming: bool = False,
+        cache: bool | None = None,
     ) -> BaseLLM:
         try:
             output = Bedrock(
                 credentials_profile_name=credentials_profile_name,
                 model_id=model_id,
+                region_name=region_name,
+                model_kwargs=model_kwargs,
+                endpoint_url=endpoint_url,
+                streaming=streaming,
+                cache=cache,
             )  # type: ignore
         except Exception as e:
             raise ValueError("Could not connect to AmazonBedrock API.") from e