diff --git a/libs/aws/langchain_aws/retrievers/bedrock.py b/libs/aws/langchain_aws/retrievers/bedrock.py
index 58906e0a..d899eadc 100644
--- a/libs/aws/langchain_aws/retrievers/bedrock.py
+++ b/libs/aws/langchain_aws/retrievers/bedrock.py
@@ -1,4 +1,4 @@
-from typing import Any, Dict, List, Optional
+from typing import Any, Dict, List, Literal, Optional, Union
 
 import boto3
 from botocore.client import Config
@@ -9,49 +9,76 @@
 from langchain_core.retrievers import BaseRetriever
 from typing_extensions import Annotated
 
+FilterValue = Union[Dict[str, Any], List[Any], int, float, str, bool, None]
+Filter = Dict[str, FilterValue]
+
+
+class SearchFilter(BaseModel):
+    """Filter configuration for retrieval."""
+
+    andAll: Optional[List["SearchFilter"]] = None
+    orAll: Optional[List["SearchFilter"]] = None
+    equals: Optional[Filter] = None
+    greaterThan: Optional[Filter] = None
+    greaterThanOrEquals: Optional[Filter] = None
+    in_: Optional[Filter] = Field(None, alias="in")
+    lessThan: Optional[Filter] = None
+    lessThanOrEquals: Optional[Filter] = None
+    listContains: Optional[Filter] = None
+    notEquals: Optional[Filter] = None
+    notIn: Optional[Filter] = Field(None, alias="notIn")
+    startsWith: Optional[Filter] = None
+    stringContains: Optional[Filter] = None
+
+    class Config:
+        allow_population_by_field_name = True
+
 
 class VectorSearchConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
     """Configuration for vector search."""
 
     numberOfResults: int = 4
+    filter: Optional[SearchFilter] = None
+    overrideSearchType: Optional[Literal["HYBRID", "SEMANTIC"]] = None
 
 
 class RetrievalConfig(BaseModel, extra="allow"):  # type: ignore[call-arg]
     """Configuration for retrieval."""
 
     vectorSearchConfiguration: VectorSearchConfig
+    nextToken: Optional[str] = None
 
 
 class AmazonKnowledgeBasesRetriever(BaseRetriever):
     """`Amazon Bedrock Knowledge Bases` retrieval.
 
-    See https://aws.amazon.com/bedrock/knowledge-bases for more info.
-
-    Args:
-        knowledge_base_id: Knowledge Base ID.
-        region_name: The aws region e.g., `us-west-2`.
-            Fallback to AWS_DEFAULT_REGION env variable or region specified in
-            ~/.aws/config.
-        credentials_profile_name: The name of the profile in the ~/.aws/credentials
-            or ~/.aws/config files, which has either access keys or role information
-            specified. If not specified, the default credential profile or, if on an
-            EC2 instance, credentials from IMDS will be used.
-        client: boto3 client for bedrock agent runtime.
-        retrieval_config: Configuration for retrieval.
-
-    Example:
-        .. code-block:: python
-
-            from langchain_community.retrievers import AmazonKnowledgeBasesRetriever
-
-            retriever = AmazonKnowledgeBasesRetriever(
-                knowledge_base_id="<knowledge-base-id>",
-                retrieval_config={
-                    "vectorSearchConfiguration": {
-                        "numberOfResults": 4
-                    }
-                },
-            )
+        See https://aws.amazon.com/bedrock/knowledge-bases for more info.
+
+        Args:
+            knowledge_base_id: Knowledge Base ID.
+            region_name: The aws region e.g., `us-west-2`.
+                Fallback to AWS_DEFAULT_REGION env variable or region specified in
+                ~/.aws/config.
+            credentials_profile_name: The name of the profile in the ~/.aws/credentials
+                or ~/.aws/config files, which has either access keys or role information
+                specified. If not specified, the default credential profile or, if on an
+                EC2 instance, credentials from IMDS will be used.
+            client: boto3 client for bedrock agent runtime.
+            retrieval_config: Configuration for retrieval.
+
+        Example:
+            .. code-block:: python
+
+                from langchain_community.retrievers import AmazonKnowledgeBasesRetriever
+
+    retriever = AmazonKnowledgeBasesRetriever(
+        knowledge_base_id="<knowledge-base-id>",
+        retrieval_config={
+            "vectorSearchConfiguration": {
+                "numberOfResults": 4
+            }
+        },
+    )
     """
 
     knowledge_base_id: str
@@ -123,12 +150,15 @@ def _filter_by_score_confidence(self, docs: List[Document]) -> List[Document]:
         return filtered_docs
 
     def _get_relevant_documents(
-        self, query: str, *, run_manager: CallbackManagerForRetrieverRun
+        self,
+        query: str,
+        *,
+        run_manager: CallbackManagerForRetrieverRun,
     ) -> List[Document]:
         response = self.client.retrieve(
             retrievalQuery={"text": query.strip()},
             knowledgeBaseId=self.knowledge_base_id,
-            retrievalConfiguration=self.retrieval_config.dict(),
+            retrievalConfiguration=self.retrieval_config.dict(exclude_none=True),
         )
         results = response["retrievalResults"]
         documents = []