diff --git a/libs/community/langchain_community/vectorstores/azuresearch.py b/libs/community/langchain_community/vectorstores/azuresearch.py
index 8ff5a9583b3..1ba6bbf1b80 100644
--- a/libs/community/langchain_community/vectorstores/azuresearch.py
+++ b/libs/community/langchain_community/vectorstores/azuresearch.py
@@ -848,7 +848,6 @@ class AzureSearch(VectorStore):
                     "semantic_hybrid".
             search_kwargs (Optional[Dict]): Keyword arguments to pass to the
                 search function. Can include things like:
-                    k: Amount of documents to return (Default: 4)
                     score_threshold: Minimum relevance threshold
                         for similarity_score_threshold
                     fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
@@ -875,6 +874,16 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
     or "semantic_hybrid_score_threshold"."""
     k: int = 4
     """Number of documents to return."""
+    search_kwargs: dict = {}
+    """Search params.
+        score_threshold: Minimum relevance threshold
+            for similarity_score_threshold
+        fetch_k: Amount of documents to pass to MMR algorithm (Default: 20)
+        lambda_mult: Diversity of results returned by MMR;
+            1 for minimum diversity and 0 for maximum. (Default: 0.5)
+        filter: Filter by document metadata
+    """
+
     allowed_search_types: ClassVar[Collection[str]] = (
         "similarity",
         "similarity_score_threshold",
@@ -907,31 +916,33 @@ class AzureSearchVectorStoreRetriever(BaseRetriever):
         run_manager: CallbackManagerForRetrieverRun,
         **kwargs: Any,
     ) -> List[Document]:
+        params = {**self.search_kwargs, **kwargs}
+
         if self.search_type == "similarity":
-            docs = self.vectorstore.vector_search(query, k=self.k, **kwargs)
+            docs = self.vectorstore.vector_search(query, k=self.k, **params)
         elif self.search_type == "similarity_score_threshold":
             docs = [
                 doc
                 for doc, _ in self.vectorstore.similarity_search_with_relevance_scores(
-                    query, k=self.k, **kwargs
+                    query, k=self.k, **params
                 )
             ]
         elif self.search_type == "hybrid":
-            docs = self.vectorstore.hybrid_search(query, k=self.k, **kwargs)
+            docs = self.vectorstore.hybrid_search(query, k=self.k, **params)
         elif self.search_type == "hybrid_score_threshold":
             docs = [
                 doc
                 for doc, _ in self.vectorstore.hybrid_search_with_relevance_scores(
-                    query, k=self.k, **kwargs
+                    query, k=self.k, **params
                 )
             ]
         elif self.search_type == "semantic_hybrid":
-            docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **kwargs)
+            docs = self.vectorstore.semantic_hybrid_search(query, k=self.k, **params)
         elif self.search_type == "semantic_hybrid_score_threshold":
             docs = [
                 doc
                 for doc, _ in self.vectorstore.semantic_hybrid_search_with_score(
-                    query, k=self.k, **kwargs
+                    query, k=self.k, **params
                 )
             ]
         else: