diff --git a/libs/community/langchain_community/vectorstores/redis/base.py b/libs/community/langchain_community/vectorstores/redis/base.py
index bcad15f369..78cbffcbc7 100644
--- a/libs/community/langchain_community/vectorstores/redis/base.py
+++ b/libs/community/langchain_community/vectorstores/redis/base.py
@@ -23,7 +23,10 @@ from typing import (
 import numpy as np
 import yaml
 from langchain_core._api import deprecated
-from langchain_core.callbacks import CallbackManagerForRetrieverRun
+from langchain_core.callbacks import (
+    AsyncCallbackManagerForRetrieverRun,
+    CallbackManagerForRetrieverRun,
+)
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.utils import get_from_dict_or_env
@@ -1464,6 +1467,37 @@ class RedisVectorStoreRetriever(VectorStoreRetriever):
             raise ValueError(f"search_type of {self.search_type} not allowed.")
         return docs
 
+    async def _aget_relevant_documents(
+        self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun
+    ) -> List[Document]:
+        if self.search_type == "similarity":
+            docs = await self.vectorstore.asimilarity_search(
+                query, **self.search_kwargs
+            )
+        elif self.search_type == "similarity_distance_threshold":
+            if self.search_kwargs["distance_threshold"] is None:
+                raise ValueError(
+                    "distance_threshold must be provided for "
+                    + "similarity_distance_threshold retriever"
+                )
+            docs = await self.vectorstore.asimilarity_search(
+                query, **self.search_kwargs
+            )
+        elif self.search_type == "similarity_score_threshold":
+            docs_and_similarities = (
+                await self.vectorstore.asimilarity_search_with_relevance_scores(
+                    query, **self.search_kwargs
+                )
+            )
+            docs = [doc for doc, _ in docs_and_similarities]
+        elif self.search_type == "mmr":
+            docs = await self.vectorstore.amax_marginal_relevance_search(
+                query, **self.search_kwargs
+            )
+        else:
+            raise ValueError(f"search_type of {self.search_type} not allowed.")
+        return docs
+
     def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]:
         """Add documents to vectorstore."""
         return self.vectorstore.add_documents(documents, **kwargs)