diff --git a/langchain/vectorstores/elastic_vector_search.py b/langchain/vectorstores/elastic_vector_search.py
index 6663e79d..d67974d1 100644
--- a/langchain/vectorstores/elastic_vector_search.py
+++ b/langchain/vectorstores/elastic_vector_search.py
@@ -177,7 +177,7 @@ class ElasticVectorSearch(VectorStore, ABC):
         except NotFoundError:
             # TODO would be nice to create index before embedding,
             # just to save expensive steps for last
-            self.client.indices.create(index=self.index_name, mappings=mapping)
+            self.create_index(self.client, self.index_name, mapping)
 
         for i, text in enumerate(texts):
             metadata = metadatas[i] if metadatas else {}
@@ -226,7 +226,9 @@ class ElasticVectorSearch(VectorStore, ABC):
         """
         embedding = self.embedding.embed_query(query)
         script_query = _default_script_query(embedding, filter)
-        response = self.client.search(index=self.index_name, query=script_query, size=k)
+        response = self.client_search(
+            self.client, self.index_name, script_query, size=k
+        )
         hits = [hit for hit in response["hits"]["hits"]]
         docs_and_scores = [
             (
@@ -281,3 +283,24 @@ class ElasticVectorSearch(VectorStore, ABC):
             texts, metadatas=metadatas, refresh_indices=refresh_indices
         )
         return vectorsearch
+
+    def create_index(self, client: Any, index_name: str, mapping: Dict) -> None:
+        version_num = client.info()["version"]["number"][0]
+        version_num = int(version_num)
+        if version_num >= 8:
+            client.indices.create(index=index_name, mappings=mapping)
+        else:
+            client.indices.create(index=index_name, body={"mappings": mapping})
+
+    def client_search(
+        self, client: Any, index_name: str, script_query: Dict, size: int
+    ) -> Any:
+        version_num = client.info()["version"]["number"][0]
+        version_num = int(version_num)
+        if version_num >= 8:
+            response = client.search(index=index_name, query=script_query, size=size)
+        else:
+            response = client.search(
+                index=index_name, body={"query": script_query, "size": size}
+            )
+        return response