diff --git a/src/vanna/opensearch/opensearch_vector.py b/src/vanna/opensearch/opensearch_vector.py
index c49e2bd7..7fab1ecd 100644
--- a/src/vanna/opensearch/opensearch_vector.py
+++ b/src/vanna/opensearch/opensearch_vector.py
@@ -155,6 +155,11 @@ def __init__(self, config=None):
     else:
       max_retries = 10
 
+    if config is not None and "es_http_compress" in config:
+      es_http_compress = config["es_http_compress"]
+    else:
+      es_http_compress = False
+
     print("OpenSearch_VectorStore initialized with es_urls: ", es_urls,
           " host: ", host, " port: ", port, " ssl: ", ssl, " verify_certs: ",
           verify_certs, " timeout: ", timeout, " max_retries: ", max_retries)
@@ -162,7 +167,7 @@ def __init__(self, config=None):
       # Initialize the OpenSearch client by passing a list of URLs
       self.client = OpenSearch(
         hosts=[es_urls],
-        http_compress=True,
+        http_compress=es_http_compress,
         use_ssl=ssl,
         verify_certs=verify_certs,
         timeout=timeout,
@@ -175,7 +180,7 @@ def __init__(self, config=None):
       # Initialize the OpenSearch client by passing a host and port
       self.client = OpenSearch(
         hosts=[{'host': host, 'port': port}],
-        http_compress=True,
+        http_compress=es_http_compress,
         use_ssl=ssl,
         verify_certs=verify_certs,
         timeout=timeout,
@@ -267,6 +272,7 @@ def get_related_ddl(self, question: str, **kwargs) -> List[str]:
         }
       }
     }
+    print(query)
     response = self.client.search(index=self.ddl_index, body=query,
                                   **kwargs)
     return [hit['_source']['ddl'] for hit in response['hits']['hits']]
@@ -279,6 +285,7 @@ def get_related_documentation(self, question: str, **kwargs) -> List[str]:
         }
       }
     }
+    print(query)
     response = self.client.search(index=self.document_index,
                                   body=query,
                                   **kwargs)
@@ -292,6 +299,7 @@ def get_similar_question_sql(self, question: str, **kwargs) -> List[str]:
         }
       }
     }
+    print(query)
     response = self.client.search(index=self.question_sql_index,
                                   body=query,
                                   **kwargs)
@@ -307,6 +315,7 @@ def get_training_data(self, **kwargs) -> pd.DataFrame:
       body={"query": {"match_all": {}}},
       size=1000
     )
+    print(query)
     # records = [hit['_source'] for hit in response['hits']['hits']]
     for hit in response['hits']['hits']:
       data.append(