diff --git a/libs/community/langchain_community/utils/google.py b/libs/community/langchain_community/utils/google.py
new file mode 100644
index 0000000000..4e68512296
--- /dev/null
+++ b/libs/community/langchain_community/utils/google.py
@@ -0,0 +1,25 @@
+"""Utilities to use Google provided components."""
+
+from importlib import metadata
+from typing import Any, Optional
+
+
+def get_client_info(module: Optional[str] = None) -> Any:
+    r"""Returns a custom user agent header.
+
+    Args:
+        module (Optional[str]):
+            Optional. The module for a custom user agent header.
+    Returns:
+        google.api_core.gapic_v1.client_info.ClientInfo
+    """
+    from google.api_core.gapic_v1.client_info import ClientInfo
+
+    langchain_version = metadata.version("langchain")
+    client_library_version = (
+        f"{langchain_version}-{module}" if module else langchain_version
+    )
+    return ClientInfo(
+        client_library_version=client_library_version,
+        user_agent=f"langchain/{client_library_version}",
+    )
diff --git a/libs/community/langchain_community/vectorstores/bigquery_vector_search.py b/libs/community/langchain_community/vectorstores/bigquery_vector_search.py
index 28edc6536c..3c77ffb917 100644
--- a/libs/community/langchain_community/vectorstores/bigquery_vector_search.py
+++ b/libs/community/langchain_community/vectorstores/bigquery_vector_search.py
@@ -16,6 +16,7 @@ from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.vectorstores import VectorStore
 
+from langchain_community.utils.google import get_client_info
 from langchain_community.vectorstores.utils import (
     DistanceStrategy,
     maximal_marginal_relevance,
@@ -30,7 +31,6 @@ DEFAULT_TOP_K = 4  # default number of documents returned from similarity search
 
 _MIN_INDEX_ROWS = 5000  # minimal number of rows for creating an index
 _INDEX_CHECK_PERIOD_SECONDS = 60  # Do not check for index more often that this.
-
 _vector_table_lock = Lock()  # process-wide BigQueryVectorSearch table lock
 
 
@@ -90,8 +90,12 @@ class BigQueryVectorSearch(VectorStore):
         try:
             from google.cloud import bigquery
 
+            client_info = get_client_info(module="bigquery-vector-search")
             self.bq_client = bigquery.Client(
-                project=project_id, location=location, credentials=credentials
+                project=project_id,
+                location=location,
+                credentials=credentials,
+                client_info=client_info,
             )
         except ModuleNotFoundError:
             raise ImportError(