diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx
index 5d09b18f56..b1ab193a80 100644
--- a/docs/docs/integrations/platforms/google.mdx
+++ b/docs/docs/integrations/platforms/google.mdx
@@ -207,15 +207,11 @@ from langchain_community.vectorstores import MatchingEngine
 > [Google BigQuery](https://cloud.google.com/bigquery),
 > BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
 >
-> Google BigQuery Vector Search
+> [Google BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro)
 > BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast but approximate results, or using brute force for exact results.
 
 > It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
 
-> This is a private preview (experimental) feature. Please submit this
-> [enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true)
-> if you want to enroll BigQuery Vector Search Experimental.
-
 We need to install several python packages.
 
 ```bash
diff --git a/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb b/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb
index af26dcaf8e..403a8d9fbd 100644
--- a/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb
+++ b/docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb
@@ -7,22 +7,12 @@
    },
    "source": [
     "# BigQuery Vector Search\n",
-    "> **BigQueryVectorSearch**:\n",
-    "BigQuery vector search lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n",
+    "> [**BigQuery Vector Search**](https://cloud.google.com/bigquery/docs/vector-search-intro) lets you use GoogleSQL to do semantic search, using vector indexes for fast approximate results, or using brute force for exact results.\n",
     "\n",
     "\n",
     "This tutorial illustrates how to work with an end-to-end data and embedding management system in LangChain, and provide scalable semantic search in BigQuery."
    ]
   },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "This is a **private preview (experimental)** feature. Please submit this\n",
-    "[enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true)\n",
-    "if you want to enroll BigQuery Vector Search Experimental."
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {