docs: add the enrollment form for`BigQueryVectorSearch` (#16240)

This PR adds the enrollment form for BigQueryVectorSearch.

docs/docs/integrations/platforms/google.mdx

@@ -210,7 +210,11 @@ from langchain_community.vectorstores import MatchingEngine
 > Google BigQuery Vector Search
 > 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.
+> 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.
 

docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb

@@ -14,6 +14,15 @@
     "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": {