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docs: add the enrollment form forBigQueryVectorSearch (#16240)

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This PR adds the enrollment form for BigQueryVectorSearch.
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Ashley Xu 2024-01-18 18:34:06 -08:00 committed by GitHub
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6
docs/docs/integrations/platforms/google.mdx
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@ -210,7 +210,11 @@ from langchain_community.vectorstores import MatchingEngine
> Google BigQuery Vector Search > 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. > 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. We need to install several python packages.

9
docs/docs/integrations/vectorstores/bigquery_vector_search.ipynb
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@ -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." "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", "cell_type": "markdown",
"metadata": { "metadata": {