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docs: BigQuery Vector Search went public review and updated docs (#16896)
Update the docs for BigQuery Vector Search
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@ -207,15 +207,11 @@ from langchain_community.vectorstores import MatchingEngine
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> [Google BigQuery](https://cloud.google.com/bigquery),
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> [Google BigQuery](https://cloud.google.com/bigquery),
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> BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
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> BigQuery is a serverless and cost-effective enterprise data warehouse in Google Cloud.
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>
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>
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> Google BigQuery Vector Search
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> [Google BigQuery Vector Search](https://cloud.google.com/bigquery/docs/vector-search-intro)
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> 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.
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> 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.
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> It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
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> It can calculate Euclidean or Cosine distance. With LangChain, we default to use Euclidean distance.
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> This is a private preview (experimental) feature. Please submit this
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> [enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true)
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> if you want to enroll BigQuery Vector Search Experimental.
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We need to install several python packages.
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We need to install several python packages.
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```bash
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```bash
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@ -7,22 +7,12 @@
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},
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},
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"source": [
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"source": [
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"# BigQuery Vector Search\n",
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"# BigQuery Vector Search\n",
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"> **BigQueryVectorSearch**:\n",
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"> [**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",
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"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",
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"\n",
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"\n",
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"\n",
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"\n",
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"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."
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"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."
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]
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]
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},
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"This is a **private preview (experimental)** feature. Please submit this\n",
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"[enrollment form](https://docs.google.com/forms/d/18yndSb4dTf2H0orqA9N7NAchQEDQekwWiD5jYfEkGWk/viewform?edit_requested=true)\n",
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"if you want to enroll BigQuery Vector Search Experimental."
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]
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},
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{
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{
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"cell_type": "markdown",
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"cell_type": "markdown",
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"metadata": {
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"metadata": {
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