From e3ff107e4fd8162a3384373d057eeac0b24ebe13 Mon Sep 17 00:00:00 2001 From: Leonid Kuligin Date: Fri, 15 Mar 2024 14:30:50 +0100 Subject: [PATCH] docs: updated google integration related imports in the documentation (#19131) updated imports in the documentation for google vertex --- docs/docs/integrations/platforms/google.mdx | 8 ++++---- .../vectorstores/google_vertex_ai_vector_search.ipynb | 6 +++--- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/docs/integrations/platforms/google.mdx b/docs/docs/integrations/platforms/google.mdx index 2e9e1a236a2..616d57b73d1 100644 --- a/docs/docs/integrations/platforms/google.mdx +++ b/docs/docs/integrations/platforms/google.mdx @@ -503,21 +503,21 @@ from langchain_google_cloud_sql_pg import PostgreSQLEngine, PostgresVectorStore ### Vertex AI Vector Search -> [Google Cloud Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview) from Google Cloud, +> [Google Cloud Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/vector-search/overview) from Google Cloud, > formerly known as `Vertex AI Matching Engine`, provides the industry's leading high-scale > low latency vector database. These vector databases are commonly > referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service. -We need to install several python packages. +Install the python package: ```bash -pip install tensorflow langchain-google-vertexai tensorflow-hub tensorflow-text +pip install langchain-google-vertexai ``` See a [usage example](/docs/integrations/vectorstores/google_vertex_ai_vector_search). ```python -from langchain_community.vectorstores import MatchingEngine +from langchain_google_vertexai import VectorSearchVectorStore ``` ### ScaNN diff --git a/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb b/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb index 235aa518ab3..05f236ef150 100644 --- a/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb +++ b/docs/docs/integrations/vectorstores/google_vertex_ai_vector_search.ipynb @@ -9,7 +9,7 @@ "\n", "This notebook shows how to use functionality related to the `Google Cloud Vertex AI Vector Search` vector database.\n", "\n", - "> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/matching-engine/overview), formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n", + "> [Google Vertex AI Vector Search](https://cloud.google.com/vertex-ai/docs/vector-search/overview), formerly known as Vertex AI Matching Engine, provides the industry's leading high-scale low latency vector database. These vector databases are commonly referred to as vector similarity-matching or an approximate nearest neighbor (ANN) service.\n", "\n", "**Note**: This module expects an endpoint and deployed index already created as the creation time takes close to one hour. To see how to create an index refer to the section [Create Index and deploy it to an Endpoint](#create-index-and-deploy-it-to-an-endpoint)" ] @@ -29,7 +29,7 @@ "metadata": {}, "outputs": [], "source": [ - "from langchain_community.vectorstores import MatchingEngine" + "from langchain_google_vertexai import VectorSearchVectorStore" ] }, { @@ -50,7 +50,7 @@ "]\n", "\n", "\n", - "vector_store = MatchingEngine.from_components(\n", + "vector_store = VectorSearchVectorStore.from_components(\n", " texts=texts,\n", " project_id=\"\",\n", " region=\"\",\n",