Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesGoogle Vertex AI MatchingEngineOn this pageGoogle Vertex AI MatchingEngineThis notebook shows how to use functionality related to the GCP Vertex AI MatchingEngine vector database.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.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 EndpointCreate VectorStore from texts‚Äãfrom langchain.vectorstores import MatchingEnginetexts = [    "The cat sat on",    "the mat.",    "I like to",    "eat pizza for",    "dinner.",    "The sun sets",    "in the west.",]vector_store = MatchingEngine.from_components(    texts=texts,    project_id="<my_project_id>",    region="<my_region>",    gcs_bucket_uri="<my_gcs_bucket>",
This notebook shows how to use functionality related to the GCP Vertex AI MatchingEngine vector database.
This notebook shows how to use functionality related to the GCP Vertex AI MatchingEngine vector database. ->: Skip to main contentü¶úÔ∏èüîó LangChainDocsUse casesIntegrationsAPICommunityChat our docsLangSmithJS/TS DocsSearchCTRLKProvidersAnthropicAWSGoogleMicrosoftOpenAIMoreComponentsLLMsChat modelsDocument loadersDocument transformersText embedding modelsVector storesActiveloop Deep LakeAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cosmos DBAzure Cognitive SearchBagelDBCassandraChromaClarifaiClickHouseDashVectorDingoDocArray HnswSearchDocArray InMemorySearchElasticsearchEpsillaFaissHologresLanceDBLLMRailsMarqoGoogle Vertex AI MatchingEngineMeilisearchMilvusMomento Vector Index (MVI)MongoDB AtlasMyScaleNeo4j Vector IndexNucliaDBOpenSearchPostgres EmbeddingPGVectorPineconeQdrantRedisRocksetScaNNSingleStoreDBscikit-learnsqlite-vssStarRocksSupabase (Postgres)TairTencent Cloud VectorDBTigrisTimescale Vector (Postgres)TypesenseUSearchValdvearchVectaravectorstoresVespaWeaviateXataZepZillizRetrieversToolsAgents and toolkitsMemoryCallbacksChat loadersComponentsVector storesGoogle Vertex AI MatchingEngineOn this pageGoogle Vertex AI MatchingEngineThis notebook shows how to use functionality related to the GCP Vertex AI MatchingEngine vector database.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.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 EndpointCreate VectorStore from texts‚Äãfrom langchain.vectorstores import MatchingEnginetexts = [    "The cat sat on",    "the mat.",    "I like to",    "eat pizza for",    "dinner.",    "The sun sets",    "in the west.",]vector_store = MatchingEngine.from_components(    texts=texts,    project_id="<my_project_id>",    region="<my_region>",    gcs_bucket_uri="<my_gcs_bucket>",