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 storesMarqoOn this pageMarqoThis notebook shows how to use functionality related to the Marqo vectorstore.Marqo is an open-source vector search engine. Marqo allows you to store and query multi-modal data such as text and images. Marqo creates the vectors for you using a huge selection of open-source models, you can also provide your own fine-tuned models and Marqo will handle the loading and inference for you.To run this notebook with our docker image please run the following commands first to get Marqo:docker pull marqoai/marqo:latestdocker rm -f marqodocker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latestpip install marqofrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Marqofrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter =
This notebook shows how to use functionality related to the Marqo vectorstore.
This notebook shows how to use functionality related to the Marqo vectorstore. ->: 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 storesMarqoOn this pageMarqoThis notebook shows how to use functionality related to the Marqo vectorstore.Marqo is an open-source vector search engine. Marqo allows you to store and query multi-modal data such as text and images. Marqo creates the vectors for you using a huge selection of open-source models, you can also provide your own fine-tuned models and Marqo will handle the loading and inference for you.To run this notebook with our docker image please run the following commands first to get Marqo:docker pull marqoai/marqo:latestdocker rm -f marqodocker run --name marqo -it --privileged -p 8882:8882 --add-host host.docker.internal:host-gateway marqoai/marqo:latestpip install marqofrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Marqofrom langchain.document_loaders import TextLoaderfrom langchain.document_loaders import TextLoaderloader = TextLoader("../../modules/state_of_the_union.txt")documents = loader.load()text_splitter =