text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversText embedding modelsAgent toolkitsToolsVector storesAlibaba Cloud OpenSearchAnalyticDBAnnoyAtlasAwaDBAzure Cognitive SearchCassandraChromaClarifaiClickHouse Vector SearchActiveloop's Deep LakeDocArrayHnswSearchDocArrayInMemorySearchElasticSearchFAISSHologresLanceDBMarqoMatchingEngineMilvusMongoDB AtlasMyScaleOpenSearchpg_embeddingPGVectorPineconeQdrantRedisRocksetSingleStoreDBscikit-learnStarRocksSupabase (Postgres)TairTigrisTypesenseVectaraWeaviateZillizGrouped by providerIntegrationsVector storesElasticSearchOn this pageElasticSearchElasticsearch is a distributed, RESTful search and analytics engine. It provides a distributed, multitenant-capable full-text search engine with an HTTP web interface and schema-free JSON documents.This notebook shows how to use functionality related to the Elasticsearch database.Installationâ€‹Check out Elasticsearch installation instructions.To connect to an Elasticsearch instance that does not require
  login credentials, pass the Elasticsearch URL and index name along with the
  embedding object to the constructor.Example:        from langchain import ElasticVectorSearch        from langchain.embeddings import OpenAIEmbeddings        embedding = OpenAIEmbeddings()        elastic_vector_search = ElasticVectorSearch(            elasticsearch_url="http://localhost:9200",            index_name="test_index",            embedding=embedding        )To connect to an Elasticsearch instance that requires login credentials,
  including Elastic Cloud, use the Elasticsearch URL format
  https://username:password@es_host:9243. For example, to connect to Elastic