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 storesVectaraOn this pageVectaraVectara is a API platform for building LLM-powered applications. It provides a simple to use API for document indexing and query that is managed by Vectara and is optimized for performance and accuracy. This notebook shows how to use functionality related to the Vectara vector database or the Vectara retriever. See the Vectara API documentation  for more information on how to use the API.import osfrom langchain.embeddings import FakeEmbeddingsfrom langchain.text_splitter import CharacterTextSplitterfrom langchain.vectorstores import Vectarafrom langchain.document_loaders import TextLoaderConnecting to Vectara from LangChainâ€‹The Vectara API provides simple API endpoints for indexing and querying, which is encapsulated in the Vectara integration.