text: EndpointSearxNG Search APISerpAPIShale ProtocolSingleStoreDBscikit-learnSlackspaCySpreedlyStarRocksStochasticAIStripeTairTelegramTigris2MarkdownTrelloTruLensTwitterTypesenseUnstructuredVectaraChat Over Documents with VectaraVectara Text GenerationVespaWeights & BiasesWeatherWeaviateWhatsAppWhyLabsWikipediaWolfram AlphaWriterYeager.aiYouTubeZepZillizIntegrationsGrouped by providerVectaraOn this pageVectaraWhat is Vectara?Vectara Overview:Vectara is developer-first API platform for building GenAI applicationsTo use Vectara - first sign up and create an account. Then create a corpus and an API key for indexing and searching.You can use Vectara's indexing API to add documents into Vectara's indexYou can use Vectara's Search API to query Vectara's index (which also supports Hybrid search implicitly).You can use Vectara's integration with LangChain as a Vector store or using the Retriever abstraction.Installation and Setupâ€‹To use Vectara with LangChain no special installation steps are required. You just have to provide your customer_id, corpus ID, and an API key created within the Vectara console to enable indexing and searching.Alternatively these can be provided as environment variablesexport VECTARA_CUSTOMER_ID="your_customer_id"export VECTARA_CORPUS_ID="your_corpus_id"export VECTARA_API_KEY="your-vectara-api-key"Usageâ€‹VectorStoreâ€‹There exists a wrapper around the Vectara platform, allowing you to use it as a vectorstore, whether for semantic search or example selection.To import this vectorstore:from langchain.vectorstores import VectaraTo create an instance of the Vectara vectorstore:vectara = Vectara(    vectara_customer_id=customer_id,