text: Skip to main contentğŸ¦œï¸�ğŸ”— LangChainDocsUse casesIntegrationsAPILangSmithJS/TS DocsCTRLKIntegrationsCallbacksChat modelsDocument loadersDocument transformersLLMsMemoryRetrieversAmazon KendraArxivAzure Cognitive SearchBM25ChaindeskChatGPT PluginCohere RerankerDocArray RetrieverElasticSearch BM25Google Cloud Enterprise SearchkNNLOTR (Merger Retriever)MetalPinecone Hybrid SearchPubMedSVMTF-IDFVespaWeaviate Hybrid SearchWikipediaZepText embedding modelsAgent toolkitsToolsVector storesGrouped by providerIntegrationsRetrieversVespaVespaVespa is a fully featured search engine and vector database. It supports vector search (ANN), lexical search, and search in structured data, all in the same query.This notebook shows how to use Vespa.ai as a LangChain retriever.In order to create a retriever, we use pyvespa to
  create a connection a Vespa service.#!pip install pyvespafrom vespa.application import Vespavespa_app = Vespa(url="https://doc-search.vespa.oath.cloud")This creates a connection to a Vespa service, here the Vespa documentation search service.
  Using pyvespa package, you can also connect to a
  Vespa Cloud instance
  or a local