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 providerIntegrationsRetrieversAmazon KendraOn this pageAmazon KendraAmazon Kendra is an intelligent search service provided by Amazon Web Services (AWS). It utilizes advanced natural language processing (NLP) and machine learning algorithms to enable powerful search capabilities across various data sources within an organization. Kendra is designed to help users find the information they need quickly and accurately, improving productivity and decision-making.With Kendra, users can search across a wide range of content types, including documents, FAQs, knowledge bases, manuals, and websites. It supports multiple languages and can understand complex queries, synonyms, and contextual meanings to provide highly relevant search results.Using the Amazon Kendra Index Retrieverâ€‹%pip install boto3import boto3from langchain.retrievers import AmazonKendraRetrieverCreate New Retrieverretriever = AmazonKendraRetriever(index_id="c0806df7-e76b-4bce-9b5c-d5582f6b1a03")Now you can use retrieved documents from Kendra indexretriever.get_relevant_documents("what is langchain")PreviousRetrieversNextArxivUsing the Amazon Kendra Index RetrieverCommunityDiscordTwitterGitHubPythonJS/TSMoreHomepageBlogCopyright Â©