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One-Stop GenAI Stack to Build Production-Ready Apps | DataStaxGo from app idea to production with 20% higher relevance and 74x faster response on the industry-leading vector database, Astra DB. Get started for free!One-Stop GenAI Stack to Build Production-Ready Apps | DataStaxProductsIntegrationsPricingStoriesResourcesDocsContact UsSign InSign InTry For FreeWorkshop September 10: Learn to Build a recommendation app using LangChain and Gemini LLMsRAG++The AI Eventis here!Join us to learn about a new Unstructured integration to make document upload a snap, an API that lets you integrate your Langflow application with external apps, and a new indexing partnership with Glean!Wed, Sept 4 NYC | Tues, Sept 24 LondonRead All About ItRSVP FOR RAG++ LONDONGenerative AI Leaders Shaping Their IndustriesOne-Stop GenAI StackA RAG API with all the data, tools, and an opinionated stack that just works. Both vector and structured data, secure, compliant, scalable, and supported. Integrated with LangChain, Vercel, GitHub Copilot and AI ecosystem leaders.Request a Custom DemoRelevant GenAI FTWMinimize hallucinations with up to 20% higher relevance, 74x faster response time, and 9x higher throughput than Pinecone all at 80% lower TCO. Read while indexing to make data updates available with zero delay.Learn MoreDevelopersRAG Made EasierAn intuitive API and powerful integrations for production-level RAG workloads.InstallInstall the API clientspythontypescriptjavapip install astrapy ConnectCreate or connect to your database & collectionspythontypescriptjava# The DataAPIClient lets you connect to your database client = DataAPIClient("AstraCS:...") db = client.get_database( "https://-.apps.astra.datastax.com" ) # create_collection() will return the newly created collection collection = db.create_collection( name="collection_test", dimension=5, ) # Or you can connect to an existing connection directly collection = astra_db.get_collection("collection_test") InsertInsert into your vector store (collection)pythontypescriptjavacollection.insert_one( { "name": "Coded Cleats", "description": "Chat bot integrated sneakers that talk to you", "$vector": [0.1, 0.15, 0.3, 0.12, 0.05], } ) FindFind documents using vector searchpythontypescriptjavadocuments = list(collection.find( sort={"$vector": [0.15, 0.1, 0.1, 0.35, 0.55]}, limit=100, )) Try For Free$300/year in free credit and no credit card required.Explore examplesTutorials and sample generative AI apps with best practices.DOCS Get started in minutes with generative AI and RAG.Fast Path to ProductionQuickly take your GenAI idea into production. Deploy on the leader in production AI workloads and support global-scale on any cloud with enterprise level security and compliance.Shape the WildGenAI should be fun! An awesome developer experience for any JavaScript, Python, Java, and C++ dev to build production GenAI apps with LangChain, GitHub, Vercel, and the leading AI ecosystem partners.Try It For FreeWhat’s NewWHITEPAPER An LLM Agent Reference Architecture: Demystifying LLM-based SystemsDemystify LLM-based apps with:Common design patterns and use cases in generative AI applicationsIn-depth architectural examples (for example, building a chatbot with docs)Important architecture considerations for GenAI appsLearn MoreBlogBuilding a Wikipedia Chatbot Using Astra DB, LangChain, and VercelLearn More SUCCESS STORYHow Priceline Helps Travelers Book Great Travel Deals with Real-Time AILearn MoreReady to get started with Vector Search? Deliver generative AI apps on the world’s most powerful, most scalable vector database.GET STARTED FOR FREEREQUEST A DEMOCompanyAbout UsLeadershipBoard of DirectorsContact UsPartnersCareersNewsroomResourcesDocsBlogEventseBooksWhitepapersWebinarsLegalSecurityCloud PartnersAmazon Web ServicesGoogle CloudMicrosoft AzureNVIDIASubscribe to AI++A newsletter for devs building apps using AI© 2024 DataStaxPrivacy PolicyTerms of UseTrademark NoticeBrand ResourcesDo Not Sell My InfoDataStax, is a registered trademark of DataStax, Inc.. Apache, Apache Cassandra, Cassandra, Apache Pulsar, and Pulsar are either registered trademarks or trademarks of the Apache Software Foundation.United States

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