Relational databases have served us well, but modern demands expose their limits. Flexibility, scalability, and AI integration? Tough! 🤯 Enter MongoDB Atlas – a flexible, intuitive, cloud-based database for today's developers. Learn why and how to switch. 👇 https://lnkd.in/ghqRMFy2 #DataModernization #GoogleCloud #MongoDB
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Data is only valuable if it’s accessible and extracting insights from unstructured data like PDFs is challenging. 😥 MongoDB Atlas Vector Search with Nomic AI offers an AI-powered solution for efficient PDF search, improving data accessibility and operational efficiency. 👇 https://lnkd.in/gKAwmAjG #mongodb #nomicai #foundationalmodels #atlas
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MongoDB is continuing to differentiate ourselves as a cloud-agnostic vector database, and we recently published some important announcements that make it easier for developers to build innovative applications with generative AI, including: - Atlas as a datastore enabling storage, query, and retrieval using natural language in ChatGPT - Collaboration with Google Gemini Code Assist and Amazon Q to quickly prototype new features and accelerate application development - Google Vertex AI Extension to harness natural language with MongoDB queries - Addition of Atlas Vector Search as a knowledge base in Amazon Bedrock - Dedicated Search Nodes on AWS, GCP, and Azure More here: #vector #database #MongoDB #AWS #GCP #Azure
Top AI Announcements at MongoDB.local NYC | MongoDB Blog
mongodb.com
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Why is everyone still so bullish on Mongo even after reaching $2 billion in revenue? The old advantage of MongoDB is the new advantage... The market continues to come to us at a rapid pace because of our foundational document data model. Data flexibility is critical not only when creating apps for today's needs but also when preparing for the rapidly evolving market as AI innovation continues to accelerate at a record pace.
MongoDB CEO Ittycheria: AI Has Reached ‘A Crucible Moment’ In Its Development.
crn.com
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Another set of fantastic of innovations from the team. Azure AI Extension GA https://lnkd.in/g_Mv64EM Automated Index Tuning Preview https://lnkd.in/gM8NGyqj In Database Embedding Generation Preview https://lnkd.in/g4ZVn3RH #msbuild #azure #azurepostgres #postgresql #genai #autonomousdatabases
Gen AI simplified: The azure_ai extension now generally available on Azure Database for PostgreSQL
techcommunity.microsoft.com
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Yes. Search #PDFs with #mongodb's Developer Data Platform.... Data is only valuable if it’s accessible and extracting insights from unstructured data like PDFs is challenging. 😥 MongoDB Atlas Vector Search with Nomic AI offers an AI-powered solution for efficient PDF search, improving data accessibility and operational efficiency. 👇 https://lnkd.in/eV5s4s_A
Search PDFs at Scale with MongoDB and Nomic | MongoDB Blog
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You've developed a new Retrieval Augmented Generation (RAG) application and discovered that generating embeddings is computationally intensive. 🤯 Have you considered how to maintain updated embeddings for relevant semantic searches? And how can you ensure scalability in this process? MongoDB offers a solution to address these challenges. Follow the link provided to learn more. https://lnkd.in/e3BYS_An #MongoDB #ProfessionalServices
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MongoDB Atlas combines operational, vector, and metadata in a single platform, making it an ideal knowledge base for Amazon Bedrock users who want to augment their generative AI experiences while also simplifying their generative AI stack. In addition, MongoDB Atlas gives developers the ability to set up dedicated infrastructure for search and vector search workloads, optimizing compute resources to scale search and database independently. A good fast tutorial on how to launch a fully managed RAG workflow with both parties.
Launch a Fully Managed RAG Workflow With MongoDB Atlas and Amazon Bedrock | MongoDB
mongodb.com
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Unleash the power of Foundation Models with Amazon Bedrock and MongoDB Atlas 🔓📚💪 Today, we are announcing the availability of #MongoDBAtlas as a vector store in Knowledge Bases for #AmazonBedrock. With MongoDB Atlas vector store integration, you can build RAG solutions to securely connect your organization’s private data sources to FMs in Amazon Bedrock to build advanced retrieval augmented generation solutions tailored to your business needs. Learn more 👉 https://go.aws/4a1qUi8 #FoundationModels #AI #CloudComputing
Build RAG applications with MongoDB Atlas, now available in Knowledge Bases for Amazon Bedrock | Amazon Web Services
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🔍 Elevate Your Database Management with MongoDB Mentor AI from Wise Duck Dev GPTs! 🔍 Attention developers and data enthusiasts! Ready to master MongoDB? Our MongoDB Mentor AI GPT is here to help you navigate the complexities of MongoDB, ensuring your databases are optimized and efficiently managed. Key Features: 🛠️ Seamless Integration: Effortlessly incorporate MongoDB best practices into your projects. 📈 Optimize Performance: Enhance your database performance with expert insights. 🌐 Comprehensive Guidance: Discover, solve, debug, and explain all things MongoDB with precision. Don’t miss out on mastering MongoDB with MongoDB Mentor AI. Start your journey today and set new standards in database management! #WiseDuckDevGPTs #GPTS #DatabaseManagement #MongoDB #TechInnovation #DevCommunity #DeveloperTools https://lnkd.in/dTeaE8SE
MongoDB Mentor AI | The Wise Duck Dev GPTs
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The intersection of GenAI and the MongoDB AI Applications Program (MAAP) represents a unique opportunity to drive innovation and accelerate the development of AI-powered solutions. By leveraging GenAI to augment the capabilities of MongoDB, developers can create more intelligent, context-aware applications that deliver unparalleled value to users. This is set to redefine the way we approach AI-driven solutions. 💡 🚀 To know in detail- https://lnkd.in/gmSt8V2G #GenAI #MongoDB #AIApplications
MongoDB Launches New Program For Enterprises To Build Modern Applications With Advanced Generative AI Capabilities
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