52 results

Collaborating to Build AI Apps: MongoDB and Partners at Google Cloud Next '24

From April 9 to April 11, Las Vegas became the center of the tech world, as Google Cloud Next '24 took over the Mandalay Bay Convention Center—and the convention’s spotlight shined brightest on gen AI. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Between MongoDB’s big announcements with Google Cloud (which included an expanded collaboration to enhance building, scaling, and deploying GenAI applications using MongoDB Atlas Vector Search and Vertex AI ), industry sessions, and customer meetings, we offered in-booth lightning talks with leaders from four MongoDB partners—LangChain, LlamaIndex, Patronus AI, and Unstructured—who shared valuable insights and best practices with developers who want to embed AI into their existing applications or build new-generation apps powered by AI. Developing next-generation AI applications involves several challenges, including handling complex data sources, incorporating structured and unstructured data, and mitigating scalability and performance issues in processing and analyzing them. The lightning talks at Google Cloud Next ‘24 addressed some of these critical topics and presented practical solutions. One of the most popular sessions was from Harrison Chase , co-founder and CEO at LangChain , an open-source framework for building applications based on large language models (LLMs). Harrison provided tips on fixing your retrieval-augmented generation (RAG) pipeline when it fails, addressing the most common pitfalls of fact retrieval, non-semantic components, conflicting information, and other failure modes. Harrison recommended developers use LangChain templates for MongoDB Atlas to deploy RAG applications quickly. Meanwhile, LlamaIndex —an orchestration framework that integrates private and public data for building applications using LLMs—was represented by Simon Suo , co-founder and CTO, who discussed the complexities of advanced document RAG and the importance of using good data to perform better retrieval and parsing. He also highlighted MongoDB’s partnership with LlamaIndex, allowing for ingesting data into the MongoDB Atlas Vector database and retrieving the index from MongoDB Atlas via LlamaParse and LlamaCloud . Guillaume Nozière - Patronus AI Andrew Zane - Unstructured Amidst so many booths, activities, and competing programming, a range of developers from across industries showed up to these insightful sessions, where they could engage with experts, ask questions, and network in a casual setting. They also learned how our AI partners and MongoDB work together to offer complementary solutions to create a seamless gen AI development experience. We are grateful for LangChain, LlamaIndex, Patronus AI, and Unstructured's ongoing partnership. We look forward to expanding our collaboration to help our joint customers build the next generation of AI applications. To learn more about building AI-powered apps with MongoDB, check out our AI Resources Hub and stop by our Partner Ecosystem Catalog to read about our integrations with these and other AI partners.

April 23, 2024

A Smarter Factory Floor with MongoDB Atlas and Google Cloud's Manufacturing Data Engine

The manufacturing industry is undergoing a transformative shift from traditional to digital, propelled by data-driven insights, intelligent automation, and artificial intelligence. Traditional methods of data collection and analysis are no longer sufficient to keep pace with the demands of today's competitive landscape. This is precisely where Google Cloud’s Manufacturing Data Engine (MDE) and MongoDB Atlas come into play, offering a powerful combination for optimizing your factory floor. Unlock the power of your factory data MDE is positioned to transform the factory floor with the power of cloud-driven insights. MDE simplifies communication with your factory floor, regardless of the diverse protocols your machines might use. It effortlessly connects legacy equipment with modern systems, ensuring a comprehensive data stream. MDE doesn't just collect data, it transforms it. By intelligently processing and contextualizing the information, you gain a clearer picture of your production processes in real-time with a historical pretext. It offers pre-built analytics and AI tools directly addressing common manufacturing pain points. This means you can start finding solutions faster, whether it's identifying bottlenecks, reducing downtime, or optimizing resource utilization. Conveniently, it also offers great support for integrations that can further enhance the potential of the data (e.g. additional data sinks). The MongoDB Atlas developer data platform enhances MDE by providing scalability and flexibility through automated scaling to adapt to evolving data requirements. This makes it particularly suitable for dynamic manufacturing environments. MongoDB’s document model can handle diverse data types and structures effortlessly while being incredibly flexible because of its native JSON format. This allows for enriching MDE data with other relevant data, such as supply chain logistics, for a deeper understanding of the factory business. You can gain immediate insights into your operations through real-time analytics, enabling informed decision-making based on up-to-date data. While MDE offers a robust solution for collecting, contextualizing, and managing industrial data, leveraging it alongside MongoDB Atlas offers tremendous advantages Inside the MDE integration Google Cloud’s Manufacturing Data Engine (MDE) acts as a central hub for your factory data. It not only processes and enriches the data with context, but also offers flexible storage options like BigQuery and Cloud Storage. Now, customers already using MongoDB Atlas can skip the hassle of application re-integration and make this data readily accessible for applications. Through this joint solution developed by Google Cloud and MongoDB, you can seamlessly move the processed streaming data from MDE to MongoDB Atlas using Dataflow jobs. MDE publishes the data via a Pub/Sub subscription, which is then picked up by a custom Dataflow job built by MongoDB. This job transforms the data into the desired format and writes it to your MongoDB Atlas cluster. Google Cloud’s MDE and MongoDB Atlas utilize compatible data structures, simplifying data integration through a shared semantic configuration. Once the data resides in MongoDB Atlas, your existing applications can access it seamlessly without any code modifications, saving you time and effort. The flexibility of MDE, combined with the scalability and ease of use of MongoDB Atlas, makes this a powerful and versatile solution for various data-driven use cases such as predictive maintenance and quality control, while still providing factory ownership of the data. Instructions on how to set up the dataflow job are available in the MongoDB github repository. Conclusion If you want to level up your manufacturing data analytics, pairing MDE with MongoDB Atlas provides a proven, easy-to-implement solution. It's easy to get started with MDE and MongoDB Atlas . MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More

April 9, 2024

Workload Isolation for More Scalability and Availability: Search Nodes Now on Google Cloud

Today we’re excited to take the next step in bringing scalable, dedicated architecture to your search experiences with the introduction of Atlas Search Nodes, now in general availability for Google Cloud. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . Since our initial announcement of Search Nodes in June of 2023, we’ve been rapidly accelerating access to the most scalable dedicated architecture, starting with general availability on AWS and now expanding to general availability on Google Cloud. We'd like to give you a bit more context on what Search Nodes are and why they're important to any search experience running at scale. Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads to enable even greater control over search workloads. They also allow you to isolate and optimize compute resources to scale search and database needs independently, delivering better performance at scale and higher availability. One of the last things developers want to deal with when building and scaling apps is having to worry about infrastructure problems. Any downtime or poor user experiences can result in lost users or revenue, especially when it comes to your database and search experience. This is one of the reasons developers turn to MongoDB, given the ease of use of having one unified system for your database and search solution. With the introduction of Atlas Search Nodes, we’ve taken the next step in providing our builders with ultimate control, giving them the ability to remain flexible by scaling search workloads without the need to over-provision the database. By isolating your search and database workloads while at the same time automatically keeping your search cluster data synchronized with operational data, Atlas Search and Atlas Vector Search eliminate the need to run a separate ETL tool, which takes time and effort to set up and is yet another fail point for your scaling app. This provides superior performance and higher availability while reducing architectural complexity and wasted engineering time recovering from sync failures. In fact, we’ve seen a 40% to 60% decrease in query time for many complex queries, while eliminating the chances of any resource contention or downtime. With just a quick button click, Search Nodes on Google Cloud offer our existing Atlas Search and Vector Search users the following benefits: Higher availability Increased scalability Workload isolation Better performance at scale Improved query performance We offer both compute-heavy search-specific nodes for relevance-based text search, as well as a memory-optimized option that is optimal for semantic and retrieval augmented generation (RAG) production use cases with Atlas Vector Search. This makes resource contention or availability issues a thing of the past. Search Nodes are easy to opt into and set up — to start, jump on into the MongoDB UI and follow the steps do the following: Navigate to your “Database Deployments” section in the MongoDB UI Click the green “+Create” button On the “Create New Cluster” page, change the radio button for Google Cloud for “Multi-cloud, multi-region & workload isolation” to enable Toggle the radio button for “Search Nodes for workload isolation” to enable. Select the number of nodes in the text box Check the agreement box Click “Create cluster” For existing Atlas Search users, click “Edit Configuration” in the MongoDB Atlas Search UI and enable the toggle for workload isolation. Then the steps are the same as noted above. Jump straight into our docs to learn more! MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More

March 28, 2024

利用工作负载隔离提高可扩展性和可用性:Search Nodes 现已在 Google Cloud 上提供

今天,我们很高兴地宣布 Atlas Search Nodes(公开预览版)现已在 Google Cloud 上提供,这离我们针对搜索体验提供可扩展的专用架构这个目标更进了一步。 自 2023 年 6 月首次宣布推出 Search Nodes 以来,我们一直在加快这个最具可扩展性的专用架构的应用速度, 先是在 AWS 上正式发布 ,现在又在 Google Cloud 上发布了它的公开预览版。让我们简单介绍一下什么是 Search Nodes,以及它为何对任何大规模运行的搜索体验非常重要。 Search Nodes 可为 Atlas Search 和 Vector Search 工作负载提供专用基础架构,让您能够对搜索工作负载拥有更大的控制力度。通过隔离并优化计算资源来独立地扩展搜索和数据库需求,从而大规模提升性能并实现更高的可用性。 在构建和扩展应用时,开发者最不愿处理的一件事情就是要担心基础架构问题。任何停机或不佳的用户体验都可导致用户流失或收入受损,在涉及数据库和搜索体验时,这种影响尤为明显。这也是开发者纷纷转向 MongoDB 的原因之一,因为它可以让开发者为数据库和搜索解决方案使用一个统一的系统。 随着 Atlas Search Nodes 的推出,我们在为构建者提供最大控制力度方面又迈出了重要一步。现在,构建者可以扩展搜索工作负载,而无需过度预配数据库,因此能够保持灵活性。利用 Atlas Search 和 Atlas Vector Search,您可以在隔离搜索和数据库工作负载的同时,自动保持搜索集群数据与操作数据的同步。这样,您就无需运行单独的 ETL 工具,也就不用耗费时间和精力进行额外设置,从而避免在扩展应用时出错。这有助于提升性能和可用性,同时降低架构复杂性,以及减少从同步失败事件中恢复所耗费的工程时间。事实上,我们已经看到许多复杂查询的查询时间减少了 40% - 60%,资源争用或停机问题也得到了解决。 只需切换一下按钮,Google Cloud 上的 Search Nodes 就能为使用 Atlas Search 和 Vector Search 的用户提供以下优势: 更高的可用性 更强的可扩展性 工作负载隔离 大规模提升性能 更好的查询性能 我们为基于相关性的文本搜索提供计算密集型且特定于搜索的节点,同时还提供内存优化选项,该选项最适合使用 Atlas Vector Search 的语义和 RAG 生产用例。这解决了一直以来存在的资源争用或可用性问题。 启用和设置 Search Nodes 非常简单,只需前往 MongoDB 用户界面并执行以下操作: 前往 MongoDB 用户界面中的“数据库部署”部分 单击绿色的“+创建”按钮 在“创建新集群”页面上,将 Google Cloud 的“多云、多区域和工作负载隔离”单选按钮切换至“开启” 将“用于工作负载隔离的 Search Nodes”单选按钮切换至“开启”。在文本框中选择节点数 勾选协议框 单击“创建集群” 对于使用 Atlas Search 的用户,请单击 MongoDB Atlas Search 用户界面中的“修改配置”,并开启工作负载隔离的切换开关。后续步骤与之前所述步骤相同。 直接跳转至我们的文档以了解更多信息 !

March 28, 2024

Building AI With MongoDB: How DevRev is Redefining CRM for Product-Led Growth

OneCRM from DevRev is purpose-built for Software-as-a-Service (SaaS) companies. It brings together previously separate customer relationship management (CRM) suites for product management, support, and software development. Built on a foundation of customizable large language models (LLMs), data engineering, analytics, and MongoDB Atlas , it connects end users, sellers, support, product owners, and developers. OneCRM converges multiple discrete business apps and teams onto a common platform. As the company states on its website “Our mission is to connect makers (Dev) to customers (Rev) . When every employee adopts a “product-thinking” mindset, customer-centricity transcends from a department to become a culture.” DevRev was founded in October 2020 and raised over $85 million in seed funding from investors such as Khosla Ventures and Mayfield. At the time, this made it the largest seed in the history of Silicon Valley. The company is led by its co-founder and CEO, Dheeraj Pandey, who was previously the co-founder and CEO of Nutanix, and by Manoj Agarwal, DevRev's co-founder and former SVP of Engineering at Nutanix. DevRev is headquartered in Palo Alto and has offices in seven global locations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. CRM + AI: Digging into the stack DevRev’s Support and Product CRM serve over 4,500 customers: Support CRM brings support staff, product managers, and developers onto an AI-native platform to automate Level 1 (L1), assist L2, and elevate L3 to become true collaborators. Product CRM brings product planning, software work management, and product 360 together so product teams can assimilate the voice of the customer in real-time. Figure 1: DevRev’s real-time dashboards empower product teams to detect at-risk customers, monitor product health, track development velocity, and more. AI is central to both the Support and Product CRMs. The company’s engineers build and run their own neural networks, fine-tuned with application data managed by MongoDB Atlas. This data is also encoded by open-source embedding models where it is used alongside OpenAI models for customer support chatbots and question-answering tasks orchestrated by autonomous agents. MongoDB partner LangChain is used to call the models, while also providing a layer of abstraction that frees DevRev engineers to effortlessly switch between different generative AI models as needed. Data flows across DevRev’s distributed microservices estate and into its AI models are powered by MongoDB change streams . Downstream services are notified in real-time of any data changes using a fully reactive, event-driven architecture. MongoDB Atlas: AI-powered CRM on an agile and trusted data platform MongoDB is the primary database backing OneCRM, managing users, customer and product data, tickets, and more. DevRev selected MongoDB Atlas from the very outset of the company. The flexibility of its data model, freedom to run anywhere, reliability and compliance, and operational efficiency of the Atlas managed service all impact how quickly DevRev can build and ship high-quality features to its customers. The flexibility of the document data model enables DevRev’s engineers to handle the massive variety of data structures their microservices need to work with. Documents are large, and each can have many custom fields. To efficiently store, index, and query this data, developers use MongoDB’s Attribute pattern and have the flexibility to add, modify, and remove fields at any time. The freedom to run MongoDB anywhere helps the engineering team develop, test, and release faster. Developers can experiment locally, then move to integration testing, and then production — all running in different environments — without changing a single line of code. This is core to DevRev’s velocity in handling over 4,000 pull requests per month: Developers can experiment and test with MongoDB on local instances — for example adding indexes or evaluating new query operators, enabling them to catch issues earlier in the development cycle. Once unit tests are complete, developers can move to temporary instances in Docker containers for end-to-end integration testing. When ready, teams can deploy to production in MongoDB Atlas. The multi-cloud architecture of Atlas provides flexibility and choice that proprietary offerings from the hyperscalers can’t match. While DevRev today runs on AWS, in the early days of the company, they evaluated multiple cloud vendors. Knowing that MongoDB Atlas could run anywhere gave them the confidence to make a choice on the platform, knowing they would not be locked into that choice in the future. With MongoDB Atlas, our development velocity is 3-4x higher than if we used alternative databases. We can get our innovations to market faster, providing our customers with even more modern and useful CRM solutions. Anshu Avinash, Founding Engineer, DevRev The HashiCorp Terraform MongoDB Atlas Provider automates infrastructure deployments by making it easy to provision, manage, and control Atlas configurations as code. “The automation provided by Atlas and Terraform means we’ve avoided having to hire a dedicated infrastructure engineer for our database layer,” says Anshu. “This is a savings we can redirect into adding developers to work on customer-facing features.” Figure 2: The reactive, event-driven microservices architecture underpinning DevRev’s AI-powered CRM platform Anshu goes on to say, “We have a microservices architecture where each microservice manages its own database and collections. By using MongoDB Atlas, we have little to no management overhead. We never even look at minor version upgrades, which Atlas does for us in the background with zero downtime. Even the major version upgrades do not require any downtime, which is pretty unique for database systems.” Discussing scalability, Anshu says, “As the business has grown, we have been able to scale Atlas, again without downtime. We can move between instance and cluster sizes as our workloads expand, and with auto-storage scaling, we don’t need to worry about disks getting full.” DevRev manages critical customer data, and so relies on MongoDB Atlas’ native encryption and backup for data protection and regulatory compliance. The ability to provide multi-region databases in Atlas means global customers get further control over data residency, latency, and high availability requirements. Anshu goes on to say, “We also have the flexibility to use MongoDB’s native sharding to scale-out the workloads of our largest customers with complete tenant isolation.” DevRev is redefining the CRM market through AI, with MongoDB Atlas playing a critical role as the company’s data foundation. You can learn more about how innovators across the world are using MongoDB by reviewing our Building AI case studies . If your team is building AI apps, sign up for the AI Innovators Program . Successful companies get access to free Atlas credits and technical enablement, as well as connections into the broader AI ecosystem.

March 27, 2024

Fireworks AI and MongoDB: The Fastest AI Apps with the Best Models, Powered By Your Data

We’re happy to announce that Fireworks AI and MongoDB are now partnering to make innovating with generative AI faster, more efficient, and more secure. Fireworks AI was founded in late 2022 by industry veterans from Meta’s PyTorch team, where they focused on performance optimization, improving the developer experience, and running AI apps at scale. This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . It’s this expertise that Fireworks AI brings to its production AI platform, curating and optimizing the industry's leading open models. Benchmarking by the company shows gen AI models running on Fireworks AI deliver up to 4x faster inference speeds than alternative platforms, with up to 8x higher throughput and scale. Models are one part of the application stack. But for developers to unlock the power of gen AI, they also need to bring enterprise data to those models. That’s why Fireworks AI has partnered with MongoDB, addressing one of the toughest challenges to adopting AI. With MongoDB Atlas , developers can securely unify operational data, unstructured data, and vector embeddings to safely build consistent, correct, and differentiated AI applications and experiences. Jointly, Fireworks AI and MongoDB provide a solution for developers who want to leverage highly curated and optimized open-source models, and combine these with their organization’s own proprietary data — and to do it all with unparalleled speed and security. Lightning-fast models from Fireworks AI: Enabling speed, efficiency, and value Developers can choose from many different models to build their gen AI-powered apps. Navigating the AI landscape to identify the most suitable models for specific tasks — and tuning them to achieve the best levels of price and performance — is complex and creates friction in building and running gen AI apps. This is one of the key pain points that Fireworks AI alleviates. With its lightning-fast inference platform, Fireworks AI curates, optimizes, and deploys 40+ different AI models. These optimizations can simultaneously result in significant cost savings , reduced latency , and improved throughput. Their platform delivers this via: Off-the-shelf models, optimized models, and add-ons: Fireworks AI provides a collection of top-quality text, embedding, and image foundation models . Developers can leverage these models or fine-tune and deploy their own, pairing them with their own proprietary data using MongoDB Atlas. Fine-tuning capabilities : To further improve model accuracy and speed, Fireworks AI also offers a fine-tuning service using its CLI to ingest JSON-formatted objects from databases such as MongoDB Atlas. Simple interfaces and APIs for development and production: The Fireworks AI playground allows developers to interact with models right in a browser. It can also be accessed programmatically via a convenient REST API. This is OpenAI API-compatible and thus interoperates with the broader LLM ecosystem. Cookbook: A simple and easy-to-use cookbook provides a comprehensive set of ready-to-use recipes that can be adapted for various use cases, including fine-tuning, generation, and evaluation. Fireworks AI and MongoDB: Setting the standard for AI with curated, optimized, and fast models With Fireworks AI and MongoDB Atlas, apps run in isolated environments ensuring uptime and privacy, protected by sophisticated security controls that meet the toughest regulatory standards: As one of the top open-source model API providers, Fireworks AI serves 66 billion tokens per day (and growing). With Atlas, you run your apps on a proven platform that serves tens of thousands of customers, from high-growth startups to the largest enterprises and governments. Together, the Fireworks AI and MongoDB joint solution enables: Retrieval-augmented generation (RAG) or Q&A from a vast pool of documents: Ingest a large number of documents to produce summaries and structured data that can then power conversational AI. Classification through semantic/similarity search: Classify and analyze concepts and emotions from sales calls, video conferences, and more to provide better intelligence and strategies. Or, organize and classify a product catalog using product images and text. Images to structured data extraction: Extract meaning from images to produce structured data that can be processed and searched in a range of vision apps — from stock photos, to fashion, to object detection, to medical diagnostics. Alert intelligence: Process large amounts of data in real-time to automatically detect and alert on instances of fraud, cybersecurity threats, and more. Figure 1: The Fireworks tutorial showcases how to bring your own data to LLMs with retrieval-augmented generation (RAG) and MongoDB Atlas Getting started with Fireworks AI and MongoDB Atlas To help you get started, review the Optimizing RAG with MongoDB Atlas and Fireworks AI tutorial, which shows you how to build a movie recommendation app and involves: MongoDB Atlas Database that indexes movies using embeddings. (Vector Store) A system for document embedding generation. We'll use the Fireworks embedding API to create embeddings from text data. (Vectorisation) MongoDB Atlas Vector Search responds to user queries by converting the query to an embedding, fetching the corresponding movies. (Retrieval Engine) The Mixtral model uses the Fireworks inference API to generate the recommendations. You can also use Llama, Gemma, and other great OSS models if you like. (LLM) Loading MongoDB Atlas Sample Mflix Dataset to generate embeddings (Dataset) We can also help you design the best architecture for your organization’s needs. Feel free to connect with your account team or contact us here to schedule a collaborative session and explore how Fireworks AI and MongoDB can optimize your AI development process.

March 26, 2024

Transforming Industries with MongoDB and AI: Telecommunications and Media

This is the second in a six-part series focusing on critical AI use cases across the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. Read part one here. The telecommunications industry operates in a landscape characterized by tight profit margins, particularly in commoditized communication and connectivity services where differentiation is minimal. With offerings such as voice, data, and internet access being largely homogeneous, telecom companies need to differentiate and diversify revenue streams to create value and stand out in the market. As digital natives disrupt traditional business models with agile and innovative approaches, established companies are not only competing among themselves but also with newcomers to deliver enhanced customer experiences and adapt to evolving consumer demands. To thrive in an environment where advanced connectivity is increasingly expected, telecom operators must prioritize cost efficiency in their Operations Support Systems (OSS) and Business Support Systems (BSS), elevate customer service standards, and enhance overall customer experiences to secure market share and gain a competitive edge. They’re not alone — media publishers, too, must streamline operations through automation while strengthening reader relationships to foster a willingness to pay for personalized and relevant content. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More Service assurance Telecommunications providers need to deliver network services at optimal quality and performance levels to meet customer expectations and service level agreements. Key aspects of service assurance include performance monitoring, quality of service (QoS) management, and predictive analytics to anticipate potential service degradation or network failures before they occur. With the increasing complexity of telecommunications networks and the growing expectations of customers for high-quality, always-on services, a new bar has been set for service assurance, requiring companies to invest heavily in solutions that can automate and optimize these processes and maintain a competitive edge. Service assurance is revolutionized by artificial intelligence (AI) through several key capabilities: Machine learning (ML) can be a powerful foundation for predictive maintenance, analyzing patterns, and predicting network failures before they occur, allowing for preemptive maintenance and significantly reducing downtime; AI techniques can also sift through complex network systems to accurately identify the root causes of issues, improving the effectiveness of troubleshooting efforts; and, with network optimization, analyzing log data to identify opportunities for improvement, raising efficiency and thus reducing operational costs and optimizing network performance in real-time. MongoDB Atlas ’s JSON-based document model is the ideal data foundation to underpin intelligent applications. It enables developers to store log data from various systems without the need for time-intensive upfront data normalization efforts and with the flexibility to deal with a wide variety of different data structures, even as they change over time. By vectorizing the data with an appropriate ML model, it's possible to reflect the healthy system state and identify log information that shows abnormal system behavior. Atlas Vector Search allows for conducting the required K-Nearest Neighbors (KNN) search in an effective way and as a fully included service of the MongoDB Atlas developer data platform . Finally, using LLM, information about the error, including the analysis of the root cause, can be expressed in natural language, making the job of understanding and fixing the problem much easier for the staff who are in charge of maintenance. Fraud detection and prevention Telecom providers today are utilizing an advanced array of techniques for detecting and preventing fraud, constantly adjusting to the dynamic nature of threat actors. Routine activities for detecting fraud consist of tracking unusual call trends and data usage, along with safeguarding against SIM swap incidents, a method frequently used for identity theft. To prevent fraud, strategies are applied at various levels, starting with stringent verification for new customers during SIM swaps or for transactions with elevated risk, taking into account the unique risk profile of each customer. Machine learning offers telecommunications companies a powerful tool to enhance their fraud detection and prevention capabilities by training ML models on historical data like call detail records (CDR). Moreover, these algorithms can assess the individual risk profile of each customer, tailoring detection and prevention strategies to their specific patterns of use. The models can adapt over time, learning from new data and emerging fraud tactics, thus enabling real-time detection and the automation of fraud prevention measures, reducing manual checks, and speeding up response times. To succeed in fraud detection, many data dimensions need to be considered, making the reaction time a critical factor in preventing the worst things from happening. So, the solution must also support fast, sub-second decisions. By vectorizing the data with an appropriate ML model, normal (healthy) business can be defined, and in turn, deviations from the norm identified, such as suspicious user activities. In addition to Atlas Vector Search, the MongoDB Query API supports stream processing , simplifying data ingestion from various sources and detecting fraud in real-time. Content discovery Today’s media organizations are expected to offer a high degree of content personalization, from streaming services to online publications and more. Viewers want intelligently selected and suggested content tailored to their interests. Using AI can significantly enhance the process of suggesting the next best article to read or show to stream. The most powerful implementations of content personalization track the behavior of the user, such as what content was searched for, how long was content displayed before the next click happened, and the categories the search falls under. Based on these parameters, similar content can be presented, or, as an alternative strategy, content from unseen areas of the portal so the user may discover new types of media and decide if they like it. To bring the right content to the right people at the right time, an automated system needs to maintain a multitude of information facets, which will lay the foundation for proper suggestions. With MongoDB and its document model, all required data points can be easily and flexibly stored in a user’s profile, in content, and in media. Ultimately, by vectorizing the content, an even more powerful system of content suggestions can be built with Atlas Vector Search, which allows for a similarity search that goes well beyond comparing just keywords or a list of attributes. Other notable use cases Differential Pricing: Gather insights into what customers are willing to spend on content or a service by conducting A/B tests and analyzing the data with an ML algorithm. This method facilitates the adoption of dynamic pricing models instead of sticking to a standard price list, thereby enhancing revenue and increasing the paying customer base. Content Summarization and Reformatting: Design a smart assistant tailored for writers, capable of providing automatic suggestions for content summaries, identifying suitable SEO keywords, and adapting articles for various specific audiences. Search Generative Experiences (SGE): Provide more dynamic, personalized, and contextually relevant search results, thus making information retrieval not only more efficient but also more engaging and useful. This can include personalization and summarization elements, as well. In conclusion, the telecommunications industry faces challenges of differentiation and revenue diversification amidst commoditized services and disruptive market forces. To thrive, telecom operators must prioritize cost efficiency, elevate customer service, and enhance experiences. Leveraging AI, MongoDB Atlas offers solutions like service assurance, fraud detection, and content discovery, empowering companies to navigate the complexities of the digital landscape, innovate, and deliver value-added services. From predictive maintenance to personalized content recommendations, MongoDB Atlas stands as a foundational tool for telecom and media companies, driving efficiency, agility, and competitiveness in a rapidly evolving market. Learn more about AI use cases for top industries in our new white paper, “ How Leading Industries are Transforming with AI and MongoDB Atlas .”

March 22, 2024

Introducing Semantic Caching and a Dedicated MongoDB LangChain Package for Gen AI Apps

We are in an unprecedented time in history where developers can build transformative AI applications quickly, without being AI experts themselves. This ability is enabling new classes of applications that can better serve customers with conversational AI for assistance and automation, advanced reasoning and analysis using AI-powered retrieval, and recommendation systems. Behind this revolution are large language models (LLMs) that can be prompted to solve for a wide range of use cases. However, LLMs have various limitations, like knowledge cutoff and a tendency to hallucinate. To overcome these limitations, they must be integrated with proprietary enterprise data sources to build reliable, relevant, and high-quality generative AI applications. That’s where MongoDB plays a critical role in the modern generative AI stack. Developers use MongoDB Atlas Vector Search as a vital part of the generative AI technique known as retrieval-augmented generation (RAG). RAG is the process of feeding LLMs the supplementary data necessary to ground their responses, ensuring they're dependable and precise. LangChain has been a critical part of this journey since the public launch of Atlas Vector Search, enabling developers to build better retriever systems powered by vector search and store conversation history in the operational database. Today, we are excited to announce support for two enhancements: Semantic cache powered by Atlas vector search, which improves the performance of your apps A dedicated LangChain-MongoDB package for Python and JS/TS developers, enabling them to build advanced applications even more efficiently The MongoDB Atlas integration with LangChain can now power all the database requirements for building modern generative AI applications: vector search, semantic caching (currently only available in Python), and conversation history. Earlier, we announced the launch of MongoDB LangChain Templates , which enable the developers to quickly deploy RAG applications, and provided a reference implementation of a basic RAG template using MongoDB Atlas Vector Search and OpenAI and a more advanced Parent-document Retrieval RAG template using MongoDB Atlas Vector Search. We are excited about our partnership with LangChain and will continue innovating. Improve LLM application performance with semantic cache Semantic cache improves the performance of LLM applications by caching responses based on the semantic meaning or context within the queries themselves. This is different from a traditional cache that works based on exact keyword matching. In the era of LLM the value of semantic cache is increasing tremendously, enabling sophisticated user experiences that closely mimic human interactions. For example, if two different users enter two different prompts, “give me suggestions for a comedy movie” and “recommend a comedy movie”, the semantic cache can understand that the intent behind the queries are same and return a similar response, even though different keywords are used, whereas a traditional cache will fail. Figure 1: Semantic cache using MongoDB Atlas Vector Search Check out this video walkthrough for the semantic cache: Accelerate development with a dedicated package With a dedicated LangChain-MongoDB package, MongoDB is even more deeply integrated with LangChain. The Python and Javascript packages contain the following LangChain Integrations: MongoDBAtlasVectorSearch ( Vector stores ) and MongoDBChatMessageHistory ( Chat Messages Memory ). In addition, the Python package includes the MongoDBAtlasSemanticCache ( LLM Caching ). The new package langchain-mongodb contains all the MongoDB-specific implementations and needs to be installed separately from langchain, which includes all the core abstractions. Earlier, everything was in the same package, making it challenging to correctly version and communicate what version should be used and whether any breaking changes were made. Find out more about the langchain-mongodb package: Python: Source code , LangChain docs , MongoDB docs Javascript: Source code , LangChain.js docs , MongoDB docs Get started today Check out this accompanying tutorial and notebook on building advanced RAG with MongoDB and LangChain, which contains a walkthrough and use cases for using semantic cache, vector search, and chat message history. Check out the “ PDFtoChat ” app to see langchain-mongodb JS in action. It allows you to have a conversation with your proprietary PDFs using AI and is built with MongoDB Atlas, LangChain.js, and TogetherAI. It’s an end-to-end SaaS-in-a-box app and includes user authentication, saving PDFs, and saving chats per PDF. Read the excellent overview of semantic caching using LangChain and MongoDB.

March 20, 2024

Transforming Industries with MongoDB and AI: Manufacturing and Motion

This is the first in a six-part series focusing on critical AI use cases across several industries . The series covers the manufacturing and motion, financial services, retail, telecommunications and media, insurance, and healthcare industries. The integration of artificial intelligence (AI) within the manufacturing and automotive industries has transformed the conventional value chain, presenting a spectrum of opportunities. Leveraging Industrial IoT, companies now collect extensive data from assets, paving the way for analytical insights and unlocking novel AI use cases, including enhanced inventory management and predictive maintenance. MongoDB.local NYC Join us in person on May 2, 2024 for our keynote address, announcements, and technical sessions to help you build and deploy mission-critical applications at scale. Use Code Web50 for 50% off your ticket! Learn More Inventory management Efficient supply chains can control operational costs and ensure on-time delivery to their customers. Inventory optimization and management is a key component in achieving these goals. Managing and optimizing inventory levels, planning for fluctuations in demand, and of course, cutting costs are all imperative goals. However, efficient inventory management for manufacturers presents complex data challenges too, primarily in forecasting demand accurately and optimizing stock levels. This is where AI can help. Figure 1: Gen AI-enabled demand forecasting with MongoDB Atlas AI algorithms can be used to analyze complex datasets to predict future demand for products or parts. Improvement in demand forecasting accuracy is crucial for maintaining optimal inventory levels. AI-based time series forecasting can assist in adapting to rapid changes in customer demand. Once the demand is known, AI can play a pivotal role in stock optimization. By analyzing historical sales data and market trends, manufacturers can determine the most efficient stock levels and even reduce human error. On top of all this existing potential, generative AI can help with generating synthetic inventory data and seasonally adjusted demand patterns. It can also help with creating scenarios to simulate supply chain disruptions. MongoDB Atlas makes this process simple. At the warehouse, the inventory can be scanned using a mobile device. This data is persisted in Atlas Device SDK and synced with Atlas using Device Sync, which is used by MongoDB customers like Grainger . Atlas Device Sync provides an offline-first seamless mobile experience for inventory tracking, making sure that inventory data is always accurate in Atlas. Once data is in Atlas, it can serve as the central repository for all inventory-related data. This repository becomes the source of data for inventory management AI applications, eliminating data silos and improving visibility into overall inventory levels and movements. Using Atlas Vector Search and generative AI, manufacturers can easily categorize products based on their seasonal attributes, cluster products with similar seasonal demand patterns, and provide context to the foundation model to improve the accuracy of synthetic inventory data generation. Predictive maintenance The most basic approach to maintenance today is reactive — assets are deliberately allowed to operate until failures actually occur. The assets are maintained as needed, making it challenging to anticipate repairs. Preventive maintenance, however, allows systems or components to be replaced based on a conservative schedule to prevent commonly occurring failures — although predictive maintenance is expensive to implement due to frequent replacement of parts before end-of-life. Figure 2: Audio-based anomaly detection with MongoDB Atlas. Scan the QR code to try it out yourself. AI offers a chance to efficiently implement predictive maintenance using data collected from IoT sensors on machinery trained to detect anomalies. ML/AI algorithms like regression models or decision trees are trained on the preprocessed data, deployed on-site for inference, and continuously analyzed sensor data. When anomalies are detected, alerts are generated to notify maintenance personnel, enabling proactive planning and execution of maintenance actions to minimize downtime and optimize equipment reliability and performance. A retrieval-augmented generation (RAG) architecture can be deployed to generate or curate the data preprocessor removing the need for specialized data science knowledge. The domain expert can provide the right prompts for the large language model. Once the maintenance alert is generated by an AI model, generative AI can come in again to suggest a repair strategy, taking spare parts inventory data, maintenance budget, and personal availability into consideration. Finally, the repair manuals can be vectorized and used to power a chatbot application that guides the technician in performing the actual repair. MongoDB documents are inherently flexible while allowing data governance when required. Since machine health prediction models require not just sensor data but also maintenance history and inventory data, the document model is a perfect fit to model such disparate data sources. During the maintenance and support process of a physical product, information such as product information and replacement parts documentation must be available and easily accessible to support staff. Full-text search capabilities provided by Atlas Search can be integrated with the support portal and help staff retrieve information from Atlas clusters with ease. Atlas Vector Search is a foundational element for effective and efficiently powered predictive maintenance models. Manufacturers can use MongoDB Atlas to explore ways of simplifying machine diagnostics. Audio files can be recorded from machines, which can then be vectorized and searched to retrieve similar cases. Once the cause is identified, they can use RAG to implement a chatbot interface that the technician can interact with and get context-aware, step-by-step guidance on how to perform the repair. Autonomous driving With the rise of connected vehicles, automotive manufacturers have been compelled to transform their business models into software-first organizations. The data generated by connected vehicles is used to create better driver assistance systems, paving the way for autonomous driving applications. However, it is challenging to create fully autonomous vehicles that can drive safer than humans. Some experts estimate that the technology to achieve level 5 autonomy is about 80% developed — but the remaining 20% will be extremely hard to achieve and will take a lot of time to perfect. Figure 3: MongoDB Atlas’s Role in Autonomous Driving AI-based image and object recognition in automotive applications face uncertainties, but manufacturers must utilize data from radar, LiDAR, cameras, and vehicle telemetry to improve AI model training. Modern vehicles act as data powerhouses, constantly gathering and processing information from onboard sensors and cameras, generating significant Big Data. Robust storage and analysis capabilities are essential to manage this data, while real-time analysis is crucial for making instantaneous decisions to ensure safe navigation. MongoDB can play a significant role in addressing these challenges. The document model is an excellent way to accommodate diverse data types such as sensor readings, telematics, maps, and model results. New fields to the documents can be added at run time, enabling the developers to easily add context to the raw telemetry data. MongoDB’s ability to handle large volumes of unstructured data makes it suitable for the constant influx of vehicle-generated information. Atlas Search provides a performant search engine to allow data scientists to iterate their perception AI models. Finally, Atlas Device Sync can be used to send configuration updates to the vehicle's advanced driving assistance system Other notable use cases AI plays a critical role in fulfilling the promise of Industry 4.0. Numerous other use cases of AI can be enabled by MongoDB Atlas, some of which include: Logistics Optimization: AI can help optimize routes resulting in reduced delays and enhanced efficiency in day-to-day delivery operations. Quality Control and Defect Detection: Computer or machine vision can be used to identify irregularities in the products as they are manufactured. This ensures that product standards are met with precision. Production Optimization: By analyzing time series data from sensors installed on production lines, waste can be identified and reduced, thereby improving throughput and efficiency. Smart After Sales Support: Manufacturers can utilize AI-driven chatbots and predictive analytics to offer proactive maintenance, troubleshooting, and personalized assistance to customers. Personalized Product Recommendations: AI can be used to analyze user behavior and preferences to deliver personalized product recommendations via a mobile or web app, enhancing customer satisfaction and driving sales. The integration of AI in manufacturing and automotive industries has revolutionized traditional processes, offering a plethora of opportunities for efficiency and innovation. With industrial IoT and advanced analytics, companies can now harness vast amounts of data to enhance inventory management and predictive maintenance. AI-driven demand forecasting ensures optimal stock levels, while predictive maintenance techniques minimize downtime and optimize equipment performance. Moreover, as automotive manufacturers work toward autonomous driving, AI-powered image recognition and real-time data analysis become paramount. MongoDB Atlas emerges as a pivotal solution, providing flexible document modeling and robust storage capabilities to handle the complexities of Industry 4.0. Beyond the manufacturing and automotive sectors, the potential of AI-enabled by MongoDB Atlas extends to logistics optimization, quality control, production efficiency, smart after-sales support, and personalized customer experiences, shaping the future of Industry 4.0 and beyond. Learn more about AI use cases for top industries in our new white paper, “ How Leading Industries are Transforming with AI and MongoDB Atlas .”

March 19, 2024

From Relational Databases to AI: An Insurance Data Modernization Journey

Imagine you’re a data architect, a developer, or a data engineer at an insurance company. Management has asked you and your team to build a new AI claim adjustment system, a customer-facing LLM-powered chatbot, and an application to streamline the underwriting process. However, doing so is far from straightforward due to the challenges you face on a daily basis. The bulk of your time is spent navigating your company’s outdated legacy systems, which were built in the 1970s and 1980s. Some of these legacy platforms were written in COBOL and CICS, and today very few people on your team know how to develop and maintain those technologies. Moreover, the data models you work with are another source of frustration. Every interaction with them is a reminder of the intricate structures that have evolved over time, making data manipulation and analysis a nightmare. In sum, legacy systems are preventing your team—and your company—from innovating and keeping up with both your industry and customer demands. Whether you’re trying to modernize your legacy systems to improve operational efficiency, or to boost developer productivity, or if you want to build AI-powered apps that integrate with large language models (LLMs), MongoDB has a solution for that. In this post, we’ll walk you through a journey that starts with a relational data model refactored into MongoDB collections, vectorization and querying of unstructured data and, finally, retrieval augmented generation (RAG) : asking large language models (LLMs) questions about data in natural language. Identifying, modernizing, and storing the data Our journey starts with an assessment of the data sources we want to work with. As shown below, we can bucket the data into three different categories: Structured legacy data: Tables of claims, coverages, billings, and more. Is your data locked in rigid relations schemas? This tutorial is a step-by-step guide on how to migrate a real-life insurance relational model with the help of MongoDB Relational Migrator , refactoring 21 tables to only five MongoDB collections. Structured data (JSON): You might have files of policies, insurance products, or forms in JSON format. Check out our docs to learn how to insert those into a MongoDB collection. Unstructured data (PDFs, Audios, Images, etc.): If you need to create and store a numerical representation (vector embedding) of, for instance, claim-related photos of accidents or PDFs of policy guidelines, you can have a look at this blog that will walk you through the process of generating embeddings of pictures of car crashes and persisting them alongside existing fields in a MongoDB collection. Figure 1: Storing different types of data into MongoDB Regardless of the original format or source, our data has finally landed into MongoDB Atlas into what we call a Converged AI Data Store, which is a platform that centrally integrates and organizes enterprise data, including vectors, that enable the development of ML- and AI-powered applications. Accessing, experimenting and interacting with the data It’s time to put the data to work. The Converged AI Data Store unlocks a plethora of use cases and efficiency gains, both for the business and for developers. The next step of the journey is about the different ways we can interact with our data: Database and Full Text Search: Learn how to run database queries, start from the basics and move up to advanced features such as facets, fuzzy search, autocomplete, highlighting, and more with Atlas Search . Vector Search: We can finally leverage unstructured data. The Image Search blog we mentioned earlier also explains how to create a Vector Search index and run vector queries against embeddings of photos. RAG: Combining Vector Search and the power of LLMs, it is possible to interact in natural language with our data (see Figure 2 below), asking complex questions and getting detailed answers. Follow this tutorial to become a RAG expert. Figure 2: Retrieval augmented generation (RAG) diagram where we dynamically combine our custom data with the LLM to generate reliable and relevant outputs Having explored all the different ways we can ask questions of the data, we made it to the end of our journey. You are now ready to modernize your company’s systems and finally be able to keep up with the business’ demands. What will you build next? If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases Build a ML-Powered Underwriting Engine in 20 Minutes with MongoDB and Databricks

March 14, 2024

Using Generative AI and MongoDB to Tackle Cybersecurity’s Biggest Challenges

This post is also available in: Deutsch , Français , Español , Português , Italiano , 한국어 , 简体中文 . In the ever-evolving landscape of cybersecurity, organizations face a multitude of challenges that demand innovative solutions harnessing cutting-edge technologies. One of the most pressing issues is the increasing sophistication of cyber threats, including malware, ransomware, and phishing attacks, which are becoming more difficult to detect and mitigate. Additionally, the rapid expansion of digital infrastructures has widened the attack surface, making it harder for security teams to monitor and protect every entry and egress point. Another significant challenge is the shortage of skilled cybersecurity professionals — estimated by independent surveys to number around 4 million staff worldwide 1 — which leaves many organizations vulnerable to attack. These challenges underscore the need for advanced technologies that can augment human efforts to secure digital assets and data. How can generative AI help? Generative AI (gen AI) has emerged as a powerful tool in addressing these cybersecurity challenges. By leveraging large language models (LLMs) to generate new data or patterns based on existing datasets, generative AI can provide innovative solutions in several key areas: Enhanced threat detection and response Generative AI can be used to create simulations of cyber threats, including sophisticated malware and phishing attacks. These simulations can help in training machine learning models to detect new and evolving threats more accurately. Furthermore, gen AI can aid in the development of automated response systems that react to threats in real time. While this will never eliminate the need for human oversight, it will reduce the need for manual intervention and toil, allowing for quicker mitigation of attacks. For example, with the appropriate oversight it can automatically apply patches to vulnerable systems or adjust firewall rules to block attack vectors. This automated rapid response capability is particularly valuable in mitigating zero-day vulnerabilities, where the window between the discovery of a vulnerability and its exploitation by attackers can be very short. Actionable learnings from security event postmortems In the aftermath of a cybersecurity incident, conducting a thorough postmortem analysis is crucial for understanding what happened, why it happened, and how similar events can be prevented in the future. Generative AI can play a pivotal role in this process by synthesizing and summarizing complex data from a multitude of sources, including logs, network traffic, and security alerts. By analyzing this data, gen AI can identify patterns and anomalies that may have contributed to the security breach, offering insights that might be overlooked by human analysts due to the sheer volume and complexity of the information. Furthermore, it can generate comprehensive reports that highlight key findings, causative factors, and potential vulnerabilities, streamlining the postmortem process. This capability not only accelerates the recovery and learning process but also enables organizations to implement more effective remediation strategies, ultimately strengthening their cybersecurity posture. Generating synthetic data for deep model training The shortage of real-world data for training cybersecurity systems is a significant hurdle. Gen AI can create realistic, synthetic data sets that mirror genuine network traffic and user behavior without exposing sensitive information. This synthetic data can be used to train detection systems, improving their accuracy and effectiveness without compromising privacy or security. Automating phishing detection Phishing remains one of the most common attack vectors. Gen AI can analyze patterns in phishing emails and websites, generating models that predict and detect phishing attempts with high accuracy. By integrating these models into email systems and web browsers, organizations can automatically filter out phishing content, protecting users from potential threats. Putting it all together: The opportunities and the risks Generative AI holds the promise of transforming cybersecurity practices by automating complex processes, enhancing threat detection and response, and providing a deeper understanding of cyber threats. As the industry continues to integrate gen AI into cybersecurity strategies, it's crucial to remain vigilant about the ethical use of this technology and the potential for misuse. Nevertheless, the benefits it offers in strengthening digital defenses are undeniable, making it an invaluable asset in the ongoing battle against cyber threats. How does MongoDB help? With MongoDB, your development teams can build and deploy robust, correct, and differentiated real-time cyber defenses faster, and at any scale. To understand how MongoDB does this, consider that the the AI technology stack comprises three layers: The underlying compute (GPUs) and LLMs The tooling to fine-tune models along with the tooling for in-context learning and inference against the trained models The AI applications and related end-user experiences MongoDB operates at the second layer of the stack. It enables customers to bring their own proprietary data to any LLM running on any computing infrastructure to build gen AI-powered cybersecurity applications. MongoDB does this by addressing the hardest problems when adopting gen AI for cybersecurity. MongoDB Atlas securely unifies operational data, unstructured data, and vector data in a single, fully managed multi-cloud platform, avoiding the need to copy and sync data between different systems. MongoDB’s document-based architecture also allows development teams to easily model relationships between your application data and vector embeddings. This allows deeper and faster analytics and insights against security-related data. Figure 1: MongoDB Atlas brings together all of the data services needed to build modern cyber security applications in a unified API and developer data platform. MongoDB’s open architecture is integrated with a rich ecosystem of AI developer frameworks, LLMs, and embedding providers. This, combined with our industry-leading multi-cloud capabilities, allows your development teams the flexibility to move quickly and avoid lock-in to any particular cloud provider or AI technology in this rapidly evolving space. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. Applying gen AI and MongoDB to real world cybersecurity applications Threat intelligence ExTrac utilizes AI-powered analytics and MongoDB Atlas to predict public safety risks by analyzing data from thousands of sources. The platform initially helped Western governments foresee conflicts but is expanding to enterprises for reputational management and more. MongoDB's document data model allows ExTrac to manage complex data efficiently, enhancing real-time threat identification. Atlas Vector Search aids in augmenting language models and managing vector embeddings for texts, images, and videos, speeding up feature development. This approach enables ExTrac to efficiently model trends, track evolving narratives, and predict risk for its customers, leveraging the flexibility and power of MongoDB to handle data of any shape and structure. Learn more in our ExTrac case study . Cybersec assessments VISO TRUST leverages AI to streamline the assessment of third-party cyber risks, making complex vendor security information quickly accessible for informed decision-making. Utilizing Amazon Bedrock and MongoDB Atlas, VISO TRUST's platform automates the due diligence of vendor security, significantly reducing the workload for security teams. Its AI-powered approach involves artifact intelligence that classifies security documents, detects organizations, and predicts security control locations within artifacts. MongoDB Atlas hosts text embeddings for a dense retrieval system that enhances the accuracy of LLMs through retrieval-augmented generation (RAG), providing instant, actionable security insights. This innovative use of technology enables VISO TRUST to offer rapid, scalable cyber risk assessments, boasting significant reductions in work and time for enterprises like InstaCart and Upwork. MongoDB's flexible document database and Atlas Vector Search play critical roles in managing and querying the vast amounts of data, supporting VISO TRUST's mission to deliver comprehensive cyber risk intelligence. Learn more in our Viso Trust case study . Steps to get started Generative AI powered by LLMs augmented with your own operational data encoded as vector embeddings is opening up many new possibilities in cyber security. If you want to learn more about the technology and its possibilities, take a look at our Atlas Vector Search learning byte . In just 10 minutes you’ll get an overview of different use cases and how to get started. 1 Hill, M. (2023, April 10). Cybersecurity workforce shortage reaches 4 million despite significant recruitment drive . CSO.

March 13, 2024

How MongoDB Enables Digital Twins in the Industrial Metaverse

The integration of MongoDB into the metaverse marks a pivotal moment for the manufacturing industry, unlocking innovative use cases across design and prototyping, training and simulation, and maintenance and repair. MongoDB's powerful capabilities — combined with Augmented Reality (AR) or Virtual Reality (VR) technologies — are reshaping how manufacturers approach these critical aspects of their operations, while also enabling the realization of innovative product features. But first: What is the metaverse, and why is it so important to manufacturers? We often use the term, "digital twin" to refer to a virtual replication of the physical world. It is commonly used for simulations and documentation. The metaverse goes one step further: Not only is it a virtual representation of a physical device or a complete factory, but the metaverse also reacts and changes in real time to reflect a physical object’s condition. The advent of the industrial metaverse over the past decade has given manufacturers an opportunity to embrace a new era of innovation, one that can enhance collaboration, visualization, and training. The industrial metaverse is also a virtual environment that allows geographically dispersed teams to work together in real time. Overall, the metaverse transforms the way individuals and organizations interact to produce, purchase, sell, consume, educate, and work together. This paradigm shift is expected to accelerate innovation and affect everything from design to production across the manufacturing industry. Here are some of the ways the metaverse — powered by MongoDB — is having an impact manufacturing. Design and prototyping Design and prototyping processes are at the core of manufacturing innovation. Within the metaverse, engineers and designers can collaborate seamlessly using VR, exploring virtual spaces to refine and iterate on product designs. MongoDB's flexible document-oriented structure ensures that complex design data, including 3D models and simulations, is efficiently stored and retrieved. This enables real-time collaboration, accelerating the design phase while maintaining the precision required for manufacturing excellence. Training and simulation Taking a digital twin and connecting it to physical assets enables training beyond traditional methods and provides immersive simulations in the metaverse that enhance skill development for manufacturing professionals. VR training, powered by MongoDB's capacity to manage diverse data types — such as time-series, key-values and events — enables realistic simulations of manufacturing environments. This approach allows workers to gain hands-on experience in a safe virtual space, preparing them for real-world challenges without affecting production cycles. Gamification is also one of the most effective ways to learn new things. MongoDB's scalability ensures that training data, including performance metrics and user feedback, is efficiently handled to continuously enlarge the training modules and the necessary resources for the ever-increasing amount of data. Maintenance and repair Maintenance and repair operations are streamlined through AR applications within the metaverse. The incorporation of AR and VR technologies into manufacturing processes amplifies the user experience, making interactions more intuitive and immersive. Technicians equipped with AR devices can access real-time information overlaid onto physical equipment, providing step-by-step guidance for maintenance and repairs. MongoDB's support for large volumes of diverse data types, including multimedia and spatial information, ensures a seamless integration of AR and VR content. This not only enhances the visual representation of data from the digital twin and the physical asset but also provides a comprehensive platform for managing the vast datasets generated during AR and VR interactions within the metaverse. Additionally, MongoDB's geospatial capabilities come into play, allowing manufacturers to manage and analyze location-based data for efficient maintenance scheduling and resource allocation. The result is reduced downtime through more efficient maintenance and improved overall operational efficiency. From the digital twin to metaverse with MongoDB The advantages of a metaverse for manufacturers are enormous, and according to Deloitte many executives are confident the industrial metaverse “ will transform research and development, design, and innovation, and enable new product strategies .” However, the realization is not easy for most companies. Challenges include managing system overload, handling vast amounts of data from physical assets, and creating accurate visualizations. The metaverse must also be easily adaptable to changes in the physical world, and new data from various sources must be continuously implemented seamlessly. Given these challenges, having a data platform that can contextualize all the data generated by various systems and then feed that to the metaverse is crucial. That is where MongoDB Atlas , the leading developer data platform, comes in, providing synchronization capabilities between physical and virtual worlds, enabling flexible data modeling, and providing access to the data via a unified query interface as seen in Figure 1. Figure 1: MongoDB connecting to a physical & virtual factory Generative AI with Atlas Vector Search With MongoDB Atlas, customers can combine three systems — database, search engine, and sync mechanisms — into one, delivering application search experiences for metaverse users 30% to 50% faster . Atlas powers use cases such as similarity search, recommendation engines, Q&A systems, dynamic personalization, and long-term memory for large language models (LLMs). Vector data is integrated with application data and seamlessly indexed for semantic queries, enabling customers to build easier and faster. MongoDB Atlas enables developers to store and access operational data and vector embeddings within a single unified platform. With Atlas Vector Search , users can generate information for maintenance, training, and all the other use cases from all possible information that is accessible. This information can come from text files such as Word, from PDFs, and even from pictures or sound streams from which an LLM then generates an accurate semantic answer. It’s no longer necessary to keep dozens of engineers busy, just creating useful manuals that are outdated at the moment a production line goes through first commissioning. Figure 2: Atlas Vector Search Transforming the manufacturing industry with MongoDB In the digital twin and metaverse-driven future of manufacturing, MongoDB emerges as a linchpin, enabling cost-effective virtual prototyping, enhancing simulation capabilities, and revolutionizing training processes. The marriage of MongoDB with AR and VR technologies creates a symbiotic relationship, fostering innovation and efficiency across design, training, and simulation. As the manufacturing industry continues its journey into the metaverse, the partnership between MongoDB and virtual technologies stands as a testament to the transformative power of digital integration in shaping the future of production. Learn more about how MongoDB is helping organizations innovate with the industrial metaverse by reading how we Build a Virtual Factory with MongoDB Atlas in 5 Simple Steps , how IIoT data can be integrated in 4 steps into MongoDB, or how MongoDB drives Innovations End-To-End in the whole Manufacturing Chain .

March 12, 2024