Atlas Charts Adds a Dedicated Hub for Managing Embedded Charts and Dashboards

Since the release of the Charts Embedding SDK in May of 2020, developers have been exploring powerful new ways to visualize and share data from their MongoDB Atlas clusters. Embedding charts and dashboards is a valuable use case for Charts users and the new Embedding Page streamlines the embedding experience for first time users and veterans alike.

Everything you need on one screen

Don’t worry if the concept of embedding within the MongoDB Charts platform is new to you. The Getting Started tab provides configuration guidance, and links to video references, code snippets, live sandboxes, and other resources to help you get started.


But just as your applications may evolve according to your needs, your embedding requirements may also change over time. Once you have set up an embedded dashboard or chart, the Items tab acts as the landing page. Think of this as a live snapshot of your current embedding environment. You’ll see a list of all of your charts grouped by their dashboards, be able to search based on title or description, and filter the list to show only dashboards.

Within each row, you can view a chart or dashboard’s embedded status, see which type of embedding is enabled, view and copy the embedding ID, and access the full suite of embedding settings available for each item. This means that you can add filters or change your embedding method without having to know exactly where every chart or related setting lives. This approach also lets you operate with confidence on one single page. How cool is that?

Authentication settings

The Charts SDK allows you to configure unauthenticated embedding for dashboards or charts, making for a painless way to share these items in a safe and controlled environment.

Depending on your use case, this setup may be a little more flexible than you’d like. The Authentication Settings tab contains authentication provider settings, giving project owners a single source of truth for adding and maintaining providers.

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Our focus for this feature is on simplicity and consolidation. We believe wholeheartedly that if we can enable you to spend less time hunting down where to configure settings or find resources, you can focus more on what really matters and build great software.

For more information on authentication options, read our documentation.

New to MongoDB Atlas Charts? Get started today by logging in to or signing up for MongoDB Atlas, deploying or selecting a cluster, and activating Charts for free.

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The Insider's Guide to MongoDB World 2022

Join us from June 7 to June 9 at MongoDB World 2022, which will be held at New York City’s Javits Center. Enjoy three packed days of keynotes, workshops, talks, technical panels, networking, community building, and more. Whether you’re eager to reconnect with your peers in person or are slightly overwhelmed by the choice of sessions and activities, you’ll find everything you need to know in this post. We will highlight special events at MongoDB World, preview what to expect and how to prepare, and provide tips on getting the most out of the conference. Plan your itinerary Space for workshops, talks, and other sessions are limited, so make sure to check out the World 2022 agenda and sign up for the activities that interest you. “Take time to create a list — and budget time between sessions,” advises Ben Flast, a MongoDB product management lead and featured World speaker . “There’s a lot going on, so have a plan to make sure to see the sessions that are most important to you.” Pick your learning path Whether they’re conference tracks, Chalk Talks, or keynotes, each event has a different audience, purpose, and skill level. The must-see keynotes from MongoDB CEO Dev Ittycheria, CTO Mark Porter, and chief product officer Sahir Azam will showcase announcements and new releases — and explain how they fit into the MongoDB ecosystem. Additionally, we are excited to announce that renowned technologist Ray Kurzweil has been confirmed as a keynote speaker. A distinguished thinker, inventor, and leader, Kurzweil has transformed multiple areas of technology, pioneering industry-leading products such as flatbed scanners, the first text-to-speech synthesizer, and much more. Don’t miss this exciting speech from a legend of the tech industry. Talks and workshops are divided into eight tracks, each of which includes a variety of sessions. The tracks include: Partner talks; the MongoDB Developer Data Platform; Community Cafe; Governance, Compliance, and Security; Industry and Solutions Data Architecture; Modern Application Development; Make It Matter; Schema Design and Modeling; and the keynote speeches. Make It Matter, a track on inclusion, diversity, equity, and accessibility (IDEA), will be held in our dedicated IDEA Lounge. “People learn in all sorts of ways,” explains Karen Huaulme, a principal developer advocate at MongoDB. “That’s why we have hour-long sessions, 15-minute lightning talks, and everything in between. Feel free to mix and match so that you can learn in a way that works for you.” See the entire World 2022 agenda and mix and match your sessions For instance, Jumpstarts are high-level tutorials that introduce newcomers to basic (but important) MongoDB skills and best practices. This year, we’re running Jumpstarts on data and schema modeling, MongoDB Atlas, and Atlas Search, all of which will be moderated by seasoned MongoDB product managers and users. In contrast, Chalk Talks are highly interactive, small-group sessions for everyone from beginners to experts. Chalk Talks tend to be short (around 30 minutes), with plenty of audience participation, whiteboarding, and free-flowing discussion. For something more immersive, try a workshop — the long meal to the Chalk Talk’s snack break. Held only on Day 3, workshops are deep dives into highly technical topics. The first two hours will set the tone with onboarding, configuration, and lectures, and the second half will center on relevant real-world scenarios and attendee needs. If you want to practice using a specific technology and figure out how to make it work in your environment, sign up for a workshop . If you’re curious about the big picture, attend a Product Announcement or a Product Vision talk. Announcements will cover individual releases, how to use them, and how they fit into the MongoDB product family. Vision talks will marry new and existing products in order to explore different themes and workflows. Examples include " Serverless: The Future of Application Development " and " Going Real-Time With MongoDB Atlas ." More information can be found on the MongoDB World Agenda , which is updated regularly. Come prepared Speakers and facilitators will be in touch in advance to share all the necessary prerequisites, whether it’s downloadable modules, syllabi, or any other required materials. “Preparation will depend on the specific event,” says Jesse Hall, a senior developer advocate at MongoDB and workshop presenter. “For example, my workshop takes a serverless approach — setting up MongoDB in JAMstack — so be sure to bring a laptop with a basic development environment (like Node.js or VSCode).” Don't miss the hallway track Let serendipity take the wheel as you mix and mingle with other attendees, speakers, customers, partners, and other industry leaders between sessions, at the Community Cafe, and elsewhere. “Don’t be afraid to explore different events or exchange ideas with new people,” Huaulme suggests. “That’s where the magic happens. Don’t be intimidated by the idea of chatting with speakers or presenters. They’re very approachable, down-to-earth, and happy to hear from you.” “Keep an open mind and an open ear, and definitely reach out to anyone wearing MongoDB swag,” says Flast. “They’re working on something interesting.” Meet MongoDB partners Get to know our partners and learn how they build the future with MongoDB. Many of our top partners will be presenting talks at MongoDB World on topics from building operational data stores to working with edge devices , and they’ll also be running booths at the Partner Promenade. These organizations include major cloud companies, along with leaders in streaming data, real-time analytics, and much more. Matt Asay, MongoDB’s vice president of Partner Marketing , encourages visitors to see how these partners help enterprises of all sizes build the future. For his part, Asay looks forward to moderating a panel with leaders from Vercel, Prisma, and Apollo GraphQL, and to learning more about how these cutting-edge companies build for — and with — developers. Try something new Check out events that are off the beaten track, like the Builders’ Fest and the Community Cafe. At both venues, you’ll be able to unleash your creativity and pick up new skills. Check out the unique workshops at the Builders’ Pods, relaxed areas with lots of comfortable chairs, tables, and monitors. In the past, participants have learned to pick locks, create ice sculptures, construct machine learning algorithms, and develop games, among other things. For Huaulme, the Builders’ Fest sessions are a personal favorite. “The last time around, I learned to pick locks, while others learned to jump rope,” she recalls. “Builders’ Fest is a great place to learn new, fun skills — not all of which are related to tech.” Builders’ Fest will also include competition alongside discovery and exploration. Head to the nearby stages, where you can choose from coding challenges (like Code Golf) and play popular video games such as MarioKart, Donkey Kong, and more. Test your skills — whether it’s your mastery of code or your fast reflexes — against your peers. Stop by the Community Cafe to recharge. Lounge with coffee, thumb through the products at the swag store, and take a break from the action. Don’t forget to check out the silk-screen booth, where you can customize T-shirt designs and watch as they’re printed before your eyes. Register today for MongoDB World, and use code ​​ MDBW22BLOG to save 25% off your tickets. We hope to see you in NYC from June 7 to June 9!

May 18, 2022
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Retrieval Augmented Generation for Claim Processing: Combining MongoDB Atlas Vector Search and Large Language Models

Following up on our previous blog, AI, Vectors, and the Future of Claims Processing: Why Insurance Needs to Understand The Power of Vector Databases , we’ll pick up the conversation right where we left it. We discussed extensively how Atlas Vector Search can benefit the claim process in insurance and briefly covered Retrieval Augmented Generation (RAG) and Large Language Models (LLMs). 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 One of the biggest challenges for claim adjusters is pulling and aggregating information from disparate systems and diverse data formats. PDFs of policy guidelines might be stored in a content-sharing platform, customer information locked in a legacy CRM, and claim-related pictures and voice reports in yet another tool. All of this data is not just fragmented across siloed sources and hard to find but also in formats that have been historically nearly impossible to index with traditional methods. Over the years, insurance companies have accumulated terabytes of unstructured data in their data stores but have failed to capitalize on the possibility of accessing and leveraging it to uncover business insights, deliver better customer experiences, and streamline operations. Some of our customers even admit they’re not fully aware of all the data in their archives. There’s a tremendous opportunity to leverage this unstructured data to benefit the insurer and its customers. Our image search post covered part of the solution to these challenges, opening the door to working more easily with unstructured data. RAG takes it a step further, integrating Atlas Vector Search and LLMs, thus allowing insurers to go beyond the limitations of baseline foundational models, making them context-aware by feeding them proprietary data. Figure 1 shows how the interaction works in practice: through a chat prompt, we can ask questions to the system, and the LLM returns answers to the user and shows what references it used to retrieve the information contained in the response. Great! We’ve got a nice UI, but how can we build an RAG application? Let’s open the hood and see what’s in it! Figure 1: UI of the claim adjuster RAG-powered chatbot Architecture and flow Before we start building our application, we need to ensure that our data is easily accessible and in one secure place. Operational Data Layers (ODLs) are the recommended pattern for wrangling data to create single views. This post walks the reader through the process of modernizing insurance data models with Relational Migrator, helping insurers migrate off legacy systems to create ODLs. Once the data is organized in our MongoDB collections and ready to be consumed, we can start architecting our solution. Building upon the schema developed in the image search post , we augment our documents by adding a few fields that will allow adjusters to ask more complex questions about the data and solve harder business challenges, such as resolving a claim in a fraction of the time with increased accuracy. Figure 2 shows the resulting document with two highlighted fields, “claimDescription” and its vector representation, “claimDescriptionEmbedding” . We can now create a Vector Search index on this array, a key step to facilitate retrieving the information fed to the LLM. Figure 2: document schema of the claim collection, the highlighted fields are used to retrieve the data that will be passed as context to the LLM Having prepared our data, building the RAG interaction is straightforward; refer to this GitHub repository for the implementation details. Here, we’ll just discuss the high-level architecture and the data flow, as shown in Figure 3 below: The user enters the prompt, a question in natural language. The prompt is vectorized and sent to Atlas Vector Search; similar documents are retrieved. The prompt and the retrieved documents are passed to the LLM as context. The LLM produces an answer to the user (in natural language), considering the context and the prompt. Figure 3: RAG architecture and interaction flow It is important to note how the semantics of the question are preserved throughout the different steps. The reference to “adverse weather” related accidents in the prompt is captured and passed to Atlas Vector Search, which surfaces claim documents whose claim description relates to similar concepts (e.g., rain) without needing to mention them explicitly. Finally, the LLM consumes the relevant documents to produce a context-aware question referencing rain, hail, and fire, as we’d expect based on the user's initial question. So what? To sum it all up, what’s the benefit of combining Atlas Vector Search and LLMs in a Claim Processing RAG application? Speed and accuracy: Having the data centrally organized and ready to be consumed by LLMs, adjusters can find all the necessary information in a fraction of the time. Flexibility: LLMs can answer a wide spectrum of questions, meaning applications require less upfront system design. There is no need to build custom APIs for each piece of information you’re trying to retrieve; just ask the LLM to do it for you. Natural interaction: Applications can be interrogated in plain English without programming skills or system training. Data accessibility: Insurers can finally leverage and explore unstructured data that was previously hard to access. Not just claim processing The same data model and architecture can serve additional personas and use cases within the organization: Customer Service: Operators can quickly pull customer data and answer complex questions without navigating different systems. For example, “Summarize this customer's past interactions,” “What coverages does this customer have?” or “What coverages can I recommend to this customer?” Customer self-service: Simplify your members’ experience by enabling them to ask questions themselves. For example, “My apartment is flooded. Am I covered?” or “How long do windshield repairs take on average?” Underwriting: Underwriters can quickly aggregate and summarize information, providing quotes in a fraction of the time. For example, “Summarize this customer claim history.” “I Am renewing a customer policy. What are the customer's current coverages? Pull everything related to the policy entity/customer. I need to get baseline info. Find relevant underwriting guidelines.” If you would like to discover more about Converged AI and Application Data Stores with MongoDB, take a look at the following resources: RAG for claim processing GitHub repository From Relational Databases to AI: An Insurance Data Modernization Journey Modernize your insurance data models with MongoDB and Relational Migrator

April 18, 2024