You Asked, We Listened. It's Here - Dark Mode for Atlas is Now Available in Public Preview

Tonya Edmonds and Sabina Friden
November 15, 2023
#Atlas

We are thrilled to announce a much-anticipated feature for MongoDB Atlas. Dark mode is now available in Public Preview for users worldwide. Dark mode has been the number one requested feature in MongoDB's feedback forum, and we've taken note. Users have tried browser plugins and other makeshift fixes, but now the wait is over. Our development team diligently worked to introduce a dark mode option, improving user experience with a new and refreshing perspective to the familiar interface of Atlas. This update—which includes 300 converted pages—is not just for our community. It also benefits us as developers, promoting a seamless dark mode experience across different tools in the developer workflow.

Dark mode is sleek and sophisticated, aligning with the preferred working styles of many of our developers. Remember that this is an ongoing project, and there may be areas within Atlas that need refining. Rest assured, we will be monitoring our feedback channels closely.

Not just a sleek interface

We took a thoughtful approach to the overall dark mode user experience, particularly with respect to accessibility considerations. We ensured that our dark mode theme met accessibility standards by checking and adjusting all text, illustrations, and UI elements for color and contrast to help reduce eye strain and address those with light sensitivities while making sure it was still easy to read.

We also focused on accommodating the overall light-to-dark background contrast while staying mindful of how they may layer or interact with other elements.

Beyond aesthetics, dark mode is a proven method for extending battery life. For our users with OLED or AMOLED screens dark mode ensures the device’s battery life stretches even further by illuminating fewer pixels and encouraging lower brightness levels.

Health benefits

A typical engineer spends no fewer than eight hours a day in front of a computer, exposing their eyes to multiple digital screens, according to data from Medium. This screen usage can lead to dry eyes, insomnia, and headaches. While dark text on a light background is best for legibility purposes, light text on a dark background helps reduce eye strain in low-light conditions.

Enable dark mode preview today

To update the theme at any time, navigate to the User Menu in the top right corner, then select User Preferences. Under Appearance, there will be three options.

  • Light Mode: This is the default color scheme.

  • Dark Mode: Our new dark theme.

  • Auto (Sync with OS): This setting will match the operating system's setting.

A few things to keep in mind

  • This is a user setting and does not impact other users within a project or organization.

  • Dark mode is not currently available for Charts, Documentation, University, or Cloud Manager.

  • Since we are releasing this in Public Preview, there might be some minor visual bugs. The goal of Public Preview releases is to generate interest and gather feedback from early adopters. It is not necessarily feature-complete and does not typically include consulting, SLAs, or technical support obligations.

  • We have conducted comprehensive internal testing, and we did not find anything that prevents users from using Atlas. While we are still making a few finishing touches feel free to share any feedback using this form.

Thank you to all our users who provided valuable feedback and waited patiently for this feature! Keep the feedback coming. We hope you enjoy dark mode, designed to improve accessibility, reduce eye strain and fatigue, and enhance readability.

We invite you to experience the difference. Try dark mode today through your MongoDB Atlas portal.

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Perfect Your CI/CD Pipelines with MongoDB's New GitHub Action and Docker Image for the Atlas CLI

Do you use GitHub Actions for your CI/CD workflows? Or build using Docker containers? If so, you’ll probably be excited to hear that MongoDB has released: 1. An official GitHub Action and 2. A dedicated Docker image for the Atlas CLI. Together, these two releases make it easier than ever to develop applications with MongoDB Atlas. Since MongoDB announced the Atlas CLI at MongoDB World in 2022, it has become one of our most popular tools for building with the Atlas developer data platform. One of the great things about the Atlas CLI is that it not only caters to the individual developer wanting a mouseless terminal experience—it also makes it easy to programmatically manage Atlas resources throughout the entire development lifecycle. With the new releases for the Atlas CLI with GitHub Actions and Docker, you can easily use the Atlas CLI to build with Atlas while still working natively within your preferred CI/CD platforms. Within GitHub Actions, you now have access to a dedicated Action that allows you to seamlessly manage Atlas resources using your favorite Atlas CLI commands. You can use the predefined workflows available or create custom workflows leveraging native Atlas CLI commands. For example, with one of the predefined workflows you can: create a project, set up the Atlas CLI with an Atlas deployment, retrieve your connection string, and tear down your project and deployment. If you use a platform other than GitHub Actions to manage your CI/CD pipelines, or simply use Docker in your toolchain, you can now also use the Atlas CLI by pulling the Docker image with just one command: docker pull mongodb/atlas From there, you can enter an interactive shell to run Atlas CLI commands as you normally would: docker run --rm -it mongodb/atlas bash atlas --help You can also find detailed information in the MongoDB Documentation on how to run Docker in interactive mode or as a daemon (detached mode) for working with the Atlas CLI. Ready to get started? You can find the Atlas CLI GitHub Action in the GitHub Marketplace and the Atlas CLI Docker image on Docker Hub . If you have any feedback on either experience, share your thoughts with us in the Atlas CLI section of the MongoDB Feedback Engine .

November 15, 2023
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Building AI With MongoDB: Integrating Vector Search And Cohere to Build Frontier Enterprise Apps

Cohere is the leading enterprise AI platform, building large language models (LLMs) which help businesses unlock the potential of their data. Operating at the frontier of AI, Cohere’s models provide a more intuitive way for users to retrieve, summarize, and generate complex information. Cohere offers both text generation and embedding models to its customers. Enterprises running mission-critical AI workloads select Cohere because its models offer the best performance-cost tradeoff and can be deployed in production at scale. Cohere’s platform is cloud-agnostic. Their models are accessible through their own API as well as popular cloud managed services, and can be deployed on a virtual private cloud (VPC) or even on-prem to meet companies where their data is, offering the highest levels of flexibility and control. Cohere’s leading Embed 3 and Rerank 3 models can be used with MongoDB Atlas Vector Search to convert MongoDB data to vectors and build a state-of-the-art semantic search system. Search results also can be passed to Cohere’s Command R family of models for retrieval augmented generation (RAG) with citations. Check out our AI resource page to learn more about building AI-powered apps with MongoDB. A new approach to vector embeddings It is in the realm of embedding where Cohere has made a host of recent advances. Described as “AI for language understanding,” Embed is Cohere’s leading text representation language model. Cohere offers both English and multilingual embedding models, and gives users the ability to specify the type of data they are computing an embedding for (e.g., search document, search query). The result is embeddings that improve the accuracy of search results for traditional enterprise search or retrieval-augmented generation. One challenge developers faced using Embed was that documents had to be passed one by one to the model endpoint, limiting throughput when dealing with larger data sets. To address that challenge and improve developer experience, Cohere has recently announced its new Embed Jobs endpoint . Now entire data sets can be passed in one operation to the model, and embedded outputs can be more easily ingested back into your storage systems. Additionally, with only a few lines of code, Rerank 3 can be added at the final stage of search systems to improve accuracy. It also works across 100+ languages and offers uniquely high accuracy on complex data such as JSON, code, and tabular structure. This is particularly useful for developers who rely on legacy dense retrieval systems. Demonstrating how developers can exploit this new endpoint, we have published the How to use Cohere embeddings and rerank modules with MongoDB Atlas tutorial . Readers will learn how to store, index, and search the embeddings from Cohere. They will also learn how to use the Cohere Rerank model to provide a powerful semantic boost to the quality of keyword and vector search results. Figure 1: Illustrating the embedding generation and search workflow shown in the tutorial Why MongoDB Atlas and Cohere? MongoDB Atlas provides a proven OLTP database handling high read and write throughput backed by transactional guarantees. Pairing these capabilities with Cohere’s batch embeddings is massively valuable to developers building sophisticated gen AI apps. Developers can be confident that Atlas Vector Search will handle high scale vector ingestion, making embeddings immediately available for accurate and reliable semantic search and RAG. Increasing the speed of experimentation, developers and data scientists can configure separate vector search indexes side by side to compare the performance of different parameters used in the creation of vector embeddings. In addition to batch embeddings, Atlas Triggers can also be used to embed new or updated source content in real time, as illustrated in the Cohere workflow shown in Figure 2. Figure 2: MongoDB Atlas Vector Search supports Cohere’s batch and real time workflows. (Image courtesy of Cohere) Supporting both batch and real-time embeddings from Cohere makes MongoDB Atlas well suited to highly dynamic gen AI-powered apps that need to be grounded in live, operational data. Developers can use MongoDB’s expressive query API to pre-filter query predicates against metadata, making it much faster to access and retrieve the more relevant vector embeddings. The unification and synchronization of source application data, metadata, and vector embeddings in a single platform, accessed by a single API, makes building gen AI apps faster, with lower cost and complexity. Those apps can be layered on top of the secure, resilient, and mature MongoDB Atlas developer data platform that is used today by over 45,000 customers spanning startups to enterprises and governments handling mission-critical workloads. What's next? To start your journey into gen AI and Atlas Vector Search, review our 10-minute Learning Byte . In the video, you’ll learn about use cases, benefits, and how to get started using Atlas Vector Search.

April 25, 2024