Atlas Search Nodes: Now with Multi-Region Availability

At MongoDB, we are continually refining our products to try and create the simplest and most seamless developer experience possible. This mantra has also been applicable to how we think about search, from the beginning with Atlas Text Search, to the announcement of the next paradigm with Atlas Vector Search. We have continued to expand this vision with the introduction of Search Nodes, initially launching on AWS, and then expanding to both Google Cloud and Microsoft Azure. Today we’re excited to take the next step in that journey with the announcement of multi-region availability on all three major cloud providers.

Search Nodes: Isolation and scale

As a quick refresher, Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, enabling 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.

Since our announcements, we’ve been thrilled with the excitement around Search Nodes and the desire for better control, flexibility, and availability for scaling both Atlas Search and Vector Search workloads. Incorporating Search Nodes into your deployment delivers workload isolation, and the ability to optimize resource usage.

A visual of the evolution from the previous coupled architecture to dedicated nodes is shown below:

Graphic titled Better aligning workload size with resourcing. On the left of the graphic, is a cylinder labled coupled architecture, which contains search, database, and vector search. Arrows point from this left component over to the right, where there is a box titled dedicated search nodes. This side contains search, database, and scalability.
Figure 1: Improved workload sizing alignment and enhanced scalability with Search Nodes

Introducing Global Availability

Another tenet of our builder's journey is making sure the flexibility, scalability, and performance with Search Nodes are available to everyone, regardless of the cloud you’re using or cloud region. Today, we’re excited to officially announce multi-region availability for Search Nodes to allow anyone to better optimize resource usage regardless of location.

Now, with multi-region availability, you can take full advantage of global scalability by no longer being limited to one geographic area. Furthermore, you now have the peace of mind by having the redundancy needed to protect yourself in the case of any unforeseen outage event, whether due to technical issues or natural disasters that could cause data center downtime.

This image is a basic world map that has MongoDB leaf logos indicating where MongoDB cloud servers are available. There are several logos in North America, one in Brazil, several in Europe, a couple in Africa, one in Asia, and one in Australia.
Figure 2: Multi-region availability on all three major cloud providers

Here is a quick video tutorial about how to enable Search Nodes, as well as take advantage of multi-region availability:


Brief tutorial on how to enable multi-region Search Nodes

With today’s announcements we’re excited to bring the power and control of dedicated Search Nodes to people using all clouds and regions across the globe. We’re excited to see the continued adoption and improved results from having greater ubiquity across your search implementations.

As always, reach out to us with any feedback, as we’d love to hear what you think!

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MongoDB AI Course in Partnership with Andrew Ng and DeepLearning.AI

MongoDB is committed to empowering developers and meeting them where they are. With a thriving community of 7 million developers across 117 regions, MongoDB has become a cornerstone in the world of database technology. Building on this foundation, we're excited to announce our collaboration with AI pioneer Andrew Ng and DeepLearning.AI, a leading educational technology company specializing in AI and machine learning. Together, we've created an informative course that bridges the gap between database technology and modern AI applications, further enhancing our mission to support developers in their journey to build innovative solutions. Introducing "Prompt Compression and Query Optimization" MongoDB’s latest course on DeepLearning.AI, Prompt Compression and Query Optimization , covers the prominent form factor of modern AI applications today: Retrieval Augmented Generation (RAG) . This course showcases how MongoDB Atlas Vector Search capabilities enable developers to build sophisticated AI applications, leveraging MongoDB as an operational and vector database. To ensure that learners taking this course are not just introduced to vector search, the course presents an approach to reducing the operational cost of running AI applications in production by a technique known as prompt compression. “RAG, or retrieval augmented generation, has moved from being an interesting new idea a few months ago to becoming a mainstream large-scale application.” — Andrew Ng, DeepLearning.AI Key course highlights RAG Applications: Learn to build and optimize the most prominent form of AI applications using MongoDB Atlas and the MongoDB Query Language(MQL). MongoDB Atlas Vector Search: Leverage the power of vector search for efficient information retrieval. MongoDB Document Model: Explore MongoDB's flexible, JSON-like document model, which represents complex data structures and is ideal for storing and querying diverse AI-related data. Prompt Compression: Use techniques to reduce the operational costs of AI applications in production environments. In this course, you'll learn techniques to enhance your RAG applications' efficiency, search relevance, and cost-effectiveness. As AI applications become more sophisticated, efficient data retrieval and processing becomes crucial. This course bridges the gap between traditional database operations and modern vector search capabilities, enabling you to confidently build robust, scalable AI applications that can handle real-world challenges. MongoDB's document model: The perfect fit for AI A key aspect of this course is that it introduces learners to MongoDB's document model and its numerous benefits for AI applications: Python-Compatible Structure: MongoDB's BSON format aligns seamlessly with Python dictionaries, enabling effortless data representation and manipulation. Schema Flexibility: Adapt to varied data structures without predefined schemas, matching the dynamic nature of AI applications. Nested Data Structures: Easily represent complex, hierarchical data often found in AI models and datasets. Efficient Data Ingestion: Directly ingest data without complex transformations, speeding up the data preparation process. Leveraging the combined insights from MongoDB and DeepLearning.AI, this course offers a perfect blend of practical database knowledge and advanced AI concepts. Who should enroll? This course is ideal for developers who: Are familiar with vector search concepts Building RAG applications and Agentic Systems Have a basic understanding of Python and MongoDB and are curious about AI Want to optimize their RAG applications for better performance and cost-efficiency This course offers an opportunity to grasp techniques in AI application development. You'll gain the skills to build more efficient, powerful, cost-effective RAG applications, from advanced query optimization to innovative prompt compression. With hands-on code, detailed walkthroughs, and real-world applications, you'll be equipped to tackle complex AI challenges using MongoDB's robust features. Take advantage of this chance to stay ahead in the rapidly evolving field of AI. Whether you're a seasoned developer or just starting your AI journey, this course will provide invaluable insights and practical skills to enhance your capabilities. Improve your AI application development skills with MongoDB's practical course. Learn to build efficient RAG applications using vector search and prompt compression. Enroll now and enhance your developer toolkit.

August 8, 2024
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Hanabi Technologies Uses MongoDB to Power AI Assistant, Hana

For all the hype surrounding generative AI, cynics tend to view the few real-world implementations as little more than “fancy chatbots.” But for Abhinav Aggarwal, CEO of Hanabi Technologies, the idea of a generative AI-powered bot that is more than just an assistant was intriguing. “I’d been using ChatGPT since it launched,” said Aggarwal. “That got me thinking: How could we make a chatbot that was like a team member?” And with that concept, Hana was born. The problem with bots “Most generative AI chatbots do not act like people; they wait for a command and give a response,” said Aggarwal. “We wanted to create a human-like chatbot that would proactively help people based on what they wanted—automating reminders, for example, or fetching time zones from your calendar to correctly schedule meetings.” Hanabi’s flagship product, Hana, is an AI assistant designed to enhance team collaboration within Google Chat, working in concert with Google Workspace and its suite of products. “Our target customers are smaller companies of between 10 and 50 people. At this size you’re not going to build your own agent from scratch,” he said. Hana integrates with Google APIs to deliver a human-like assistant that chimes in with helpful interventions, such as automatically setting reminders and making sure meetings are booked in the right time zone for each participant. “Hana is designed to bring AI to smaller companies and help them collaborate in a space where they are already working—Google Workspace,” Aggarwal explained. The MongoDB Atlas solution For Hana to act like a member of the team, Hanabi needed to process massive amounts of data to support advanced features like retrieval-augmented generation (RAG) for better information retrieval across Google Docs and many other sources. And with a rapidly growing user base of over 600 organizations and 17,000+ installs, Hanabi also required a secure, scalable, and high-performing data storage solution. MongoDB Atlas provided a flexible document model, built-in vector database, and scalable cloud-based infrastructure, freeing Hanabi engineers to build new features for Hana rather than focusing on rote tasks like data extract, transform, and load processes or manual scaling and provisioning. Now, MongoDB Atlas handles a variety of responsibilities: Scalability and security: MongoDB Atlas’s auto-scaling and automatic backup features have enabled Hanabi to seamlessly grow its user base without the need for manual database management. RAG: MongoDB Atlas plays a critical role in Hana’s RAG functionality. The platform enables Hanabi to split Google Docs into small sections, create embeddings, and store these sections in Atlas’s vector database. Development Processes: According to Aggarwal, MongoDB’s flexibility in managing changing schemas has been essential to the company’s fast-paced development cycle. Data Visualization: Using MongoDB Atlas Charts has enabled Hanabi to create comprehensive dashboards for real-time data visualization. This has helped the team track usage, set reminders, and optimize performance without needing to build a manual dashboard. Impact and results With MongoDB Atlas, Hanabi can successfully scale Hana to meet the demands of its rapidly expanding user base. The integration is also enabling Hana to offer powerful features like automatic interactions with customers, advanced information retrieval from Google Docs, and manually added memory snippets, making it an essential tool for teams around the world. Next steps Hanabi plans to continue integrating more tools into Hana while expanding its reach to personal Gmail users. The company is also rolling out a new automatic-interaction feature, further enhancing Hana’s ability to proactively assist users without direct commands. MongoDB Atlas remains a key component of Hanabi’s stack, alongside Google Kubernetes Engine, NestJS, and LangChain, enabling Hanabi to focus on innovating to improve the customer experience. Tech Stack MongoDB Atlas Google Kubernetes Engine NestJS LangChain Are you building AI apps? Join the MongoDB AI Innovators Program today! Successful participants gain access to free MongoDB Atlas credits, technical enablement, and invaluable connections within the broader AI ecosystem. If your company is interested in being featured, we’d love to hear from you. Connect with us at ai_adopters@mongodb.com.

November 21, 2024