MongoDB Enterprise Advanced in Google Distributed Cloud Hosted

Abhinav Mehla, Rutuja Rajwade, and Dan Mckean
February 6, 2024 | Updated: February 21, 2024

Today, we’re excited to strengthen our "run anywhere" approach and deepen our relationship with Google Cloud by announcing that MongoDB Enterprise Advanced is now available for use within Google Distributed Cloud Hosted (GDC Hosted).

"Google Cloud is happy to welcome MongoDB as a preferred partner for our Google Distributed Cloud Hosted product," said Rohan Grover, Director of Product for GDC Hosted. "MongoDB's powerful document database aligns with our data analytics focus, empowering our shared customers to unlock the full potential of their sensitive data in an air-gapped private cloud."

GDC Hosted is Google Cloud’s air-gapped private cloud that does not require connectivity to Google Cloud or the public internet to manage the infrastructure, services, APIs, or tooling. GDC Hosted enables public sector organizations and regulated enterprises to address strict data residency and security requirements, while continuing to deliver innovation to their users.

MongoDB Enterprise Advanced combines the power of MongoDB —– the leading NoSQL, document-oriented database that supports a variety of data structures — with an industry-leading offering catering to customers with the most advanced security and data sovereignty needs. As a flexible and scalable solution, MongoDB allows diverse datasets to be stored in a schemaless format, ensuring easy data manipulation and real-time analytics.

Together, GDC Hosted and MongoDB Enterprise Advanced offer a solution that enables users to scale their operations while adhering to the strictest data governance and security standards. The bridge between GDC Hosted and MongoDB Enterprise Advanced is Kubernetes: GDC Hosted is built on Kubernetes allowing teams to self-manage MongoDB through the use of the MongoDB Enterprise Kubernetes Operator.

The MongoDB Enterprise Kubernetes Operator is the only officially supported way to run Enterprise Advanced deployments of MongoDB in Kubernetes. To enable customers to manage deployments within their environment of choice (GDC Hosted in this case), the operator works in conjunction with the MongoDB self-hosted Ops Manager, which the operator can also install and manage in Kubernetes. This gives customers the ability to deploy, monitor, back up, and scale MongoDB.

The Enterprise Operator drastically simplifies both the setup and day-two operations like upgrades, making it possible to run MongoDB in Kubernetes with far less Kubernetes expertise. Creation and configuration of database deployments can be managed via a Git repo, saving developers from needing the permissions or knowledge needed to work directly with Kubernetes.

By leveraging the Enterprise Kubernetes Operator, users can manage their MongoDB deployments with even greater power and scale, and maximize their investment in both MongoDB and Google Cloud.

GDC Hosted is built to meet high regulatory, durability, and availability requirements, which aligns with MongoDB Enterprise Advanced’s commitment to giving users the tools and support they need to have complete control over the management and security of their self-managed MongoDB environments. While MongoDB Atlas is the best way to run MongoDB on Google Cloud, MongoDB Enterprise Advanced in GDC Hosted is the best option for teams that need absolute self-managed control over data governance and compliance, while still allowing for scalability.

Once you have GDC Hosted up and running, you can get started with MongoDB Enterprise Advanced through the MongoDB Enterprise Advanced listing in the GDC Hosted Marketplace. Alternatively, teams can access MongoDB Enterprise Advanced through the Google Cloud Platforms Marketplace.

MongoDB customers who want to get started using Enterprise Advanced in their GDC Hosted environments will need to sign up for a MongoDB Enterprise Advanced license through MongoDB first. For more information, reach out to gcp@mongodb.com.

To learn more about the Enterprise Kubernetes Operator, visit our documentation. To learn more about Enterprise Advanced, visit our product page or download the latest version.

← Previous

DocsGPT: Migrating One of the Industry’s Most Popular Open Source AI Assistants to Atlas Vector Search

Since its founding in 2019, Arc53 has focused on building predictive AI/ML solutions for its clients, with use cases ranging from recommendation engines to fraud detection. But it was with OpenAI’s launch of ChatGPT in November 2022 that the company saw AI rapidly take a new direction. As Arc53 co-founder Alex Tushynski explains, “It was no surprise to see generative AI suddenly capture market attention. Suddenly developers and data teams were being challenged to bring their companies’ own proprietary data to gen AI models, in what we now call retrieval-augmented generation (RAG) . But this involved them building new skills and disciplines. It wasn’t easy as they had to stitch together all of their different databases, data lakes, file systems, and search engines, and then figure out how to feed data from those systems into their shiny new vector stores. Then they had to orchestrate all of these components to build a complete solution. We identified an opportunity to abstract this complexity away from them. So DocsGPT was born.” DocsGPT is an open-source documentation assistant that makes it easy for developers to build conversational user experiences with natural language processing (NLP) directly on top of their data. That can be a chatbot on a company website for customer support or as an interface into internal data repositories to help boost employee productivity. Developers simply connect their data sources to DocsGPT to experiment with different embedding and large language models to optimize for their specific use case. LLM options currently include ChatGPT 3.5 and 4, along with DocsGPT-7B, based on Mistral. In addition to the choice of models, developers can choose where they deploy DocsGPT. They can download the open source code to run in their own environment or consume DocsGPT as a managed service from Arc53. Figure 1:   DocsGPT tech stack The freedom developers enjoy with DocsGPT is reflected in its levels of adoption. Since its release last year, the project has accumulated close to 14,000 GitHub stars and built a vibrant community with over 100 independent contributors. Tushynski says, “DocsGPT counts the UK government’s Department of Work and Pensions, pharmaceutical industry solution provider NoDeviation, and nearly 20,000 other users.” Tushynski and team selected MongoDB Atlas as the database for the DocsGPT managed service. “We’ve used MongoDB in many of our prior predictive AI projects. Its flexibility to store data of any structure, scale to huge data sets, and ease of use for both developers and data scientists means we can deliver richer AI-driven solutions faster. Using it to underpin DocsGPT was an obvious choice. As developers connect their documentation to DocsGPT, MongoDB stores all of the metadata, along with chat history and user account information.” Migrating from Elasticsearch to MongoDB Atlas Vector Search With the release of Atlas Vector Search , the DocsGPT team is now migrating its vector database from Elasticsearch into MongoDB Atlas. Tushynski says, “MongoDB is a proven OLTP database handling high read and write throughput with transactional guarantees. Bringing these capabilities to vector search and real-time gen AI apps is massively valuable. Atlas is able to handle highly dynamic workloads with rapidly changing embeddings in ways Elasticsearch cannot. The latency Elasticsearch exhibits as it merges updates into existing indexes means the app is often retrieving stale data, impacting the quality and reliability of model outputs.” Tushynski goes on to say, “We’ve experimented with a number of standalone vector databases. There are some good technologies there, but again, they don’t meet our needs when working with highly dynamic genAI apps. We often see users wanting to change embedding models as their apps evolve — a process that means re-encoding the data and updating the vector search index. For example, we’ve migrated our own default embedding models from OpenAI to multiple open-source models hosted on Hugging Face and now to BGE. MongoDB’s OLTP foundations make this a fast, simple, and hassle-free process.” The unification and synchronization of source 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. Alex Tushynski, co-founder, Arc53 Tushynski discusses the importance of embedding models in his blog post, Amplify DocsGPT with optimal embeddings . The post includes an example of how one customer was able to improve measured user experience by 50% simply by updating their embedding model. Figure 2:   Demonstrating the impact of vector embedding choices “One of the standout features of MongoDB Atlas in this context is its adeptness in handling multiple embeddings. The ability to link various embeddings directly with one or more LLMs without the necessity for separate collections or tables is a powerful feature," Tushynski says. "This approach not only streamlines the data architecture but also eliminates the need for data duplication, a common challenge in traditional database setups. By facilitating the storage and management of multiple embeddings, it allows for a more seamless and flexible interaction between different LLMs and their respective embeddings.” Being part of AI Innovators program , the DocsGPT engineering team gets free Atlas credits as well as access to technical expertise to help support their migration. The AI Innovators program is open to any startup that is building AI with MongoDB. Check out our AI resource page to learn more about building AI-powered apps with MongoDB.

February 6, 2024
Next →

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