MongoDB, Inc., today at MongoDB.local London announced new capabilities, performance improvements, and a data-streaming integration for MongoDB Atlas Vector Search that make it even faster and easier for developers to build generative AI applications. Organizations of all sizes have rushed to adopt MongoDB Atlas Vector Search as part of a unified solution to process data for generative AI applications since being announced in preview in June of this year.
MongoDB Atlas Vector Search has made it even easier for developers to aggregate and filter data, improving semantic information retrieval and reducing hallucinations in AI-powered applications. With new performance improvements for MongoDB Atlas Vector Search, the time it takes to build indexes is now significantly reduced by up to 85 percent to help accelerate application development. Additionally, MongoDB Atlas Vector Search is now integrated with fully managed data streams from Confluent Cloud to make it easier to use real-time data from a variety of sources to power AI applications.
“It has been really exciting to see the overwhelmingly positive response to the preview version of MongoDB Atlas Vector Search as our customers eagerly move to incorporate generative AI technologies into their applications and transform their businesses—without the complexity and increased operational burden of ‘bolting on’ yet another software product to their technology stack. Customers are telling us that having the capabilities of a vector database directly integrated with their operational data store is a game changer for their developers,” said Sahir Azam, Chief Product Officer at MongoDB. “This customer response has inspired us to iterate quickly with new features and improvements to MongoDB Atlas Vector Search, helping to make building application experiences powered by generative AI even more frictionless and cost effective.”
Many organizations today are on a mission to invent new classes of applications that take advantage of generative AI to meet end-user expectations. However, the large language models (LLMs) that power these applications require up-to-date, proprietary data in the form of vectors—numerical representations of text, images, audio, video, and other types of data.
Working with vector data is new for many organizations, and single-purpose vector databases have emerged as a short-term solution for storing and processing data for LLMs. However, adding a single-purpose database to their technology stack requires developers to spend valuable time and effort learning the intricacies of developing with and maintaining each point solution. For example, developers must synchronize data across data stores to ensure applications can respond in real time to end-user requests, which is difficult to implement and can significantly increase complexity, cost, and potential security risks.
Many single-purpose databases also lack the flexibility to run as a managed service on any major cloud provider for high performance and resilience, severely limiting long-term infrastructure options. Because of these challenges, organizations from early-stage startups to established enterprises want the ability to store vectors alongside all of their data in a flexible, unified, multi-cloud developer data platform to quickly deploy applications and improve operational efficiency.
MongoDB Atlas Vector Search addresses these challenges by providing the capabilities needed to build generative AI applications on any major cloud provider for high availability and resilience with significantly less time and effort. MongoDB Atlas Vector Search provides the functionality of a vector database integrated as part of a unified developer data platform, allowing teams to store and process vector embeddings alongside virtually any type of data to more quickly and easily build generative AI applications.
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Dataworkz, Drivly, ExTrac, Inovaare Corporation, NWO.ai, One AI, VISO Trust, and many other organizations are already using MongoDB Atlas Vector Search in preview to build AI-powered applications for reducing public safety risk, improving healthcare compliance, surfacing intelligence from vast amounts of content in multiple languages, streamlining customer service, and improving corporate risk assessment. The updated capabilities for MongoDB Atlas Vector Search further accelerate generative AI application development:
- Increase the accuracy of information retrieval for generative AI applications: Whether personalized movie recommendations, quick responses from chatbots for customer service, or tailored options for food delivery, application end-users today expect accurate, up-to-date, and highly engaging experiences that save them time and effort. Generative AI is helping developers deliver these capabilities, but the LLMs powering applications can hallucinate (i.e., generate inaccurate information that is not useful) because they lack the necessary context to provide relevant information. By extending MongoDB Atlas’s unified query interface, developers can now create a dedicated data aggregation stage with MongoDB Atlas Vector Search to filter results from proprietary data and significantly improve the accuracy of information retrieval to help reduce LLM hallucinations in applications.
- Accelerate data indexing for generative AI applications: Generating vectors is the first step in preparing data for use with LLMs. Once vectors are created, an index must be built for the data to be efficiently queried for information retrieval—and when data changes or new data is available, the index must then be updated. The unified and flexible document data model powering MongoDB Atlas Vector Search allows operational data, metadata, and vector data to be seamlessly indexed in a fully managed environment to reduce complexity. With new performance improvements, the time it takes to build an index with MongoDB Atlas Vector Search is now reduced by up to 85 percent to help accelerate developing AI-powered applications.
- Use real-time data streams from a variety of sources for AI-powered applications: Businesses use Confluent Cloud’s fully managed, cloud-native data streaming platform to power highly engaging, responsive, real-time applications. As part of the Connect with Confluent partner program, developers can now use Confluent Cloud data streams within MongoDB Atlas Vector Search as an additional option to provide generative AI applications ground-truth data (i.e. accurate information that reflects current conditions) in real time from a variety of sources across their entire business. Configured with a fully managed connector for MongoDB Atlas, developers can make applications more responsive to changing conditions and provide end user results with greater accuracy.