MongoDB Unveils Generative AI Features for Atlas Vector Search

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.
MongoDB on a Mission to Equip 85 Mn Developers with New Skills

Data forms the crux of generative AI applications. And, MongoDB’s vector search capabilities are driving generative AI by transforming diverse data types like text, images, videos, and audio files into numerical vectors, simplifying AI processing and enabling efficient relevance-based searches. The company has unveiled a series of features in MongoDB Atlas Vector Search that offer several benefits for generative AI application development. 

Boost Information Accuracy for LLMs: Generative AI applications aim to provide precise and engaging experiences, but they can sometimes hallucinate information due to a lack of context. By expanding MongoDB Atlas’s query capabilities, developers can create a dedicated data aggregation stage with MongoDB Atlas Vector Search. This helps filter results from proprietary data, significantly improving information accuracy and reducing inaccuracies in AI applications.

Speed Up Data Indexing for Generative AI Applications: Generating vectors is crucial for preparing data for use with LLMs. After creating vectors, an efficient index must be built for data retrieval. MongoDB Atlas Vector Search’s unified document data model simplifies the indexing process for operational data, metadata, and vector data, facilitating faster development of AI-powered applications.

Use Real-Time Data Streams: Developers can leverage Confluent Cloud’s managed data streaming platform to power real-time applications. Through the Connect with Confluent partnership, Confluent Cloud data streams can be integrated into MongoDB Atlas Vector Search. This integration offers generative AI applications access to real-time, accurate data from various sources across a business. By using a fully managed connector for MongoDB Atlas, developers can make their applications more responsive and provide users with more precise results that reflect current conditions.

Read more: MongoDB Ups the Ante with Vector Search for Generative AI 

Customer Success Stories

Several organizations are utilizing MongoDB Atlas Vector Search to enhance their services. Data innovators company Dataworkz is merging data, transformations, and AI to create high-quality, LLM-ready data for AI applications, while Auto API platform Drivly is employing AI embeddings and Atlas Vector Search to empower AI car-buying assistants. Risk analytics firm ExTrac is using it to augment LLMs and analyze various data modalities, including text, images, and videos, for real-time threat identification. Inovaare Corporation leverages MongoDB to improve healthcare compliance operations, thanks to the capabilities of Atlas Vector Search in reporting and data-driven insights. NWO.ai enhances its consumer intelligence platform by integrating Atlas Vector Search to search and analyze embeddings for real-time insights. Finally, One AI uses it to enable semantic search and information retrieval, enhancing customer experiences. Cyber security company VISO Trust is also employing it to provide comprehensive vendor security information, streamlining decision-making for risk assessments.

Read more: Is MongoDB Vector Search the Panacea for all LLM Problems? 

Shritama Saha
Shritama Saha is a technology journalist who is keen to learn about AI and analytics play. A graduate in mass communication, she is passionate to explore the influence of data science on fashion, drug development, films, and art.

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