Our team can help integrate the operational database and vector search in a single, unified, and fully managed platform with a MongoDB native interface that leverages large language models (LLMs) through popular frameworks.
Atlas Vector Search lets you search unstructured data. You can create vector embeddings with machine learning models like OpenAI and Hugging Face, and store them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, dynamic personalization, and other use cases.
With Atlas Vector Search, developers can build AI-powered experiences while accessing all the data they need through a unified and consistent developer experience in the form of the MongoDB Query API. Our new $vectorSearch aggregation stage makes it even easier for those already using MongoDB.
Store vector embeddings right next to your source data and metadata with the power of the document model. Vector embeddings are integrated with application data and seamlessly indexed for semantic queries, enabling you to build easier and faster.
Atlas Vector Search is built on the MongoDB Atlas developer data platform. Easily automate provisioning, patching, upgrades, scaling, security, and disaster recovery while providing deep visibility into performance for both the database and Vector Search so you can focus on building applications.