ATLAS
Vector Search
Build intelligent applications powered by semantic search and generative AI over any type of data.
Featured Integrations
Key use cases for Atlas Vector Search
Atlas Vector Search lets you search unstructured data. You can create vector embeddings with machine learning models like OpenAI and Hugging Face, and store and index them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, dynamic personalization, and other use cases.
Vector Search simplified
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.
Remove operational heavy lifting
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.Robust ecosystem of AI integrations
Atlas Vector Search accelerates your journey to building advanced search and generative AI applications by integrating with a wide variety of top LLMs and frameworks.
Cohere
Vector Embeddings generated by Cohere can be stored in MongoDB Atlas Vector Search to build high-performance Generative AI applications.
Nomic
Nomic provides the ability to visualize and explore vector embedding data easily in the web browser, as well as generate vector embeddings via thegpt4all. It works easily with Atlas Vector Search.
Microsoft Semantic Kernel
Semantic Kernel is an SDK that simplifies building LLM application with programming languages like C# and python. Atlas Vector search integrates to provide “memory” for LLM applications.
“With Atlas Vector Search, we now possess a battle-tested vector-metadata database, refined over a decade, effectively addressing our dense retrieval requirements. There's no need to deploy a new database, as our vectors and artifact metadata can be seamlessly stored alongside each other."
Co-Founder & CTO at VISO TRUST
“We are using AI embeddings and Vector Search to go beyond full-text search with semantic meaning, and give context and memory to generative AI car-buying assistants. We are very excited that MongoDB has added Vector Search to Atlas, which greatly simplifies our engineering efforts.”
Founder & CTO, Drivly Inc.
FAQ
What is semantic search?
Semantic search is the practice of searching on the meaning of data rather than the data itself.
What is a vector?
A vector is a numeric representation of data and associated context that can be efficiently searched for using advanced algorithms.
How does Atlas Vector Search differ from Atlas Search?
Atlas Vector Search allows searching through data based on semantic meaning captured in vectors, whereas Atlas Search allows for keyword search (i.e., based on the actual text and any defined synonym mappings).
What is KNN?
What is $vectorSearch and how does it differ from the knnBeta operator in $search?
$vectorSearch is a new aggregation stage in MongoDB Atlas that lets you execute an Approximate Nearest Neighbor query with MongoDB Query Language filtering (e.g., “$eq” or “$gte”). This stage will be supported on Atlas clusters version 6.0 and higher. The knnBeta operator in $search will continue to be supported as well.
What is ANN?
ANN stands for "Approximate Nearest Neighbors" and it is an approach to finding similar vectors that trades accuracy in favor of performance. This is one of the core algorithms used to power Atlas Vector Search. Our algorithm for Approximate Nearest Neighbor search uses the Hierarchical Navigable Small World (HNSW) graphs.
Which Vector embeddings does Atlas Search support?
Atlas Vector Search Supports embeddings from any provider that is under the 2048-dimension limit on the service.
Does Vector Search work with images, media files, and other types of data?
Yes, Atlas Vector Search can query any kind of data that can be turned into an embedding. One of the benefits of the document model is that you can store your embeddings right alongside your rich data in your documents.
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