ATLAS
Vector Search
Build intelligent applications powered by semantic search and generative AI over any type of data using a full-featured vector database.
What is Atlas Vector Search?
Integrate your operational database and vector search in a single, unified, fully managed platform with full vector database capabilities. Store your operational data, metadata, and vector embeddings on Atlas while using Atlas Vector Search to build intelligent gen AI-powered applications.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.
The Versatility of Atlas as a Vector Database
Rather than use a standalone or bolt-on vector database, the versatility of our platform empowers users to store their operational data, metadata, and vector embeddings on Atlas and seamlessly use Atlas Vector Search to index, retrieve, and build performant gen AI applications.
_Spot.svg?auto=format%252Ccompress)
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.
LangChain
MongoDB Atlas Vector Search integrates with LangChain to provide “Long term memory” to LLMs and as a store for chat conversations.
LlamaIndex
MongoDB Atlas Vector Search integrates with LlamaIndex to provide “Long term memory” to LLMs as well as provide a store for document chunks.
OpenAI
Vector Embeddings generated by OpenAI can be stored in MongoDB Atlas Vector Search to build high-performance Generative AI applications.
Hugging Face
Hugging Face provides access to many open source models that can be easily used for generating vector embeddings and storing them in Atlas Vector Search.
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.
“It was incredibly easy to deploy our search data to Atlas Search Nodes, requiring only a few button clicks. Furthermore, the memory requirements of vector search can now match our Atlas Search Node deployment exactly. This is a crucial consideration for keeping vector search fast and streamlined.”
Senior Software Engineer, VISO TRUST
“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).
Can I use MongoDB Atlas instead of a standalone vector database?
Yes, MongoDB Atlas is a vector database. Atlas is a fully managed, multi-cloud developer data platform with a rich array of capabilities that includes text or lexical and vector search. Rather than use a standalone or bolt-on vector database, the versatility of our platform empowers users to store their operational data, metadata, and vector embeddings on Atlas and seamlessly use Atlas Vector Search to index, retrieve, and build performant gen AI applications.
What is KNN?
What is $vectorSearch and how does it differ from the knnBeta operator in $search?
$vectorSearch is a new aggregation stage in MongoDB Atlas that lets you execute an Approximate Nearest Neighbor (ANN) query with MongoDB Query Language filtering (e.g., “$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.
What are Search Nodes?
Search Nodes provide dedicated infrastructure for Atlas Search and Vector Search workloads, allowing you to optimize compute resources and fully scale search needs independent of the database. Search Nodes provide better performance at scale, delivering workload isolation, higher availability, and the ability to better optimize resource usage.
Which Vector embeddings does Atlas Search support?
Atlas Vector Search Supports embeddings from any provider that is under the 4096-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.
Get the most out of Atlas
Power more data-driven experiences and insights with the rest of our developer data platform.
Database
Start with the multi-cloud database service built for resilience, scale, and the highest levels of data privacy and security.
Charts
Bring your data to life instantly. Create, share, and embed visualizations for real-time insights and business intelligence.
Data Federation
Included only in paid tiersAnalyze rich data easily across Atlas and AWS S3. Combine, transform and enrich data from multiple sources without complex integrations.
Register Now
_Spot_BS_Mist.svg?auto=format%252Ccompress)