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Vector Search

Build intelligent applications powered by semantic search and generative AI over any type of data using a full-featured vector database.
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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.Watch 3-minute video

Featured Integrations

Hugging Face
Microsoft Semantic Kernel

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Key use cases for Atlas Vector Search

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.What is retrieval augmented generation?
Vector Search simplified

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.Vector Search explained in 3 minutes
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Workload isolation for more scalability and availability

Set up dedicated infrastructure for Atlas Search and Vector Search workloads. Optimize compute resources to scale search and database independently, delivering better performance at scale and higher availability.View the docs
Illustration of robot representing AI applications

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.
Avoid the synchronization tax

Avoid the synchronization tax

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.What is a document database?
Remove operational heavy lifting

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.Learn how to build intelligent 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 logo image.


LangChain-MongoDB is a dedicated package that provides “long-term memory” capabilities for LLMs — vector store, conversation history, and semantic caching.

LlamaIndex logo image.


MongoDB Atlas Vector Search integrates with LlamaIndex to provide “long-term memory” to LLMs as well as provide a store for document chunks.

OpenAI logo image.


Vector embeddings generated by OpenAI can be stored in MongoDB Atlas Vector Search to build high-performance generative AI applications.

Hugging Face logo image.

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 logo image.


Vector embeddings generated by Cohere can be stored in MongoDB Atlas Vector Search to build high-performance generative AI applications.

Microsoft Semantic Kernel logo image.

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.

AWS logo image.

Amazon Bedrock Knowledge Base

Knowledge Bases for Amazon Bedrock is a fully managed capability that allows for implementing the entire RAG workflow, from ingestion to retrieval. Atlas Vector Search integrates natively and securely.

“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.”
Pierce Lamb
Senior Software Engineer, VISO TRUST
Read the whole story
“We want to make it possible for users of our customers’ knowledge base to receive instant, trustworthy, and accurate answers to their questions using conversational search powered by MongoDB Atlas Vector Search and Generative AI capabilities.”
Saravana Kumar
CEO, Kovai
“We were initially looking at other vendors for vector search. However, once we saw MongoDB’s Vector Search it was a no-brainer — since we know we’re going to move everything to Atlas, we knew we should just consolidate everything there.”
Mars Lan
Co-founder & CTO, Metaphor Data
“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."
Russell Sherman
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.”
Nathan Clevenger
Founder & CTO, Drivly Inc.

Resources for building AI-powered applications

Discover how to leverage MongoDB to streamline development for the next generation of AI-powered applications.
View resources


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.
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?
KNN stands for "K Nearest Neighbors," which is the algorithm frequently used to find vectors near one another.
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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.
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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.
Learn more
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.

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Ready to get started?

Head over to our tutorial to see how you can quickly create embeddings of your MongoDB data and search it with our Vector Search capability.Get Started