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Vector Search

Build intelligent applications powered by semantic search and generative AI over any type of data.
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What is Atlas Vector Search?
Build semantic search and AI-powered applications by integrating 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.
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Featured Integrations

Hugging Face

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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 them in Atlas for retrieval augmented generation (RAG), semantic search, recommendation engines, dynamic personalization, and other use cases.
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.
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.
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.

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.


MongoDB Atlas Vector Search integrates with LangChain to provide “Long term memory” to LLMs and as a store for chat conversations.

Cohere logo image.


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

Nomic logo image.


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 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.

“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
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“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.
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What is semantic search?
Semantic search is the concept of searching on the meaning of data rather than the data itself.
What is a vector?
A vector is a numeric representation of your data that can be searched over using advanced machine learning algorithms.
What is KNN?
KNN stands for "K Nearest Neighbors," which is the algorithm frequently used to find vectors near one another. Learn more
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.
What Vector Embeddings does Atlas Search support?
Atlas Vector Search Supports embeddings from any provider that are under the 2048 dimension width 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.
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