ATLAS
Vector Search
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.
MongoDB Atlas Vector Search voted most loved vector database
Once again, Atlas Vector Search takes the prize as the most loved vector database according to the new 2024 State of AI report from Retool.Read the BlogFeatured Integrations
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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.
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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
LangChain
LangChain-MongoDB is a dedicated package that provides “long-term memory” capabilities for LLMs — vector store, conversation history, and semantic caching.
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.
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.
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.
“Everything in gen AI is new — you can’t just go to GitHub and repurpose code others have written. Only MongoDB Atlas gives us the flexibility and scale at the data platform layer to experiment in how to harness one of the biggest technical advancements the industry has ever seen.”
“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.”
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What does the Future of Search look like?
Read our white paper to learn how generative AI is shaping the future of search, and how to future-proof your own search strategy.FAQ
What is semantic search?
What is a vector?
How does Atlas Vector Search differ from Atlas Search?
Can I use MongoDB Atlas instead of a standalone vector database?
What's the difference between K-Nearest Neighbor Search and Approximate K-Nearest Neighbor Search? When to use what?
KNN stands for "K Nearest Neighbors," which is the algorithm frequently used to find vectors near one another. Learn more.
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) graph for efficient indexing and querying of millions of vectors.
In MongoDB Atlas, you can implement exact K nearest neighbor search via the $vectorSearch stage. This method would guarantee to return the exact closest vectors to a query vector, with the number of vectors specified by the variable limit. Exact vector search query execution can maintain sub-second latency for unfiltered queries up to 10,000 documents. It can also provide low-latency responses for highly selective filters that restrict a broad set of documents into 10,000 documents or less, ordered by vector relevance.
What is $vectorSearch and how does it differ from the knnBeta operator in $search?
What is ANN?
What are Search Nodes?
Which Vector embeddings does Atlas Search support?
Does Vector Search work with images, media files, and other types of data?
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