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Table of Contents
Vector search: what is it?
Intelligent semantic search over unstructured data (text, photos, and audio) is made possible by generative AI using vectors. Building recommendation systems, anomaly detection, and conversational AI all depend on vectors. MongoDB’s intrinsic capabilities enable a wide range of use cases that result in transformational user experiences.
The combined strength of MongoDB and vectors
Unmatched ease of use
Stay away from the tax on synchronization. There is no need to sync data between your operational and vector databases because Atlas Vector Search is integrated into the core database, which saves time, simplifies things, and guards against mistakes. Both your vector and operational data remain in one location.
Strong query capabilities
For strong hybrid search use cases in a single database, effortlessly integrate vector searches with filters on meta-data, graph lookups, aggregation pipelines, geo-spatial search, and lexical search.
Better scaling for applications that use vector searches
The distributed design of MongoDB scales vector search separately from the core database, in contrast to other systems. This leads to better performance at scale by enabling genuine workload segregation and vector query optimization.
Study up on Search Nodes
Vector database that is ready for enterprise use
High availability and security are integrated. You can be guaranteed that your workloads are operating with the same dependable enterprise-grade security and availability that MongoDB is renowned for because vector data is stored directly in Atlas alongside your operational data.
Customer success stories for Atlas Vector Search
Deploying our search data to Atlas Search Nodes just required a few mouse clicks, making it exceedingly simple. Additionally, vector search’s memory needs can now precisely match our Atlas Search Node deployment. This is an important factor to maintain vector search efficient and quick.
Use cases for vector search
- See every use case
- Look for Semantic Search
- focuses on meaning and gives priority to user intent by figuring out why users are seeking as well as what they enter. This allows for the provision of more precise and contextual search results.