Embeddings convert text or images into numeric vectors that capture semantic similarity. Vector databases make it efficient to find the closest matches at scale.
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Vector Database
Vector databases support semantic search and retrieval by comparing embedding vectors. They are a common infrastructure component for RAG systems and recommendation engines.
CategorySystems
Reading time5 min read
Last updatedJan 31, 2025
Definition
A database optimized to store and search vector embeddings for similarity-based retrieval.
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Operational considerations
PineconeKey concerns include indexing strategy, latency, data freshness, and privacy controls when storing proprietary information.
FAQ
Do vector databases replace traditional databases?
No. They complement relational and document databases by enabling similarity search rather than transactional queries.
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