Definition
A specialized storage and retrieval system designed to manage high-dimensional embeddings, enabling RAG pipelines to perform efficient semantic similarity searches rather than exact keyword matching. It utilizes advanced indexing algorithms to find the nearest mathematical neighbors to a query vector within a latent space.
Unlike a relational database that matches exact values, a vector database identifies 'mathematical closeness' in high-dimensional space.
"A multi-dimensional library where books aren't filed alphabetically by title, but physically clustered by the similarity of their themes and meanings."
- Embeddings(Prerequisite)
- HNSW (Hierarchical Navigable Small World)(Component)
- Cosine Similarity(Component)
- Semantic Retrieval(Component)
Conceptual Overview
A specialized storage and retrieval system designed to manage high-dimensional embeddings, enabling RAG pipelines to perform efficient semantic similarity searches rather than exact keyword matching. It utilizes advanced indexing algorithms to find the nearest mathematical neighbors to a query vector within a latent space.
Disambiguation
Unlike a relational database that matches exact values, a vector database identifies 'mathematical closeness' in high-dimensional space.
Visual Analog
A multi-dimensional library where books aren't filed alphabetically by title, but physically clustered by the similarity of their themes and meanings.