Definition
A specialized database designed to store, index, and query high-dimensional vector embeddings, enabling semantic search within RAG pipelines by calculating mathematical proximity between a query and indexed documents. It necessitates a trade-off between retrieval speed and precision, typically utilizing Approximate Nearest Neighbor (ANN) algorithms to maintain performance at scale.
Unlike a relational database matching text strings, a vector store matches the mathematical 'meaning' or context of data.
"A Multidimensional Warehouse where items are not shelved alphabetically, but are physically positioned closer to other items that share similar characteristics or 'vibes'."
- Embeddings(Prerequisite)
- Cosine Similarity(Component)
- HNSW (Hierarchical Navigable Small World)(Component)
- Retrieval-Augmented Generation (RAG)(System Context)
Conceptual Overview
A specialized database designed to store, index, and query high-dimensional vector embeddings, enabling semantic search within RAG pipelines by calculating mathematical proximity between a query and indexed documents. It necessitates a trade-off between retrieval speed and precision, typically utilizing Approximate Nearest Neighbor (ANN) algorithms to maintain performance at scale.
Disambiguation
Unlike a relational database matching text strings, a vector store matches the mathematical 'meaning' or context of data.
Visual Analog
A Multidimensional Warehouse where items are not shelved alphabetically, but are physically positioned closer to other items that share similar characteristics or 'vibes'.