Definition
A mathematical encoding of semantic meaning where unstructured data is transformed into a high-dimensional numerical array, enabling RAG systems to calculate conceptual proximity between user queries and stored knowledge. Trade-off: Higher dimensionality increases semantic precision and nuance but significantly raises computational latency and storage costs during retrieval.
Distinct from geometric vectors in physics; focuses on latent semantic positioning within a transformer's embedding space.
"A multi-dimensional coordinate map where 'Apple' the fruit is physically near 'Banana' but distant from 'Apple' the tech company."
- Embedding Model(Prerequisite)
- Cosine Similarity(Component)
- Vector Database(Component)
- Dimensionality(Component)
Conceptual Overview
A mathematical encoding of semantic meaning where unstructured data is transformed into a high-dimensional numerical array, enabling RAG systems to calculate conceptual proximity between user queries and stored knowledge. Trade-off: Higher dimensionality increases semantic precision and nuance but significantly raises computational latency and storage costs during retrieval.
Disambiguation
Distinct from geometric vectors in physics; focuses on latent semantic positioning within a transformer's embedding space.
Visual Analog
A multi-dimensional coordinate map where 'Apple' the fruit is physically near 'Banana' but distant from 'Apple' the tech company.