Definition
Neural Search is a retrieval paradigm that utilizes deep learning models to map queries and documents into a shared high-dimensional vector space, enabling semantic retrieval based on conceptual meaning rather than literal keyword matching. While it excels at capturing intent and context, it involves a trade-off of higher computational latency and 'black-box' matching logic compared to traditional lexical search.
Semantic vector matching based on meaning, not keyword frequency (BM25).
"A multidimensional star map where concepts with similar 'vibes' are physically clustered together, even if they use different vocabulary."
- Vector Embeddings(Prerequisite)
- Dense Retrieval(Component)
- Approximate Nearest Neighbor (ANN)(Component)
- Hybrid Search(Optimization Strategy)
Conceptual Overview
Neural Search is a retrieval paradigm that utilizes deep learning models to map queries and documents into a shared high-dimensional vector space, enabling semantic retrieval based on conceptual meaning rather than literal keyword matching. While it excels at capturing intent and context, it involves a trade-off of higher computational latency and 'black-box' matching logic compared to traditional lexical search.
Disambiguation
Semantic vector matching based on meaning, not keyword frequency (BM25).
Visual Analog
A multidimensional star map where concepts with similar 'vibes' are physically clustered together, even if they use different vocabulary.