Definition
A retrieval methodology in RAG pipelines that utilizes high-dimensional, non-zero numerical embeddings to identify relevant context based on semantic proximity rather than lexical overlap. It excels at capturing conceptual relationships but requires higher computational overhead and can struggle with exact keyword or acronym matching compared to sparse methods.
Semantic concept matching via latent space coordinates, not keyword counting like BM25.
"A multidimensional star map where related concepts are physical clusters of stars, regardless of what the stars are named."
- Embedding Model(Prerequisite)
- Cosine Similarity(Mathematical Component)
- Approximate Nearest Neighbor (ANN)(Scaling Component)
- Hybrid Search(Optimization Pattern)
Conceptual Overview
A retrieval methodology in RAG pipelines that utilizes high-dimensional, non-zero numerical embeddings to identify relevant context based on semantic proximity rather than lexical overlap. It excels at capturing conceptual relationships but requires higher computational overhead and can struggle with exact keyword or acronym matching compared to sparse methods.
Disambiguation
Semantic concept matching via latent space coordinates, not keyword counting like BM25.
Visual Analog
A multidimensional star map where related concepts are physical clusters of stars, regardless of what the stars are named.