Definition
The representation of text or data within a high-dimensional latent space where proximity is determined by conceptual relationships rather than lexical overlap. In RAG, this allows for the retrieval of documents that are contextually relevant to a query even if they share no common keywords, trading off the deterministic precision of exact-match search for broader contextual recall.
Conceptual intent vs. literal keyword matching.
"A 3D star map where stars (data points) are clustered into constellations based on their 'topic' rather than their alphabetical name."
- Vector Embeddings(Prerequisite)
- Cosine Similarity(Component)
- Latent Space(Prerequisite)
- Semantic Chunking(Component)
Conceptual Overview
The representation of text or data within a high-dimensional latent space where proximity is determined by conceptual relationships rather than lexical overlap. In RAG, this allows for the retrieval of documents that are contextually relevant to a query even if they share no common keywords, trading off the deterministic precision of exact-match search for broader contextual recall.
Disambiguation
Conceptual intent vs. literal keyword matching.
Visual Analog
A 3D star map where stars (data points) are clustered into constellations based on their 'topic' rather than their alphabetical name.