Definition
A retrieval methodology that uses high-dimensional vector embeddings to identify relevant context based on conceptual meaning and intent rather than literal keyword matching. It enables RAG systems to handle synonyms and polysemy, though it faces a trade-off between increased recall and the risk of retrieving 'thematically similar' but factually irrelevant noise.
Meaning-based retrieval vs. Keyword-based (Lexical) matching.
"A 3D star map where stars (data points) are physically clustered into constellations based on their chemical composition (meaning) rather than their names."
- Vector Embedding(Prerequisite)
- Cosine Similarity(Component)
- Dense Retrieval(Component)
- Hybrid Search(Optimization Strategy)
Conceptual Overview
A retrieval methodology that uses high-dimensional vector embeddings to identify relevant context based on conceptual meaning and intent rather than literal keyword matching. It enables RAG systems to handle synonyms and polysemy, though it faces a trade-off between increased recall and the risk of retrieving 'thematically similar' but factually irrelevant noise.
Disambiguation
Meaning-based retrieval vs. Keyword-based (Lexical) matching.
Visual Analog
A 3D star map where stars (data points) are physically clustered into constellations based on their chemical composition (meaning) rather than their names.