Definition
Dense embeddings are continuous numerical representations where every dimension typically contains a non-zero value, capturing the semantic essence of data in a high-dimensional vector space. In RAG pipelines, they enable semantic retrieval by mapping queries and documents to a shared space where proximity indicates contextual similarity, though they involve a trade-off of higher computational overhead and less interpretability compared to sparse keyword-based methods.
Unlike sparse embeddings (e.g., BM25) which rely on keyword frequency, dense embeddings capture 'meaning' and 'intent' through latent mathematical relationships.
"A high-dimensional star map where constellations represent concepts, and 'distance' is measured by how closely two ideas relate rather than the words used to describe them."
- Vector Database(Component)
- Cosine Similarity(Prerequisite)
- Sparse Embeddings(Contrast)
- Bi-Encoder(Prerequisite)
Conceptual Overview
Dense embeddings are continuous numerical representations where every dimension typically contains a non-zero value, capturing the semantic essence of data in a high-dimensional vector space. In RAG pipelines, they enable semantic retrieval by mapping queries and documents to a shared space where proximity indicates contextual similarity, though they involve a trade-off of higher computational overhead and less interpretability compared to sparse keyword-based methods.
Disambiguation
Unlike sparse embeddings (e.g., BM25) which rely on keyword frequency, dense embeddings capture 'meaning' and 'intent' through latent mathematical relationships.
Visual Analog
A high-dimensional star map where constellations represent concepts, and 'distance' is measured by how closely two ideas relate rather than the words used to describe them.