Definition
Embeddings are high-dimensional numerical vectors that transform discrete data into a continuous latent space, allowing RAG pipelines to mathematically calculate semantic similarity between queries and source documents. Architectural trade-offs involve balancing high-dimensionality for nuanced accuracy against the increased storage costs and retrieval latency inherent in larger vector spaces.
Not 'embedded systems' or hardware; these are dense mathematical representations of semantic meaning.
"GPS coordinates in a multi-dimensional library where books with similar themes are physically located at the same latitude and longitude."
- Vector Database(Storage Infrastructure)
- Cosine Similarity(Distance Metric)
- Tokenization(Prerequisite)
- Semantic Search(Applied Use Case)
Conceptual Overview
Embeddings are high-dimensional numerical vectors that transform discrete data into a continuous latent space, allowing RAG pipelines to mathematically calculate semantic similarity between queries and source documents. Architectural trade-offs involve balancing high-dimensionality for nuanced accuracy against the increased storage costs and retrieval latency inherent in larger vector spaces.
Disambiguation
Not 'embedded systems' or hardware; these are dense mathematical representations of semantic meaning.
Visual Analog
GPS coordinates in a multi-dimensional library where books with similar themes are physically located at the same latitude and longitude.