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Dimensionality Reduction

The process of transforming high-dimensional vector embeddings into a lower-dimensional representation to reduce computational overhead, storage costs, and search latency in vector databases while attempting to preserve the semantic structure of the data.

Definition

The process of transforming high-dimensional vector embeddings into a lower-dimensional representation to reduce computational overhead, storage costs, and search latency in vector databases while attempting to preserve the semantic structure of the data.

Disambiguation

In RAG, this is not about compressing file sizes like a .zip; it is about reducing the number of numerical features (the vector length) used to represent a text chunk.

Visual Metaphor

"Flattening a 3D topographic model into a 2D contour map: you lose the physical depth, but the essential boundaries and shapes remain recognizable."

Key Tools
PCA (Principal Component Analysis)UMAPt-SNEScikit-learnPyTorch (Autoencoders)Cohere (via Matryoshka Embeddings)
Related Connections

Conceptual Overview

The process of transforming high-dimensional vector embeddings into a lower-dimensional representation to reduce computational overhead, storage costs, and search latency in vector databases while attempting to preserve the semantic structure of the data.

Disambiguation

In RAG, this is not about compressing file sizes like a .zip; it is about reducing the number of numerical features (the vector length) used to represent a text chunk.

Visual Analog

Flattening a 3D topographic model into a 2D contour map: you lose the physical depth, but the essential boundaries and shapes remain recognizable.

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