gist_correlation_matrix

A sorted correlation matrix reveals the complete relational structure of an embedding set by encoding every pairwise relationship. It converts isolated top-K similarity scores into a full relational map, exposing clusters, boundaries, and hierarchy patterns for human exploration.

To make embedding clusters visually legible in a correlation matrix, apply greedy decorrelation sorting. This reorders the matrix into a near-diagonal structure, directly exposing cluster boundaries and hierarchy patterns within the interactive heatmap.

The best way to explore all pairwise relationships in an embedding space is rendering a sorted correlation matrix as an interactive heatmap. This supports hover and drill-down exploration to surface relationship strength and supporting metadata.

Yes, a correlation matrix serves effectively as an output layer for graph-integrated search. Computing corr_matrix via embeddings @ embeddings.T generates a navigable information artifact that reveals the complete relational structure of search results.

Compute a correlation matrix from an (N, D) matrix by calculating embeddings @ embeddings.T. Then apply a greedy decorrelation or coverage-based ordering to render a sorted, interactive heatmap for similarity visualization.