ArrowSpace

Spectral vector search that augments nearest-neighbour search with graph Laplacian features. Computes a Laplacian over the item graph and uses the Rayleigh quotient to produce a λτ (lambda-tau) score per item, enabling search that respects both semantic similarity and structural role.

When to Use This Skill

• Cosine or L2 similarity misses latent structure in your embeddings
• You want graph-based retrieval with spectral awareness
• You need to characterise the spectral properties of an embedding space
• You are building RAG pipelines where contextual role matters alongside semantic content

How It Works

Step 1: Install and import

pip install arrowspace