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arrowspace

authentic

Spectral vector search using graph Laplacian eigenstructure. Use when cosine/L2 similarity misses latent structure in your embeddings.

When to use

• 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

When not to use

• API examples target the reviewed 0.28 interface; [immutable upstream skill](https://github.com/Genefold/arrowspace-skills/blob/8d9e8e30be314292e2ee60b84f0c163963660d41/SKILL.md) documents the builder, graph return value and query-aware sea…
• Fit graph parameters and tau on held-out retrieval evidence; λτ signatures alone do not prove better recall or structural importance.
• ArrowSpace is batch-oriented and not designed for real-time indexing of streaming data.

Skill page

Source