claudegoodies
Skill

arrowspace

From sickn33

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

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sickn33/agentic-awesome-skills
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plugins/agentic-awesome-skills-claude/skills/arrowspace/SKILL.md

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The instructions Claude Code reads when this skill runs.

# 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

```bash
pip install arrowspace
```

```python
from arrowspace import ArrowSpaceBuilder
import numpy as np
```

### Step 2: Prepare your data

Pass an (N, d) float64 NumPy array of embedding vectors:

```python
items = np.array([[0.1, 0.2, 0.3],
                  [0.0, 0.5, 0.1],
                  [0.9, 0.1, 0.0]], dtype=np.float64)
```

### Step 3: Configure graph parameters

```python
graph_params = {"eps": 0.2, "k": 6, "topk": 3, "p": 2.0, "sigma": 1.0}
builder = ArrowSpaceBuilder(items, graph_params=graph_params)
aspace = builder.build()
```

### Step 4: Query

```python
lambdas = aspace.lambdas()           # array indexed by insertion order
sorted_res = aspace.lambdas_sorted()  # (score, index) pairs ascending
```

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