spectralyte 0.1.0

Illuminating the geometry of your embedding space

spectralyte

Illuminating the geometry of your embedding space.

Most RAG failures are not model failures — they are geometry failures. If your embedding space is anisotropic, over-clustered, or low-dimensional, no amount of prompt engineering or model upgrades will fix the underlying retrieval problem.

Spectralyte makes the invisible geometry visible.

from spectralyte import Spectralyte

audit = Spectralyte(embeddings)   # numpy array of shape (n, d)
report = audit.run()
report.summary()

Spectralyte Audit Report
════════════════════════════════════════
  Anisotropy Score       0.61   ⚠ HIGH
  Effective Dimensions   43 / 1536
  Density CV             0.84   ⚠ CLUSTERED
  Retrieval Stability    0.58   ⚠ BRITTLE
  Intrinsic Dimension    31
════════════════════════════════════════
  2 critical issues detected.
  Run report.plot() for full visualization.
  Run audit.transform('whiten') to fix anisotropy.

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Why Spectralyte

When a RAG pipeline returns wrong results, engineers have three debugging tools: inspect individual queries, run ad-hoc similarity searches, or stare at raw vectors. None of them answer the foundational question: is my embedding space geometrically healthy?

Spectralyte answers that question with five geometric metrics derived from linear algebra and manifold theory — all computed locally, with no API calls and no cost beyond the compute already used to generate your embeddings.

Metric	What It Catches
Anisotropy	Vectors clustered in one direction — cosine similarity loses discriminative power
Effective Dimensionality	Space is lower-dimensional than expected — distinct content collapses together
Density Distribution	Tight clusters with voids — small query changes flip entire result sets
Retrieval Sensitivity	Unstable regions — rephrasing a query returns completely different documents
Intrinsic Dimensionality	True manifold complexity — guides dimensionality reduction decisions

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Installation

pip install spectralyte

Requires Python 3.9+. Core dependencies: numpy, scipy, scikit-learn, matplotlib.

For large indices (>50k embeddings), install the optional FAISS backend:

pip install spectralyte[faiss]

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Quick Start

import numpy as np
from spectralyte import Spectralyte

# Works with any embedding source — OpenAI, sentence-transformers, Cohere, etc.
# Just pass a numpy array of shape (n, d)
embeddings = np.load("my_embeddings.npy")

# Run the full geometric audit
audit = Spectralyte(embeddings)
report = audit.run()

# Human-readable summary
report.summary()

# Visualize all five metrics
report.plot()

# Export structured results
report.export("audit.json")

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Fixing Detected Issues

For the two most common problems, Spectralyte can fix them directly — no re-embedding required.

# Fix anisotropy via whitening transform
fixed = audit.transform(embeddings, strategy="whiten")

# Fix anisotropy via All-but-the-Top (ABTT)
fixed = audit.transform(embeddings, strategy="abtt", k=3)

# Re-index with fixed embeddings
# your_vector_db.upsert(fixed)

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Individual Metrics

Each metric is independently importable if you only need one.

from spectralyte.metrics import anisotropy, effective_dimensionality
from spectralyte.metrics import density, sensitivity, intrinsic_dim

# Anisotropy score (0 = isotropic, 1 = fully anisotropic)
result = anisotropy.compute(embeddings)
print(result.score)

# Effective dimensionality via SVD
result = effective_dimensionality.compute(embeddings, variance_threshold=0.95)
print(result.effective_dims, "/", result.nominal_dims)

# Retrieval Sensitivity Index
result = sensitivity.compute(embeddings, k=10, epsilon=0.05, m=5)
print(result.mean_stability)

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Use Cases

Pre-deployment audit — catch geometry problems before your RAG pipeline reaches production.

Embedding model comparison — evaluate models on your specific corpus, not just generic benchmarks.

Chunking strategy validation — compare geometric health across different chunking approaches.

Index drift monitoring — detect when new documents degrade the geometric structure of your index.

Debugging retrieval failures — localize whether failures are geometric (brittle zones) or content-based (missing documents).

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Configuration

audit = Spectralyte(
    embeddings,
    k=10,                    # nearest neighbors for density and sensitivity
    sensitivity_epsilon=0.05, # perturbation scale as fraction of mean k-NN distance
    sensitivity_m=5,         # perturbations per embedding for RSI
    variance_threshold=0.95, # cumulative variance for effective dimensionality
    sample_size=None,        # subsample for large indices (auto-set for n > 50k)
    random_seed=42           # reproducibility
)

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The Math

Spectralyte implements five geometric metrics with rigorous mathematical foundations:

• Anisotropy — Gram matrix formulation: measures average pairwise cosine similarity across all embedding pairs
• Effective Dimensionality — SVD-based: minimum principal components explaining 95% of variance
• Density Distribution — k-NN distance coefficient of variation + Local Outlier Factor
• Retrieval Sensitivity Index — Jaccard stability of top-k results under Gaussian perturbation
• Intrinsic Dimensionality — TwoNN estimator (Facco et al. 2017): fits d_int from nearest-neighbor distance ratios

Full mathematical derivations are available in the documentation.

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Roadmap

• Anisotropy metric
• Effective dimensionality
• Density distribution
• Retrieval Sensitivity Index
• Intrinsic dimensionality (TwoNN)
• Whitening transform
• All-but-the-Top (ABTT) transform
• CLI entrypoint (spectralyte audit embeddings.npy)
• HTML report export
• FAISS backend for large indices
• Pinecone / Qdrant / Weaviate native integration
• Continuous drift monitoring

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Contributing

Contributions are welcome. Please open an issue before submitting a PR so we can discuss the change.

git clone https://github.com/UniqueRed/spectralyte
cd spectralyte
pip install -e ".[dev]"
pytest

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Citation

If you use Spectralyte in research, please cite:

@software{spectralyte2026,
  author = {Thoppe, Adhviklal},
  title = {Spectralyte: Illuminating the Geometry of Your Embedding Space},
  year = {2026},
  url = {https://github.com/UniqueRed/spectralyte}
}

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License

MIT License. See LICENSE for details.