dyf-rs 0.10.0

Density Yields Features - Rust core for structure discovery in embedding spaces

DYF-RS - Density Yields Features (Rust Core)

Rust-accelerated core for DYF. Discover structure in embedding spaces using density-based LSH.

• Dense: Core items in well-populated semantic regions
• Bridge: Transitional items connecting different clusters
• Orphan: Unique items with no semantic neighbors

Installation

pip install dyf-rs

For the full Python package with serialization, embedding generation, and LLM labeling:

pip install dyf

Quick Start

import numpy as np
from dyf_rs import DensityClassifier

# Your embeddings (e.g., from sentence-transformers)
embeddings = np.random.randn(10000, 384).astype(np.float32)

# Find structure
classifier = DensityClassifier(embedding_dim=384)
classifier.fit(embeddings)

# What did we find?
print(classifier.report())
# Corpus: 10000 items
#   Dense: 9500 (95.0%)
#   Bridge: 450 (4.5%)
#   Orphan: 50 (0.5%)

# Get indices
bridges = classifier.get_bridge()  # Transitional items
orphans = classifier.get_orphans() # Unique items

Performance

Dataset	Time	Per item
60K embeddings (384d)	~60ms	1.0 µs

~4x faster than pure Python/sklearn.

API

DensityClassifier

DensityClassifier(
    embedding_dim: int,
    initial_bits: int = 14,      # LSH resolution
    recovery_bits: int = 8,      # Coarser recovery resolution
    dense_threshold: int = 10,   # Min bucket size for "dense"
    seed: int = 31
)

# Methods
classifier.fit(embeddings)
classifier.fit_arrow(arrow_array)  # Zero-copy from PyArrow
classifier.get_dense()             # Dense item indices
classifier.get_bridge()            # Bridge item indices
classifier.get_orphans()           # Orphan item indices
classifier.get_bucket_id(idx)      # Which bucket is item in?
classifier.report()                # Summary statistics

See Also

• dyf - Full Python package with serialization, configs, and LLM labeling
• Curvo FDA Navigator - DYF in action on 2.69M FDA medical devices

License

Proprietary