eisenstein-embed 0.1.0

Enhanced static embeddings with a 5-layer matching cascade

eisenstein-embed

Enhanced static embeddings with a 5-layer matching cascade. Drop-in enhancement of Model2Vec.

What It Does

Replaces Model2Vec's single-path encoding with a progressive cascade:

1. EXACT — string match (free, catches identical queries)
2. BITVECTOR — 64-bit Hamming distance (<1μs, 93.8% typo accuracy, zero ML deps)
3. SEMANTIC — Model2Vec dense vectors (catches paraphrases)
4. DOMAIN — per-domain vocabulary weighting (SIF from corpus, not Zipf estimate)
5. DEADBAND — cosine similarity cache (skip 30-60% redundant encoding)

Each layer only runs when previous layers fail → best speed + accuracy combination.

Install

# Core only (bitvector + exact, no ML deps):
pip install eisenstein-embed

# With Model2Vec semantic layer:
pip install eisenstein-embed[model2vec]

Quick Start

from eisenstein_embed import EisensteinModel

# Zero-config — works without any ML library:
model = EisensteinModel()
result = model.match("triangel", ["triangle", "square", "circle"])
# → MatchResult(best_match="triangle", score=0.94, method="bitvector")

# With Model2Vec for semantic matching:
model = EisensteinModel.from_model2vec("minishlab/potion-base-8M")
vectors = model.encode(["hello world", "greetings earth"])

# Domain-aware matching:
model.add_domain("fleet", texts=["deploy micro model", "plato room tile"])

Why Fork Model2Vec?

Model2Vec is brilliant — static embeddings at 500x the speed of transformers. We enhance it with techniques from our PLATO system:

• TUTOR bitvector matching (1965) catches typos embeddings miss, at 50x less cost
• Domain-aware SIF learns word importance from your corpus, not Zipf's law
• Deadband caching skips redundant encoding in conversational systems
• SplineLinear quantization compresses embedding tables 20x vs Model2Vec's 4x int8
• BMA drift detection knows when your embeddings have gone stale

Standalone vs PLATO

Standalone: Works without any PLATO infrastructure. Just numpy.

With PLATO: Domain profiles auto-populate from room history. BMA monitors embedding quality. Collective inference compares embeddings across agents. Learn more →

Architecture

eisenstein_embed/
├── cascade.py               # 5-layer cascade matcher
├── bitvector.py             # TUTOR-style 64-bit fingerprints + Hamming distance
├── domain_sif.py            # Corpus-driven SIF weighting
├── deadband_cache.py        # Cosine similarity cache
├── bma_monitor.py           # BMA drift detection
├── eisenstein_quantize.py   # SplineLinear embedding compression
├── static_model.py          # Drop-in StaticModel replacement
└── utils.py                 # Shared utilities

Benchmarks

Method	Typo Accuracy	Hit Rate	Speed	Size	Dependencies
Exact string match	43.8%	43.8%	~0μs	0KB	None
Bitvector (this)	93.8%	86.2%	~21μs	~0KB	None
Model2Vec semantic	68.8%	86.2%	~33μs	~400MB	model2vec
Eisenstein V3 (ours)	—	71.2%	~3.4ms	627KB	torch
Full cascade	95%+	90%+	varies	varies	optional

Benchmarks on 80 fleet-domain queries from 68 SuperInstance repos.

Mesh Protocol

eisenstein-embed works standalone. Install plato-core to enable ecosystem meshing:

pip install eisenstein-embed[mesh]

When co-installed, eisenstein-embed auto-registers:

• matchers.eisenstein-cascade — 5-layer cascade matcher
• matchers.eisenstein-bitvector — bitvector fingerprinting
• encoders.eisenstein — EisensteinModel as encoder

Other packages can discover and use these capabilities automatically.

License

MIT

Credits

Built by SuperInstance / Cocapn Fleet. Bitvector technique inspired by TUTOR (1965, PLATO system, University of Illinois).