dcee 1.0.2

Delta-Compressed Embedding Engine — compressed approximate similarity search for correlated embeddings

DCEE — Delta-Compressed Embedding Engine

Introduction

DCEE (Delta-Compressed Embedding Engine) targets embeddings that sit together in semantic space—document chunks, chats, logs, or clustered corpora—where sequential delta coding plus quantization shrinks storage versus raw float32 vectors. The pipeline clusters vectors (MiniBatch k-means), orders points inside each cluster to keep deltas small, stores keyframes + deltas, and at query time uses keyframe routing with optional Adaptive Margin Probing (AMP) to widen cluster search when scores are ambiguous. Math runs on CuPy when available, otherwise NumPy.

On correlated synthetic benchmarks in this repo (benchmark_dcee.py, 50,000 normalized vectors, Recall@5 vs exact inner-product neighbors), a tuned DCEE+AMP configuration achieved ~96% recall with ~4× smaller on-disk payload than storing uncompressed float32 norms (see compressed size column below). Latency and recall depend on hardware, n_probe, quantization, and dataset; treat these as example numbers, not guarantees for every workload.

Example benchmark snapshot (internal script, same queries for all methods)

Method	Recall@5	P50 (ms)	P95 (ms)	QPS (approx.)	Build (s)	Size (MB)
DCEE+AMP (tuned)	96.4%	1.37	1.95	422	12.57	6.40
FAISS IndexFlatIP	100.0%	0.53	0.79	1897	0.01	25.60
FAISS HNSW (M=32, ef=64)	100.0%	0.09	0.11	10689	0.63	39.21
FAISS IVF-Flat (nprobe=8)	90.6%	0.03	0.03	36364	0.48	26.47

Takeaway: DCEE trades some recall versus exact flat search for much smaller index bytes; graph/IVF methods can be faster but use different memory/compute tradeoffs. Reproduce or tune with benchmark_dcee.py (and tune_dcee.py) on your own data.

Install

From PyPI (recommended):

pip install dcee

Install a specific release:

pip install "dcee>=0.1.0"

Dependencies (pulled in automatically): numpy, scikit-learn, tqdm. Python 3.10+.

Optional GPU acceleration: install a CuPy wheel that matches your CUDA toolkit (e.g. cupy-cuda12x). If CuPy is not installed, DCEE runs on NumPy (CPU).

Development (editable install from a clone):

git clone https://github.com/arjun988/DCEE.git
cd DCEE
pip install -e ".[dev]"

Quick start

import numpy as np
from dcee import DCEEConfig, DCEEEngine, is_gpu_available

print("GPU:", is_gpu_available())

emb = np.random.randn(10_000, 128).astype(np.float32)
emb /= np.linalg.norm(emb, axis=1, keepdims=True)

cfg = DCEEConfig.tuned_for(len(emb), emb.shape[1])
engine = DCEEEngine(cfg)
engine.build(emb)

q = emb[0]
for idx, score in engine.search(q, top_k=5):
    print(idx, score)

engine.save("index.dce2")

loaded = DCEEEngine.from_file("index.dce2")
print(loaded.search(q, top_k=3))

Configuration

• DCEEConfig: defaults for dim, n_clusters, keyframe_every, quantization, n_probe, n_probe_max, AMP (adaptive_probe, adaptive_probe_margin), top_k_refine, verbose.
• DCEEConfig.tuned_for(n_vectors, dim): heuristic scale-aware defaults.

Set verbose=False for quiet builds and loads.

License

See LICENSE in the repository.