TurboQuant Memory

Compress and accelerate vector search in memory/RAG systems using TurboQuant (ICLR 2026) — near-optimal vector quantization with 5-8x compression and 98%+ search accuracy. Uses blockwise Hadamard rotation + Lloyd-Max scalar quantization. Use when: (1) optimizing embedding storage size, (2) speeding up semantic search, (3) user mentions "compress embeddings", "quantize vectors", "memory optimization", "faster search", "TurboQuant", "vector compression", or "embedding compression", (4) reducing memory footprint of RAG systems. Works with any embedding model (Gemini, OpenAI, Cohere, local) and any dimension ≥ 128. No GPU required. numpy only.

Compress embedding vectors 5-8x with 98%+ search accuracy using TurboQuant (Google, ICLR 2026).

Quick Start

1. Run tests

python3 scripts/turboquant.py

15 built-in tests: FWHT correctness, MSE distortion, IP correlation, recall, compression ratio, determinism.

2. Validate on your data

python3 scripts/validate.py --db /path/to/memory.sqlite --auto-detect --bits 5

Auto-detects sqlite-vec vec0 tables, analyzes distribution, reports quantization quality and recall.

3. Quantize a memory database

python3 scripts/memory_quantize.py --db /path/to/memory.db --bits 5 --benchmark
python3 scripts/memory_quantize.py --db /path/to/memory.db --bits 5 --migrate

4. Integrate into code

from turboquant import TurboQuantMSE

# Initialize (deterministic — same seed = same quantization)
tq = TurboQuantMSE(dim=3072, bits=5)

# Quantize for storage
stored = tq.quantize(embedding_vector)  # float32 → compressed

# Reconstruct
reconstructed = tq.dequantize(stored)   # compressed → float32

# Search: query stays float32, database is quantized
q_rot = tq.rotation.apply(query)
for doc in database:
    score = doc['norm'] * doc['scale'] * np.dot(q_rot, tq.codebook[doc['indices']])

Recommended Configuration

Preset	Mode	Bits	R@1	Compression	Use Case
Default	MSE	5	98%	6.4x	Most memory/RAG search
Conservative	MSE	6	98%+	5.3x	High-fidelity retrieval
Aggressive	MSE	4	92%	8.0x	Large-scale, storage-constrained

Parameters

Parameter	Default	Description
dim	auto-detect	Embedding dimension (768, 1536, 3072, etc.)
bits	5	Bits per coordinate. See table above.
seed	42	Rotation seed. Same seed = reproducible quantization.

Algorithm

Blockwise Hadamard Rotation → Lloyd-Max Scalar Quantization

1. Split vector into power-of-2 blocks (e.g., 3072 = 3 × 1024)
2. Per block: random sign flip + Fast Walsh-Hadamard Transform (fully invertible)
3. Per-vector scale normalization
4. Lloyd-Max optimal scalar quantizer per coordinate (precomputed codebook for N(0,1))
5. Pack indices into compact bit representation

Key properties:

• Data-oblivious: no training or calibration needed
• Fully invertible: zero information loss from rotation
• Near-optimal: within 2.7x of Shannon information-theoretic lower bound
• Deterministic: same seed = same output

See references/algorithm.md for full details.

Benchmark (Gemini embedding-001, 3072-dim, 112 vectors)

Bits	MSE	Cosine	R@1	R@5	R@10	Bytes/vec	Compression
3	1.1e-5	0.982	88%	90%	91%	1,160	10.6x
4	3.2e-6	0.995	92%	93%	93%	1,544	8.0x
5	8.2e-7	0.999	98%	96%	96%	1,928	6.4x
6	2.2e-7	1.000	96%	98%	98%	2,312	5.3x
7	8e-8	1.000	100%	98%	99%	2,696	4.6x
8	3e-8	1.000	98%	98%	99%	3,080	4.0x

Compatibility

• Python 3.9+, numpy only (no scipy, no GPU)
• Any embedding dimension ≥ 128
• Any embedding model (Gemini, OpenAI, Cohere, sentence-transformers, etc.)
• SQLite / sqlite-vec vec0 tables (auto-detected)

References

• TurboQuant paper: arXiv:2504.19874 (ICLR 2026)
• PolarQuant paper: arXiv:2502.02617 (AISTATS 2026)