vectorzip 0.1.1

High-performance RAG vector compression

VectorZip

VectorZip is a high-performance Python library designed to optimize vector database storage and query latency in Retrieval-Augmented Generation (RAG) pipelines. By applying post-hoc mathematical projections to high-dimensional embedding outputs, VectorZip shrinks vector footprints by 4x to 48x and accelerates CPU search speeds by up to 6x, with negligible loss in semantic retrieval fidelity.

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Resources

• Documentation & Website: https://sergimichi.github.io/vectorzip/
• Architecture & Mathematical Foundations: https://sergimichi.github.io/vectorzip/how-it-works/
• Empirical Benchmarks: https://sergimichi.github.io/vectorzip/benchmarks/
• API Reference: https://sergimichi.github.io/vectorzip/api/
• Source Repository: https://github.com/sergimichi/vectorzip

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Installation

pip install vectorzip

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Compression Modes & Performance Matrix

VectorZip integrates multiple compression backends under a single unified, easy-to-use API. Choose the method that best matches your target model and database constraints:

Compression Mode	Method String	Target Model Type	Compression Factor	Search Speedup (CPU)	Retrieval Fidelity
Spectral DCT (Flagship)	"dct"	Standard / Non-Matryoshka	4x - 12x	6.02x	High
Matryoshka Slicing	"matryoshka"	Native Matryoshka models	4x - 24x	6.02x	High
Principal Component Analysis	"pca"	Closed-domain restricted	4x - 12x	6.02x	High (Local)
Product Quantization	"pq"	General dense vectors	8x - 16x	4.20x	Moderate
Scalar Quantization	"sq8"	General dense vectors	4x	1.0x (Baseline)	High
Hybrid Spectral+SQ8	"dct+sq8"	Standard / Non-Matryoshka	12x - 48x	6.02x	High

Note: Search Speedup is measured over a database of 100,000 document vectors on CPU compared to raw high-dimensional scalar quantized (SQ8) search.

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1-Minute Quickstart

VectorZip provides a high-level model wrapper, VectorZipModel, that acts as a direct, drop-in replacement for standard SentenceTransformer models, automating offline calibration and online query compression:

1. Calibrate and Compress Database Index

from vectorzip import VectorZipModel

# Wrap your model, specify target dimensions, and choose your method
model = VectorZipModel("BAAI/bge-m3", n_components=128, method="dct+sq8")

# Calibrate and compress your corpus (calibration runs automatically on first batch)
documents = ["VectorZip enables high-performance compression.", "Vector search is now 6x faster."]
compressed_embeddings = model.encode(documents)

# Save the calibrated JSON configuration for production
model.save_pretrained("./vectorzip_calibrated_model")

2. Compress Online Queries in Production

from vectorzip import VectorZipModel

# Load the pre-calibrated JSON configuration in your production environment
production_model = VectorZipModel.from_pretrained("./vectorzip_calibrated_model")

# Compress query embedding instantly to uint8 for database search
query_vector = production_model.encode(["Fast vector search"])

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License

VectorZip is released under the Apache 2.0 License.