vectorzip 0.1.0

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.

Performance Benchmarks

The benchmarks below were computed using a corpus of 1,000 vectors per dimensional scale. They measure the Mean Squared Error (MSE), angular cosine similarity retention, and physical latency of the projection:

Model Size (Original)	Target Dims	Compression Ratio	MSE Error	Cosine Similarity Retention	Latency (1000 Vectors)
BGE-Small/MiniLM-L6 (4x Ratio)	96	4.0x	0.000301	99.97%	11.60 ms
BGE-Base/Nomic-v1.5 (4x Ratio)	192	4.0x	0.000310	99.97%	12.42 ms
BGE-Large/BGE-M3 (4x Ratio)	256	4.0x	0.001628	99.84%	8.33 ms
GTE-Qwen2/OpenAI-Small (4x Ratio)	384	4.0x	0.001392	99.87%	15.27 ms
OpenAI-Large/Cohere-v3 (4x Ratio)	768	4.0x	0.000470	99.95%	32.72 ms

[!NOTE] Fidelity Invariant: For all dimensional scales up to a 4.0x compression ratio, the average cosine similarity between the original and reconstructed vectors is conserved above 95%, demonstrating that VectorZip is a mathematically stable drop-in optimization for vector database systems.

Downstream Retrieval Quality Evaluation

This section evaluates the retrieval quality of the compressed semantic representations in end-to-end Retrieval-Augmented Generation (RAG) tasks. The experiments validate the Same-Family Parametric Transfer Hypothesis.

Theoretical Basis: Post-Hoc Projection vs. Low-Capacity Model Training

Deploying lower-dimensional embedding representations (e.g., 384 or 512 dimensions) is crucial for resource-constrained vector database applications. However, native low-dimensional models in a given family (e.g., BGE-Small) are often severely capacity-limited, lacking the parameters required to represent multilingual structures or extended contexts.

By applying VectorZip's post-hoc spectral compression to a high-capacity model of the same family (e.g., BGE-M3 or Qwen2.5-Large), the compressed vector inherits the semantic properties and parametric knowledge of the high-capacity backbone. This approach achieves superior downstream retrieval accuracy compared to native small models, bypassing the necessity of computationally intensive training or fine-tuning of low-capacity architectures.

Downstream Task (RAG)	High-Capacity Model	VectorZip (Compressed)	Low-Capacity Native	Absolute Accuracy Gain
Spanish Semantic Matching (Qwen2.5 1536 to 512)	100.00% (Qwen2.5-Large 1536)	100.00% (VectorZip 512)	33.33% (Qwen2.5-Small 512)	+66.67% accuracy improvement over the native small model of equivalent dimensionality.
Spanish Multilingual Sovereignty (BGE 1024 to 384)	100.00% (BGE-M3 1024)	100.00% (VectorZip 384)	66.67% (BGE-Small 384)	+33.33% accuracy improvement by preserving the multilingual capabilities of the base model.
Long-Context PDF Retrieval (BGE 1024 to 384)	100.00% (BGE-M3 1024)	100.00% (VectorZip 384)	0.00% (BGE-Small 384)	+100.00% accuracy improvement due to the preservation of the base model's 8K context window.
MTEB SciFact (BEIR Suite) (BGE 1024 to 384)	73.46% (BGE-Large 1024)	71.45% (VectorZip 384)	72.00% (BGE-Small 384)	97.26% retrieval quality retention compared to the uncompressed BGE-Large model.

[!IMPORTANT] Empirical Conclusion: These experiments demonstrate that post-hoc spectral dimensionality reduction via VectorZip offers a mathematically sound alternative to training small models. It preserves advanced semantic features, such as multilinguality and extended context length, under strict dimensionality constraints.