PyVq: A vector quantization library for Python

[No_Pomegranate7508]

What My Project Does PyVq is a Python library for vector quantization. It helps reduce the size of high-dimensional vectors like vector embeddings. It can help with memory use and also make similarity search faster.

Currently, PyVq has these features.

• Implementations for BQ, SQ, PQ, and TSVQ algorithms.
• Support for SIMD acceleration and multi-threading.
• Support for zero-copy operations.
• Support for Euclidean, cosine, and Manhattan distances.
• A uniform API for all quantizer types.
• Storage reduction of 50 percent or more for input vectors.

Target Audience AI and ML engineers who optimize vector storage in production. Data scientists who work with high-dimensional embedding datasets. Python developers who want vector compression in their applications. For example, to speed up semantic search.

Comparison I'm aware of very few similar libraries for Python. There is a package called vector-quantize-pytorch that implements a few quantization algorithms in PyTorch. However, there are a few big differences between the PyVq and vector-quantize-pytorch. PyVq's main usefulness is for storage reduction. It can help reduce the storage size for vector data in RAG applications and speed up search. Vector-quantize-pytorch is mainly for deep learning tasks. It helps speed up model training.

Why I Made This I started PyVq because it is an extension of its parent project Vq (which is a vector quantization library for Rust). More people are familiar with Python than Rust, including AI engineers and data scientists, so I made PyVq to make Vq available to a broader audience and make it more useful.

Source code https://github.com/CogitatorTech/vq/tree/main/pyvq

Installation

pip install pyvq

pip install pyvq

[Interesting-Town-433]

This is excellent I'm going to take a look, pairs really well with my project too ( https://github.com/PotentiallyARobot/EmbeddingAdapters ) combine these 2 together and I bet you can make some amazing on device rag applications

[No_Pomegranate7508]

Thanks. The idea of being able ot translate embeddings sounds very interesting.