LibVQ

title: LibVQ slug: libvq category: sdks-libraries source_url: https://github.com/staoxiao/LibVQ images:

• https://opengraph.githubassets.com/1/staoxiao/LibVQ tags:
• vector-quantization
• neural-search
• ann

Description

LibVQ is an open-source library for dense-retrieval–oriented vector quantization. It provides training and indexing components that optimize vector quantization for retrieval quality, and can serve as the core of high-performance approximate nearest neighbor (ANN) search and vector database systems.

Features

• Dense retrieval–oriented vector quantization

  • Designed specifically to improve retrieval quality compared with conventional VQ methods (e.g., IVF, PQ, OPQ).
  • Targets real-time and memory-efficient dense retrieval scenarios.

• Knowledge distillation–based learning

  • Uses knowledge distillation to learn VQ parameters from off-the-shelf embeddings.
  • Can directly operate on existing dense embeddings without modifying upstream models.
  • Aims to achieve strong retrieval metrics (e.g., MRR@10, Recall@10/100) compared to other VQ-based ANN indexes.

• Flexible usage modes

  • Train only VQ/index parameters while keeping encoders fixed.
  • Jointly adapt and train the query encoder together with the index.
  • Supports different training strategies (e.g., contrastive index training, distillation-based index training).

• Rich input condition support

  • Works with only off-the-shelf embeddings when no extra signals are available.
  • Optionally leverages extra supervision such as:
    • Relevance labels.
    • Source queries.
  • Can be configured for both labeled and no-label (unlabeled) training settings.

• PyTorch-based training

  • Training pipeline implemented with PyTorch.
  • Configurable for different computation resources and training setups.

• FAISS-backed ANN deployment

  • Exports trained VQ parameters to FAISS-based indexes (e.g., IndexPQ, IndexIVFPQ).
  • Resulting indexes are directly deployable for large-scale dense retrieval.
  • Integrates with common ANN backends similar to FAISS, ScaNN, etc.

• Example workflows and benchmarks

  • Example pipelines for constructing and training indexes (documented in the Docs and examples folders).
  • MSMARCO example demonstrating:
    • IVFPQ and PQ settings with a fixed compression ratio (e.g., 96x compression).
    • Multiple training recipes, including:
      • contrastive_index
      • distill_index
      • distill_index_nolabel
      • contrastive_index-and-query-encoder
      • distill_index-and-query-encoder
      • distill_index-and-query-encoder_nolabel
    • Reported metrics such as MRR@10, Recall@10, Recall@100 for these methods and for baseline FAISS/ScaNN indexes.

• Simple installation from source

  • Installable via pip after cloning the repository:
    • git clone https://github.com/staoxiao/LibVQ.git
    • cd LibVQ
    • pip install .

Installation

git clone https://github.com/staoxiao/LibVQ.git
cd LibVQ
pip install .

Pricing

LibVQ is an open-source library; no pricing information or paid plans are specified.