WEAVESS

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title: WEAVESS slug: weavess category: benchmarks-evaluation brand: lsyhprum source_url: https://github.com/Lsyhprum/WEAVESS featured: false images:

• https://opengraph.githubassets.com/1/Lsyhprum/WEAVESS tags:
• ann
• benchmark
• similarity-search

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Description

WEAVESS is an open-source benchmarking and evaluation framework for graph-based approximate nearest neighbor (ANN) search methods. It provides the code, datasets, optimal parameters, and experimental setup used in the paper “A Comprehensive Survey and Experimental Comparison of Graph-based Approximate Nearest Neighbor Search”. The framework is aimed at researchers and practitioners who need fair, reproducible comparisons of vector indexing algorithms for large-scale vector similarity search, vector databases, and AI search applications.

Features

• Comprehensive experimental framework

  • Implements the full experimental pipeline used in the referenced survey paper.
  • Provides code, datasets, and detailed experimental configurations.
  • Ensures fair comparison by reimplementing all evaluated algorithms under a unified design pattern, programming language (with a noted exception for the IEH hash table), and common experimental setup.

• Implemented / evaluated algorithms

  • Supports benchmarking of thirteen representative graph-based ANNS algorithms:
    • KGraph
    • FANNG
    • NSG (Navigating Spreading-out Graph)
    • NSSG
    • DPG
    • Vamana
    • EFANNA
    • IEH
    • NSW (Navigable Small World)
    • HNSW (Hierarchical NSW)
    • NGT-panng
    • NGT-onng
    • SPTAG-KDT
    • SPTAG-BKT
    • HCNNG
    • KDRG
  • Includes links or references to original papers and, where available, original code repositories for each algorithm.

• Standardized implementation details

  • Algorithms reimplemented in a consistent way to reduce implementation bias in comparisons.
  • Uses the same set of implementation “tricks” and optimizations across methods where applicable.

• Real-world datasets

  • Includes eight real-world ANN datasets commonly used in prior work.
  • Each dataset is pre-split into:
    • Base data (indexing corpus)
    • Query data (evaluation queries)
  • Provides ground truth neighbor data for evaluation, in the form of top-20 or top-100 nearest neighbors.

• Synthetic datasets for scalability studies

  • Provides twelve synthetic datasets to analyze:
    • Scalability of each algorithm with respect to dataset size.
    • Performance under varying data distributions and characteristics.

• Evaluation focus

  • Designed for large-scale vector similarity search and ANN benchmarking.
  • Suitable for evaluating indexing methods used in vector databases and AI search systems.

Use Cases

• Research on graph-based ANN algorithms and their comparative performance.
• Reproducing or extending the experiments from the WEAVESS survey paper.
• Benchmarking new ANN algorithms against a standardized suite of baselines and datasets.
• Evaluating vector indexing strategies for production vector databases or search applications.

Pricing

• Open-source: No pricing information is provided; the project is available as open-source software via GitHub.