kannolo 0.1.0

kANNolo is designed for easy prototyping of ANN Search algorithms while ensuring high effectiveness and efficiency over both leanred and sparse vectors.
Documentation

kANNolo is a research-oriented library for Approximate Nearest Neighbors (ANN) search written in Rust 🦀. It is explicitly designed to combine usability with performance effectively. Designed with modularity and researchers in mind, kANNolo makes prototyping new ANN search algorithms and data structures easy. kANNolo supports both dense and sparse embeddings seamlessly. It implements the HNSW graph index and Product Quantization.

Details on how to use kANNolo's core engine in Rust 🦀 can be found in docs/RustUsage.md.

Details on how to use kANNolo's Python interface can be found in docs/PythonUsage.md.

Resources

Check out our docs folder for more detailed guide on use to use kANNolo directly in Rust, replicate the results of our paper, or use kANNolo with your custom collection.

📚 Bibliography

Leonardo Delfino, Domenico Erriquez, Silvio Martinico, Franco Maria Nardini, Cosimo Rulli and Rossano Venturini. "kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search." Proc. ECIR. 2025. To Appear.

Citation License

The source code in this repository is subject to the following citation license:

By downloading and using this software, you agree to cite the under-noted paper in any kind of material you produce where it was used to conduct a search or experimentation, whether be it a research paper, dissertation, article, poster, presentation, or documentation. By using this software, you have agreed to the citation license.

arXiv

@article{delfino2025kannolo,
  title={kANNolo: Sweet and Smooth Approximate k-Nearest Neighbors Search},
  author={Delfino, Leonardo and Erriquez, Domenico and Martinico, Silvio and Nardini, Franco Maria and Rulli, Cosimo and Venturini, Rossano},
  journal={arXiv preprint arXiv:2501.06121},
  year={2025}
}