Densim is a library for efficient similarity search and clustering of dense vectors, which are numerical representations of data such as images, text, or audio. It supports various methods for similarity search, such as exact search, approximate search, binary search, and product quantization, as well as cosine similarity, Jaccard distance, and additive quantization. The library can handle large-scale datasets that do not fit in RAM, and provides tools for evaluation and parameter tuning.

Contributions to Densim are welcome! Please see the CONTRIBUTING.md file for guidelines.

Densim is licensed under the MIT License. See the LICENSE file for details.