Show HN: BMP – Fast, Exact Learned Sparse Retrieval for RAG

We built BMP, a fast and memory-efficient search engine for learned sparse retrieval — written in Rust and with Python bindings.

It supports exhaustive (non-approximate) search over large collections like MS MARCO, without dropping query terms or pruning the index.

Features:

- Full support for SPLADE, uniCOIL, CSV, and similar models

- No static pruning – keeps full index fidelity

- No term dropping – every token counts

- Runs fast thanks to block-max pruning

- Usable from Python

- Pre-built indexes available from CIFF-Hub: https://github.com/pisa-engine/ciff-hub/

Backed by the paper: Faster Learned Sparse Retrieval with Block-Max Pruning (SIGIR 2024) - https://arxiv.org/pdf/2405.01117

Would love feedback, issues, or contributions!