Getting Better with Baguetter - New Retrieval Testing Framework

Baguetter is an open-source, Python-based retrieval testing framework designed to unify sparse, dense, and hybrid search experimentation through a single extensible interface. Created to address limitations in existing tools encountered during work on BMX and hybrid-search fusion algorithms, it supports lexical retrieval, semantic embeddings, reranking, and embedding quantization while aiming to remain lightweight and performant. Forked from the retriv project, Baguetter adds keyword-search implementations including BM25S and BMX, and uses USearch and Faiss for dense retrieval. The framework can be installed with pip, indexed with documents, and used to compare retrieval approaches through a common API. For evaluation, it relies on ranx and supports metrics including nDCG, precision, mean reciprocal rank, and recall, while providing Hugging Face dataset wrappers for benchmarks such as MTEB and tools to evaluate and save results from alternative index implementations.

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