Introducing jev-reranker: Reranking and Relevance Filtering for RAG

Jev-reranker is a Python library built around TypeSafe.AI’s Jev model to improve retrieval-augmented generation pipelines by reranking search results and filtering documents unlikely to answer a query before they are sent to an LLM. Its relevance-filtering mode uses instruction-guided scoring and configurable thresholds to remove superficially related but unhelpful passages, reducing token use and potentially limiting distracting context. On the 50-query NanoHotpotQA benchmark, the library’s filtering approach achieved nDCG@10 of 0.975 while retaining an average of 7.62 out of 100 candidate documents per query, removing about 92% without losing labeled positive evidence present in the retrieval pool. The library supports listwise and pointwise scoring, input splitting for long contexts, bounded concurrency, and retry handling, with listwise scoring presented as the default based on the author’s tests. The author emphasizes that thresholds and instructions should be evaluated for each application, particularly where omitted evidence carries risk, and positions Jev as a configurable middle ground between general-purpose LLM decisions and task-specific model training.

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