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Jev Reranker — shinpr

A CLI that reranks, filters, or extractively compresses JSON retrieval results by asking Jev separate Noul questions, then lets Rust apply the order and thresholds, reading stdin and writing stdout.
Stars
4
Language
Rust
License
MIT
Reading time
3 min
Published by
Community
Author
shinpr
Last verified
Sep 2026

This is one of several Jev listings for the same project. The canonical entry is Jev Reranker — hotchpotch.

About this project

Choose the search results worth passing to your LLM. jev-reranker uses TypeSafe AI's Jev to rerank retrieved documents, remove candidates that contain no usable evidence, or extract query-specific passages for your LLM. It reads a JSON array from stdin and writes a JSON array to stdout. Choose the field that contains the text; IDs, source paths, retrieval scores, and other metadata pass through unchanged. Use it after BM25, vector search, or any command that emits a JSON array of candidate objects.

Quoted from the project's README. Read the full README.

Highlights

  • Three modes: rerank orders by a 0-to-1 rerankScore, filter drops candidates without usable evidence, and compress keeps relevant sentences or lines.
  • Field mapping flags select the text field and a context field, while IDs, source paths, and retrieval scores pass through unchanged.
  • Rerank and filter make one sequential request per 30 candidates by default; compression scores every sentence or line and can need more requests.
  • Filter keeps candidates with evidenceScore >= 0.5 in input order by default, and --threshold accepts 0 to 1.
  • Retries 429 and 529 responses up to twice after 250 ms and 500 ms waits; other errors are not retried.

Quickstart

bash
npm install --global jev-reranker
export TYPESAFE_API_KEY="YOUR_API_KEY"
printf '%s\n' '[{"text":"Build artifacts are cached locally."},{"text":"Access tokens expire after one hour."}]' | jev-reranker --query "How long do access tokens last?"

Watch out

MIT-licensed. Needs Node.js 14+ and a TYPESAFE_API_KEY; each invocation makes its own API requests, --top does not reduce API work, and long documents can send repeated copies of context in compression batches.

Questions about this entry

Short answers drawn from the linked project or page.

Reactions & coverage

Posts, threads, and videos about this entry from around the web.

X: Search and tagging on keep.md

X: Jev reranks station suggestions in TrainLCD

YouTube: Jev in the reranking step of a RAG pipeline: why cosine similarity fails and how a steerable reranker compares. A Colab notebook comes with it.

X: LinkedIn bookmarks search

From the Jev guides

Original write-ups on what this entry is for.

From the Jev AI model community

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