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RAGFreeOpen Source
RERANKERS
A unified API for all common reranking and cross-encoder models
Apache 2.0
ABOUT
Reranking is a critical step in high-quality RAG pipelines, but each reranking method — cross-encoders, RankGPT, FlashRank, Cohere API, ColBERT — has its own API, dependencies, and deployment requirements. Rerankers eliminates this fragmentation with a single Reranker class that provides a consistent rank() interface across 10+ reranking backends, allowing developers to easily experiment with, benchmark, and switch between reranking strategies without changing application code.
INSTALL
pip install "rerankers[transformers]"
pip install "rerankers[all]"
INTEGRATION GUIDE
1. Rerank initial retrieval results from vector search or BM25 to improve RAG pipeline quality
2. Experiment with different reranking strategies (cross-encoder, RankGPT, FlashRank, ColBERT) using a single API
3. Deploy CPU-friendly reranking with FlashRank ONNX models for low-latency production use
4. Use API-based reranking from Cohere, Jina, or Voyage without managing local model infrastructure
5. Benchmark and compare reranking approaches on custom datasets to select the best strategy for a given domain
TAGS
rerankingretrievalragcross-encoderinformation-retrievalnlppythonsearch