Rerankers
One Python API for cross-encoder, ColBERT, LLM and hosted rerankers, with optional per-backend installs.
About
Swapping rerankers in a retrieval pipeline becomes a one-string change with this Python package from Ben Clavie at Answer.AI, which puts many approaches behind one Reranker class and a rank(query, docs) method. Supported families include cross-encoders, T5 sequence-to-sequence rankers, ColBERT and PyLate late-interaction models, FlashRank's CPU-friendly ONNX models, LLM listwise rankers through RankGPT and RankLLM (RankZephyr, RankVicuna), layerwise LLM rankers, the MonoQwen2-VL multimodal ranker, and hosted APIs from Cohere, Jina, Mixedbread, Pinecone and Isaacus. Results arrive as a RankedResults object with scores, ranks, IDs and attached metadata, plus a top_k helper and an async variant. The base install has zero dependencies; extras such as transformers, flashrank, api or rankllm pull in only the backends you need, so it rarely fights with packages already installed. Local models run on CPU or GPU depending on the backend, and API rankers need a vendor key. Licensed Apache-2.0, it suits anyone tuning RAG or search who wants to benchmark rerankers side by side. Development has slowed, with the latest fixes merged in late 2025.
Should you use Rerankers?
Pick it when
Use Rerankers when your RAG pipeline retrieves roughly the right documents but in the wrong order, and you want to try cross-encoders, ColBERT, T5, or LLM rankers behind one API to find which helps your data.
Look elsewhere when
Skip it if first-stage recall is the problem, since reranking cannot surface documents that were never retrieved; look at RAGatouille or better embeddings. For a hosted reranking endpoint with no models to run, Pinecone offers one.
Alternatives to Rerankers
- RAGatouille
Builds and trains full ColBERT indexes for first-stage retrieval, fixing recall problems that a reranker over bad candidates cannot.
- FlagEmbedding (BGE)
Supplies BGE reranker models you can call directly, fewer abstractions but tied to one model family instead of a swappable API.
- Superlinked SIE
Serves reranking models over HTTP next to embeddings, better for sharing across services than an in-process library, but one more server to run.
- Pinecone
Hosts reranking models next to managed vector search, with no model serving on your side, but closed source and billed by usage.
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Details
- Category
- RAG & Document Retrieval
- Price
- Free
- Platform
- Local/Desktop
- Difficulty
- Easy (2/5)
- License
- Apache-2.0
- Added
- Apr 3, 2026