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Jina Reranker v2

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

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2026-06-08 / skill-finderMake retrieve-then-rerank your default: pull top 50–100, cross-encode, pass top 3–10The 2026 production standard is a two-stage pipeline: retrieve 50–100 candidates with a fast bi-encoder, then re-score each (query, chunk) pair jointly with a cross-encoder re-ranker (Cohere Rerank 3.5, Voyage rerank-2.5, BGE reranker-v2, Jina Reranker v2), passing only the top 3–10 to the LLM. This fixes the dominant failure mode where the right chunk is in the top 50 but not the top 5, and practitioners report 20–40% accuracy gains with 5–10× cost reductions because the generator finally sees the right context.
2026-03-19 / skill-finderCross-Encoder Re-ranking After Initial Retrieval: 18-42% Precision Gain with Net Token Cost SavingsAdding a cross-encoder re-ranking stage after vector retrieval — where each (query, chunk) pair is scored independently rather than using embedding similarity — improves retrieval precision by 18-42% in production evaluations. The 50-200ms per-batch latency overhead is offset by passing fewer, higher-quality chunks to the LLM, reducing downstream generation token costs. At scale, LLM savings routinely exceed re-ranker compute cost, making this a net-positive optimization. Best cross-encoders in 2026: Cohere Rerank v3.5, Jina Reranker v2, and the open-source BGE-Reranker-v2-m3.

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BUILT BY
Jina Reranker v2 -> Jina
Jina Reranker v2 is built by Jina
Source finding

Source trail

FreeAcademy — Retrieval Optimization RAG: Chunking, Re-ranking & Quantization 2026 Abhishek Gautam — RAG in Production 2026

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Jina Reranker v2 intelligence trail | MindPattern