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name: inference description: "Use rag_retrieval Reranker inference APIs to score query-document pairs, rerank passages, inspect RankedResults, and debug model_type/device/dtype issues." disable-model-invocation: true metadata: disco-role: operating license: MIT

rag-retrieval inference

Use this sub-skill when a task asks to use the installed rag_retrieval reranker, score query-document pairs, rerank RAG passages, choose a reranker model_type, interpret RankedResults, or debug inference failures for BGE, BCE, Gemma, MiniCPM, or ColBERT-named rerankers.

Fast routing

• Start with API reference for constructor signatures, model type mappings, return objects, and current limitations.
• Use workflows for copy-ready scoring, reranking, long-document, LLM prompt, cutoff-layer, and no-download validation patterns.
• Use troubleshooting when model_type is unsupported, ColBERT fails, downloads start unexpectedly, devices/dtypes misbehave, queries are too long, empty docs return a dict, BCE scores look normalized, LLM prompt/cutoff behavior is unclear, or Pydantic warnings appear.

Scope

This sub-skill covers inference only: rag_retrieval.Reranker, cross-encoder rankers, LLM rankers, automatic model-type inference, query-document scoring, document reranking, long-document strategies, normalization, LLM prompt/cutoff-layer behavior, result helpers, and safe no-download environment checks.

Do not use this sub-skill for embedding training, reranker fine-tuning, or ColBERT training. Route those requests to sibling training sub-skills when present. Also do not promise ColBERT inference support: the current package has a colbert mapping name, but the ColBERT ranker is not registered or implemented for inference in the installed surface.