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Unverified50% confidenceSolutionExact time

在RAG流程返回大量候选文档片段时，可先按相关性排序再截取前3-5项传入大模型以控制输入长度

1

Sources

50%

Confidence

Long-term

Relevance

8/18/2026

First Seen

Sources

Dify列表操作节点详解：数组过滤排序截取实战教程

Related Claims

Unverified文本分块策略、检索召回数量（Top-K）、重排序模型（Reranker）是影响RAG系统最终效果的三个关键变量75% similar Unverified相比RAG提交prompt有硬上限，图谱工具调用允许模型按需调用任意次数以挖掘所需信息74% similar Unverified检索增强少样本（RAG-augmented Few-shot）方法从历史处理记录中动态检索最相关案例作为示例，使模型持续适应新类型异常而无需重新训练72% similar Unverified在RAG场景中，分块大小、检索Top-K数量、相似度阈值、是否引入Rerank重排序模型等参数相互影响，没有通用最优解70% similar Unverified召回加重排（Recall + Rerank）先用 embedding 或关键词检索召回 Top 10 候选，再交给大模型做最终裁决，与RAG思路一致70% similar

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