chunking-strategy

仓库创建 2025年10月21日最近提交 1 个月前SkillHot 收录 20 天前

▸ 精选理由

直接提升检索质量与召回/精确率评估，实用性强。

这个 Skill 做什么

为检索增强生成系统提供文档切分与重叠策略建议。

给 RAG 系统和向量数据库提供实用的文档切分策略：推荐分片大小（常见 256–1024 token）、重叠比例和语义边界检测方法。构建检索增强生成或处理大文档时用来决定怎么切、怎么合并，以提高检索的召回与精确度。特色是还能验证语义连贯性并评估检索指标，避免盲目按字节或页数切分。

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Provides chunking strategies for RAG systems. Generates chunk size recommendations (256-1024 tokens), overlap percentages (10-20%), and semantic boundary detection methods. Validates semantic coherence and evaluates retrieval precision/recall metrics. Use when building retrieval-augmented generation systems, vector databases, or processing large documents.

数据与抓取RAG文档切分向量检索通用

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# Chunking Strategy for RAG Systems

## Overview

Provides chunking strategies for RAG systems, vector databases, and document processing. Recommends chunk sizes, overlap percentages, and boundary detection methods; validates semantic coherence; evaluates retrieval metrics.

## When to Use

Use when building or optimizing RAG systems, vector search pipelines, document chunking workflows, or performance-tuning existing systems with poor retrieval quality.

## Instructions

### Choose Chunking Strategy

Select based on document type and use case:

1. **Fixed-Size Chunking** (Level 1)
   - Use for simple documents without clear structure
   - Start with 512 tokens and 10-20% overlap
   - Adjust

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