📄 中文摘要
在 RAG 系统中，语言模型（LLM）的智能程度取决于其检索能力，而检索的有效性又与知识分块的方式密切相关。知识的切分方式直接影响到 AI 的知识获取能力。不同的分块策略适用于不同的场景，合理选择分块方法能够提升信息检索的效率和准确性。该文提供了各种分块策略的实用指南，帮助用户在不同情况下选择最合适的分块方式，从而优化 AI 系统的表现。
RAG 分块策略
发布: 2026年2月14日
📄 English Summary
RAG Chunking Strategies
In RAG systems, the intelligence of the language model (LLM) is contingent upon its retrieval capabilities, which in turn depend on how knowledge is chunked. The method of chunking directly influences the AI's ability to acquire knowledge. Different chunking strategies are suited for various scenarios, and selecting the appropriate method can enhance the efficiency and accuracy of information retrieval. This article provides a practical guide to various chunking strategies, assisting users in choosing the most suitable approach for different contexts to optimize the performance of AI systems.