📄 中文摘要
RAPTOR是一种新兴的架构，旨在解决传统检索增强生成（RAG）系统在处理长文档和复杂推理时的局限性。传统RAG系统在信息分散于多个片段时，往往无法提供足够的上下文，从而影响大型语言模型（LLM）生成高质量答案的能力。RAPTOR通过层次化的检索机制，能够更有效地整合和利用分散的信息，提升生成答案的准确性和相关性。这一架构为构建更智能的AI系统提供了新的思路，尤其在需要处理复杂信息的场景中展现出其独特优势。
理解RAPTOR：RAG系统中层次检索的强大架构
📄 English Summary
# Understanding RAPTOR: A Powerful Architecture for Hierarchical Retrieval in RAG Systems
RAPTOR is an emerging architecture designed to address the limitations of traditional Retrieval-Augmented Generation (RAG) systems when dealing with long documents and complex reasoning. Traditional RAG systems often struggle to provide sufficient context when relevant information is spread across multiple fragments, impacting the ability of large language models (LLMs) to generate high-quality answers. RAPTOR employs a hierarchical retrieval mechanism that effectively integrates and utilizes dispersed information, enhancing the accuracy and relevance of generated responses. This architecture offers new insights for building smarter AI systems, particularly in scenarios requiring the handling of complex information.