We present CAISSON, a novel hierarchical approach to Retrieval-Augmented Generation (RAG) that transforms traditional single-vector search into a multi-view clustering framework. At its core, CAISSON leverages dual Self-Organizing Maps (SOMs) to create complementary organizational views of the document space, where each view captures different aspects of document relationships through specialized embeddings. The first view processes combined text and metadata embeddings, while the second operates on metadata enriched with concept embeddings, enabling a comprehensive multi-view analysis that captures both fine-grained semantic relationships and high-level conceptual patterns. This dual-view approach enables more nuanced document discovery by combining evidence from different organizational perspectives. To evaluate CAISSON, we develop SynFAQA, a framework for generating synthetic financial analyst notes and question-answer pairs that systematically tests different aspects of information retrieval capabilities. Drawing on HotPotQA's methodology for constructing multi-step reasoning questions, SynFAQA generates controlled test cases where each question is paired with the set of notes containing its ground-truth answer, progressing from simple single-entity queries to complex multi-hop retrieval tasks involving multiple entities and concepts. Our experimental results demonstrate substantial improvements over both basic and enhanced RAG implementations, particularly for complex multi-entity queries, while maintaining practical response times suitable for interactive applications.

翻译：我们提出CAISSON，一种新颖的检索增强生成（RAG）分层方法，它将传统的单向量搜索转化为多视图聚类框架。CAISSON的核心利用双自组织映射（SOM）创建文档空间的互补组织视图，每个视图通过专门的嵌入捕获文档关系的不同方面。第一个视图处理组合的文本和元数据嵌入，而第二个视图则对经过概念嵌入增强的元数据进行操作，从而实现全面的多视图分析，捕获细粒度的语义关系和高层次的概念模式。这种双视图方法通过结合来自不同组织视角的证据，实现了更细致的文档发现。为了评估CAISSON，我们开发了SynFAQA，这是一个用于生成合成金融分析师笔记和问答对的框架，它系统地测试信息检索能力的不同方面。借鉴HotPotQA构建多步推理问题的方法，SynFAQA生成受控测试用例，其中每个问题都与包含其真实答案的笔记集配对，从简单的单实体查询逐步发展到涉及多个实体和概念的复杂多跳检索任务。我们的实验结果表明，相较于基础和增强的RAG实现，CAISSON取得了显著改进，特别是在复杂的多实体查询方面，同时保持了适用于交互式应用的实用响应时间。