This work addresses the challenge of capturing the complexities of legal knowledge by proposing a multi-layered embedding-based retrieval method for legal and legislative texts. Creating embeddings not only for individual articles but also for their components (paragraphs, clauses) and structural groupings (books, titles, chapters, etc), we seek to capture the subtleties of legal information through the use of dense vectors of embeddings, representing it at varying levels of granularity. Our method meets various information needs by allowing the Retrieval Augmented Generation system to provide accurate responses, whether for specific segments or entire sections, tailored to the user's query. We explore the concepts of aboutness, semantic chunking, and inherent hierarchy within legal texts, arguing that this method enhances the legal information retrieval. Despite the focus being on Brazil's legislative methods and the Brazilian Constitution, which follow a civil law tradition, our findings should in principle be applicable across different legal systems, including those adhering to common law traditions. Furthermore, the principles of the proposed method extend beyond the legal domain, offering valuable insights for organizing and retrieving information in any field characterized by information encoded in hierarchical text.

本研究解决了捕捉法律知识复杂性的问题，提出了一种基于多层嵌入的法律及立法文本检索方法。通过为单个条款及其组成部分（段落、条款）和结构分组（书籍、标题、章节等）创建嵌入，旨在以不同的粒度捕捉法律信息的细微之处，从而提升法律信息检索的准确性和有效性。尽管研究主要集中在巴西的立法方法及巴西宪法，但该方法的原则也适用于不同法律体系并可推广至其他领域的信息组织和检索。 

多层嵌入式检索解锁法律知识