2024 LegalDocumentRAGMultiGraphMulti

• (Yang & Chung, 2024) ⇒ Chia Jeng Yang, and Timothy Chung. (2024). “Legal Document RAG: Multi-Graph Multi-Agent Recursive Retrieval through Legal Clauses.” In: Medium - Enterprise RAG.

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Notes

• The blogpost introduces a multi-agent system designed to intelligently navigate legal documents, using recursive retrieval to handle interconnected legal clauses and legal references.
• The blogpost describes the challenges of legal document analysis, particularly the need to recursively retrieve clauses and footnotes that reference each other, which is common in regulatory documents.
• The blogpost details the use of a lexical graph to represent the document’s structure, allowing hierarchical relationships between sections, clauses, and other elements like footers to be modeled and traversed.
• The blogpost highlights the importance of definitions in legal documents and creates a definitions graph to manage legal terms that often have specialized meanings within different contexts.
• The blogpost demonstrates a specific use case involving a regulatory document from the Malaysian Central Bank, where the system is used to answer a complex compliance-related question.
• The blogpost contrasts its multi-agent recursive system with traditional Retrieval-Augmented Generation (RAG) models, showing that traditional models can miss key legal references due to a lack of recursive retrieval.
• The blogpost explains the different agents used in the system, including the Definition Agent, Router Agent, Recursive Retrieval Agent, and Answering Agent, each with a specific role in retrieving, linking, and summarizing legal content.
• The blogpost emphasizes the use of BM25 and keyword retrieval methods, alongside vector-based search, to improve the precision of legal term retrieval in dense, repetitive text.
• The blogpost shows how the Recursive Retrieval Agent identifies and retrieves linked clauses, such as those found in footnotes, ensuring that all references in legal documents are fully explored.
• The blogpost highlights how the multi-graph system is built using technologies like Reducto.AI, WhyHow.AI, LlamaIndex, and LangGraph to facilitate the agentic workflow for complex document analysis.
• The blogpost suggests the potential of this system in legal technology, particularly for improving the accuracy, context understanding, and memory in systems designed for analyzing legal texts through recursive, intelligent search methods.

Cited By

• http://scholar.google.com/scholar?q=%222024%22+Legal+Document+RAG%3A+Multi-Graph+Multi-Agent+Recursive+Retrieval+through+Legal+Clauses

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2024 LegalDocumentRAGMultiGraphMulti	Timothy Chung Chia Jeng Yang			Legal Document RAG: Multi-Graph Multi-Agent Recursive Retrieval through Legal Clauses						2024