针对印度法律文本设计分域混合检索系统，提升法律推理准确性与可解释性。
Domain-Partitioned Hybrid RAG for Legal Reasoning: Toward Modular and Explainable Legal AI for India
- 按司法、法典、刑法三类划分领域，分别优化检索模块。
- 在40题测试中达到70%正确率，显著优于单一RAG的37.5%。
- 结合知识图谱实现引用链与跨领域推理，适合法律AI研究者使用。
印度法律研究涉及长篇且异构的文档，涵盖宪法条文、刑法典及判例，纯关键词或仅基于嵌入的检索系统难以支撑结构化法律推理。现有检索增强生成（RAG）方法虽改善了事实依据，但在多跳推理、引用链和跨领域依赖方面仍存在不足。本文提出一种面向印度法律研究的分域混合RAG与知识图谱架构，集成三个专用RAG管道：最高法院判例、成文法与宪法条文、印度刑法典（IPC），每类均针对领域特性优化检索。为支持关系推理，构建基于Neo4j的法律知识图谱，捕捉判例、法条、罪名、法官与引用之间的结构化关系。由大模型驱动的智能调度器动态引导查询在各检索模块与知识图谱间流转，融合证据生成有依据且带引用的回复。我们在来自权威印度法律来源的40道合成法律问答基准上评估系统，并采用‘大模型作为法官’框架进行评测。结果表明，该混合架构达到70%通过率，远超仅用RAG基线的37.5%，在完整性与法律推理质量上均有显著提升。这证明将分域检索与结构化关系知识结合，可为印度司法语境下的高级法律AI提供可扩展、可解释的基础。
原文摘要 · Abstract (English)
Legal research in India involves navigating long and heterogeneous documents spanning statutes, constitutional provisions, penal codes, and judicial precedents, where purely keyword-based or embedding-only retrieval systems often fail to support structured legal reasoning. Recent retrieval augmented generation (RAG) approaches improve grounding but struggle with multi-hop reasoning, citation chaining, and cross-domain dependencies inherent to legal texts. We propose a domain partitioned hybrid RAG and Knowledge Graph architecture designed specifically for Indian legal research. The system integrates three specialized RAG pipelines covering Supreme Court case law, statutory and constitutional texts, and the Indian Penal Code, each optimized for domain specific retrieval. To enable relational reasoning beyond semantic similarity, we construct a Neo4j based Legal Knowledge Graph capturing structured relationships among cases, statutes, IPC sections, judges, and citations. An LLM driven agentic orchestrator dynamically routes queries across retrieval modules and the knowledge graph, fusing evidence into grounded and citation aware responses. We evaluate the system using a 40 question synthetic legal question answer benchmark curated from authoritative Indian legal sources and assessed via an LLM as a Judge framework. Results show that the hybrid architecture achieves a 70 percent pass rate, substantially outperforming a RAG only baseline at 37.5 percent, with marked improvements in completeness and legal reasoning quality. These findings demonstrate that combining domain partitioned retrieval with structured relational knowledge provides a scalable and interpretable foundation for advanced legal AI systems in the Indian judicial context.
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