🤖 AI Summary
Traditional RAG systems struggle with complex, multi-hop queries in knowledge-intensive domains—such as cross-entity association or author-wide document retrieval—due to their limited capacity for structured reasoning and semantic aggregation. To address this, we propose INRAExplorer, an agent-based RAG framework grounded in domain-specific knowledge graphs for agricultural, food, and environmental science literature. It integrates LLM agents with dynamic, multi-tool orchestration—including iterative retrieval, author-wide collection, and relational inference—as well as automated knowledge graph construction and multi-step reasoning algorithms. Our key contribution lies in the tight coupling of agent architecture with a curated, structured knowledge graph, enabling interpretable, graph-aware multi-hop question answering. Evaluated on real-world scientific corpora, INRAExplorer significantly improves answer completeness and accuracy for complex queries, while supporting high-level semantic search and cross-document information synthesis.
📝 Abstract
Conventional Retrieval-Augmented Generation (RAG) systems enhance Large Language Models (LLMs) but often fall short on complex queries, delivering limited, extractive answers and struggling with multiple targeted retrievals or navigating intricate entity relationships. This is a critical gap in knowledge-intensive domains. We introduce INRAExplorer, an agentic RAG system for exploring the scientific data of INRAE (France's National Research Institute for Agriculture, Food and Environment). INRAExplorer employs an LLM-based agent with a multi-tool architecture to dynamically engage a rich knowledge base, through a comprehensive knowledge graph derived from open access INRAE publications. This design empowers INRAExplorer to conduct iterative, targeted queries, retrieve exhaustive datasets (e.g., all publications by an author), perform multi-hop reasoning, and deliver structured, comprehensive answers. INRAExplorer serves as a concrete illustration of enhancing knowledge interaction in specialized fields.