优化农业研究：一种基于RAG的菌根真菌信息获取方法
Optimizing Agricultural Research: A RAG-Based Approach to Mycorrhizal Fungi Information
AI总结：
本研究针对农业领域菌根真菌信息获取需求，设计了基于RAG的双层策略系统，结合语义检索与结构化数据提取，可高效获取AMF相关农业信息，助力可持续农业决策与创新。
摘要：
检索增强生成（Retrieval-Augmented Generation，RAG）是自然语言处理（natural language processing，NLP）领域的一种变革性方法，它将神经信息检索与生成式语言建模相结合，以提升响应的上下文准确性和事实可靠性。与受静态训练语料库限制的传统大语言模型（Large Language Models，LLMs）不同，基于RAG的系统可动态整合特定领域的外部知识源，从而克服时间和学科层面的局限。本研究中，我们展示了专为Mycophyto定制的RAG系统的设计与评估，该系统聚焦于推进与丛枝菌根真菌（arbuscular mycorrhizal fungi，AMF）相关的农业应用。这类真菌可增强养分吸收、提升植物在非生物和生物胁迫下的恢复力，并助力土壤健康，在可持续农业中发挥着关键作用。我们的系统采用双层策略落地：（i）利用向量嵌入，从农学和生物技术语料库中进行语义检索并增强特定领域内容；（ii）通过结构化数据提取，捕获预设的实验元数据，如接种方法、孢子密度、土壤参数和产量结果。这种混合方法确保生成的响应不仅在语义上匹配，还能得到结构化实验证据的支撑。为保障可扩展性，嵌入向量存储在高性能向量数据库中，可从不断更新的文献库中实现近实时检索。实证评估表明，所提出的流水线能够检索并综合与AMF和作物系统（如番茄（Solanum lycopersicum））互作相关的高度相关信息。该框架凸显了人工智能驱动的知识发现在加速农业生态创新、提升可持续农业系统决策能力方面的潜力。
英文摘要：
Retrieval-Augmented Generation (RAG) represents a transformative approach within natural language processing (NLP), combining neural information retrieval with generative language modeling to enhance both contextual accuracy and factual reliability of responses. Unlike conventional Large Language Models (LLMs), which are constrained by static training corpora, RAG-powered systems dynamically integrate domain-specific external knowledge sources, thereby overcoming temporal and disciplinary limitations. In this study, we present the design and evaluation of a RAG-enabled system tailored for Mycophyto, with a focus on advancing agricultural applications related to arbuscular mycorrhizal fungi (AMF). These fungi play a critical role in sustainable agriculture by enhancing nutrient acquisition, improving plant resilience under abiotic and biotic stresses, and contributing to soil health. Our system operationalizes a dual-layered strategy: (i) semantic retrieval and augmentation of domain-specific content from agronomy and biotechnology corpora using vector embeddings, and (ii) structured data extraction to capture predefined experimental metadata such as inoculation methods, spore densities, soil parameters, and yield outcomes. This hybrid approach ensures that generated responses are not only semantically aligned but also supported by structured experimental evidence. To support scalability, embeddings are stored in a high-performance vector database, allowing near real-time retrieval from an evolving literature base. Empirical evaluation demonstrates that the proposed pipeline retrieves and synthesizes highly relevant information regarding AMF interactions with crop systems, such as tomato (Solanum lycopersicum). The framework underscores the potential of AI-driven knowledge discovery to accelerate agroecological innovation and enhance decision-making in sustainable farming systems.
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