AgriIR：面向领域特定知识检索的可扩展框架

摘要

本文介绍AgriIR——一种可配置的检索增强生成框架，该框架在保持灵活性与低计算成本的同时，能够提供基于事实的领域特定答案。与传统大型单体模型不同，AgriIR将信息获取流程分解为声明式模块化阶段：查询优化、子查询规划、检索、合成与评估。该设计使实践者无需修改架构即可将框架适配至新的知识领域。我们的参考实施方案聚焦印度农业信息获取，将10亿参数语言模型与自适应检索器、领域感知智能体目录相集成。该系统强制实施确定性引用机制，集成遥测技术保障透明度，并包含自动化部署资源以确保可审计、可复现的运行。通过强调架构设计与模块化控制，AgriIR证明了精心设计的流程即使在受限资源下也能实现领域精准且可信的检索。我们认为该方法通过提升检索增强生成系统的可及性、可持续性与可问责性，为"农业人工智能"提供了典范。

English

This paper introduces AgriIR, a configurable retrieval augmented generation (RAG) framework designed to deliver grounded, domain-specific answers while maintaining flexibility and low computational cost. Instead of relying on large, monolithic models, AgriIR decomposes the information access process into declarative modular stages -- query refinement, sub-query planning, retrieval, synthesis, and evaluation. This design allows practitioners to adapt the framework to new knowledge verticals without modifying the architecture. Our reference implementation targets Indian agricultural information access, integrating 1B-parameter language models with adaptive retrievers and domain-aware agent catalogues. The system enforces deterministic citation, integrates telemetry for transparency, and includes automated deployment assets to ensure auditable, reproducible operation. By emphasizing architectural design and modular control, AgriIR demonstrates that well-engineered pipelines can achieve domain-accurate, trustworthy retrieval even under constrained resources. We argue that this approach exemplifies ``AI for Agriculture'' by promoting accessibility, sustainability, and accountability in retrieval-augmented generation systems.

AgriIR：面向领域特定知识检索的可扩展框架

AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval

摘要

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