AgriIR: A Scalable Framework for Domain-Specific Knowledge Retrieval

TL;DR

AgriIR is a modular, scalable retrieval-augmented generation framework tailored for domain-specific knowledge access in agriculture, emphasizing flexibility, low cost, and transparency.

Contribution

The paper presents AgriIR, a configurable, modular RAG framework that enables domain-specific knowledge retrieval without modifying core architecture.

Findings

Achieves domain-accurate, trustworthy answers with low computational cost.

Supports flexible adaptation to new knowledge verticals.

Includes deterministic citation and transparency features.

Abstract

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.