Intent Aware Context Retrieval for Multi-Turn Agricultural Question Answering

TL;DR

This paper introduces Krishi Sathi, an AI-powered agricultural chatbot that uses intent-aware context retrieval and multi-turn dialogue to provide personalized, accessible advice to Indian farmers in multiple languages, with high accuracy and quick response times.

Contribution

It presents a novel multi-turn, intent-aware retrieval-augmented generation system tailored for rural Indian farmers, integrating speech and text in multiple languages for improved accessibility.

Findings

Achieved 97.53% query response accuracy

Attained 91.35% relevance and personalization

Maintained under 6 seconds response time

Abstract

Indian farmers often lack timely, accessible, and language-friendly agricultural advice, especially in rural areas with low literacy. To address this gap in accessibility, this paper presents a novel AI-powered agricultural chatbot, Krishi Sathi, designed to support Indian farmers by providing personalized, easy-to-understand answers to their queries through both text and speech. The system's intelligence stems from an IFT model, subsequently refined through fine-tuning on Indian agricultural knowledge across three curated datasets. Unlike traditional chatbots that respond to one-off questions, Krishi Sathi follows a structured, multi-turn conversation flow to gradually collect the necessary details from the farmer, ensuring the query is fully understood before generating a response. Once the intent and context are extracted, the system performs Retrieval-Augmented Generation (RAG) by first fetching information from a curated agricultural database and then generating a tailored response using the IFT model. The chatbot supports both English and Hindi languages, with speech input and output features (via ASR and TTS) to make it accessible for users with low literacy or limited digital skills. This work demonstrates how combining intent-driven dialogue flows, instruction-tuned models, and retrieval-based generation can improve the quality and accessibility of digital agricultural support in India. This approach yielded strong results, with the system achieving a query response accuracy of 97.53%, 91.35% contextual relevance and personalization, and a query completion rate of 97.53%. The average response time remained under 6 seconds, ensuring timely support for users across both English and Hindi interactions.