In Bangladesh, many farmers continue to face challenges in accessing timely, expert-level agricultural guidance. This paper presents KrishokBondhu, a voice-enabled, call-centre-integrated advisory platform built on a Retrieval-Augmented Generation (RAG) framework, designed specifically for Bengali-speaking farmers. The system aggregates authoritative agricultural handbooks, extension manuals, and NGO publications; applies Optical Character Recognition (OCR) and document-parsing pipelines to digitize and structure the content; and indexes this corpus in a vector database for efficient semantic retrieval. Through a simple phone-based interface, farmers can call the system to receive real-time, context-aware advice: speech-to-text converts the Bengali query, the RAG module retrieves relevant content, a large language model (Gemma 3-4B) generates a context-grounded response, and text-to-speech delivers the answer in natural spoken Bengali. In a pilot evaluation, KrishokBondhu produced high-quality responses for 72.7% of diverse agricultural queries covering crop management, disease control, and cultivation practices. Compared to the KisanQRS benchmark, the system achieved a composite score of 4.53 (vs. 3.13) on a 5-point scale, a 44.7% improvement, with especially large gains in contextual richness (+367%) and completeness (+100.4%), while maintaining comparable relevance and technical specificity. Semantic similarity analysis further revealed a strong correlation between retrieved context and answer quality, emphasizing the importance of grounding generative responses in curated documentation. KrishokBondhu demonstrates the feasibility of integrating call-centre accessibility, multilingual voice interaction, and modern RAG techniques to deliver expert-level agricultural guidance to remote Bangladeshi farmers, paving the way toward a fully AI-driven agricultural advisory ecosystem.

翻译：在孟加拉国，许多农民在获取及时、专家级的农业指导方面仍然面临挑战。本文介绍了KrishokBondhu，一个基于检索增强生成（RAG）框架构建的、支持语音的、集成呼叫中心的咨询平台，专为孟加拉语农民设计。该系统汇集了权威的农业手册、推广指南和非政府组织出版物；应用光学字符识别（OCR）和文档解析流水线对内容进行数字化和结构化处理；并将该语料库索引到向量数据库中，以实现高效的语义检索。通过一个简单的基于电话的界面，农民可以呼叫系统以获取实时、情境感知的建议：语音转文本模块将孟加拉语查询转换为文本，RAG模块检索相关内容，一个大语言模型（Gemma 3-4B）生成基于上下文的回答，文本转语音模块则以自然的孟加拉语口语形式播报答案。在一项试点评估中，KrishokBondhu针对涵盖作物管理、病害防治和耕作实践等多样化的农业问题，在72.7%的案例中给出了高质量的回答。与KisanQRS基准相比，该系统在5分制上获得了4.53的综合得分（基准为3.13），提升了44.7%，尤其在上下文丰富度（+367%）和完整性（+100.4%）方面取得了显著进步，同时在相关性和技术特异性方面保持了可比水平。语义相似性分析进一步揭示了检索到的上下文与回答质量之间的强相关性，强调了将生成式回答根植于精选文档的重要性。KrishokBondhu证明了整合呼叫中心的可访问性、多语言语音交互和现代RAG技术，为偏远的孟加拉国农民提供专家级农业指导的可行性，为迈向完全由人工智能驱动的农业咨询生态系统铺平了道路。