RAG-Based Academic Knowledge Assistant
Retrieval-Augmented Generation (RAG) assistant for university courseware and research paper question answering with source citation.
Project Overview
Built using LangChain, vector databases (ChromaDB/FAISS), and LLMs. Indexes academic textbooks, lecture slides, and research papers, allowing students and faculty to query complex technical topics with hallucination-free responses and line-item source citations. ### Practical Student Engineering Solution Fully functional hardware demonstration prototype equipped with dedicated microcontrollers, precision sensors, actuators, and an interactive cloud/mobile telemetry dashboard. ### Key Learning Outcomes - Embedded C / MicroPython firmware architecture and sensor interfacing - IoT telemetry protocols (MQTT / HTTP / BLE / LoRaWAN) - Power regulation, hardware debugging, and PCB design principles - Comprehensive technical documentation aligned with IEEE academic standards
Modern commercial and industrial infrastructure requires automated, real-time sensing and telemetry. Conventional manual monitoring suffers from high latency, human error, and lack of predictive fault visibility for RAG-Based Academic Knowledge Assistant.
A turnkey engineering prototype integrating high-precision sensor modules, dedicated microcontroller unit, and cloud telemetry protocols to automate measurement, trigger alert thresholds, and provide remote dashboard control.