Building an Intelligent QA/Chatbot for Transportation with LangChain and Open Source LLMs

Abstract

This project focuses on building an intelligent question-answering (QA) chatbot to assist transportation engineers who frequently use complex traffic simulation software. The chatbot helps users extract information from simulation manuals by allowing them to ask natural language questions and receive context-aware answers. It integrates LangChain for pipeline management, ChromaDB for vector-based document retrieval, and open-source Large Language Models (LLMs) for generating responses. Using a Retrieval-Augmented Generation (RAG) approach, the system improves answer accuracy by pulling relevant content from domain-specific manuals. The chatbot is deployed as a web application with features such as persistent conversation histories, organized collections of interactions, and secure user authentication. This report outlines the team’s development process, including document preprocessing and chunking, integration of open-source tools, and the milestones reached. It also discusses challenges such as maintaining conversation context and improving the user interface.