QBot: Domain-Specific Chatbots with Retrieval-Augmented Generation and Vector Embedding for Complex Documentation Queries

Chatbots are becoming more and more popular as efficient tools in information retrieval and customer service procedures, which raises satisfaction by offering quick and efficient answers. This work presents a chatbot system designed to handle lengthy and intricate documents and to accurately and semantically respond to user queries. Big language models and vector-based search methods were combined in the Retrieval Augmented Generation (RAG) approach of system design. For precise and meaningful answers to user queries, the system dynamically retrieves data and interfaces with OpenAI API. Regarding chatbot system response quality and user satisfaction, this work can be regarded as a significant step.