Python-Based Telegram Frequently Asked Questions (FAQ) Chatbot Using Text Retrieval

Abstract

This study presents the implementation of a Telegram FAQ chatbot developed in Python to address the difficulty of providing rapid and consistent responses to recurring public-service questions. The system stores question-and-answer pairs in memory, extracts FAQ entries from free-form text, and retrieves the closest answer using text similarity through Python's difflib library. The prototype also provides inline menus, an interactive quiz, FAQ reset and update commands, and an optional large language model refinement stage constrained by the retrieved context. The research followed an implementation-oriented prototyping approach comprising requirements identification, code analysis, system design, implementation review, and scenario-based functional inspection. Static analysis identified 14 principal functions, 273 built-in FAQ pairs, and 100 quiz questions. The implementation analysis indicates that lightweight retrieval can support a practical and resource-efficient FAQ automation prototype while maintaining answers within the available knowledge base. Nevertheless, production deployment requires stronger secret management, persistent storage, access control, logging, and empirical evaluation using real user questions. The study contributes a replicable architecture for controlled FAQ automation on Telegram and establishes priorities for subsequent reliability and usability testing.