Percorrer por autor "Dias, Dinis Rodrigues"
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- Digital assistant with artificial intelligence techniquesPublication . Dias, Dinis Rodrigues; Ferreira, Artur Jorge; Leite, Nuno Miguel da Costa de SousaAbstract The design, implementation, and assessment of a modular Digital Assistant (DA), developed in Python, that can process natural language in speech and text, being optimized for the Windows desktop environment are presented in this dissertation. The DA performs tasks like retrieving weather data and launching applications, where the system combines Large Language Models (LLM) to interpret user requests and dynamically choose between conversational responses and function execution. To ensure modularity, extensibility, and maintainability, a layered architecture was used to organize the functionality, reasoning engine, conversation handling, and graphical user interface modules. To maintain responsiveness and user control even during lengthy operations, the assistant uses asynchronous execution, supports both text and voice input, and can output speech synthesis. The implementation places a strong emphasis on sound software engineering techniques, such as modular contracts, interface-first design, and reliable error handling. The secure handling of Application Programming Interface (API) keys and the lack of persistent memory protect privacy are also addressed. Experimental evaluation shows near real-time responses from contemporary LLM backends, sub-second latency for functionality modules, and high accuracy in differentiating between function calls and conversations. Additionally, qualitative validation verifies that the system satisfies its non-functional requirements for modularity, robustness, and user experience, and that the Graphical User Interface (GUI) is responsive and the speech features are usable. In conclusion, the project produces a useful, expandable, and intuitive digital assistant that connects conversational Artificial Intelligence (AI) and desktop task automation, providing a solid basis for upcoming improvements like cross-platform deployment, sophisticated speech recognition, and runtime model selection.