Abstract

We introduce a novel agent-based framework that leverages conversational data to enhance Large Language Models (LLMs) with personalized knowledge, enabling the creation of Artificial Personal Intelligence (API) systems. The proposed framework addresses the challenge of collecting and analysing unstructured conversational data by utilizing LLM agents and embeddings to efficiently process, organize, and extract insights from conversations. The system architecture integrates knowledge data aggregation and agent-based conversational data extraction. The knowledge data aggregation method employs LLMs and embeddings to create a dynamic, multi-level hierarchy for organizing information based on conceptual similarity and topical relevance. The agent-based component utilizes an LLM Agent to handle user queries, extracting relevant information and generating specialized theme datasets for comprehensive analysis. The framework's effectiveness is demonstrated through empirical analysis of real-world conversational data. While the proposed framework presents significant advancements, limitations such as the need for high-quality conversational data and limited user interaction testing are acknowledged. Future research should explore optimization techniques, the integration of additional conversational qualities, and extensive real-world testing to further enhance the framework's capabilities in the domain of Artificial Personal Intelligence, ultimately enabling more personalized and context-aware AI assistance.

Applied Human Factors and Ergonomics AHFE, Artificial Intelligence and Social Computing Book

http://doi.org/10.54941/ahfe1004649

Conference Presentation

The video was played at the AHFE 2024 conference in Nice on the 26th of July

General Architecture

Conversation Graph

Agent Architecture

Partner Universities

Cite this paper: Kurylek, B., Camara, A., Nandi, A., Markopoulos, E. (2024). A Novel Agent-Based Framework for Conversational Data Analysis and Personal AI Systems. In: Tareq Ahram, Jay Kalra and Waldemar Karwowski (eds) Artificial Intelligence and Social Computing. AHFE (2024) International Conference. AHFE Open Access, vol 122. AHFE International, USA.

Access it Here: http://doi.org/10.54941/ahfe1004649