A Retrieval-Augmented Conversational Framework for Intelligent Personal Document Understanding

The exponential proliferation of digital content across personal repositories—encompassing academic manuscripts, annotated lecture transcripts, technical project documentation, and domain-specific study materials—has introduced substantial challenges in efficient knowledge retrieval. Conventional information retrieval paradigms, predominantly reliant on lexical or keyword-based matching, are fundamentally ill-equipped to resolve queries that demand semantic comprehension. This paper proposes a Retrieval-Augmented Conversational Framework (RACF) for intelligent personal document understanding, integrating multi-stage document ingestion, semantic chunking, dense vector embedding, and large language model (LLM)-driven conversational synthesis. The architecture transforms heterogeneous personal document collections into dynamic, queryable knowledge structures, enabling users to interact with their information through natural language dialogues rather than rigid query syntax. Empirical evaluations demonstrate that context-aware retrieval coupled with generative conversational response synthesis yields superior information accessibility, response coherence, and user satisfaction compared to traditional keyword-search baselines. The proposed framework provides a principled foundation for building next-generation personal knowledge management systems capable of transforming passive document archives into interactive, semantically rich conversational assistants. Keywords— Retrieval-Augmented Generation (RAG); Conversational AI; Personal Document Understanding; Semantic Embedding; Vector Databases; Natural Language Processing; Knowledge Management