Domain-Specific Graph RAG Pipelines: Optimized Approaches for Building Efficient Personal Knowledge Repositories

Managing personal data, including notes, calendar events, to-do lists, and other personal information, has become increasingly complex and challenging. In response to this issue, we propose a framework using RAG that enables a large language model (LLM) to efficiently query this data without requiring training on the personal data itself. Conventional retrieval systems, including those leveraging vector-based retrieval-augmented generation (RAG), are effective at handling basic queries but struggle to deliver coherent global abstractions, integrate diverse knowledge sources, and account for temporal nuances. This research explores domain-specific Graph-based RAG frameworks that incorporate a knowledge graph to better model relationships, thereby enabling more comprehensive reasoning. By optimizing graph construction and modeling temporal entities for enhanced understanding, this research aims to advance the capabilities of RAG systems beyond the limitations of vector-based approaches for personal knowledge management.