GRASP: GRAPH RETRIEVAL ARCHITECTURE FOR SMART PERSONAL-AI

The evolution of personal AI Agents from simple retrieval tools to context-aware "digital twins" is currently hindered by the structural fragmentation of user data and the inherent limitations of probabilistic models. Contemporary architectures, predominantly based on standard Retrieval-Augmented Generation (RAG), rely on vector similarity matching that often fails to capture the rigorous topological and temporal dependencies essential for complex reasoning. Furthermore, the opacity of neural parameters presents significant challenges regarding auditability, hallucination, and data sovereignty, particularly in light of emerging regulatory frameworks and laws. To address these systemic deficiencies, this thesis introduces GRASP (Graph Retrieval Architecture for Smart Personal-ai), a framework that redefines the interplay between generative models and knowledge representation. Diverging from purely neural approaches, this research proposes a neuro-symbolic architecture where LLMs function not only as generative engines, but as semantic parsers designed to populate and query a deterministic Personal Knowledge Graph (PKG). This shift establishes a "knowledge-centric" ecosystem where heterogeneous data streams—textual, temporal, and visual—are synthesized into a unified, navigable semantic topology. The methodology details the construction of an end-to-end pipeline that ingests atomic source items to synthesize an explicit epistemic memory. A key innovation includes a Visual-Symbolic Transduction pipeline, which employs multimodal models to translate raw visual inputs into queryable graph nodes, effectively bridging the gap between pixel-based memory and conceptual reasoning. The retrieval phase is governed by a local GraphRAG runtime, which combines vector embedding with graph traversal to ground generative outputs in verifiable evidence. By decoupling the reasoning engine from the storage layer, the proposed architecture guarantees rigorous data isolation and enables granular knowledge governance, allowing for the precise excision of information without model retraining. Validated against the PersonalQA benchmark, a synthetic dataset designed for multi-hop reasoning tasks, GRASP demonstrates that anchoring neural capabilities to structured symbolic logic significantly enhances the verifiability, explainability, and contextual accuracy of personal AI agents.