Optimizing GDPR Compliance Retrieval with Hybrid Graph-Augmented RAG Systems

Learn how to optimize GDPR compliance retrieval systems through a comprehensive comparison of three Retrieval-Augmented Generation (RAG) pipeline approaches in this conference presentation. Explore why legal queries requiring precision, provenance, and multi-hop reasoning demand sophisticated retrieval strategies beyond basic vector similarity search. Examine a baseline implementation using Qdrant vector retrieval that captures semantic similarity but struggles with exact matches, acronyms, and cross-references essential for regulatory interpretation. Discover how a hybrid pipeline incorporating lexical lookup and LLM-based re-ranking improves recall and precision, yet still faces challenges with structural navigation of legal documents. Analyze the most advanced variant that integrates a Neo4j knowledge graph alongside vector storage, modeling hierarchical relationships and explicit connections between legal provisions to retrieve contextually linked articles and definitions for more complete answers and clearer citations. Gain insights into evaluation criteria, benchmark design, and error analysis methodologies, while learning practical implementation patterns including schema choices, retrieval orchestration, and guardrails that extend beyond GDPR applications. Access the original presentation slides and acquire a concrete playbook for selecting and benchmarking embedding-only, hybrid, and graph-enhanced RAG systems for high-stakes compliance search scenarios.