Optimizing GDPR Compliance Retrieval with Hybrid Graph‑Augmented RAG Systems | KI Reply | Agrawal

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VIEW ORIGINAL SLIDES: https://docs.google.com/presentation/d/1WVBxlwvCHc-lt4yyt9F_a_eKkVX2NOp-ChGWiPFvkwM/edit?slide=id.g3821790bc06_0_116#slide=id.g3821790bc06_0_116 _____ Legal queries demand precision, provenance, and multi‑hop reasoning. Focusing on GDPR, we compare three Retrieval‑Augmented Generation pipelines and show why each step matters in practice. A baseline uses Qdrant vector retrieval to capture semantic similarity, but it can miss exact matches, acronyms, and cross‑references critical to regulatory interpretation. A hybrid pipeline adds lexical lookup and LLM‑based re‑ranking to improve recall and precision, yet struggles with questions that require navigating the law’s structure. The most capable variant integrates a Neo4j knowledge graph alongside the vector store, modeling hierarchy and explicit relationships between provisions. This graph‑augmented RAG retrieves contextually linked articles and definitions, yielding more complete answers and clearer citations. We outline evaluation criteria, benchmark design, and error analysis, then share implementation patterns—schema choices, retrieval orchestration, and guardrails—that generalize beyond GDPR. Attendees will leave with a concrete playbook for selecting and benchmarking embedding‑only, hybrid, and graph‑enhanced RAG systems for high‑stakes compliance search. _____ This video is a part of a conference series from Qdrant's #VectorSpaceDay 2025. Read about the full event recap here: https://qdrant.tech/blog/vector-space-day-2025-recap/ and check out all other speaker presentations in this playlist: https://www.youtube.com/playlist?list=PL9IXkWSmb36-peUPGzdzjAZ0dDmaSnzUw "