Cite Before You Write: Agentic RAG That Picks Graph vs. Vector on the Fly
Paper: Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review (Nagori et al., 2025) One‑line: The authors wrap a hybrid RAG pipeline (Neo4j GraphRAG + FAISS VectorRAG) inside an agent (Llama‑3.3‑70B) that decides per query which retriever to use, then instruction‑tunes generation (Mistral‑7B) and quantifies uncertainty via bootstrapped evaluation. It’s open‑source and genuinely useful. Why this paper matters (beyond research circles) Business pain: Knowledge workers drown in PDFs. Static “semantic search + summarize” tools miss citation structure and provenance; worse, they hallucinate under pressure. What’s new: Dynamic query routing between graph queries (Cypher over Neo4j) and semantic + keyword retrieval (FAISS + BM25 + rerank). Then DPO nudges the generator to prefer grounded answers. So what: For regulated sectors (healthcare, finance, legal), this is a pattern you can implement today for auditable reviews with traceable sources and tunable confidence bands. The blueprint (concrete, reproducible) Ingestion: Pull bibliometrics (DOI, title, abstract, year, authors, PDF URL, source) from PubMed, arXiv, Google Scholar. Deduplicate and filter by cosine similarity of TF‑IDF keywords (keep top‑quartile relevance). ...