Post by AYMAN ISMAILI

Shipping Sovereign Enterprise RAG: Event-Driven, Self-Correcting, Air-Gapped Just completed a production-grade Retrieval-Augmented Generation (RAG) pipeline designed for industrial-scale technical document processing—with zero dependency on public APIs. The Challenge: Standard RAG systems fail enterprise deployments due to: Data sovereignty breaches (IP leakage to third-party APIs) Structural decay in complex documents (tables, matrices, hierarchical layouts) Hallucination rates that exceed business risk tolerance The Solution: A multi-agent, event-driven architecture that delivers: 100% Air-Gapped Processing — all inference on-premise Event-Driven Decoupling — non-blocking ingestion via Celery + Redis workers Self-Correcting Verification — automated hallucination detection + fallback routing Hybrid Retrieval + RRF — semantic + BM25 + cross-encoder reranking Production UI — React SPA + Streamlit dev dashboard + comprehensive eval harness (MLflow) 35% Hallucination Reduction — vs. naive baseline via Self-RAG verification Tech Stack: Python (FastAPI, Celery, Pydantic) Docker (multi-stage, production-optimized) Qdrant (vector DB) + BM25 Ollama (local LLM inference) MLflow evaluation pipeline React + TypeScript frontend Event-driven architecture at scale What I Built: Non-blocking ingestion pipeline handling 1000+ pages per file Multi-phase deployment (dev → staging → production) Comprehensive evaluation framework with domain-specific metrics Self-contained airgap topology (validated runbook included) GitHub: https://lnkd.in/ezgTs8Ez If you're building RAG systems at scale, solving data sovereignty challenges, or scaling LLM infrastructure—let's talk. #RAG #GenerativeAI #LLM #Python #FastAPI #Docker #VectorDB #EnterpriseAI #MachineLearning #BackendEngineering #SystemsArchitecture #OpenSource #TechLeadership