Self-Healing RAGOps

🚀 Overview & Core Idea Self-Healing RAGOps introduces a new generation of Retrieval-Augmented Generation systems. It solves the long-standing reliability issues in LLM pipelines by adding autonomous monitoring, anomaly detection, and self-healing behaviors. Inspired by Zero-Touch Management (ZSM) from 6G networks, the system becomes an intelligent, self-managing AI ecosystem. The architecture includes a Data Plane (DP) for retrieval and generation, and a Control Plane (CP) for autonomous healing and governance. 📌 Why Traditional RAG Systems Fail Retrieval often misses important passages (low coverage@k). Chunking can split concepts incorrectly, reducing semantic clarity. The LLM may hallucinate even when evidence exists. Systems lack real-time health monitoring or anomaly alerts. No auto-correction: humans must manually tune prompts, chunk sizes, or parameters. Long context windows cause drift and degrade output quality. Retrieval pipelines degrade over time without noticing. 🧠 Key Innovations Introduced by Self-Healing RAGOps 1. Two-Plane Architecture Data Plane: ingestion, chunking, embeddings, Qdrant indexing, retrieval, LLM answer generation. Control Plane: telemetry, anomaly detection, policy-driven healing, reinforcement-based validation, learning memory. 2. Autonomous Telemetry Monitors every query in real time: Coverage@k Faithfulness Hallucination score Semantic drift Latency Token usage Cost per query Governance score 3. Real-Time Anomaly Detection Identifies: Hallucination spikes Retrieval collapse Missing context Semantic drift Latency spikes High cost anomalies Low governance score 4. Self-Healing Actions Control Plane executes corrective actions automatically: Tighten the prompt Increase or decrease top-k retrieval github URL https://github.com/03066207138/Self-healing-Ragops-with-ZSM