About This Architecture

Production-grade AI narrative generator for Suspicious Activity Reports (SAR) combines Llama 3.1 models with LangChain RAG framework, ChromaDB vector embeddings, and Kubernetes orchestration. Data flows from PostgreSQL audit storage through FastAPI backend to Streamlit analyst dashboard, with Redis caching session state and prompts for sub-second response times. Architecture implements SHAP explainability for model transparency, RBAC authentication for compliance controls, and LangChain callbacks for prompt tracing—critical for regulated financial institutions. Fork this Kubernetes deployment diagram on Diagrams.so to customize vector database sizing, add GPU node pools for model inference, or integrate your organization's SAR templates and ML typology patterns.

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How do I architect a Kubernetes-based AI system for generating compliant SAR narratives with explainability?

Deploy Llama 3.1 models with LangChain RAG framework on Kubernetes, using ChromaDB for SAR template embeddings, FastAPI for orchestration, PostgreSQL for audit trails, and SHAP for model explainability. This diagram shows production-grade layer separation with RBAC and monitoring.