About This Architecture

Intelligent debugging and RCA platform leveraging AWS infrastructure with multi-AZ deployment across VPCs, combining FastAPI backends, Celery workers, and advanced LLM processing. User requests flow through WAF and CloudFront CDN to ALB and API Gateway, routing to React + Vite frontend with Monaco Code Editor and Pyodide client execution. Traceback parsing triggers Celery workers queued in Redis, feeding embeddings into Qwen/Llama/Phi LLMs with Sarvam AI translation, guardrails, and RAG pipeline backed by ChromaDB vector database. This architecture demonstrates high-availability patterns with standby replicas across AZ-1 and AZ-2, Redis caching, RDS feedback storage, and S3 object storage for model artifacts. Fork and customize this diagram on Diagrams.so to adapt the LLM stack, adjust subnet sizing, or integrate additional AWS services like SageMaker or Bedrock. The design balances real-time debugging responsiveness with asynchronous AI processing, ideal for teams building intelligent error analysis at scale.

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How do I design a scalable AWS architecture for intelligent debugging and root cause analysis using LLMs and RAG?

This diagram shows a production-grade multi-AZ AWS architecture combining FastAPI backends, Celery async workers, and LLMs (Qwen/Llama/Phi) with RAG powered by ChromaDB. It demonstrates security best practices (WAF, CloudFront, private subnets), high availability (standby replicas, RDS failover), and intelligent processing (traceback parsing, embedding generation, safety guardrails).