graph-rca
Log-to-resolution RAG system over 200 annotated production incidents — LLM parses raw logs into a causal DAG stored in MongoDB; ChromaDB (HNSW index) retrieves runbook context scoped to that graph traversal before generating a root-cause report. Output quality validated by a 3-model judge ensemble (Qwen3:32b, GPT-4o-mini, Llama-3.1-70B) across 9 reproducible experiment scripts; ships with CPU/GPU Docker Compose variants and one-command ./run.sh setup.
- Problem
- Manual analysis of production incident logs for root cause determination is time-consuming and inconsistent.
- Tech
- PythonFastAPILangChainChromaDBMongoDB
- Impact
- Automated root-cause reports with high-quality validation and easy deployment, reducing resolution time.





