llm-reliability-platform
Currently in development: a production-oriented LLMOps/RAG project for document-based AI assistants with source-grounded answers, evaluation workflows, observability, cost/token tracking, and deployment topics.
Overview
The goal of this project is to demonstrate practical backend and GenAI engineering ability through a reliable document-based RAG system. Instead of presenting a simple chatbot, the platform is designed around traceable answers, evaluation, provider handling, operational visibility, and deployment-readiness.
Tech stack
PythonFastAPIPostgreSQLpgvectorRAGEmbeddingsVector SearchLangChainLangGraphOpenTelemetryPrometheusGrafanaDockerCaddyHetznerCloudflare
What the project demonstrates
- •Document ingestion flow with upload, parsing, chunking, embeddings, and vector search.
- •RAG answers designed to include sources and avoid unsupported responses.
- •Evaluation workflows for citation behavior, answer quality, regression risk, and reliability checks.
- •Provider routing, fallback handling, token/cost tracking, rate limits, and observability as production-oriented reliability topics.
Architecture direction
- •Python/FastAPI backend with typed schemas, service layers, health endpoints, and API documentation.
- •PostgreSQL/pgvector foundation for relational data, embeddings, and similarity search.
- •Observability stack planned with OpenTelemetry, Prometheus, Grafana, structured logs, request IDs, and trace IDs.
- •Docker-oriented deployment path with Caddy, Hetzner, Cloudflare, runbooks, and operational documentation.