Enterprise AI & SRE
DevOps AI RAG Architect Studio
Design, customize, and generate a production-ready Local RAG (Retrieval Augmented Generation) knowledge engine for DevOps & SRE workflows. Package the entire codebase, deployment orchestrations, and monitoring dashboards dynamically.
⚙️ RAG Pipeline Configuration
🐳 DevOps & Production Infrastructure
Generate multi-stage Dockerfiles for Frontend and Backend + docker-compose.yml for local clustering.
Create complete ConfigMaps, Secrets, Deployments, ClusterIP Services, Ingress Routes, and Horizontal Pod Autoscalers (HPA).
Include a GitHub Actions workflow with unit testing (`pytest`), Trivy vulnerability scans, container builds, and deployment.
Integrate a prometheus scrape configuration and export a complete Grafana analytics dashboard JSON payload.
💡 Interactive RAG Flowchart
Click configuration sliders/toggles to visualize RAG pipeline updates.
Upload Documents
➔
Recursive Splitter
➔
Text Embeddings
Vector Database
➔
Similarity Search
➔
Context Injection
➔
Local LLM Inference
➔
Streamlit Web Interface
.py
⚡ LLM SRE Cheat Sheet
# Verify local Ollama running models:
ollama list
# Pre-pull LLM models in automation scripts:
ollama pull llama3:8b
# Check Vector Store size & index details:
du -sh ./chroma_data