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Complete automated RAG setup orchestrator in 8 phases for new projects
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Based on SOC occupation classification
Scale up or scale down Azure RAG configurations (Search, Log Analytics, Insights) and manage budgets/alerts automatically. Reversible changes with cost calculation before applying.
PowerShell helper for obtaining Azure Blob Storage credentials via Azure CLI. Provides connection strings used by RAG indexers and document upload pipelines to access Blob Storage.
Design document ingestion and indexing workflows for Azure AI Search, including chunking, metadata strategy, and incremental reindexing guidance.
Exposes RAG functionality as a REST API for external applications. Provides HTTP endpoints for document search and query with JSON request/response, async processing, CORS support, and observability metrics.
Monitors, diagnoses and troubleshoots RAG system health. Verifies Azure AI Search connectivity, index status, configuration, and provides real-time monitoring with actionable error reports.
Professional executive report generation using Claude Opus 4.7. Generates high-quality DOCX reports with professional formatting, compelling narratives, and quantified impact metrics. Perfect for client presentations and stakeholder communication.
| name | rag-orchestration |
| description | Complete automated RAG setup orchestrator in 8 phases for new projects |
| applyTo | **/*.agent.md |
RAG Reference: Retrieval-augmented Generation (RAG) in Azure AI Search - Microsoft Learn
Status: Production Version: 1.0 Last Updated: Mayo 13, 2026
Orquestador de automatización completa para setup RAG (Retrieval Augmented Generation). Lleva al usuario desde "Tengo documents" a "Puedo consultar mi RAG" en 8 phases con cero interacción manual en Azure portal.
Este skill:
knowledge/ e indexa todos los formatos (PDF, Word, Excel, Markdown, Código, PowerPoint).env con templates de endpoints Azureknowledge/)rag-cost-analyst para validar presupuesto vs coste realknowledge/ (pdfs, procedimientos, codigo, presentaciones).env con templates de credentials.env listo para que el usuario rellene credentialsfrom pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).parent / ".github" / "skills" / "rag-orchestration"))
from orchestrator import RAGOrchestrator
orchestrator = RAGOrchestrator()
exit_code = orchestrator.run()
python .github/skills/rag-orchestration/orchestrator.py
python run-rag.py --agent onboarding