awesome-copilot

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.

Hybrid-professional SharePoint integration for RAG. Two modes: Professional (Azure Search indexer, real-time sync, no duplication) or Local (download to knowledge/, coexists with traditional docs)

Expert RAG validator: verifies that agents, instructions, skills, and RAG implementations comply with Microsoft RAG best practices and repository guidelines.

Validates and optimizes Azure RAG deployment architecture for cost efficiency and performance. Reviews service tiers, scaling, redundancy, and recommends right-sizing before deployment.

Comprehensive Azure cost analysis, forecasting, and optimization recommendations. Analyzes infrastructure costs, model inference costs, and identifies savings opportunities.

Bicep IaC templates to deploy Azure OpenAI, AI Search, and Application Insights. Reusable across any RAG project. Includes main.bicep and deploy.sh orchestration.

Complete automated RAG setup orchestrator in 8 phases for new projects

Interactive conversational RAG query engine for Q&A over documents

Build and evaluate a conversational QA layer over indexed enterprise knowledge with grounding, citation handling, and response quality checks.

Interactive CLI for searching and querying documents indexed in a RAG system using Azure AI Search and Azure OpenAI. Supports hybrid search, source tracking, response generation, and UTF-8 compatibility on Windows.

Reusable Python modules for agent instrumentation: metrics collection, Application Insights integration, observability logging. Used by all agents to capture tokens, latency, cost, errors.

Plan and scaffold Azure resources for a production-ready RAG baseline with Azure OpenAI, Azure AI Search, Storage, and observability defaults.