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463 lines (463 loc) · 17.6 KB
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{
"version": "1.0.0",
"last_updated": "2025-10-22",
"kb_sections": {
"architecture-patterns": {
"title": "Core Architecture Patterns",
"description": "Foundational patterns for building enterprise-grade Copilot solutions",
"repositories": {
"existing": [
{
"name": "three-layer-ai-framework",
"url": "https://github.com/maree217/three-layer-ai-framework",
"description": "Production-ready enterprise AI framework with three-layer architecture",
"status": "production",
"stars": 1,
"topics": ["Microsoft Copilot Stack", "Production Deployment"],
"tech_stack": ["Python", "Azure AI"]
},
{
"name": "azure-ai-foundry-showcase",
"url": "https://github.com/maree217/azure-ai-foundry-showcase",
"description": "Azure AI Foundry implementations for strategic forecasting",
"status": "production",
"stars": 0,
"topics": ["Microsoft Copilot Stack", "Azure AI Foundry"],
"tech_stack": ["Python", "Azure AI Foundry"]
},
{
"name": "enterprise-agent-toolkit",
"url": "https://github.com/maree217/enterprise-agent-toolkit",
"description": "Professional AI consultancy toolkit with case studies and templates",
"status": "production",
"stars": 0,
"topics": ["Multi-Agent Systems"],
"tech_stack": ["TypeScript", "Azure"]
},
{
"name": "skmultiagents",
"url": "https://github.com/maree217/skmultiagents",
"description": "Multi-Agents Demo using Semantic Kernel and Azure AI Foundry Agents Service",
"status": "fork-to-enhance",
"stars": 0,
"topics": ["Multi-Agent Systems", "Semantic Kernel"],
"tech_stack": ["Python", "Semantic Kernel"]
}
],
"planned": [
{
"name": "semantic-kernel-production-patterns",
"description": "Production patterns for Semantic Kernel (setup, plugins, memory, orchestration, deployment, testing)",
"priority": "P0",
"status": "planned",
"topics": ["Microsoft Copilot Stack", "Semantic Kernel"],
"tech_stack": ["Python", "C#", "Semantic Kernel", "Azure OpenAI"]
},
{
"name": "rag-architecture-blueprints",
"description": "Advanced RAG patterns (hybrid search, reranking, query expansion, guardrails)",
"priority": "P0",
"status": "planned",
"topics": ["RAG Architectures"],
"tech_stack": ["Python", "Azure AI Search", "Pinecone", "Redis"]
},
{
"name": "multi-agent-orchestration-cookbook",
"description": "AutoGen + LangGraph + Azure Agent Service patterns",
"priority": "P2",
"status": "planned",
"topics": ["Multi-Agent Systems"],
"tech_stack": ["Python", "AutoGen", "LangGraph"]
},
{
"name": "azure-copilot-production-stack",
"description": "Full production deployment (API Gateway, caching, circuit breaker, observability)",
"priority": "P2",
"status": "planned",
"topics": ["Production Deployment"],
"tech_stack": ["Azure", "Bicep", "Terraform", "Redis"]
}
]
}
},
"use-cases": {
"title": "Real-World Use Cases",
"description": "Detailed case studies from production deployments across industries",
"repositories": {
"existing": [
{
"name": "enterprise-ai-analytics-platform",
"url": "https://github.com/maree217/enterprise-ai-analytics-platform",
"description": "Production-ready Enterprise AI Analytics Platform with Three-Layer Architecture",
"status": "production",
"stars": 1,
"case_study": "Financial Services / Cross-Industry",
"tech_stack": ["Python", "Azure AI"]
},
{
"name": "agentic-data-platform",
"url": "https://github.com/maree217/agentic-data-platform",
"description": "Autonomous, self-healing data platform with AI agents",
"status": "production",
"stars": 0,
"case_study": "Multi-step Automation",
"tech_stack": ["HTML", "Python"]
},
{
"name": "strategic-forecasting-ai",
"url": "https://github.com/maree217/strategic-forecasting-ai",
"description": "AI-powered strategic decision support with scenario planning",
"status": "production",
"stars": 0,
"case_study": "Strategic Intelligence",
"tech_stack": ["Python"]
},
{
"name": "ram-digital-twin",
"url": "https://github.com/maree217/ram-digital-twin",
"description": "AI-Powered Digital Twin with RAG, vector search, and lead intelligence",
"status": "production",
"stars": 0,
"case_study": "Consultancy / Knowledge Management",
"tech_stack": ["Python"]
}
],
"planned": [
{
"name": "compliance-qa-production-system",
"description": "Financial services compliance Q&A with PII detection, faithfulness scoring, citations",
"priority": "P1",
"status": "planned",
"case_study": "Financial Services - Enterprise Knowledge Management",
"tech_stack": ["Python", "Azure AI Content Safety", "Azure AI Search"]
},
{
"name": "housing-repair-automation-agents",
"description": "Multi-agent repair request workflow for housing associations",
"priority": "P2",
"status": "planned",
"case_study": "Housing Association - Multi-Step Process Automation",
"tech_stack": ["Python", "Power Automate", "AutoGen"]
}
]
}
},
"challenges": {
"title": "Technical Challenges & Solutions",
"description": "Common production challenges with specific tools and battle-tested solutions",
"repositories": {
"existing": [
{
"name": "universal-conversation-coach",
"url": "https://github.com/maree217/universal-conversation-coach",
"description": "Real-time conversation coaching with Core Audio Taps, Whisper, GPT-4",
"status": "production",
"stars": 0,
"challenge": "Performance & Real-time Processing",
"tech_stack": ["JavaScript", "OpenAI"]
},
{
"name": "stealth-sales-coach",
"url": "https://github.com/maree217/stealth-sales-coach",
"description": "Local AI-powered sales coaching with real-time insights",
"status": "production",
"stars": 0,
"challenge": "Local AI Processing",
"tech_stack": ["Python"]
}
],
"planned": [
{
"name": "llm-evaluation-production-toolkit",
"description": "RAGAS, faithfulness metrics, LLM-as-judge, regression testing, hallucination detection",
"priority": "P0",
"status": "planned",
"challenge": "Evaluation & Quality",
"tech_stack": ["Python", "RAGAS", "PromptBench"]
},
{
"name": "semantic-cache-production-patterns",
"description": "Redis semantic caching for 40-60% cost savings and latency optimization",
"priority": "P1",
"status": "planned",
"challenge": "Performance & Scale",
"tech_stack": ["Python", "Redis", "Azure Cache for Redis"]
},
{
"name": "ai-security-guardrails-framework",
"description": "PII detection, prompt injection prevention, RBAC patterns",
"priority": "P1",
"status": "planned",
"challenge": "Security & Governance",
"tech_stack": ["Python", "Azure AI Content Safety", "Microsoft Purview"]
},
{
"name": "persistent-memory-patterns",
"description": "Solutions to persistent memory problem - Redis state, Cosmos DB, conversation history",
"priority": "P2",
"status": "planned",
"challenge": "Memory & State Management",
"tech_stack": ["Python", "Redis", "Cosmos DB", "Semantic Kernel"]
}
]
}
},
"adrs": {
"title": "Architectural Decision Records",
"description": "Critical architectural decisions with criteria, trade-offs, and recommendations",
"repositories": {
"existing": [],
"planned": [
{
"name": "ai-architecture-decision-framework",
"description": "Interactive decision trees, trade-off calculators, cost models for AI architecture",
"priority": "P1",
"status": "planned",
"adr_topics": ["All ADRs", "RAG vs Fine-Tuning", "Agent Complexity"],
"tech_stack": ["Python", "Streamlit", "Jupyter"]
},
{
"name": "rag-vs-finetuning-production-comparison",
"description": "Side-by-side implementations of same use case with both approaches",
"priority": "P3",
"status": "planned",
"adr_topics": ["RAG vs Fine-Tuning"],
"tech_stack": ["Python", "Azure OpenAI", "Azure ML"]
}
]
}
},
"evolution": {
"title": "Evolution & Emerging Patterns",
"description": "Cutting-edge patterns including GraphRAG, Small LLMs, Agentic RAG, MCP",
"repositories": {
"existing": [
{
"name": "graphiti",
"url": "https://github.com/maree217/graphiti",
"description": "Build Real-Time Knowledge Graphs for AI Agents",
"status": "fork",
"stars": 0,
"pattern": "GraphRAG",
"tech_stack": ["Python"]
},
{
"name": "web-eval-agent",
"url": "https://github.com/maree217/web-eval-agent",
"description": "MCP server that autonomously evaluates web applications",
"status": "fork",
"stars": 0,
"pattern": "MCP Integration",
"tech_stack": ["Python"]
},
{
"name": "mcp-use",
"url": "https://github.com/maree217/mcp-use",
"description": "Easiest way to interact with MCP servers with custom agents",
"status": "fork",
"stars": 0,
"pattern": "MCP Integration",
"tech_stack": ["Python"]
}
],
"planned": [
{
"name": "graphrag-production-patterns",
"description": "Knowledge graph + RAG integration patterns (Neo4j + Azure AI Search)",
"priority": "P3",
"status": "planned",
"pattern": "GraphRAG",
"tech_stack": ["Python", "Neo4j", "Azure Cosmos DB Graph"]
},
{
"name": "small-llm-local-deployment-guide",
"description": "Llama 3.1 8B, Phi-3, fine-tuning for privacy-focused local deployment",
"priority": "P3",
"status": "planned",
"pattern": "Small Language Models",
"tech_stack": ["Python", "Ollama", "LM Studio", "Azure ML"]
},
{
"name": "mcp-copilot-integration-patterns",
"description": "Semantic Kernel + MCP, AutoGen + MCP integration patterns",
"priority": "P3",
"status": "planned",
"pattern": "MCP Integration",
"tech_stack": ["Python", "MCP SDK", "Semantic Kernel", "FastAPI"]
}
]
}
},
"implementation": {
"title": "Implementation Guides",
"description": "Step-by-step implementation guides with production-ready code examples",
"repositories": {
"existing": [
{
"name": "harringey-voicechatbot-AZURE",
"url": "https://github.com/maree217/harringey-voicechatbot-AZURE",
"description": "VoiceRAG pattern with Azure AI Search and Azure OpenAI realtime preview",
"status": "fork",
"stars": 0,
"guide": "VoiceRAG Implementation",
"tech_stack": ["Python", "Azure"]
},
{
"name": "genai-stack",
"url": "https://github.com/maree217/genai-stack",
"description": "Langchain + Docker + Neo4j + Ollama stack",
"status": "fork",
"stars": 0,
"guide": "Full Stack Setup",
"tech_stack": ["Python", "Docker"]
}
],
"planned": [
{
"name": "kb-implementation-examples",
"description": "Working implementations of every code example in the KB (50+ examples)",
"priority": "P0",
"status": "planned",
"guide": "All KB Implementation Guides",
"tech_stack": ["Python", "Azure SDK", "Jupyter"]
},
{
"name": "prompt-flow-production-templates",
"description": "Production-ready Prompt Flow templates (RAG, agent, evaluation flows)",
"priority": "P3",
"status": "planned",
"guide": "Prompt Flow Deployment",
"tech_stack": ["Python", "Azure AI Studio", "Azure ML"]
}
]
}
},
"metrics": {
"title": "Metrics & Measurement Framework",
"description": "Observable systems with quality, performance, cost, and business metrics",
"repositories": {
"existing": [],
"planned": [
{
"name": "copilot-metrics-framework",
"description": "Observable dashboards, metric collection, alerting (Application Insights, Grafana)",
"priority": "P2",
"status": "planned",
"metrics": ["Quality", "Performance", "Cost", "Business"],
"tech_stack": ["Python", "Azure Monitor", "Application Insights", "Prometheus", "Grafana"]
},
{
"name": "llm-cost-calculator",
"description": "Interactive cost calculator for Azure OpenAI (RAG, agent, caching ROI models)",
"priority": "P4",
"status": "planned",
"metrics": ["Cost Analysis"],
"tech_stack": ["Python", "Streamlit", "Azure Cost Management API"]
}
]
}
}
},
"thought_leadership": {
"title": "Thought Leadership Articles",
"description": "LinkedIn articles with accompanying code implementations",
"repository": {
"name": "microsoft-ai-reality-check",
"status": "planned",
"priority": "P0",
"articles": [
{
"number": 1,
"title": "The One Unsolved Problem Blocking AI Agents: Memory",
"kb_sections": ["challenges", "evolution"],
"topics": ["Persistent Memory", "State Management"]
},
{
"number": 2,
"title": "Microsoft, AWS, ServiceNow: Why They're All Selling You the Same Thing",
"kb_sections": ["evolution"],
"topics": ["Platform Commoditization", "Vendor Lock-in"]
},
{
"number": 3,
"title": "Why Your AI Still Can't Touch Customer Data",
"kb_sections": ["challenges", "use-cases"],
"topics": ["Security", "PII Leakage", "Compliance"]
},
{
"number": 4,
"title": "AI Projects Fail Because of Politics, Not Technology",
"kb_sections": ["use-cases"],
"topics": ["Organizational Politics", "Change Management"]
},
{
"number": 5,
"title": "Stop Defaulting to RAG: An AI Architecture Decision Framework",
"kb_sections": ["adrs"],
"topics": ["RAG vs Fine-Tuning", "Architecture Decisions"]
},
{
"number": 6,
"title": "Why I Still Don't Know How to Prompt",
"kb_sections": ["challenges"],
"topics": ["Prompt Engineering", "Intellectual Honesty"]
}
]
}
},
"education": {
"title": "BA GenAI Transformation Course",
"description": "5-week program for business analysts and project managers",
"repository": {
"name": "ba-genai-transformation-course",
"url": "https://github.com/maree217/transformed",
"current_url": "http://65.109.4.220/ba-course/",
"status": "migrate-to-github",
"priority": "P2",
"weeks": [
{
"week": 1,
"title": "Strategic Discovery",
"kb_sections": ["use-cases", "adrs"]
},
{
"week": 2,
"title": "Workflow Foundations",
"kb_sections": ["architecture-patterns"]
},
{
"week": 3,
"title": "Rapid Prototyping",
"kb_sections": ["implementation"]
},
{
"week": 4,
"title": "Delivery Pipelines",
"kb_sections": ["architecture-patterns"]
},
{
"week": 5,
"title": "Agentic Operations",
"kb_sections": ["architecture-patterns"]
}
]
}
},
"stats": {
"total_repositories": 48,
"existing_repos": 16,
"planned_repos": 20,
"thought_leadership_repos": 1,
"education_repos": 1,
"forks_to_enhance": 5,
"external_references": 60,
"kb_sections": 7,
"priority_distribution": {
"P0": 5,
"P1": 4,
"P2": 5,
"P3": 4,
"P4": 2
}
}
}