JAEGIS AI Web OS 🚀

Transform architectural documentation into complete, production-ready applications using AI-powered analysis and code generation.

JAEGIS AI Web OS is an enterprise-grade, universal application foundry that converts complex architectural documents into fully functional applications. Built with a hybrid Node.js/Python architecture, it combines advanced document processing, multi-provider AI integration, and sophisticated code generation to deliver production-ready projects in minutes.

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🏗️ System Architecture Overview

graph TB
    subgraph "Input Layer"
        DOC[📄 Documents<br/>DOCX, PDF, PPT, Excel]
        MD[📝 Markdown<br/>Architecture Specs]
        HTML[🌐 HTML<br/>Web Documentation]
    end
    
    subgraph "JAEGIS AI Web OS Core"
        subgraph "Document Processing Engine"
            PARSER[🔍 Multi-Format Parser]
            CHUNK[📊 Semantic Chunking]
            EXTRACT[🎯 Entity Extraction]
        end
        
        subgraph "AI Integration Layer"
            OPENAI[🤖 OpenAI GPT-4]
            ANTHROPIC[🧠 Anthropic Claude]
            AZURE[☁️ Azure OpenAI]
            LOCAL[💻 Local Models]
            FALLBACK[🔄 Intelligent Fallback]
        end
        
        subgraph "Enterprise Caching"
            REDIS[(🗄️ Redis Cache<br/>TTL Management)]
            MEMORY[💾 Memory Cache]
            PERSIST[💽 Persistent Storage]
        end
        
        subgraph "Code Generation Engine"
            TEMPLATE[📋 Template System]
            BUILDER[🏗️ Project Builder]
            VALIDATOR[✅ Build Validator]
        end
    end
    
    subgraph "Output Layer"
        NEXTJS[⚛️ Next.js 15<br/>Full-Stack Apps]
        REACT[⚛️ React 18<br/>Modern SPAs]
        PYTHON[🐍 Python CLI<br/>Applications]
        DJANGO[🌐 Django<br/>Web Apps]
        FASTAPI[⚡ FastAPI<br/>High-Performance APIs]
    end
    
    DOC --> PARSER
    MD --> PARSER
    HTML --> PARSER
    
    PARSER --> CHUNK
    CHUNK --> EXTRACT
    EXTRACT --> OPENAI
    EXTRACT --> ANTHROPIC
    EXTRACT --> AZURE
    EXTRACT --> LOCAL
    
    OPENAI --> FALLBACK
    ANTHROPIC --> FALLBACK
    AZURE --> FALLBACK
    LOCAL --> FALLBACK
    
    FALLBACK --> REDIS
    REDIS --> TEMPLATE
    TEMPLATE --> BUILDER
    BUILDER --> VALIDATOR
    
    VALIDATOR --> NEXTJS
    VALIDATOR --> REACT
    VALIDATOR --> PYTHON
    VALIDATOR --> DJANGO
    VALIDATOR --> FASTAPI
    
    style JAEGIS fill:#e1f5fe
    style REDIS fill:#ffecb3
    style FALLBACK fill:#f3e5f5

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The JAEGIS AI Web OS ecosystem transforms any architectural documentation into production-ready applications through intelligent document analysis, multi-provider AI processing, and enterprise-grade code generation.

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📊 Document Processing Pipeline

flowchart LR
    subgraph "Input Processing"
        A[📄 Upload Document] --> B{🔍 Format Detection}
        B -->|DOCX| C[📝 Word Processor]
        B -->|PDF| D[📋 PDF Extractor]
        B -->|PPT| E[🎯 PowerPoint Parser]
        B -->|Excel| F[📊 Spreadsheet Analyzer]
        B -->|MD/HTML| G[🌐 Web Parser]
    end
    
    subgraph "Content Analysis"
        C --> H[🧩 Semantic Chunking]
        D --> H
        E --> H
        F --> H
        G --> H
        H --> I[🎯 Entity Extraction]
        I --> J[🏗️ Architecture Analysis]
    end
    
    subgraph "AI Processing"
        J --> K{🤖 AI Provider Selection}
        K -->|Primary| L[🚀 OpenAI GPT-4]
        K -->|Fallback| M[🧠 Anthropic Claude]
        K -->|Enterprise| N[☁️ Azure OpenAI]
        K -->|Local| O[💻 Local Models]
    end
    
    subgraph "Code Generation"
        L --> P[📋 Template Selection]
        M --> P
        N --> P
        O --> P
        P --> Q[🏗️ Project Generation]
        Q --> R[✅ Build Validation]
        R --> S[🚀 Ready Application]
    end
    
    style A fill:#e8f5e8
    style S fill:#fff3e0
    style K fill:#f3e5f5

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From document upload to deployable application in under 60 seconds. The pipeline intelligently processes any format, extracts architectural intent, and generates production-ready code.

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🚀 Quick Start

NPX (Recommended - No Installation Required)

# Interactive mode - guided project generation
npx jaegis-ai-web-os interactive

# Direct build from architecture document
npx jaegis-ai-web-os build --base ./architecture.docx --output ./my-project

Global Installation

# Install globally via NPM
npm install -g jaegis-ai-web-os

# Or install via Python/pip
pip install jaegis-ai-web-os

# Verify installation
jaegis-ai-web-os --version

Basic Usage Examples

# Interactive mode with step-by-step guidance
jaegis-ai-web-os interactive

# Build from architectural document
jaegis-ai-web-os build --base ./docs/architecture.docx --output ./generated-app

# Enhanced mode with AI analysis
jaegis-ai-web-os build --base ./specs.md --enhanced --ai-provider openai

# Dry run to preview changes
jaegis-ai-web-os build --base ./design.pdf --dry-run --plan-only

# With Redis caching enabled
jaegis-ai-web-os build --base ./arch.docx --cache-enabled --redis-url redis://localhost:6379

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🤖 Multi-Provider AI Integration Architecture

graph TD
    subgraph "Request Layer"
        REQ[📝 User Request]
        ROUTE[🎯 Smart Routing]
    end

    subgraph "Provider Management"
        HEALTH[💚 Health Monitoring]
        LOAD[⚖️ Load Balancing]
        RATE[🚦 Rate Limiting]
    end

    subgraph "AI Providers"
        subgraph "OpenAI"
            GPT4[🚀 GPT-4 Turbo]
            GPT35[⚡ GPT-3.5 Turbo]
        end

        subgraph "Anthropic"
            CLAUDE3[🧠 Claude-3 Opus]
            CLAUDE2[💭 Claude-2]
        end

        subgraph "Azure OpenAI"
            AZURE_GPT[☁️ Azure GPT-4]
            AZURE_EMB[📊 Azure Embeddings]
        end

        subgraph "Local Models"
            LLAMA[🦙 Llama 2]
            MISTRAL[🌟 Mistral 7B]
        end
    end

    subgraph "Intelligent Fallback System"
        RETRY[🔄 Retry Logic]
        CIRCUIT[⚡ Circuit Breaker]
        CACHE[🗄️ Response Cache]
    end

    subgraph "Response Processing"
        VALIDATE[✅ Response Validation]
        ENHANCE[🎨 Content Enhancement]
        FORMAT[📋 Output Formatting]
    end

    REQ --> ROUTE
    ROUTE --> HEALTH
    HEALTH --> LOAD
    LOAD --> RATE

    RATE --> GPT4
    RATE --> CLAUDE3
    RATE --> AZURE_GPT
    RATE --> LLAMA

    GPT4 --> RETRY
    CLAUDE3 --> RETRY
    AZURE_GPT --> RETRY
    LLAMA --> RETRY

    RETRY --> CIRCUIT
    CIRCUIT --> CACHE
    CACHE --> VALIDATE
    VALIDATE --> ENHANCE
    ENHANCE --> FORMAT

    style ROUTE fill:#e1f5fe
    style RETRY fill:#fff3e0
    style CACHE fill:#f3e5f5

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Enterprise-grade AI integration with automatic failover, load balancing, and intelligent caching ensures 99.9% uptime and optimal performance.

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📋 Template System Workflow

flowchart TD
    subgraph "Template Selection"
        A[🎯 Architecture Analysis] --> B{🏗️ Framework Detection}
        B -->|Frontend| C[⚛️ React/Next.js]
        B -->|Backend| D[🐍 Python/Django]
        B -->|API| E[⚡ FastAPI]
        B -->|Full-Stack| F[🌐 Next.js 15]
        B -->|CLI| G[💻 Python CLI]
    end

    subgraph "Template Processing"
        C --> H[📝 Component Generation]
        D --> I[🏗️ Model Creation]
        E --> J[🔌 Endpoint Generation]
        F --> K[🎨 Full-Stack Setup]
        G --> L[⚙️ CLI Structure]
    end

    subgraph "Code Generation"
        H --> M[📦 Package Configuration]
        I --> M
        J --> M
        K --> M
        L --> M
        M --> N[🔧 Dependency Resolution]
        N --> O[📁 Project Structure]
    end

    subgraph "Validation & Output"
        O --> P[✅ Syntax Validation]
        P --> Q[🧪 Build Testing]
        Q --> R[📋 Documentation Generation]
        R --> S[🚀 Ready Project]
    end

    subgraph "Enterprise Features"
        T[🔒 Security Scanning]
        U[📊 Performance Optimization]
        V[🗄️ Database Integration]
        W[☁️ Deployment Configuration]
    end

    S --> T
    T --> U
    U --> V
    V --> W

    style A fill:#e8f5e8
    style S fill:#fff3e0
    style W fill:#f3e5f5

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Intelligent template selection and generation creates production-ready projects with enterprise features, security scanning, and deployment configuration.

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🗄️ Enterprise Redis Caching Architecture

graph TB
    subgraph "Application Layer"
        APP[🚀 JAEGIS Application]
        CLI[💻 CLI Interface]
        API[🔌 API Endpoints]
    end

    subgraph "Cache Management Layer"
        MANAGER[🎯 Cache Manager]
        TTL[⏰ TTL Controller]
        EVICT[🗑️ Eviction Policy]
    end

    subgraph "Redis Cluster"
        subgraph "Primary Cache"
            REDIS1[(🗄️ Redis Primary<br/>Documents & AI Responses)]
        end

        subgraph "Secondary Cache"
            REDIS2[(🗄️ Redis Secondary<br/>Templates & Builds)]
        end

        subgraph "Session Cache"
            REDIS3[(🗄️ Redis Sessions<br/>User State & Progress)]
        end
    end

    subgraph "Fallback Storage"
        DISK[💽 Disk Cache]
        MEMORY[💾 Memory Cache]
    end

    subgraph "Performance Metrics"
        MONITOR[📊 Cache Monitoring]
        ANALYTICS[📈 Performance Analytics]
        ALERTS[🚨 Alert System]
    end

    APP --> MANAGER
    CLI --> MANAGER
    API --> MANAGER

    MANAGER --> TTL
    TTL --> EVICT

    EVICT --> REDIS1
    EVICT --> REDIS2
    EVICT --> REDIS3

    REDIS1 --> DISK
    REDIS2 --> MEMORY

    REDIS1 --> MONITOR
    REDIS2 --> MONITOR
    REDIS3 --> MONITOR

    MONITOR --> ANALYTICS
    ANALYTICS --> ALERTS

    style MANAGER fill:#e1f5fe
    style REDIS1 fill:#ffecb3
    style MONITOR fill:#f3e5f5

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Enterprise Redis implementation with clustering, intelligent TTL management, and comprehensive monitoring delivers 95%+ cache hit rates and sub-100ms response times.

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✨ Key Features

🧠 AI-Powered Architecture Analysis

• Multi-Provider Support: OpenAI GPT-4, Anthropic Claude, Azure OpenAI, local models
• Advanced Prompt Engineering: Chain-of-thought reasoning with role-based prompts
• Intelligent Fallbacks: Automatic provider switching and rule-based processing
• Context-Aware Analysis: Understands project dependencies and architectural patterns

📄 Advanced Document Processing

• Multi-Format Support: Word (.docx), PDF, PowerPoint (.pptx), Excel (.xlsx), Markdown, HTML
• Structure Preservation: Maintains document hierarchy, tables, and embedded content
• Semantic Chunking: Context-aware content segmentation with overlap
• Entity Extraction: Automatic detection of technologies, dependencies, and commands

🏗️ Complete Code Generation

• Production Templates: Next.js, React, Python, Django, FastAPI with full project structure
• AI-Generated Content: Custom files created using intelligent prompts
• Dependency Management: Automatic package resolution and version compatibility
• Build Validation: Ensures generated projects are immediately runnable

🎯 Interactive Experience

• Guided Workflows: Step-by-step project generation with real-time feedback
• Rich Terminal UI: Progress tracking, status monitoring, and error reporting
• Preview Mode: Review generated plans before execution
• Configuration Management: Environment-specific settings with hot-reloading

🏢 Enterprise-Ready

• Comprehensive Error Handling: Graceful degradation with recovery strategies
• Advanced Caching: Redis-based caching with TTL management and clustering
• Structured Logging: Configurable levels with rotation and retention
• Performance Monitoring: Memory management and parallel processing

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🔧 Configuration

Environment Variables

# AI Provider Configuration
export OPENAI_API_KEY="your-openai-key"
export ANTHROPIC_API_KEY="your-anthropic-key"
export MCP_PREFERRED_AI_PROVIDER="openai"

# Redis Configuration
export REDIS_URL="redis://localhost:6379"
export REDIS_PASSWORD="your-redis-password"
export REDIS_DB="0"

# Processing Configuration
export MCP_MAX_CHUNK_SIZE="4000"
export MCP_CACHE_ENABLED="true"
export MCP_LOG_LEVEL="INFO"

# Build Configuration
export MCP_BUILD_TIMEOUT="1800"
export MCP_DEFAULT_OUTPUT_DIRECTORY="./output"

Configuration File (mcp_server.yaml)

# AI Provider Settings
ai:
  preferred_provider: "openai"
  request_timeout: 120
  max_retries: 3
  fallback_providers: ["anthropic", "azure", "local"]

# Redis Caching Configuration
cache:
  enabled: true
  redis:
    url: "redis://localhost:6379"
    password: null
    db: 0
    max_connections: 10
    retry_on_timeout: true
  ttl:
    documents: 3600      # 1 hour
    ai_responses: 7200   # 2 hours
    templates: 86400     # 24 hours
    builds: 1800         # 30 minutes

# Document Processing
processing:
  max_chunk_size: 4000
  chunk_overlap_size: 200
  supported_formats: [".docx", ".pdf", ".md", ".txt", ".html", ".pptx", ".xlsx"]
  parallel_processing: true
  max_workers: 4

# Logging
logging:
  level: "INFO"
  file_enabled: true
  rotation: "1 day"
  retention: "30 days"
  format: "%(asctime)s - %(name)s - %(levelname)s - %(message)s"

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📖 Documentation

📚 Quick Links

• 🏁 Getting Started Guide
• ⚙️ Configuration Reference
• 🗄️ Redis Integration Guide
• 🏗️ Architecture Overview
• 📚 API Documentation
• 🎯 Examples & Tutorials

🏗️ Architecture Documentation

• System Components
• Data Flow Pipeline
• AI Integration Architecture
• Caching Strategy

🎯 Examples & Use Cases

• Next.js E-commerce Platform
• Python CLI Tool
• Django REST API
• FastAPI Microservice