Initial commit: LLM Automation Docs & Remediation Engine v2.0

Features:
- Automated datacenter documentation generation
- MCP integration for device connectivity
- Auto-remediation engine with safety checks
- Multi-factor reliability scoring (0-100%)
- Human feedback learning loop
- Pattern recognition and continuous improvement
- Agentic chat support with AI
- API for ticket resolution
- Frontend React with Material-UI
- CI/CD pipelines (GitLab + Gitea)
- Docker & Kubernetes deployment
- Complete documentation and guides

v2.0 Highlights:
- Auto-remediation with write operations (disabled by default)
- Reliability calculator with 4-factor scoring
- Human feedback system for continuous learning
- Pattern-based progressive automation
- Approval workflow for critical actions
- Full audit trail and rollback capability

src/datacenter_docs/utils/config.py

@@ -0,0 +1,60 @@
+"""
+Configuration management using Pydantic Settings
+"""
+
+from pydantic_settings import BaseSettings
+from typing import List
+from functools import lru_cache
+
+
+class Settings(BaseSettings):
+    """Application settings"""
+    
+    # MongoDB
+    MONGODB_URL: str = "mongodb://admin:password@localhost:27017"
+    MONGODB_DATABASE: str = "datacenter_docs"
+    
+    # Redis
+    REDIS_URL: str = "redis://localhost:6379/0"
+    
+    # MCP Server
+    MCP_SERVER_URL: str
+    MCP_API_KEY: str
+    
+    # Anthropic Claude API
+    ANTHROPIC_API_KEY: str
+    
+    # CORS
+    CORS_ORIGINS: List[str] = ["*"]
+    
+    # Application
+    LOG_LEVEL: str = "INFO"
+    DEBUG: bool = False
+    
+    # API Configuration
+    API_HOST: str = "0.0.0.0"
+    API_PORT: int = 8000
+    WORKERS: int = 4
+    
+    # LLM Configuration
+    MAX_TOKENS: int = 4096
+    TEMPERATURE: float = 0.3
+    MODEL: str = "claude-sonnet-4-20250514"
+    
+    # Vector Store
+    VECTOR_STORE_PATH: str = "./data/chroma_db"
+    EMBEDDING_MODEL: str = "sentence-transformers/all-MiniLM-L6-v2"
+    
+    # Celery
+    CELERY_BROKER_URL: str = "redis://localhost:6379/0"
+    CELERY_RESULT_BACKEND: str = "redis://localhost:6379/0"
+    
+    class Config:
+        env_file = ".env"
+        case_sensitive = True
+
+
+@lru_cache()
+def get_settings() -> Settings:
+    """Get cached settings instance"""
+    return Settings()

src/datacenter_docs/utils/database.py

@@ -0,0 +1,115 @@
+"""
+MongoDB Database Connection and Utilities
+"""
+
+import logging
+from typing import Optional
+from motor.motor_asyncio import AsyncIOMotorClient
+from beanie import init_beanie
+
+from .api.models import (
+    Ticket,
+    DocumentationSection,
+    ChatSession,
+    SystemMetric,
+    AuditLog
+)
+
+logger = logging.getLogger(__name__)
+
+
+class Database:
+    """MongoDB Database Manager"""
+    
+    client: Optional[AsyncIOMotorClient] = None
+    
+    @classmethod
+    async def connect_db(cls, mongodb_url: str, database_name: str = "datacenter_docs"):
+        """
+        Connect to MongoDB and initialize Beanie
+        
+        Args:
+            mongodb_url: MongoDB connection string
+            database_name: Database name
+        """
+        try:
+            # Create Motor client
+            cls.client = AsyncIOMotorClient(mongodb_url)
+            
+            # Test connection
+            await cls.client.admin.command('ping')
+            logger.info(f"Connected to MongoDB at {mongodb_url}")
+            
+            # Initialize Beanie with document models
+            await init_beanie(
+                database=cls.client[database_name],
+                document_models=[
+                    Ticket,
+                    DocumentationSection,
+                    ChatSession,
+                    SystemMetric,
+                    AuditLog
+                ]
+            )
+            
+            logger.info("Beanie ODM initialized successfully")
+            
+            # Create indexes
+            await cls._create_indexes()
+            
+        except Exception as e:
+            logger.error(f"Failed to connect to MongoDB: {e}")
+            raise
+    
+    @classmethod
+    async def _create_indexes(cls):
+        """Create additional indexes if needed"""
+        try:
+            # Beanie creates indexes automatically from model definitions
+            # But we can create additional ones here if needed
+            
+            # Text search index for tickets
+            db = cls.client.datacenter_docs
+            await db.tickets.create_index([
+                ("title", "text"),
+                ("description", "text"),
+                ("resolution", "text")
+            ])
+            
+            logger.info("Additional indexes created")
+            
+        except Exception as e:
+            logger.warning(f"Failed to create some indexes: {e}")
+    
+    @classmethod
+    async def close_db(cls):
+        """Close database connection"""
+        if cls.client:
+            cls.client.close()
+            logger.info("MongoDB connection closed")
+
+
+# Dependency for FastAPI
+async def get_database():
+    """
+    FastAPI dependency to get database instance
+    Not needed with Beanie as models are directly accessible
+    """
+    return Database.client
+
+
+# Initialize database on startup
+async def init_db(mongodb_url: str, database_name: str = "datacenter_docs"):
+    """
+    Initialize database connection
+    
+    Usage:
+        await init_db("mongodb://localhost:27017")
+    """
+    await Database.connect_db(mongodb_url, database_name)
+
+
+# Close database on shutdown
+async def close_db():
+    """Close database connection"""
+    await Database.close_db()