🌲 Silvanus Platform - Technical Specification

{
    "header": {
        "alg": "RS256",
        "typ": "JWT",
        "kid": "key_id"
    },
    "payload": {
        "sub": "user_uuid",
        "email": "user@archwood.com",
        "roles": ["admin", "user"],
        "departments": ["finance", "operations"],
        "iat": 1234567890,
        "exp": 1234571490,
        "iss": "silvanus.archwood.com",
        "aud": "silvanus-api"
    },
    "signature": "..."
}
                

-- Key PostgreSQL Configuration Parameters
max_connections = 200
shared_buffers = 8GB
effective_cache_size = 24GB
maintenance_work_mem = 2GB
checkpoint_completion_target = 0.9
wal_buffers = 16MB
default_statistics_target = 100
random_page_cost = 1.1
effective_io_concurrency = 200
work_mem = 20MB
min_wal_size = 1GB
max_wal_size = 4GB

-- Connection Pooling (PgBouncer)
pool_mode = transaction
max_client_conn = 1000
default_pool_size = 25
reserve_pool_size = 5
reserve_pool_timeout = 3
server_lifetime = 3600
                

class SilvanusAgent:
    """
    Main AI Agent implementation for Silvanus platform
    """
    
    def __init__(self):
        self.llm = AzureFoundryAI(
            endpoint=AZURE_AI_ENDPOINT,
            api_key=AZURE_AI_KEY,
            model="gpt-4-custom-silvanus"
        )
        self.intent_classifier = IntentClassifier()
        self.action_executor = ActionExecutor()
        self.context_manager = ContextManager()
    
    async def process_request(self, user_input: str, user_context: dict) -> dict:
        """
        Process user request through AI pipeline
        """
        # 1. Extract intent and entities
        intent = await self.intent_classifier.classify(user_input)
        entities = await self.extract_entities(user_input)
        
        # 2. Retrieve context
        context = await self.context_manager.get_context(
            user_id=user_context['user_id'],
            conversation_id=user_context['conversation_id']
        )
        
        # 3. Generate response
        response = await self.llm.generate(
            prompt=self.build_prompt(intent, entities, context),
            temperature=0.7,
            max_tokens=500
        )
        
        # 4. Execute actions if needed
        if intent.requires_action:
            action_result = await self.action_executor.execute(
                action=intent.action,
                parameters=entities
            )
            response = self.merge_action_result(response, action_result)
        
        # 5. Update context
        await self.context_manager.update_context(
            user_id=user_context['user_id'],
            interaction={"input": user_input, "response": response}
        )
        
        return response
                

{
    "workflow_id": "expense_approval",
    "name": "Expense Approval Process",
    "version": "1.0",
    "triggers": [
        {
            "type": "api",
            "endpoint": "/api/v1/workflows/expense/submit"
        }
    ],
    "stages": [
        {
            "id": "validation",
            "name": "Validate Expense",
            "type": "automated",
            "actions": [
                {
                    "type": "validate",
                    "rules": [
                        {"field": "amount", "operator": "<=", "value": 10000},
                        {"field": "receipt", "operator": "exists"}
                    ]
                }
            ],
            "on_success": "manager_approval",
            "on_failure": "rejection"
        },
        {
            "id": "manager_approval",
            "name": "Manager Approval",
            "type": "human",
            "assignee": "${submitter.manager}",
            "timeout": "48h",
            "actions": [
                {"type": "approve", "next": "finance_review"},
                {"type": "reject", "next": "rejection"},
                {"type": "request_info", "next": "validation"}
            ]
        },
        {
            "id": "finance_review",
            "name": "Finance Review",
            "type": "human",
            "assignee": "role:finance_team",
            "condition": "${amount} > 1000",
            "timeout": "72h",
            "actions": [
                {"type": "approve", "next": "payment"},
                {"type": "reject", "next": "rejection"}
            ]
        },
        {
            "id": "payment",
            "name": "Process Payment",
            "type": "automated",
            "actions": [
                {
                    "type": "integration",
                    "service": "payment_service",
                    "method": "process_expense"
                }
            ],
            "on_success": "completed",
            "on_failure": "error"
        }
    ]
}
                

import structlog

logger = structlog.get_logger()

# Standard log format
{
    "timestamp": "2026-01-15T10:30:45.123Z",
    "level": "INFO",
    "service": "user_service",
    "trace_id": "abc123xyz",
    "span_id": "def456",
    "user_id": "uuid",
    "method": "POST",
    "path": "/api/v1/users",
    "status_code": 201,
    "duration_ms": 125,
    "message": "User created successfully",
    "metadata": {
        "department": "finance",
        "roles": ["user"]
    }
}

# Log levels and usage
- DEBUG: Detailed diagnostic information
- INFO: General informational messages
- WARNING: Warning messages for potential issues
- ERROR: Error events but application continues
- CRITICAL: Critical problems causing shutdown
                

# Security headers configuration
Content-Security-Policy: default-src 'self'; script-src 'self' 'unsafe-inline' https://cdn.azure.com; style-src 'self' 'unsafe-inline';
X-Content-Type-Options: nosniff
X-Frame-Options: DENY
X-XSS-Protection: 1; mode=block
Strict-Transport-Security: max-age=31536000; includeSubDomains; preload
Referrer-Policy: strict-origin-when-cross-origin
Permissions-Policy: geolocation=(), microphone=(), camera=()
                

apiVersion: apps/v1
kind: Deployment
metadata:
  name: silvanus-api
  namespace: production
spec:
  replicas: 3
  strategy:
    type: RollingUpdate
    rollingUpdate:
      maxSurge: 1
      maxUnavailable: 0
  selector:
    matchLabels:
      app: silvanus-api
  template:
    metadata:
      labels:
        app: silvanus-api
        version: v1.0.0
    spec:
      containers:
      - name: silvanus-api
        image: archwood.azurecr.io/silvanus-api:1.0.0
        ports:
        - containerPort: 8000
        env:
        - name: DJANGO_SETTINGS_MODULE
          value: "config.settings.production"
        - name: DATABASE_URL
          valueFrom:
            secretKeyRef:
              name: silvanus-secrets
              key: database-url
        resources:
          requests:
            memory: "512Mi"
            cpu: "500m"
          limits:
            memory: "1Gi"
            cpu: "1000m"
        livenessProbe:
          httpGet:
            path: /health
            port: 8000
          initialDelaySeconds: 30
          periodSeconds: 10
        readinessProbe:
          httpGet:
            path: /ready
            port: 8000
          initialDelaySeconds: 5
          periodSeconds: 5
---
apiVersion: v1
kind: Service
metadata:
  name: silvanus-api-service
  namespace: production
spec:
  selector:
    app: silvanus-api
  ports:
  - protocol: TCP
    port: 80
    targetPort: 8000
  type: LoadBalancer
                

-- Performance-critical indexes
CREATE INDEX idx_users_email ON users(email);
CREATE INDEX idx_users_department ON users(department_id);
CREATE INDEX idx_users_manager ON users(manager_id);
CREATE INDEX idx_users_active ON users(is_active) WHERE is_active = true;

CREATE INDEX idx_sessions_user ON auth_sessions(user_id);
CREATE INDEX idx_sessions_token ON auth_sessions(token_hash);
CREATE INDEX idx_sessions_expires ON auth_sessions(expires_at) WHERE revoked = false;

CREATE INDEX idx_audit_user_time ON audit_logs(user_id, created_at DESC);
CREATE INDEX idx_audit_resource ON audit_logs(resource_type, resource_id);

CREATE INDEX idx_workflows_status ON workflows(status) WHERE status IN ('PENDING', 'IN_PROGRESS');
CREATE INDEX idx_workflows_assignee ON workflows(assignee_id, status);

-- Composite indexes for common queries
CREATE INDEX idx_users_dept_active ON users(department_id, is_active);
CREATE INDEX idx_audit_user_action_time ON audit_logs(user_id, action, created_at DESC);
                

# Required Environment Variables

# Django Settings
DJANGO_SETTINGS_MODULE=config.settings.production
SECRET_KEY=
DEBUG=False
ALLOWED_HOSTS=silvanus.archwood.com,*.archwood.internal

# Database
DATABASE_URL=postgresql://user:password@host:5432/silvanus
DATABASE_POOL_SIZE=20
DATABASE_MAX_OVERFLOW=5

# Redis
REDIS_URL=redis://:password@redis-host:6379/0
REDIS_CACHE_URL=redis://:password@redis-host:6379/1

# Azure Services
AZURE_STORAGE_ACCOUNT=archwoodstorage
AZURE_STORAGE_KEY=
AZURE_KEY_VAULT_URL=https://archwood-kv.vault.azure.net/
AZURE_AI_ENDPOINT=https://archwood.cognitiveservices.azure.com/
AZURE_AI_KEY=

# Authentication
JWT_SECRET_KEY=
JWT_ALGORITHM=RS256
JWT_ACCESS_TOKEN_LIFETIME=3600
JWT_REFRESH_TOKEN_LIFETIME=604800

# Monitoring
APPLICATION_INSIGHTS_KEY=
LOG_LEVEL=INFO

# Email
EMAIL_HOST=smtp.office365.com
EMAIL_PORT=587
EMAIL_USE_TLS=True
EMAIL_HOST_USER=silvanus@archwood.com
EMAIL_HOST_PASSWORD=

# External APIs
SLACK_WEBHOOK_URL=https://hooks.slack.com/services/xxx
TEAMS_WEBHOOK_URL=https://outlook.office.com/webhook/xxx

# Feature Flags
ENABLE_MFA=True
ENABLE_AI_AGENT=True
ENABLE_ADVANCED_ANALYTICS=False
                

// Azure Log Analytics KQL Queries

// API Response Time Analysis
requests
| where timestamp > ago(1h)
| where name startswith "POST /api/v1/"
| summarize 
    avg_duration = avg(duration),
    p95_duration = percentile(duration, 95),
    p99_duration = percentile(duration, 99),
    count = count()
    by bin(timestamp, 5m), name
| render timechart

// Error Rate by Endpoint
requests
| where timestamp > ago(24h)
| where resultCode >= 400
| summarize 
    error_count = count(),
    error_rate = count() * 100.0 / toscalar(requests | where timestamp > ago(24h) | count())
    by resultCode, name
| order by error_count desc

// User Activity Heatmap
customEvents
| where timestamp > ago(7d)
| where name == "UserAction"
| extend hour = hourofday(timestamp)
| extend day = dayofweek(timestamp)
| summarize actions = count() by day, hour
| render heatmap

// Database Query Performance
dependencies
| where timestamp > ago(1h)
| where type == "SQL"
| extend query_type = extract(@"^(\w+)", 1, data)
| summarize 
    avg_duration = avg(duration),
    max_duration = max(duration),
    count = count()
    by query_type
| order by avg_duration desc

// AI Agent Performance
customMetrics
| where timestamp > ago(1h)
| where name == "SilvanusAgent.ResponseTime"
| summarize 
    avg_response = avg(value),
    max_response = max(value),
    requests = count()
    by bin(timestamp, 5m)
| render timechart