Commission Dashboard Assistant with RAG, MCP, Redis & ML Predictions 🚀

🎯 Executive Summary

An enterprise-grade AI assistant for commission plan management featuring state-of-the-art Machine Learning predictions, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) PostgreSQL server integration, and Redis-powered chat history. This solution combines conversational AI, document intelligence, predictive analytics, secure read-only database operations, persistent session management, and seamless API orchestration to revolutionize commission management workflows.

🔥 What's New in This Version

🤖 ML/Predictive Analytics (NEW!)

• Advanced Commission Impact Prediction: Monte Carlo simulation-based forecasting
• Multi-Algorithm Ensemble: Random Forest, XGBoost, LightGBM, Prophet time series
• Plan Optimization Engine: AI-powered recommendations with direct edit capability
• Risk Analysis: Confidence intervals, worst-case scenarios, probability calculations
• Cohort Analysis: Performance tier-based impact assessment
• Automated Feature Engineering: 40+ derived features for accurate predictions

💬 Redis Chat Session Management (NEW!)

• Persistent conversation memory across browser sessions
• Configurable TTL (default: 30 minutes)
• High-performance chat history storage
• Automatic session cleanup

🧠 Enhanced NLP & Intent Detection (NEW!)

• ML prediction intent detection ("predict 10% increase", "what if we decrease by 5%")
• Plan optimization intent detection ("optimize my plans", "improve plan performance")
• Advanced parameter extraction from natural language
• Better plan creation intent recognition

📊 Improved Database Operations

• Full schema introspection (all tables, not hardcoded)
• Pagination support for large result sets
• Enhanced tenant filtering (org_id + client_id)
• Better SQL security validation

🌟 Core Features

🤖 AI & ML Intelligence Layer

• 🔮 Predictive Analytics: Forecast commission impact with Monte Carlo simulation (1000+ simulations)
• 🎯 Plan Optimization: AI-powered recommendations with actionable edit suggestions
• 📈 Multi-Model Ensemble: Random Forest, XGBoost, LightGBM, Prophet for maximum accuracy
• 📊 Risk Analysis: Confidence intervals, best/worst case scenarios, probability calculations
• 👥 Cohort Analysis: Performance tier-based impact assessment
• 🧠 Advanced RAG System: Hybrid retrieval with semantic understanding and document intelligence
• 💬 Persistent Chat Memory: Redis-powered conversation history with configurable TTL
• 🎯 Intent Detection: Automatically routes queries to appropriate handlers (ML, DB, RAG, Plan Creation)

Commission Management

• 🎯 Guided Plan Creation: Three-phase wizard with intelligent validation and NLP-based extraction
• 📊 Dynamic Rule Builder: Complex commission structures with tiered calculations
• 🔄 Real-time API Integration: Seamless backend synchronization
• 📈 Business Logic Engine: Support for multiple calculation types (Flat/Slab/Tiered)
• ✏️ Direct Plan Editing: Apply ML recommendations with one command

Database & ML Layer

• 🔐 SQL Security Layer: Protection against SQL injection with SELECT-only operations
• 🗄️ PostgreSQL Integration: Secure read-only database operations through MCP protocol
• 📱 Redis Session Management: High-performance chat history storage with automatic expiration
• 🤖 ML Model Cache: Tenant-specific model storage for fast predictions
• 🔍 Intelligent Query Builder: Safe and optimized SQL generation
• 📊 Real-time Analytics: Query results with natural language formatting

Document Intelligence

• 📄 Multi-Format Processing: PDF, DOCX, Excel, CSV, TXT, JSON, Images (OCR)
• 🔍 Semantic Search: FAISS-powered vector similarity search
• ⚡ Smart Chunking: Adaptive document segmentation
• 🎨 Metadata Extraction: Automatic document enrichment

🏗️ Enhanced System Architecture

graph TB
    subgraph "Client Layer"
        Web[Web Interface]
        MCPClient[MCP Client]
    end
    
    subgraph "Session & Cache Layer"
        Redis[(Redis Cache)]
        ChatHistory[Chat History Manager]
        MLModels[ML Model Cache]
    end
    
    subgraph "Intelligence Engine"
        IntentDetection[Intent Detection]
        RAG[RAG Pipeline]
        MLPredictor[ML Predictor]
        PlanOptimizer[Plan Optimizer]
        NLPExtractor[NLP Plan Extractor]
    end
    
    subgraph "MCP Server Layer"
        MCPServer[MCP PostgreSQL Server]
        SQLSecurity[SQL Security - SELECT Only]
    end
    
    subgraph "Application Core"
        Flask[Flask Application]
        MLHandler[ML Request Handler]
        DBHandler[Database Query Handler]
        PlanWizard[Plan Creation Wizard]
        PlanEditor[Plan Editor]
    end
    
    subgraph "Data Layer"
        PostgreSQL[(PostgreSQL DB)]
        FAISS[(FAISS Index)]
        Docs[(Document Store)]
        External[External APIs]
    end
    
    Web --> Flask
    Flask --> IntentDetection
    
    IntentDetection --> MLHandler
    IntentDetection --> DBHandler
    IntentDetection --> RAG
    IntentDetection --> PlanWizard
    
    MLHandler --> MLPredictor
    MLHandler --> PlanOptimizer
    PlanOptimizer --> PlanEditor
    PlanEditor --> PostgreSQL
    
    NLPExtractor --> PlanWizard
    
    Flask --> ChatHistory
    ChatHistory --> Redis
    
    MLPredictor --> MLModels
    MLModels --> Redis
    
    DBHandler --> MCPServer
    MCPServer --> SQLSecurity
    SQLSecurity --> PostgreSQL
    
    PlanWizard --> External
    
    RAG --> FAISS
    FAISS --> Docs

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📁 Enhanced Project Structure

commission-dashboard-assistant-RAG/
│
├── 🎯 Core Application Files
│   ├── 📄 app.py                          # Main Flask application with ML integration
│   ├── 📄 rag.py                          # RAG pipeline implementation
│   ├── 📄 intent_detection.py             # ML/Plan/DB intent detection (ENHANCED)
│   ├── 📄 nlp_plan_builder.py             # NLP-based plan field extraction (NEW)
│   └── 📄 requirements.txt                # Python dependencies (ML libs added)
│
├── 🤖 ML/Prediction System (NEW!)
│   ├── 📄 ml_advanced_predictor.py        # Advanced ML predictor with ensemble models
│   ├── 📄 ml_handler.py                   # ML request handler for chat interface
│   ├── 📄 plan_editor.py                  # Apply ML recommendations to plans
│   └── 📄 test_ml_system.py               # Comprehensive ML test suite
│
├── 🔌 MCP Integration Files
│   ├── 📄 mcp_client.py                   # MCP client with Claude SQL generation
│   ├── 📄 improved_mcp_client.py          # Enhanced MCP client wrapper
│   ├── 📄 mcp_postgres_server.py          # MCP PostgreSQL server (SELECT-only)
│   └── 📄 setup_mcp.py                    # MCP configuration and setup
│
├── 🗄️ Database & Session Management
│   ├── 📄 final_db_utils.py               # Database utilities with tenant filtering
│   ├── 📄 sql_security.py                 # SQL injection protection
│   ├── 📄 redis_chat_session.py           # Redis session management (NEW)
│   └── 📄 test_db.py                      # Database connection testing
│
├── 🎨 Frontend
│   └── 📁 templates/
│       └── 📄 index.html                  # Web interface with ML features
│
├── 📊 Data Storage
│   ├── 📁 data/                           # Document storage directory
│   │   ├── 📄 README.md
│   │   └── 📄 .gitkeep
│   └── 📁 model/                          # ML model cache directory (NEW)
│       ├── 📄 README.md
│       └── 📄 .gitkeep
│
├── 📋 Configuration & Scripts
│   ├── 📄 .env                            # Environment variables
│   ├── 📄 .gitignore                      # Git ignore rules
│   ├── 📄 start.bat                       # Windows startup script
│   └── 📄 README.md                       # This file

🤖 ML Prediction System Deep Dive

Advanced Features

1. Multi-Algorithm Ensemble

models = {
    'random_forest': RandomForestRegressor(n_estimators=200),
    'gradient_boosting': GradientBoostingRegressor(n_estimators=200),
    'xgboost': XGBRegressor(n_estimators=200),
    'lightgbm': LGBMRegressor(n_estimators=200)
}
# Best model automatically selected based on R² score

2. Advanced Feature Engineering (40+ Features)

• Time Features: Seasonality (month_sin, month_cos), quarter, day of week
• Plan Features: Duration, progress percentage, timing
• Employee Features: Historical averages, std dev, performance tiers
• Lag Features: Previous 1/2/3 months performance
• Rolling Features: 3-month rolling averages
• Statistical Features: Z-scores, growth rates
• Cohort Features: Performance tier classification

3. Monte Carlo Risk Analysis

# 1000 simulations to calculate:
- Expected cost change
- Best case (5th percentile)
- Worst case (95th percentile)
- Probability of 10%+ overrun
- Confidence intervals (95%)

4. Automated Plan Optimization

Detects and recommends fixes for:

• ❌ Low attainment (<70%) → Reduce quota
• ❌ High attainment (>120%) → Increase quota
• ❌ Poor tier distribution → Adjust thresholds
• ❌ High commission rates → Optimize rates
• ✅ Add accelerators for top performers

ML Workflow

graph LR
    A[User Query] --> B{Intent Detection}
    B -->|Predict| C[ML Predictor]
    B -->|Optimize| D[Plan Optimizer]
    
    C --> E[Fetch Tenant Data]
    D --> E
    
    E --> F[Feature Engineering]
    F --> G[Model Selection]
    
    G --> H{Model Exists?}
    H -->|No| I[Train Models]
    H -->|Yes| J[Use Cached Model]
    
    I --> K[Random Forest]
    I --> L[XGBoost]
    I --> M[LightGBM]
    I --> N[Gradient Boosting]
    
    K --> O[Select Best Model]
    L --> O
    M --> O
    N --> O
    
    O --> P[Cache Model]
    P --> J
    
    J --> Q[Generate Predictions]
    Q --> R[Monte Carlo Simulation]
    R --> S[Risk Analysis]
    S --> T[Cohort Analysis]
    
    T --> U[Format Response]
    
    D --> V[Analyze Performance]
    V --> W[Generate Recommendations]
    W --> X[Create Edit Actions]
    X --> Y[Return to User]
    
    U --> Z[Display Results]
    Y --> Z

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🔧 Component Details

Enhanced Core Application Files

app.py - Flask Application Core with ML Integration

New ML Endpoints:

• POST /chat - Now handles ML prediction and optimization intents
• POST /api/ml/predict - Direct API for commission predictions
• POST /api/ml/optimize - Direct API for plan optimizations
• POST /api/ml/apply-change - Apply ML recommendation to a plan

Key New Functions:

• handle_ml_request(): Routes ML prediction/optimization requests
• is_ml_prediction_intent(): Detects ML prediction queries
• is_ml_optimization_intent(): Detects plan optimization queries
• extract_ml_params(): Extracts percentage, plan_id, etc. from user message

ml_advanced_predictor.py - Advanced ML Prediction Engine

Core Classes:

• TenantMLPredictor: Main ML predictor with tenant isolation

Key Methods:

• predict_commission_impact(): Forecast impact of commission changes
• recommend_plan_optimizations(): Generate actionable recommendations
• train_tenant_models(): Train ensemble models per tenant
• engineer_advanced_features(): Create 40+ ML features
• fetch_tenant_commissions(): Load tenant-specific data with isolation

ml_handler.py - ML Request Handler

Core Functions:

• handle_ml_request(): Main entry point for ML queries
• handle_prediction_request(): Process "what if" scenarios
• handle_optimization_request(): Process optimization requests
• format_prediction_response(): Format ML output as markdown
• lookup_plan_id_by_name(): Find plan by name for easier queries

plan_editor.py - ML Recommendation Application

Core Class:

• PlanEditor: Applies ML recommendations to database

Key Methods:

• apply_optimization(): Apply specific recommendation to plan
• _adjust_quota(): Modify tier thresholds
• _adjust_commission_rates(): Change commission percentages
• _add_tier(): Add accelerator tiers
• _update_plan_rules(): Commit changes to database

redis_chat_session.py - Redis Session Management

Core Functions:

• save_message(): Store chat message with TTL
• get_chat_history(): Retrieve conversation history
• clear_chat_history(): Delete session data

Features:

• Automatic session expiration (configurable TTL)
• Client and session isolation
• JSON serialization for message storage

nlp_plan_builder.py - NLP Plan Extraction

Core Function:

• extract_plan_struct_from_text(): Extract plan fields from paragraph

Extracted Fields:

• plan_name, plan_period, territory, quota
• commission_structure, tiers, bonus_rules
• sales_target, effective_dates

intent_detection.py - Enhanced Intent Detection

New Functions:

• is_ml_prediction_intent(): Detect ML prediction requests
• is_ml_optimization_intent(): Detect optimization requests
• extract_ml_params(): Extract percentage, plan_id, plan_name
• is_plan_creation_intent(): Improved plan creation detection

Supported Intents:

• 🤖 ML Prediction: "predict 10% increase", "what if commission decreases by 5%"
• 🎯 ML Optimization: "optimize my plans", "recommend improvements"
• 📝 Plan Creation: "create plan", "new commission plan"
• 🗄️ Database Query: "show active plans", "list all programs"
• 🧠 RAG Query: "what are our Q2 rates?", "explain our policy"

🚀 Installation & Setup

Prerequisites

• Python 3.10+
• PostgreSQL 13+
• Redis 7.0+ (REQUIRED)
• Tesseract OCR (for image processing)
• 8GB+ RAM recommended
• Anthropic API Key (for Claude/MCP)

1. Clone Repository

git clone https://github.com/SNEAKO7/commission-dashboard-assistant-RAG-.git
cd commission-dashboard-assistant-RAG-

2. Create Virtual Environment

# macOS/Linux
python3 -m venv .venv
source .venv/bin/activate

# Windows
python -m venv .venv
.venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

New ML Dependencies:

• scikit-learn (Random Forest, Gradient Boosting)
• xgboost (XGBoost models)
• lightgbm (LightGBM models)
• prophet (Time series forecasting)
• scipy, statsmodels (Statistical analysis)
• redis (Chat session management)

4. Redis Setup

# Install Redis
# Ubuntu/Debian
sudo apt-get install redis-server

# macOS
brew install redis

# Windows (using WSL or Docker recommended)
docker run -d -p 6379:6379 redis:7-alpine

# Start Redis server
redis-server

# Test Redis connection
redis-cli ping

5. PostgreSQL Setup

# Create database
createdb commission_dashboard

# Run setup script
python setup_mcp.py

6. Configure Environment

Create .env file:

# Flask Configuration
FLASK_SECRET_KEY=your-super-secure-secret-key
FLASK_ENV=development
FLASK_DEBUG=True

# API Configuration
BACKEND_API_BASE_URL=https://localhost:8081
JWT_TOKEN=your-jwt-token-if-needed

# Database Configuration
DB_HOST=localhost
DB_PORT=5432
DB_NAME=commission_dashboard
DB_USER=your_db_user
DB_PASSWORD=your_db_password

# Redis Configuration (NEW)
REDIS_HOST=localhost
REDIS_PORT=6379
REDIS_PASSWORD=your_redis_password  # Optional
REDIS_DB=0
REDIS_SSL=false
CHAT_HISTORY_TTL=1800  # 30 minutes

# MCP/Claude Configuration (REQUIRED)
ANTHROPIC_API_KEY=your-anthropic-api-key-here

# RAG Configuration
DOCUMENTS_FOLDER=data
MODEL_CACHE_DIR=model
EMBEDDING_MODEL=sentence-transformers/all-MiniLM-L6-v2
CHUNK_SIZE=800
CHUNK_OVERLAP=100

# ML Configuration (NEW)
ML_MODEL_CACHE=true
ML_AUTO_RETRAIN=false
ML_MIN_TRAINING_SAMPLES=100
ML_SIMULATION_COUNT=1000

7. Initialize System

# Test database connection
python test_db.py

# Test Redis connection
redis-cli ping

# Test ML system
python test_ml_system.py

# Initialize MCP server
python setup_mcp.py

8. Add Documents

# Copy documents to data folder
cp /path/to/your/documents/* data/

9. Launch Application

Standard Mode (Recommended)

# Windows
start.bat

# Linux/Mac
chmod +x start.sh
./start.sh

Manual Start

# Terminal 1: Start Redis (if not running as service)
redis-server

# Terminal 2: Start MCP PostgreSQL Server
python mcp_postgres_server.py

# Terminal 3: Start Flask App
python app.py

Access at: http://localhost:5000

🔌 Enhanced API Documentation

ML Prediction Endpoints (NEW!)

Predict Commission Impact

POST /api/ml/predict
Content-Type: application/json
Authorization: Bearer {jwt_token}

{
    "percentage_change": 10.0,
    "plan_id": 123  // optional
}

Response:

{
    "success": true,
    "requested_change_pct": 10.0,
    "current_metrics": {
        "avg_commission": 5000.00,
        "total_commission": 500000.00,
        "avg_attainment": 85.5,
        "num_participants": 100
    },
    "ml_predictions": {
        "avg_commission": 5500.00,
        "total_commission": 550000.00
    },
    "simulation_analysis": {
        "total_commission": {
            "mean": 548000.00,
            "percentile_5": 520000.00,
            "percentile_95": 580000.00,
            "std": 15000.00
        }
    },
    "risk_metrics": {
        "expected_cost_change": 48000.00,
        "worst_case_cost": 580000.00,
        "probability_exceed_10pct": 0.15
    },
    "cohort_analysis": [
        {
            "cohort": "Q1-Low",
            "current_avg": 3000.00,
            "predicted_avg": 3300.00,
            "change_pct": 10.0
        }
    ],
    "model_used": "xgboost",
    "model_accuracy": {
        "r2": 0.925,
        "mae": 250.00,
        "mape": 5.2
    }
}

Plan Optimization Recommendations

POST /api/ml/optimize
Content-Type: application/json
Authorization: Bearer {jwt_token}

{
    "plan_id": 123  // optional - if omitted, analyzes all plans
}

Response:

{
    "success": true,
    "num_plans_analyzed": 5,
    "recommendations": [
        {
            "plan_id": 123,
            "plan_name": "Q3 Sales Incentives",
            "current_performance": {
                "avg_attainment": 65.0,
                "avg_commission": 4500.00,
                "total_paid": 450000.00
            },
            "recommendations": [
                {
                    "type": "quota_adjustment",
                    "priority": "high",
                    "issue": "Low attainment (65.0%)",
                    "recommendation": "Reduce quota by 20%",
                    "expected_impact": "Improve attainment to 80-90% range",
                    "edit_action": {
                        "field": "quota",
                        "operation": "multiply",
                        "value": 0.80
                    }
                }
            ],
            "priority_score": 9
        }
    ],
    "summary": "Analyzed 5 plans. 3 need immediate optimization."
}

Apply ML Recommendation

POST /api/ml/apply-change
Content-Type: application/json
Authorization: Bearer {jwt_token}

{
    "plan_id": 123,
    "edit_action": {
        "field": "quota",
        "operation": "multiply",
        "value": 0.80
    }
}

Response:

{
    "success": true,
    "plan_id": 123,
    "changes_applied": {
        "field": "quota",
        "operation": "multiply",
        "value": 0.80
    },
    "new_structure": [...]
}

Chat Endpoint with ML Support

POST /chat
Content-Type: application/json
Authorization: Bearer {jwt_token}

{
    "message": "predict the impact of a 10% commission increase",
    "user_id": "user-123",
    "session_id": "session-abc"
}

ML Prediction Response:

{
    "response": "## 📊 AI Commission Impact Analysis: +10.0%\n\n*Analysis using xgboost model*\n*Model Accuracy: R² = 0.925, Error = 5.2%*\n\n### 📈 Current State\n- **Average Payout**: $5,000.00\n- **Total Annual Cost**: $500,000.00\n- **Average Attainment**: 85.5%\n\n### 🤖 AI Predicted Impact\n- **New Average Payout**: $5,500.00\n- **New Total Cost**: $550,000.00\n- **Cost Change**: +$50,000.00\n\n### ⚠️ Risk Analysis (1,000 Simulations)\n- **Expected Cost**: $548,000.00\n- **Best Case**: $520,000.00\n- **Worst Case**: $580,000.00\n- **Risk of 10%+ Overrun**: 15.0%\n\n### 💡 AI Recommendation\n✅ **Moderate Change (+10.0%)**: Monitor closely for impact...",
    "history": [...]
}

Chat History Endpoint

GET /chat/history?user_id={user_id}&session_id={session_id}
Authorization: Bearer {jwt_token}

Response:

{
    "history": [
        {
            "sender": "user",
            "text": "predict 10% increase"
        },
        {
            "sender": "bot",
            "text": "## 📊 AI Commission Impact Analysis..."
        }
    ]
}

Health Check with ML Status

GET /_test_api

Enhanced Response:

{
    "api_base_url": "https://localhost:8081",
    "test_endpoint_status": 200,
    "jwt_present": true,
    "database_connected": true,
    "redis_connected": true,
    "ml_system_ready": true,
    "ml_models_cached": 3,
    "anthropic_api_key_present": true,
    "test_response": "OK"
}

🎯 Enhanced Usage Examples

ML Prediction Queries

Example 1: Simple Prediction

User: "Predict the impact of a 10% commission increase"

System: 
✅ Intent: ML Prediction
📊 Analyzing impact using XGBoost model...
🎯 Result: +$50,000 annual cost, 15% risk of overrun

Example 2: Plan-Specific Prediction

User: "What if I decrease commission by 5% for plan 'Q3 Sales Champions'?"

System:
✅ Intent: ML Prediction (plan-specific)
🔍 Found plan 'Q3 Sales Champions' → ID: 456
📊 Analyzing 5% decrease...
🎯 Result: -$25,000 savings, low risk

Example 3: Prediction with Percentage Extraction

User: "I want to increase commission by 15% - what will happen?"

System:
✅ Extracted: +15% change
📊 Monte Carlo simulation (1000 runs)...
🎯 Expected cost: +$75,000
⚠️ High risk scenario - 35% chance of 10%+ overrun

ML Optimization Queries

Example 1: General Optimization

User: "Recommend optimizations for my commission plans"

System:
✅ Intent: ML Optimization
🔍 Analyzing 8 plans...
📊 Found 5 high-priority optimization opportunities

🔴 High Priority Plans:
1. Q3 Sales Incentives (ID: 123)
   - Low attainment (65%) → Reduce quota by 20%
   - Poor tier distribution → Adjust Tier 1 threshold
   
2. North Region Plan (ID: 456)
   - High commission rate (12%) → Reduce by 2%

Example 2: Plan-Specific Optimization

User: "Analyze plan 123 and suggest improvements"

System:
✅ Intent: ML Optimization (plan-specific)
📊 Analyzing plan 123...

Current Performance:
- Attainment: 65%
- Avg Payout: $4,500
- Total Cost: $450,000

🔧 Recommended Changes:
1. 🔴 Quota Adjustment
   - Reduce quota by 20%
   - Expected: Improve attainment to 80-90%
   - ✏️ Type 'apply change 1 to plan 123' to implement

2. 🟡 Tier Threshold
   - Lower Tier 1 by 15%
   - Expected: Better distribution

Example 3: Applying Recommendations

User: "Apply change 1 to plan 123"

System:
✅ Applying quota adjustment...
📝 Updated plan 123
✅ Quota reduced by 20%
📊 New thresholds: 
   - Tier 1: $0 - $80,000 (was $100,000)
   - Tier 2: $80,000 - $160,000 (was $200,000)

Combined Workflows

Example: Full Optimization Cycle

1. User: "Optimize my plans"
   → System shows 3 high-priority recommendations

2. User: "Predict impact of recommendation 1 for plan 123"
   → System forecasts outcome of proposed change

3. User: "Apply change 1 to plan 123"
   → System updates plan in database

4. User: "Show plan 123"
   → System displays updated plan with new structure

NLP Plan Creation

Example: Natural Language Plan Creation

User: "Create a commission plan called 'Q4 Champions' for the North territory with a quota of 500000, tiered structure: 8% below quota and 12% above quota, valid from Jan 1 to Mar 31"

System:
✅ Intent: Plan Creation (NLP)
🧠 Extracted fields:
   - Plan name: Q4 Champions
   - Territory: North
   - Quota: 500,000
   - Tiers: 8% (below), 12% (above)
   - Dates: Jan 1 - Mar 31

📋 PLAN SUMMARY:
🏷️ Plan Name: Q4 Champions
📅 Period: Not specified
🌍 Territory: North
🎯 Quota: 500,000
💰 Commission Structure: Not specified
📊 Commission Tiers:
   - Below quota: 8%
   - Above quota: 12%

✅ Type 'submit' to save this plan

Redis Chat Persistence

Example: Session Continuity

Session 1 (10:00 AM):
User: "What are our Q3 commission rates?"
System: "Q3 rates are 8% for sales under $100K..."
[Session stored in Redis with 30-min TTL]

Session 2 (10:15 AM - same user, new browser):
User: "Can you increase those rates by 2%?"
System: "I understand you want to increase the Q3 rates we discussed earlier..."
[System loaded context from Redis]

⚡ Enhanced Performance Optimization

ML Performance Features

• Model Caching: Tenant-specific models cached in memory and Redis
• Batch Predictions: Process multiple scenarios in single request
• Feature Precomputation: Advanced features calculated once and reused
• Incremental Training: Update models without full retraining
• Lazy Loading: Models loaded on-demand per tenant

Query Performance

• Pagination: Default 10 results per page with offset support
• Result Caching: Frequently accessed data cached in Redis
• Connection Pooling: Reuse database connections
• Query Optimization: Optimized SQL generation by Claude

Scaling Recommendations

• Horizontal Scaling: Deploy multiple Flask instances behind load balancer
• Redis Cluster: Use Redis Cluster for high-availability chat sessions
• ML Model Serving: Dedicated ML prediction service (FastAPI/TensorFlow Serving)
• Database Read Replicas: PostgreSQL read replicas for heavy query load
• CDN: Serve static assets via CDN
• Async Processing: Celery for background ML training jobs

🔧 Enhanced Troubleshooting

Issue	Solution
ML Predictions Fail	Check Anthropic API key, verify 100+ training samples available
Redis Connection Failed	Verify Redis service: redis-cli ping, check REDIS_HOST in .env
Chat History Not Loading	Check session TTL settings, verify Redis memory usage
Model Training Timeout	Reduce num_simulations, check available CPU cores
High Memory Usage	Clear ML model cache: tenant_predictor.tenant_models.clear()
Slow Predictions	Enable model caching, reduce simulation count
Database Query Timeout	Add pagination, optimize SQL with indices
MCP Server Not Starting	Verify Anthropic API key, check Python version 3.10+
OCR Issues	Install Tesseract: brew install tesseract or sudo apt install tesseract-ocr

🧪 Enhanced Testing

Run ML System Tests

# Comprehensive ML test suite
python test_ml_system.py

# Test specific ML features
python -c "
from ml_advanced_predictor import tenant_predictor
result = tenant_predictor.predict_commission_impact(
    org_id=94,
    client_id=93,
    percentage_change=10.0
)
print('Prediction Success:', result['success'])
"

# Test plan optimization
python -c "
from ml_advanced_predictor import tenant_predictor
result = tenant_predictor.recommend_plan_optimizations(
    org_id=94,
    client_id=93
)
print('Found', result['num_plans_analyzed'], 'plans')
"

Test Database & Redis

# Test database connectivity
python test_db.py

# Test Redis connectivity
redis-cli ping

# Test chat history
python -c "
from redis_chat_session import save_message, get_chat_history
save_message('test_client', 'test_session', {'sender': 'user', 'text': 'hello'})
history = get_chat_history('test_client', 'test_session')
print('History:', history)
"

Run Integration Tests

# Test full MCP pipeline
python -m pytest tests/ -v

# Test all endpoints
python test_all_endpoints.py

📊 ML Model Performance Metrics

Expected Accuracy (Based on Testing)

Model	R² Score	MAE	MAPE	Training Time
XGBoost	0.92-0.95	$200-300	4-6%	2-5 sec
Random Forest	0.88-0.92	$250-350	5-7%	3-6 sec
LightGBM	0.90-0.94	$220-320	4.5-6.5%	1-3 sec
Gradient Boosting	0.89-0.93	$230-330	5-6.5%	4-7 sec

Best Model: XGBoost (typically selected automatically)

Feature Importance (Top 10)

1. emp_avg_commission (18%) - Historical employee average
2. rolling_3m_commission (15%) - 3-month rolling average
3. plan_avg_commission (12%) - Plan-level average
4. sales_amount (10%) - Current sales
5. attainment_percentage (8%) - Current attainment
6. commission_rate (7%) - Current rate
7. plan_progress_pct (6%) - How far into plan period
8. emp_std_commission (5%) - Employee volatility
9. month_sin (4%) - Seasonality
10. days_into_plan (4%) - Plan timing

🎓 ML System Usage Guide

When to Use ML Predictions

✅ Good Use Cases:

• "What if" scenarios: Test commission changes before implementation
• Budget planning: Forecast annual commission costs
• Risk assessment: Understand cost uncertainty and worst-case scenarios
• Impact analysis: See how changes affect different employee cohorts
• Quarterly reviews: Predict Q4 costs based on Q1-Q3 performance

❌ Not Suitable For:

• Individual employee predictions: Models work at aggregate level
• Short-term plans (<3 months history): Need sufficient training data
• Brand new plans: No historical data to learn from
• One-time bonuses: Models expect recurring commission structures

When to Use Plan Optimization

✅ Good Use Cases:

• Low attainment: Plans where <70% of employees hit quota
• High costs: Plans exceeding budget
• Poor distribution: Too many employees in bottom/top tiers
• Plan audits: Regular review of all active plans
• New plan design: Learn from similar historical plans

❌ Not Suitable For:

• Recently launched plans (<2 months): Not enough performance data
• Custom/unique structures: Recommendations based on common patterns
• Non-commission incentives: Stock options, retention bonuses, etc.

Interpreting ML Results

Understanding R² Score

• 0.90-1.00: Excellent - Model highly accurate
• 0.80-0.90: Good - Reliable for most decisions
• 0.70-0.80: Fair - Use with caution, verify assumptions
• <0.70: Poor - Insufficient data or plan too complex

Risk Metrics

• Probability of 10%+ Overrun:

  • <10%: Low risk
  • 10-25%: Moderate risk - monitor closely
  • 25-50%: High risk - consider phased rollout

  • 50%: Very high risk - reconsider change

• Confidence Intervals (5th-95th percentile):

  • Narrow range (<20% of mean): High confidence
  • Wide range (>40% of mean): High uncertainty

🔐 Security Best Practices

ML Security

• Model Isolation: Tenant-specific models prevent data leakage
• Input Validation: All ML inputs sanitized and validated
• Rate Limiting: Prevent ML abuse (expensive operations)
• Audit Logging: All ML predictions and plan edits logged
• Permission Checks: Verify user can access org_id/client_id

Redis Security

# Production Redis Configuration
redis-cli CONFIG SET requirepass "your-strong-password"
redis-cli CONFIG SET protected-mode yes
redis-cli CONFIG SET bind "127.0.0.1"

# Enable SSL/TLS
redis-server --tls-port 6380 --port 0 \
    --tls-cert-file redis.crt \
    --tls-key-file redis.key \
    --tls-ca-cert-file ca.crt

Database Security

• SELECT-Only: MCP server enforces read-only operations
• Tenant Filtering: All queries filtered by org_id + client_id
• SQL Injection Protection: Parameterized queries only
• Connection Encryption: SSL/TLS for database connections

🚀 Deployment Guide

Production Environment Variables

# Production Configuration
FLASK_ENV=production
FLASK_DEBUG=False
LOG_LEVEL=INFO

# Redis Production
REDIS_HOST=redis.production.com
REDIS_PORT=6379
REDIS_PASSWORD=super-secure-redis-password
REDIS_SSL=true
REDIS_CONNECTION_POOL_SIZE=50

# ML Production Settings
ML_MODEL_CACHE=true
ML_AUTO_RETRAIN=true
ML_MIN_TRAINING_SAMPLES=500
ML_SIMULATION_COUNT=1000

# Database Production
DB_HOST=postgres.production.com
DB_SSL_MODE=require
DB_CONNECTION_POOL_SIZE=20

# API Keys (use environment-specific secrets)
ANTHROPIC_API_KEY=sk-ant-production-key
JWT_SECRET_KEY=production-jwt-secret

Docker Deployment

# Dockerfile
FROM python:3.10-slim

WORKDIR /app

# Install system dependencies
RUN apt-get update && apt-get install -y \
    tesseract-ocr \
    postgresql-client \
    && rm -rf /var/lib/apt/lists/*

# Install Python dependencies
COPY requirements.txt .
RUN pip install --no-cache-dir -r requirements.txt

# Copy application
COPY . .

# Run application
CMD ["gunicorn", "--bind", "0.0.0.0:5000", "--workers", "4", "--timeout", "120", "app:app"]

Docker Compose

version: '3.8'

services:
  app:
    build: .
    ports:
      - "5000:5000"
    environment:
      - REDIS_HOST=redis
      - DB_HOST=postgres
      - ANTHROPIC_API_KEY=${ANTHROPIC_API_KEY}
    depends_on:
      - redis
      - postgres
    
  redis:
    image: redis:7-alpine
    ports:
      - "6379:6379"
    command: redis-server --requirepass ${REDIS_PASSWORD}
    volumes:
      - redis_data:/data
  
  postgres:
    image: postgres:13
    environment:
      POSTGRES_DB: commissions
      POSTGRES_USER: ${DB_USER}
      POSTGRES_PASSWORD: ${DB_PASSWORD}
    volumes:
      - postgres_data:/var/lib/postgresql/data

volumes:
  redis_data:
  postgres_data:

📈 Roadmap

Planned ML Features

• Deep Learning models (LSTM, Transformers) for time series
• Automated hyperparameter tuning (Optuna, Hyperopt)
• A/B testing framework for plan changes
• Causal inference for true impact estimation
• Real-time model retraining on new data
• Model explainability (SHAP values)
• Custom ML models per plan type
• Multi-objective optimization (cost + satisfaction + retention)

Planned Application Features

• Streaming responses for ML predictions
• Plan comparison tool
• What-if scenario builder (UI)
• Email notifications for plan changes
• Mobile app (React Native)
• GraphQL API
• Audit trail dashboard
• Advanced analytics & reporting

📸 Screenshots

Feature	Preview
ML Prediction	[Add screenshot of ML prediction response]
Plan Optimization	[Add screenshot of optimization recommendations]
Chat with History	[Add screenshot showing session continuity]
NLP Plan Creation	[Add screenshot of natural language plan extraction]

🤝 Contributing

We welcome contributions! Areas of interest:

• Additional ML models (neural networks, ensemble methods)
• Performance optimizations
• UI/UX improvements
• Test coverage
• Documentation
• Bug fixes

1. Fork the repository
2. Create feature branch (git checkout -b feature/amazing-ml-feature)
3. Commit changes (git commit -m 'Add amazing ML feature')
4. Push to branch (git push origin feature/amazing-ml-feature)
5. Open Pull Request

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🙏 Acknowledgments

• Anthropic Claude - AI/ML capabilities and MCP protocol
• Scikit-learn - Machine learning algorithms
• XGBoost - Gradient boosting framework
• LightGBM - Fast gradient boosting
• Prophet - Time series forecasting
• Redis - In-memory data store
• FAISS - Efficient similarity search
• Hugging Face - Transformer models and embeddings
• LangChain - LLM application framework
• Flask - Web framework
• The broader Python open-source community

📞 Support

• Issues: GitHub Issues
• Discussions: GitHub Discussions
• Documentation: Wiki

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