BuiltByRays™

Chatbot Readme

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🚀 Overview

The CFO AI Assistant is a powerful chatbot that knows everything in your vault and can augment it with real-time insights. It provides clients with instant access to their complete financial operating system through natural language conversations.

🏗️ Architecture

Backend (Python/Flask)

  • rag-backend/chatbot.py: Main Flask application with AI chatbot logic
  • rag-backend/embedder.py: Document embedding and vector search functionality
  • rag-backend/loader.py: Document loading from vault directory
  • rag-backend/requirements.txt: Python dependencies

Frontend (HTML/CSS/JavaScript)

  • electron-app/chat.html: Chat interface
  • electron-app/chat-styles.css: Chat UI styling
  • electron-app/chat.js: Chat functionality and API integration

Startup Scripts

  • start_chatbot.py: Main startup script with dependency management
  • start_chatbot.bat: Windows batch file
  • start_chatbot.sh: Linux/Mac shell script

🎯 Features

Core Functionality

  • RAG (Retrieval-Augmented Generation): Queries vault knowledge base
  • Real-time Chat: Instant responses with typing indicators
  • Vault Integration: Access to all 19 vault sections
  • Conversation History: Maintains context across sessions
  • Voice Input: Speech-to-text capability
  • File Attachments: Support for document uploads

AI Capabilities

  • Context-Aware Responses: Uses relevant vault content
  • Multi-Section References: Can reference multiple vault sections
  • Confidence Scoring: Indicates response confidence level
  • Vault Augmentation: Suggests content updates when needed

User Interface

  • Modern Design: Beautiful, responsive chat interface
  • Mobile-Friendly: Works on all device sizes
  • Vault Panel: Quick access to vault sections
  • Status Indicators: Connection and typing status
  • Smooth Animations: Professional user experience

🛠️ Installation & Setup

Prerequisites

  • Python 3.8 or higher
  • Node.js (for Electron app)
  • Git

Quick Start

  1. Clone the repository:

    git clone <repository-url>
cd BuiltByRAVE_CFO_OS

  2. Start the chatbot:

    python start_chatbot.py

  3. Access the chat interface:

    • Web interface: http://localhost:5000
    • Electron app: Open electron-app/chat.html

Manual Setup

  1. Install Python dependencies:

    cd rag-backend
pip install -r requirements.txt

  2. Set up environment variables:

    export OPENAI_API_KEY="your-openai-api-key"

  3. Start the Flask server:

    python chatbot.py

📁 Vault Integration

The chatbot has access to all vault sections:

  • 00-Home: Welcome and navigation
  • 01-Scope: Service scope and agreements
  • 02-KPIs_Goals: Key performance indicators
  • 03-Payment_Incentives: Payment terms and structures
  • 04-Scenarios_UseCases: Real-world scenarios
  • 05-FAQ: Common questions and answers
  • 06-Redline_Agreement: Legal agreements
  • 07-Roadmap: Strategic roadmap
  • 08-Engagements: Active and completed engagements
  • 09-Financials: Financial data and analysis
  • 10-Business_Development: Growth strategies
  • 11-Marketing: Marketing strategies
  • 12-Operations: Operational processes
  • 13-Human_Resources: HR and team management
  • 14-Technology: Tech infrastructure
  • 15-Legal_Compliance: Legal and compliance
  • 16-Strategic_Initiatives: Strategic projects
  • 17-Client_Relations: Client relationship management
  • 18-Performance_Analytics: Performance metrics

🔧 API Endpoints

Chat Endpoints

  • POST /chat: Send message to AI assistant
  • GET /health: Health check and status
  • POST /vault/query: Direct vault queries
  • POST /vault/update: Update vault knowledge base

Request Format

{
  "message": "What are my current KPIs?",
  "client_context": "CFO OS Client - BuiltByRays™"
}

Response Format

{
  "response": "Based on your vault, your current KPIs include...",
  "vault_sections_referenced": ["02", "18"],
  "confidence": "high"
}

🎨 Customization

Styling

  • Modify chat-styles.css for visual changes
  • Update color schemes in CSS variables
  • Customize animations and transitions

Functionality

  • Extend chatbot.py for additional AI features
  • Modify embedder.py for different vector databases
  • Update chat.js for enhanced frontend features

Vault Integration

  • Add new vault sections in the loader
  • Update section descriptions in the frontend
  • Modify AI prompts for specific use cases

🔒 Security & Privacy

Data Protection

  • All conversations are logged locally
  • No sensitive data is stored externally
  • API keys are managed securely

Access Control

  • Client-specific contexts
  • Vault section permissions
  • Conversation history management

📊 Analytics & Logging

Conversation Logs

  • Timestamp and user messages
  • AI responses and confidence levels
  • Vault sections referenced
  • Error tracking and debugging

Performance Metrics

  • Response times
  • Vault query success rates
  • User engagement patterns
  • Feature usage statistics

🚀 Deployment

Local Development

python start_chatbot.py

Production Deployment

  1. Set up a production server
  2. Configure environment variables
  3. Use a process manager (PM2, systemd)
  4. Set up reverse proxy (nginx)
  5. Configure SSL certificates

Docker Deployment

FROM python:3.9-slim
WORKDIR /app
COPY rag-backend/ .
RUN pip install -r requirements.txt
EXPOSE 5000
CMD ["python", "chatbot.py"]

🔧 Troubleshooting

Common Issues

  1. Connection Failed

    • Check if Flask server is running
    • Verify port 5000 is available
    • Check firewall settings

  2. Vault Not Loading

    • Ensure vault directory exists
    • Check file permissions
    • Verify document formats (.md, .txt)

  3. AI Responses Not Working

    • Verify OpenAI API key
    • Check internet connection
    • Review API rate limits

  4. Chat Interface Issues

    • Clear browser cache
    • Check JavaScript console
    • Verify CORS settings

Debug Mode

export FLASK_DEBUG=1
python chatbot.py

📈 Future Enhancements

Planned Features

  • Multi-language Support: International client support
  • Advanced Analytics: Business intelligence dashboard
  • Integration APIs: Connect with external systems
  • Mobile App: Native iOS/Android applications
  • Voice Assistant: ElevenLabs integration
  • Document Analysis: PDF and document processing

Technical Improvements

  • Vector Database: Pinecone or Weaviate integration
  • Model Optimization: Fine-tuned models for CFO domain
  • Caching: Redis for improved performance
  • Scalability: Kubernetes deployment
  • Monitoring: Prometheus and Grafana

🤝 Contributing

  1. Fork the repository
  2. Create a feature branch
  3. Make your changes
  4. Test thoroughly
  5. Submit a pull request

📞 Support

For technical support or questions:

  • Check the troubleshooting section
  • Review the logs in chatbot_logs.json
  • Contact the development team

📄 License

This project is proprietary software for BuiltByRays™ CFO OS clients.

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