RAVANA AGI

Conversational AI Communication Framework

Conversational AI Communication Framework

Table of Contents

  1. Introduction
  2. Project Structure
  3. Core Components
  4. Architecture Overview
  5. Detailed Component Analysis
  6. Communication Message Structure
  7. Integration Patterns
  8. Troubleshooting Guide
  9. Conclusion

Introduction

The Conversational AI Communication Framework is a modular system designed to enable seamless interaction between AI agents and external platforms such as Discord and Telegram. It serves as a communication bridge between the main RAVANA system and user-facing interfaces, facilitating task delegation, emotional intelligence exchange, and real-time notifications. The framework supports multiple communication channels, prioritized messaging, and cross-platform user management, making it suitable for complex AI coordination scenarios.

Project Structure

The Conversational AI module is organized into distinct subdirectories based on functional responsibilities. This modular design enables clear separation of concerns and facilitates independent development and testing of components.

Diagram sources

Section sources

Core Components

The framework consists of several core components that work together to enable robust conversational AI capabilities:

  • RAVANACommunicator: Central communication bridge handling message routing between the AI system and external services
  • Communication Channels: Specialized channels for different types of data flow (memory service, shared state, message queue)
  • Platform Bots: Interface adapters for Discord and Telegram platforms
  • Emotional Intelligence Module: Handles mood processing and persona management
  • Memory System: Manages chat history and user context persistence

These components are configured through a centralized JSON configuration file that defines platform settings, communication parameters, and emotional intelligence behavior.

Section sources

Architecture Overview

The Conversational AI Communication Framework follows a layered architecture with clear separation between communication, processing, and presentation layers. The system acts as an intermediary between the main RAVANA AI system and user-facing messaging platforms.

Diagram sources

Detailed Component Analysis

RAVANACommunicator Analysis

The RAVANACommunicator class serves as the central communication hub, managing all interactions between the conversational AI and the main RAVANA system. It implements a multi-channel communication strategy to ensure reliable message delivery.

Diagram sources

Section sources

Communication Flow Analysis

The framework implements a sophisticated message processing pipeline that handles various types of communications with appropriate routing and processing logic.

Diagram sources

Communication Message Structure

The framework uses a standardized message structure to ensure consistent communication across all components. The CommunicationMessage class defines the format for all inter-system messages.

Diagram sources

Section sources

Integration Patterns

The framework supports several integration patterns for connecting with the main RAVANA system and external platforms:

Task Delegation Pattern

When the conversational AI needs to delegate a task to the main system:

task = {
    "task_id": "task_123",
    "task_description": "Analyze user sentiment",
    "parameters": {
        "user_id": "user_456",
        "conversation_history": [...]
    }
}
communicator.send_task_to_ravana(task)

Thought Exchange Pattern

For sharing insights and cognitive processes between systems:

thought = {
    "thought_type": "insight",
    "payload": {
        "description": "Noticed user prefers concise responses",
        "evidence": "User frequently interrupts long messages"
    },
    "metadata": {
        "user_id": "user_456",
        "timestamp": "2024-01-15T10:30:00"
    }
}
communicator.send_thought_to_ravana(thought)

Emotional Context Synchronization

To maintain emotional continuity across system boundaries:

emotional_data = {
    "user_id": "user_456",
    "current_mood": "frustrated",
    "mood_intensity": 0.7,
    "detected_cues": ["short_responses", "exclamation_points"],
    "recommended_response_style": "empathetic"
}
communicator.send_emotional_context_to_ravana(emotional_data)

Section sources

Troubleshooting Guide

Common issues and their solutions when working with the Conversational AI Communication Framework:

Connection Issues

Symptom: RAVANACommunicator fails to start or maintain connection Solution:

  • Verify the IPC channel name matches in both systems
  • Check that required services are running
  • Ensure proper permissions for communication channels

Message Delivery Failures

Symptom: Messages are not being delivered or processed Solution:

  • Check the message queue for backlog
  • Verify message callbacks are properly registered
  • Confirm message format adheres to CommunicationMessage structure
  • Review logs for serialization errors

Platform Integration Problems

Symptom: Discord or Telegram bots not responding Solution:

  • Validate API tokens in config.json
  • Check platform-specific configuration settings
  • Ensure bot has necessary permissions in the server/channel
  • Verify network connectivity to platform APIs

Performance Issues

Symptom: High latency in message processing Solution:

  • Monitor message queue size and processing rate
  • Consider increasing message_timeout in configuration
  • Review the number of pending tasks
  • Optimize message content size

Section sources

Conclusion

The Conversational AI Communication Framework provides a robust foundation for building intelligent, multi-platform conversational agents. Its modular architecture, standardized message formats, and comprehensive error handling make it suitable for complex AI coordination scenarios. The framework effectively bridges the gap between the cognitive capabilities of the main RAVANA system and user-facing communication platforms, enabling seamless interaction through multiple channels. By following the documented integration patterns and troubleshooting guidelines, developers can successfully implement and maintain sophisticated conversational AI applications.

Referenced Files in This Document