Multi-modal Actions

Table of Contents

1. Introduction
2. Project Structure
3. Core Components
4. Architecture Overview
5. Detailed Component Analysis
6. Dependency Analysis
7. Performance Considerations
8. Troubleshooting Guide
9. Conclusion

Introduction

This document provides a comprehensive analysis of the multi-modal actions system within the RAVANA repository. The system enables the processing and analysis of non-text content such as images and audio, and supports cross-modal integration for advanced cognitive functions. The architecture leverages external AI services, particularly Google's Gemini, to extract semantic meaning from visual and auditory data. These capabilities are integrated into a broader autonomous agent framework that supports decision-making, knowledge management, and emotional intelligence. The system is designed to handle both individual media files and batch processing of directories, with robust error handling and performance optimization features.

Project Structure

The multi-modal functionality is distributed across several key modules in the RAVANA project. The core action definitions are located in the core/actions directory, while the actual processing logic resides in the services module. The enhanced action manager orchestrates the execution flow and integrates multi-modal capabilities with the broader agent system. Additional multi-modal memory functionality is implemented in the episodic memory module, providing persistent storage and retrieval of processed media content.

Referenced Files in This Document

• core/actions/multi_modal.py
• services/multi_modal_service.py
• core/enhanced_action_manager.py
• core/llm.py
• core/config.json
• modules/episodic_memory/memory.py
• modules/episodic_memory/multi_modal_service.py