Design Architecture for Browser-Based AI Agent System with Ollama Integration

[A-Hem]

Overview

Design and implement a browser-based AI agent system that observes, learns from, and eventually automates user interactions using Ollama models with multimodal capabilities.

Core Components Required

1. Browser Integration Layer

• Chrome extension/plugin development to:
  • Track and record user interactions
  • Capture DOM events and page states
  • Record navigation patterns
  • Store form inputs and interactions
  • Implement secure data handling for sensitive information

2. Multimodal Input Processing

• Video input processing:
  • Webcam feed capture
  • User gesture recognition
  • Visual context understanding
• Audio input processing:
  • Voice command recognition
  • Natural language processing
  • Context extraction from user explanations

3. Ollama Model Integration

Required models to consider:

• llama2 for NLP and command understanding
• llava for visual processing
• whisper for audio transcription
• mixtral for task orchestration

4. Persistent Memory System

• Implementation of a vector database for:
  • Long-term storage of user patterns
  • Context retrieval
  • Task templating
  • Interaction history

5. Task Automation Engine

• Pattern recognition system
• Workflow templating
• Action validation
• Safety checks
• User notification system

Technical Requirements

1. Browser Extension:

   • Chrome Manifest V3 compliance
   • Real-time interaction tracking
   • Secure data handling
   • Privacy controls

2. Local Ollama Setup:

   • Model management
   • API integration
   • Resource optimization
   • Model switching logic

3. Storage System:

   • Vector database implementation
   • Efficient query system
   • Data compression
   • Privacy-first architecture

4. Integration Architecture:

   • Microservices design
   • Event-driven communication
   • Scalable processing pipeline
   • Error handling and recovery

Implementation Phases

1. Phase 1: Core Infrastructure

   • Set up basic browser extension
   • Implement Ollama integration
   • Create basic storage system

2. Phase 2: Data Collection

   • Implement interaction tracking
   • Add video/audio capture
   • Develop data processing pipeline

3. Phase 3: Learning System

   • Pattern recognition
   • Task templating
   • Basic automation rules

4. Phase 4: Automation

   • Workflow execution
   • Safety validation
   • User notification system

Security Considerations

• User data privacy
• Secure storage
• Permission management
• Data encryption
• Automation safety checks

Success Metrics

• Accuracy of task recognition
• Speed of automation
• User satisfaction
• Resource utilization
• Error rates

Next Steps

1. Set up development environment
2. Create initial browser extension prototype
3. Implement basic Ollama integration
4. Develop storage system architecture
5. Create basic pattern recognition system