Adds AI concepts roadmap documentation

Introduces a roadmap outlining advanced AI/ML concepts to enhance the existing AI agent.

The roadmap details potential implementations, use cases, file structures, and suggested learning resources for concepts like RLHF, multi-agent systems, memory systems, planning, neural information retrieval, computer vision, code generation, adversarial AI, meta-learning, and causal AI.

It also includes practical implementation suggestions and prioritizes these concepts into phases to provide a structured approach for future development.

Author: nullchimp

docs/ai-roadmap.md

@@ -0,0 +1,177 @@
+# AI Concepts Roadmap
+
+This document outlines advanced AI concepts that would complement the existing implementation and provide valuable learning opportunities.
+
+## Current Implementation Overview
+
+The AI agent currently includes:
+- LLM integration with chat capabilities
+- RAG (Retrieval-Augmented Generation) with Memgraph
+- MCP (Model Context Protocol) integration
+- Session management and persistence
+- Web-based UI with real-time chat
+- Tool system with dynamic loading
+- Authentication and API endpoints
+
+## 🧠 Advanced AI/ML Concepts to Implement
+
+### 1. Reinforcement Learning from Human Feedback (RLHF)
+- **Implementation**: Add a feedback system where users can rate responses
+- **Learning**: Train a reward model to improve response quality over time
+- **Files to create**: `src/core/rlhf/` with reward modeling and preference learning
+- **Use case**: Continuously improve your agent's responses based on user feedback
+
+### 2. Multi-Agent Systems & Agent Orchestration
+- **Implementation**: Create specialized sub-agents for different tasks (research, coding, analysis)
+- **Coordination**: Implement agent-to-agent communication and task delegation
+- **Files to create**: `src/core/multi_agent/` with agent coordination logic
+- **Use case**: Handle complex multi-step tasks by coordinating multiple specialized agents
+
+### 3. Memory Systems & Long-term Learning
+- **Episodic Memory**: Store and retrieve past conversation experiences
+- **Semantic Memory**: Build knowledge graphs from conversations
+- **Working Memory**: Implement attention mechanisms for context management
+- **Files to extend**: `src/core/memory/` with different memory types
+- **Use case**: Enable your agent to learn and remember across sessions
+
+### 4. Planning & Reasoning Systems
+- **Chain-of-Thought**: Implement structured reasoning patterns
+- **Tree of Thoughts**: Explore multiple reasoning paths
+- **MCTS (Monte Carlo Tree Search)**: For complex decision-making
+- **Files to create**: `src/core/planning/` with reasoning algorithms
+- **Use case**: Solve complex problems requiring multi-step reasoning
+
+### 5. Neural Information Retrieval
+- **Dense Passage Retrieval**: Implement bi-encoder architecture
+- **Re-ranking Models**: Cross-encoder for better relevance scoring
+- **Hybrid Search**: Combine dense and sparse retrieval
+- **Files to extend**: `src/core/rag/retrieval/` with advanced retrieval
+- **Use case**: Dramatically improve RAG performance and relevance
+
+### 6. Computer Vision & Multimodal AI
+- **Image Understanding**: OCR, object detection, scene analysis
+- **Vision-Language Models**: Integrate CLIP-style models
+- **Document Analysis**: Parse complex documents with layout understanding
+- **Files to create**: `src/core/vision/` and `src/tools/vision_tools.py`
+- **Use case**: Analyze images, documents, and visual content
+
+### 7. Code Generation & Analysis
+- **Abstract Syntax Tree (AST) Analysis**: Deep code understanding
+- **Code Embeddings**: Vector representations of code
+- **Program Synthesis**: Generate code from natural language
+- **Files to create**: `src/tools/code_analysis/` with AST and synthesis tools
+- **Use case**: Advanced coding assistance and code review
+
+### 8. Adversarial AI & Safety
+- **Prompt Injection Detection**: Identify malicious inputs
+- **Output Safety Filtering**: Content moderation and safety
+- **Alignment Testing**: Ensure AI behavior matches intentions
+- **Files to create**: `src/core/safety/` with safety mechanisms
+- **Use case**: Build robust, safe AI systems
+
+### 9. Meta-Learning & Few-Shot Learning
+- **In-Context Learning**: Adapt to new tasks from examples
+- **Gradient-Based Meta-Learning**: MAML-style adaptation
+- **Prompt Engineering**: Automated prompt optimization
+- **Files to create**: `src/core/meta_learning/` with adaptation algorithms
+- **Use case**: Quickly adapt to new domains and tasks
+
+### 10. Causal AI & Reasoning
+- **Causal Inference**: Identify cause-effect relationships
+- **Counterfactual Reasoning**: "What if" scenario analysis
+- **Causal Discovery**: Learn causal structures from data
+- **Files to create**: `src/core/causal/` with causal reasoning
+- **Use case**: Enable sophisticated reasoning about cause and effect
+
+## 🎯 Practical Implementation Suggestions
+
+### Start with High-Impact, Low-Complexity
+1. **Memory Systems** - Extend your existing RAG to include conversation memory
+2. **Multi-Agent Coordination** - Create specialized agents for different tool categories
+3. **Advanced Retrieval** - Implement re-ranking in your existing RAG system
+
+### Medium-Term Projects
+4. **Computer Vision** - Add image analysis capabilities to your tool suite
+5. **Planning Systems** - Implement chain-of-thought reasoning
+6. **RLHF** - Add user feedback and learning mechanisms
+
+### Advanced Research Topics
+7. **Meta-Learning** - Few-shot adaptation to new domains
+8. **Causal AI** - Deep reasoning about relationships
+9. **Adversarial Safety** - Robust security measures
+
+## 🛠 Specific Next Steps
+
+Based on your current architecture, recommended starting points:
+
+1. **Enhanced Memory System**: Extend your `core/rag/` to include episodic memory
+2. **Multi-Agent Tools**: Create specialized agents in `tools/agents/`
+3. **Advanced Retrieval**: Add re-ranking to your existing Memgraph setup
+
+## Implementation Priorities
+
+### Phase 1: Foundation Enhancement (1-2 months)
+- **Memory Systems**: Episodic and semantic memory
+- **Advanced RAG**: Re-ranking and hybrid search
+- **Planning**: Basic chain-of-thought reasoning
+
+### Phase 2: Agent Intelligence (2-3 months)
+- **Multi-Agent Systems**: Specialized agent coordination
+- **Computer Vision**: Multimodal capabilities
+- **Code Analysis**: AST and program synthesis
+
+### Phase 3: Advanced Learning (3-4 months)
+- **RLHF**: Human feedback integration
+- **Meta-Learning**: Few-shot adaptation
+- **Safety Systems**: Adversarial robustness
+
+### Phase 4: Research Topics (4+ months)
+- **Causal AI**: Deep reasoning capabilities
+- **Advanced Planning**: MCTS and complex reasoning
+- **Specialized Applications**: Domain-specific implementations
+
+## Technical Architecture Considerations
+
+### Integration Points
+- Extend existing `src/core/` modules
+- Leverage current tool system for new capabilities
+- Maintain session management compatibility
+- Build on existing RAG infrastructure
+
+### Performance Considerations
+- Implement caching for expensive operations
+- Use async operations for concurrent processing
+- Consider GPU acceleration for ML models
+- Optimize memory usage for large-scale operations
+
+### Testing Strategy
+- Unit tests for each new component
+- Integration tests with existing systems
+- Performance benchmarks
+- Safety and robustness testing
+
+## Learning Resources
+
+### Recommended Reading
+- "Artificial Intelligence: A Modern Approach" - Russell & Norvig
+- "Deep Learning" - Goodfellow, Bengio & Courville
+- "Reinforcement Learning: An Introduction" - Sutton & Barto
+- "Pattern Recognition and Machine Learning" - Bishop
+
+### Online Courses
+- CS229 (Stanford) - Machine Learning
+- CS224N (Stanford) - Natural Language Processing
+- CS231N (Stanford) - Computer Vision
+- CS285 (Berkeley) - Deep Reinforcement Learning
+
+### Research Papers
+- Attention Is All You Need (Transformers)
+- BERT: Pre-training of Deep Bidirectional Transformers
+- GPT-3: Language Models are Few-Shot Learners
+- Constitutional AI: Harmlessness from AI Feedback
+
+## Conclusion
+
+This roadmap provides a structured path for implementing advanced AI concepts while building on your existing foundation. Each concept offers unique learning opportunities and practical applications that will enhance both your understanding and your AI agent's capabilities.
+
+Start with the high-impact, low-complexity implementations and gradually work toward more advanced research topics as your expertise grows.