# 🚀 Nocturnal Archive - Enhanced Capabilities

## Overview

Nocturnal Archive has been significantly enhanced with advanced AI reasoning capabilities that bring it to parity with modern AI assistants. The system now includes dynamic tool selection, multi-step reasoning, code execution, and sophisticated context management.

## 🧠 New Core Capabilities

### 1. Advanced Reasoning Engine
- **Multi-step problem decomposition**: Breaks complex problems into manageable sub-tasks
- **Dynamic strategy selection**: Chooses optimal approach based on problem type
- **Real-time execution monitoring**: Tracks progress and handles errors gracefully
- **Self-correction and refinement**: Adapts approach based on intermediate results

### 2. Dynamic Tool Framework
- **Intelligent tool selection**: Automatically chooses best tool for each task
- **Multi-language code execution**: Python, JavaScript, Bash, SQL support
- **Safe execution environment**: Sandboxed with security restrictions
- **Tool composition**: Chains multiple tools for complex workflows

### 3. Advanced Context Management
- **Long-term memory persistence**: Remembers information across sessions
- **Knowledge graph construction**: Builds entity relationships over time
- **Semantic search**: Finds relevant context using meaning, not just keywords
- **Cross-session continuity**: Maintains context across different interactions

### 4. Enhanced File Operations
- **Safe file system access**: Read, write, list, search operations
- **Content analysis**: Automatic file type detection and analysis
- **Security validation**: Prevents access to sensitive system files
- **Pattern matching**: Advanced file search capabilities
- **Workspace API**: REST endpoints documented in [`FILES_API.md`](./FILES_API.md) for listing, upload, preview, and delete workflows

## 🔧 Technical Architecture

### Service Structure
```
src/services/
├── reasoning_engine/          # Multi-step problem solving
│   ├── reasoning_engine.py   # Core reasoning logic
│   ├── problem_decomposer.py # Problem breakdown
│   ├── strategy_selector.py  # Strategy selection
│   └── execution_monitor.py  # Execution tracking
├── tool_framework/            # Dynamic tool management
│   ├── tool_manager.py       # Tool selection & execution
│   ├── code_execution_tool.py # Safe code execution
│   ├── file_operations_tool.py # File system operations
│   ├── web_search_tool.py    # Web search capabilities
│   ├── data_analysis_tool.py # Data analysis tools
│   └── api_calls_tool.py     # HTTP API integration
└── context_manager/          # Advanced context management
    ├── advanced_context.py   # Main context manager
    ├── knowledge_graph.py    # Entity relationship graph
    ├── memory_manager.py     # Long-term memory
    └── entity_tracker.py     # Entity extraction
```

## 🚀 New API Endpoints

### Reasoning Engine
- `POST /api/reasoning/solve` - Solve complex problems with multi-step reasoning
- `GET /api/reasoning/session/{session_id}` - Get reasoning session status

### Tool Framework
- `POST /api/tools/execute` - Execute tools with dynamic selection
- `GET /api/tools/available` - List available tools and capabilities
- `GET /api/tools/performance` - Get tool performance statistics

### Context Management
- `POST /api/context/process` - Process interactions and update context
- `GET /api/context/retrieve` - Retrieve relevant context for queries
- `GET /api/context/session/{session_id}` - Get session context

### Enhanced Chat
- `POST /api/enhanced-chat` - Chat with advanced reasoning capabilities

## 💡 Usage Examples

### 1. Multi-Step Problem Solving
```python
# Solve a complex problem using reasoning engine
response = await client.post("/api/reasoning/solve", json={
    "problem_description": "Analyze the performance of our web application and suggest optimizations",
    "context": {"application_type": "web", "tech_stack": "python"}
})

# The system will:
# 1. Decompose the problem into sub-tasks
# 2. Select appropriate tools (code analysis, performance monitoring, etc.)
# 3. Execute each step with monitoring
# 4. Synthesize results into actionable recommendations
```

### 2. Dynamic Tool Execution
```python
# Execute code with automatic tool selection
response = await client.post("/api/tools/execute", json={
    "task_description": "Analyze this CSV file and generate a summary report",
    "context": {"file_path": "data.csv", "analysis_type": "statistical"}
})

# The system will:
# 1. Select file_operations_tool to read the CSV
# 2. Select data_analysis_tool to analyze the data
# 3. Select code_execution_tool to generate the report
# 4. Chain the tools together for a complete solution
```

### 3. Context-Aware Conversations
```python
# Chat with persistent context
response = await client.post("/api/enhanced-chat", json={
    "message": "What did we discuss about the machine learning project?",
    "session_id": "ml_project_session",
    "use_advanced_reasoning": True
})

# The system will:
# 1. Retrieve relevant context from previous interactions
# 2. Use knowledge graph to find related entities
# 3. Apply reasoning to provide comprehensive answer
# 4. Update context with new information
```

## 🔒 Security Features

### Code Execution Safety
- **Sandboxed environment**: Code runs in isolated containers
- **Security validation**: Blocks dangerous imports and functions
- **Resource limits**: Timeouts and memory restrictions
- **Path restrictions**: Prevents access to sensitive system files

### File Operations Security
- **Path validation**: Prevents directory traversal attacks
- **Size limits**: Maximum file size restrictions
- **Extension filtering**: Only allows safe file types
- **Access control**: Blocks access to system directories

## 📊 Performance Monitoring

### Tool Performance Tracking
- **Execution time**: Track how long each tool takes
- **Success rates**: Monitor tool reliability
- **Usage patterns**: Understand which tools are most used
- **Error analysis**: Track and analyze failures

### Context Management Metrics
- **Memory usage**: Track context storage requirements
- **Search performance**: Monitor context retrieval speed
- **Graph complexity**: Track knowledge graph growth
- **Session activity**: Monitor active user sessions

## 🎯 Key Improvements Over Original

### 1. Dynamic Reasoning vs Static Responses
- **Before**: Predefined responses to specific queries
- **After**: Dynamic problem decomposition and multi-step reasoning

### 2. Tool Selection vs Manual Integration
- **Before**: Fixed service architecture with predefined workflows
- **After**: Intelligent tool selection based on task requirements

### 3. Context Awareness vs Session Isolation
- **Before**: Basic session management
- **After**: Sophisticated memory and knowledge graph management

### 4. Code Execution vs Analysis Only
- **Before**: Could only analyze and discuss code
- **After**: Can execute code, analyze files, and perform dynamic operations

## 🚀 Getting Started

### 1. Start the Enhanced Server
```bash
cd /path/to/nocturnal-archive
python src/services/enhanced_main.py
```

### 2. Test the New Capabilities
```bash
# Test reasoning engine
curl -X POST "http://localhost:8003/api/reasoning/solve" \
  -H "Content-Type: application/json" \
  -d '{"problem_description": "How can I optimize my Python code for better performance?"}'

# Test tool execution
curl -X POST "http://localhost:8003/api/tools/execute" \
  -H "Content-Type: application/json" \
  -d '{"task_description": "Read and analyze the contents of README.md"}'

# Test enhanced chat
curl -X POST "http://localhost:8003/api/enhanced-chat" \
  -H "Content-Type: application/json" \
  -d '{"message": "Help me understand machine learning concepts", "use_advanced_reasoning": true}'
```

### 3. View API Documentation
Visit `http://localhost:8003/docs` for interactive API documentation.

## 🔮 Future Enhancements

### Planned Features
- **Multi-modal reasoning**: Support for images, audio, and video
- **Advanced ML integration**: Custom model training and inference
- **Real-time collaboration**: Multi-user context sharing
- **Plugin system**: Extensible tool framework
- **Performance optimization**: Caching and parallel execution

### Integration Opportunities
- **IDE plugins**: Direct integration with development environments
- **Browser extensions**: Web-based tool access
- **Mobile apps**: Mobile-optimized interfaces
- **API marketplace**: Third-party tool integration

## 📈 Success Metrics

### Technical Metrics
- **Response time**: < 2 seconds for most operations
- **Tool success rate**: > 95% for standard operations
- **Context accuracy**: > 90% relevance for context retrieval
- **Memory efficiency**: < 100MB per active session

### User Experience Metrics
- **Problem resolution**: > 80% of complex problems solved
- **User satisfaction**: > 4.5/5 rating
- **Feature adoption**: > 60% of users using enhanced features
- **Session retention**: > 70% of users return within 24 hours

---

**Nocturnal Archive Enhanced** - Now with the reasoning capabilities of modern AI assistants! 🚀
