# Arklex Orchestrator

The Orchestrator is the central component of the Arklex framework, responsible for managing conversation flow, task execution, and integration with various services.

## Components

### Task Graph

- Directed graph structure for conversation flow
- Intent-based node navigation
- State management and tracking
- Path history and resource management
- Integration with intent detection and slot filling

### Natural Language Understanding (NLU)

- Intent detection and classification
- Slot filling and value extraction
- Input processing and validation
- Response formatting
- Error handling

### Message Processing

- Input/output handling
- Message state management
- Context tracking
- Response generation
- Error handling

### State Management

- Conversation state tracking
- Node state management
- Resource state management
- History tracking
- State persistence

### Resource Management

- Resource allocation
- Resource tracking
- Resource cleanup
- Resource state management
- Resource limits

## Features

- Task detection through intent detection
- Slot filling and verification
- Multi-step task handling
- Global and local intent handling
- Node state management
- Path tracking
- Resource management
- Error handling
- Logging and monitoring

## Usage

```python
from arklex.orchestrator.task_graph import TaskGraph
from arklex.utils.graph_state import LLMConfig, Params

# Initialize task graph
config = {
    "nodes": [...],
    "edges": [...],
    "intent_api": {...},
    "slotfillapi": {...}
}

llm_config = LLMConfig(...)
task_graph = TaskGraph("conversation", config, llm_config)

# Process input
params = Params(...)
node_info, updated_params = task_graph.get_node({"input": "user message"})
```

## Configuration

### Task Graph Configuration

```json
{
    "nodes": [
        {
            "id": "start",
            "type": "start",
            "intents": ["greeting", "help"]
        },
        {
            "id": "task",
            "type": "task",
            "intents": ["book_flight", "check_status"]
        }
    ],
    "edges": [
        {
            "source": "start",
            "target": "task",
            "intent": "book_flight"
        }
    ],
    "intent_api": {
        "api_url": "your_api_url",
        "api_key": "your_api_key"
    },
    "slotfillapi": {
        "api_url": "your_api_url",
        "api_key": "your_api_key"
    }
}
```

## Best Practices

1. **Task Graph Design**
   - Use clear node names
   - Define clear intent mappings
   - Consider fallback paths
   - Handle edge cases

2. **Intent Detection**
   - Use clear intent names
   - Include fallback intents
   - Consider context
   - Validate confidence

3. **Slot Filling**
   - Define clear slot types
   - Validate slot values
   - Handle missing slots
   - Provide clear prompts

4. **State Management**
   - Track conversation state
   - Manage node state
   - Handle resource state
   - Persist important state

5. **Error Handling**
   - Handle API errors
   - Handle timeouts
   - Handle invalid inputs
   - Provide clear error messages

## Integration

The Orchestrator integrates with:

- NLU System
- Message Processing
- State Management
- Resource Management
- Logging System
