---

title: "ServiceAI Platform Code Documentation" project: "ServiceAI Platform" module: "Core AI Engine" version: "1.0.0" author: "ServiceAI Development Team" last_updated: "2024-03-15" status: "Stable"

ServiceAI Core AI Engine Documentation

Overview

The ServiceAI Core AI Engine is the central component of the ServiceAI Platform, responsible for natural language processing, intent recognition, and automated response generation. This documentation provides detailed information about the implementation, architecture, and usage of the Core AI Engine.

Table of Contents

1. Architecture
2. Components
3. Implementation
4. API Reference
5. Examples
6. Testing
7. Dependencies
8. Contributing

Architecture

High-Level Design

The Core AI Engine follows a modular architecture with the following key components:

graph TD
    A[Input Processor] --> B[NLP Engine]
    B --> C[Intent Classifier]
    C --> D[Response Generator]
    D --> E[Output Formatter]
    B --> F[Entity Extractor]
    F --> C

Key Components

1. Input Processor

   • Handles incoming requests
   • Validates input format
   • Preprocesses text data

2. NLP Engine

   • Tokenization
   • Part-of-speech tagging
   • Dependency parsing

3. Intent Classifier

   • Intent recognition
   • Confidence scoring
   • Context management

4. Response Generator

   • Template selection
   • Dynamic content insertion
   • Response optimization

Components

Input Processor

Class Definition

class InputProcessor:
    """
    Handles the processing and validation of incoming requests.
    
    Attributes:
        supported_languages (List[str]): List of supported language codes
        max_input_length (int): Maximum allowed input length
        
    Methods:
        validate_input(text: str) -> bool
        preprocess_text(text: str) -> str
        detect_language(text: str) -> str
    """
    
    def __init__(self, config: Dict[str, Any]):
        """
        Initialize the InputProcessor.
        
        Args:
            config: Configuration dictionary containing:
                - supported_languages: List of supported language codes
                - max_input_length: Maximum allowed input length
                - preprocessing_rules: Custom preprocessing rules
        """
        self.supported_languages = config['supported_languages']
        self.max_input_length = config['max_input_length']
        self._preprocessing_rules = config['preprocessing_rules']
        
    def validate_input(self, text: str) -> bool:
        """
        Validate the input text.
        
        Args:
            text: Input text to validate
            
        Returns:
            bool: True if input is valid, False otherwise
            
        Raises:
            ValueError: If input is empty or exceeds max length
        """
        if not text:
            raise ValueError("Input text cannot be empty")
            
        if len(text) > self.max_input_length:
            raise ValueError(f"Input text exceeds maximum length of {self.max_input_length}")
            
        return True

Usage Example

# Initialize processor
config = {
    'supported_languages': ['en', 'es', 'fr'],
    'max_input_length': 1000,
    'preprocessing_rules': {}
}
processor = InputProcessor(config)

# Process input
try:
    text = "Hello, I need help with my account"
    if processor.validate_input(text):
        processed_text = processor.preprocess_text(text)
        language = processor.detect_language(text)
except ValueError as e:
    print(f"Error processing input: {e}")

NLP Engine

Class Definition

class NLPEngine:
    """
    Natural Language Processing engine for text analysis.
    
    Attributes:
        model (Model): Loaded NLP model
        tokenizer (Tokenizer): Text tokenizer
        
    Methods:
        tokenize(text: str) -> List[str]
        pos_tag(tokens: List[str]) -> List[Tuple[str, str]]
        parse_dependencies(text: str) -> Dict[str, Any]
    """
    
    def __init__(self, model_path: str):
        """
        Initialize the NLP Engine.
        
        Args:
            model_path: Path to the pre-trained model
        """
        self.model = self._load_model(model_path)
        self.tokenizer = self.model.get_tokenizer()
        
    def tokenize(self, text: str) -> List[str]:
        """
        Tokenize input text.
        
        Args:
            text: Input text to tokenize
            
        Returns:
            List of tokens
        """
        return self.tokenizer.tokenize(text)

Implementation

Core Classes

ServiceAI

class ServiceAI:
    """
    Main class for the ServiceAI Core Engine.
    
    Attributes:
        input_processor (InputProcessor): Input processing component
        nlp_engine (NLPEngine): NLP processing component
        intent_classifier (IntentClassifier): Intent classification component
        response_generator (ResponseGenerator): Response generation component
        
    Methods:
        process_request(text: str) -> Dict[str, Any]
        train(data: List[Dict[str, Any]]) -> None
        evaluate(test_data: List[Dict[str, Any]]) -> Dict[str, float]
    """
    
    def __init__(self, config: Dict[str, Any]):
        """
        Initialize the ServiceAI engine.
        
        Args:
            config: Configuration dictionary containing component settings
        """
        self.input_processor = InputProcessor(config['input_processor'])
        self.nlp_engine = NLPEngine(config['nlp_engine'])
        self.intent_classifier = IntentClassifier(config['intent_classifier'])
        self.response_generator = ResponseGenerator(config['response_generator'])
        
    def process_request(self, text: str) -> Dict[str, Any]:
        """
        Process an incoming request.
        
        Args:
            text: Input text to process
            
        Returns:
            Dictionary containing:
                - intent: Detected intent
                - confidence: Confidence score
                - response: Generated response
                - metadata: Additional processing information
        """
        # Validate and preprocess input
        self.input_processor.validate_input(text)
        processed_text = self.input_processor.preprocess_text(text)
        
        # Perform NLP analysis
        tokens = self.nlp_engine.tokenize(processed_text)
        pos_tags = self.nlp_engine.pos_tag(tokens)
        
        # Classify intent
        intent_result = self.intent_classifier.classify(processed_text)
        
        # Generate response
        response = self.response_generator.generate(
            intent_result['intent'],
            intent_result['entities']
        )
        
        return {
            'intent': intent_result['intent'],
            'confidence': intent_result['confidence'],
            'response': response,
            'metadata': {
                'tokens': tokens,
                'pos_tags': pos_tags,
                'processing_time': time.time()
            }
        }

Configuration

Default Configuration

DEFAULT_CONFIG = {
    'input_processor': {
        'supported_languages': ['en', 'es', 'fr'],
        'max_input_length': 1000,
        'preprocessing_rules': {
            'lowercase': True,
            'remove_punctuation': True,
            'normalize_whitespace': True
        }
    },
    'nlp_engine': {
        'model_path': 'models/nlp/base',
        'use_gpu': True,
        'batch_size': 32
    },
    'intent_classifier': {
        'model_type': 'transformer',
        'confidence_threshold': 0.85,
        'max_context_length': 5
    },
    'response_generator': {
        'template_path': 'templates/responses',
        'use_dynamic_templates': True,
        'max_response_length': 500
    }
}

API Reference

Public Methods

process_request

Process an incoming customer request.

def process_request(text: str) -> Dict[str, Any]:
    """
    Process a customer request and generate a response.
    
    Args:
        text: Input text from the customer
        
    Returns:
        Dictionary containing:
            - intent: Detected intent
            - confidence: Confidence score
            - response: Generated response
            - metadata: Processing information
            
    Raises:
        ValueError: If input is invalid
        ProcessingError: If processing fails
    """

train

Train the AI engine on new data.

def train(data: List[Dict[str, Any]]) -> None:
    """
    Train the AI engine on new data.
    
    Args:
        data: List of training examples, each containing:
            - text: Input text
            - intent: Correct intent
            - entities: Named entities
            - response: Expected response
            
    Raises:
        TrainingError: If training fails
    """

Examples

Basic Usage

from serviceai import ServiceAI

# Initialize the engine
config = DEFAULT_CONFIG
engine = ServiceAI(config)

# Process a request
request = "I need to reset my password"
result = engine.process_request(request)

print(f"Intent: {result['intent']}")
print(f"Confidence: {result['confidence']}")
print(f"Response: {result['response']}")

Custom Configuration

# Custom configuration
custom_config = {
    'input_processor': {
        'supported_languages': ['en'],
        'max_input_length': 500
    },
    'intent_classifier': {
        'confidence_threshold': 0.9
    }
}

# Merge with default config
config = {**DEFAULT_CONFIG, **custom_config}
engine = ServiceAI(config)

Testing

Unit Tests

import unittest

class TestServiceAI(unittest.TestCase):
    def setUp(self):
        self.engine = ServiceAI(DEFAULT_CONFIG)
        
    def test_input_validation(self):
        with self.assertRaises(ValueError):
            self.engine.process_request("")
            
    def test_intent_classification(self):
        result = self.engine.process_request("Reset password")
        self.assertEqual(result['intent'], 'password_reset')
        self.assertGreater(result['confidence'], 0.85)

Integration Tests

def test_end_to_end():
    engine = ServiceAI(DEFAULT_CONFIG)
    
    # Test basic flow
    request = "I need to reset my password"
    result = engine.process_request(request)
    
    assert result['intent'] == 'password_reset'
    assert 'response' in result
    assert len(result['metadata']['tokens']) > 0
    
    # Test error handling
    with pytest.raises(ValueError):
        engine.process_request("" * 2000)  # Exceeds max length

Dependencies

Required Packages

[dependencies]
numpy = "^1.21.0"
torch = "^1.9.0"
transformers = "^4.5.0"
spacy = "^3.1.0"
pydantic = "^1.8.0"

[dev-dependencies]
pytest = "^6.2.0"
black = "^21.5b2"
mypy = "^0.910"

System Requirements

• Python 3.8+
• CUDA 11.0+ (for GPU support)
• 8GB RAM minimum
• 2GB disk space

Contributing

Development Setup

1. Clone the repository

   git clone https://github.com/serviceai/core-engine.git
   cd core-engine

2. Create virtual environment

   python -m venv venv
   source venv/bin/activate  # Linux/Mac
   venv\Scripts\activate  # Windows

3. Install dependencies

   pip install -r requirements.txt
   pip install -r requirements-dev.txt

Code Style

• Follow PEP 8 guidelines
• Use type hints
• Document all public methods
• Maintain test coverage above 90%

Pull Request Process

1. Create feature branch
2. Make changes
3. Run tests and linting
4. Submit PR with description
5. Address review comments

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Last Updated: March 15, 2024 Version: 1.0.0