AI Conversational System Development

In this chapter, you will learn how to develop a Chinese celebrity AI conversational system.

🎥 Tutorial Video

📋 Learning Content

1. Environment Preparation

Hugging Face Configuration

1. Visit Hugging Face to register an account
2. Generate access token:
   • Visit Token Settings Page
   • Click "New token"
   • Select "Write" permission
   • Generate token and save it

Important Note

The token is very important, please save it carefully. You will not be able to view the complete token again after closing the page.

Development Environment Configuration

Required Software

1. Python 3.8+
2. Git
3. Cursor editor

Dependency Installation

# Create virtual environment
python -m venv venv

# Activate virtual environment
# Windows:
venv\Scripts\activate
# Linux/Mac:
source venv/bin/activate

# Install dependencies
pip install gradio huggingface_hub

2. Project Creation

Hugging Face Space Setup

1. Log in to Hugging Face
2. Click "New Space"
3. Fill in basic information:
   • Owner: Your username
   • Space name: role-play-chat
   • SDK: Gradio
   • Hardware: CPU basic
   • Visibility: Public

Local Project Configuration

# Clone Space repository
git clone https://huggingface.co/spaces/your-username/role-play-chat
cd role-play-chat

# Create project structure
mkdir -p {assets,utils,tests}
touch main.py character_profiles.py dialogue_system.py

3. System Architecture Design

4. Core Functionality Implementation

Dialogue System

class DialogueSystem:
    def __init__(self):
        self.history = []
        self.current_character = None

    async def process_message(self, message: str) -> str:
        """Process user message and generate response"""
        if not self.current_character:
            return "Please select a conversation character first"

        # Add to history
        self.history.append({"role": "user", "content": message})

        # Generate response
        response = await self._generate_response(message)
        self.history.append({"role": "assistant", "content": response})

        return response

    def manage_context(self, max_length: int = 2048):
        """Manage dialogue context length"""
        context_length = sum(len(msg["content"]) for msg in self.history)

        while context_length > max_length and len(self.history) > 1:
            removed = self.history.pop(0)
            context_length -= len(removed["content"])

Character System

class Character:
    def __init__(self, name: str, profile: dict):
        self.name = name
        self.background = profile.get("background", "")
        self.personality = profile.get("personality", [])
        self.speaking_style = profile.get("speaking_style", {})

    def get_prompt(self) -> str:
        """Generate character prompt"""
        return f"""
        You are now playing {self.name}.
        Background: {self.background}
        Personality: {', '.join(self.personality)}
        Speaking style: {self.speaking_style}
        """

5. Testing and Deployment

Testing Specifications

# tests/test_dialogue.py
import pytest
from dialogue_system import DialogueSystem

def test_dialogue_initialization():
    system = DialogueSystem()
    assert system.history == []
    assert system.current_character is None

@pytest.mark.asyncio
async def test_message_processing():
    system = DialogueSystem()
    system.current_character = Character("Test Character", {})

    response = await system.process_message("Hello")
    assert isinstance(response, str)
    assert len(system.history) == 2

Deployment Process

1. Submit code to GitHub
2. Check deployment status
3. Monitor system performance

6. Best Practices

Code Standards

# Good naming and commenting example
class DialogueManager:
    """Dialogue manager responsible for processing user input and generating responses

    Attributes:
        history (List[Dict]): Dialogue history
        character (Character): Currently selected character
        max_history_length (int): Maximum history length
    """

    def __init__(self, max_history_length: int = 100):
        self.history = []
        self.character = None
        self.max_history_length = max_history_length

Performance Optimization

from functools import lru_cache

@lru_cache(maxsize=1000)
def get_character_profile(character_id: str) -> dict:
    """Get character profile information (with caching)"""
    return load_character_profile(character_id)

# Asynchronous processing
async def process_messages(messages: list) -> list:
    """Process multiple messages in parallel"""
    tasks = [process_single(msg) for msg in messages]
    return await asyncio.gather(*tasks)

Security Practices

from pydantic import BaseModel, validator

class UserInput(BaseModel):
    message: str
    character_id: str = None

    @validator('message')
    def validate_message(cls, v):
        if len(v) > 1000:
            raise ValueError('Message length cannot exceed 1000 characters')
        if not v.strip():
            raise ValueError('Message cannot be empty')
        return v.strip()

Project Features

• Support for multi-character dialogue
• Intelligent context management
• Real-time response processing
• Complete error handling mechanism

This AI conversational system project demonstrates how to build a complete AI application, covering the entire process from environment configuration to deployment and maintenance.