AI Debate Chat

A powerful Python package for creating AI-powered debate and chat applications with advanced knowledge retrieval capabilities.

Installation

pip install ai-debate-chat

Features

• Flexible AI Models: Support for multiple AI models including OpenAI, Claude, DeepSeek, local models, and G4F (GPT for Free)
• Smart Information Retrieval: Automatic research on debate topics using Wikipedia, Google, and DuckDuckGo
• Vector Store Database: Efficient storage and retrieval of debate knowledge using FAISS and embeddings
• Conversational Memory: Manages conversation history for contextual responses
• Multi-Perspective Analysis: Presents various viewpoints on complex topics
• Interactive Mode: Built-in interactive terminal interface for immediate use

Quick Start

from ai_debate_chat.debate_chat import AIDebateBot

# Initialize with OpenAI (requires API key)
bot = AIDebateBot(
    topic="Climate Change",
    model_choice="openai",
    api_key="your-api-key"
)

# Generate a response to a question
response = bot.generate_response("What are the main arguments for carbon taxes?")
print(response)

# Or use the interactive mode
bot.run_interactive()

Advanced Usage

Model Options

# Use Claude AI
bot = AIDebateBot(topic="Artificial Intelligence Ethics", model_choice="claude", api_key="your-anthropic-key")

# Use a local model
bot = AIDebateBot(topic="Space Exploration", model_choice="local", local_model_path="path/to/your/model")

# Use G4F (GPT for Free)
bot = AIDebateBot(topic="Quantum Computing", model_choice="g4f")

Custom Memory Management

from langchain.memory import ConversationBufferMemory

# Create custom memory
memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)

# Initialize bot with custom memory
bot = AIDebateBot(
    topic="Cryptocurrency",
    model_choice="openai",
    api_key="your-api-key",
    memory=memory,
    max_memory_items=15  # Control memory size
)

# Clear memory when needed
bot.clear_memory()

ML Pipeline Details

The package includes a sophisticated machine learning pipeline that:

1. Collects information from multiple sources:
   • Wikipedia articles
   • Google search results
   • DuckDuckGo search results
2. Processes content:
   • Text cleaning and preprocessing
   • Removal of non-essential elements
   • Natural language processing with NLTK
3. Creates knowledge embeddings:
   • Document chunking for better retrieval
   • FAISS vector storage for efficient similarity search
   • HuggingFace embeddings for semantic understanding

Requirements

• Python 3.10 or higher
• Key dependencies:
  • langchain-community
  • langchain-huggingface
  • langchain-text-splitters
  • faiss-cpu
  • transformers
  • g4f (optional)
  • sentence-transformers
  • torch
  • nltk
  • beautifulsoup4

License

MIT License

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.