README.md

metadata

title: Status Law Gbot
emoji: 💬
colorFrom: yellow
colorTo: purple
sdk: gradio
sdk_version: 5.23.0
app_file: app.py
pinned: false

Status Law Assistant

An intelligent chatbot based on Hugging Face and LangChain for legal consultations using information from the Status Law company website.

📝 Description

Status Law Assistant is a smart chatbot that answers user questions about Status Law company's legal services. The bot uses RAG (Retrieval-Augmented Generation) technology to find relevant information in a knowledge base created from the official website content.

✨ Key Features

• Automatic creation and updating of knowledge base from status.law website content
• Intelligent information retrieval for query responses
• Context-aware response generation
• Advanced multilingual support:
  • Automatic language detection
  • Native language response generation
  • Built-in translation system with fallback mechanism
  • Support for 30+ languages
• Customizable text generation parameters
• Model switching system with automatic fallback
• Fine-tuning capabilities based on chat history
• Multiple model support:
  • Llama 2 7B Chat (primary): Optimized for dialogues
  • Zephyr 7B: Enhanced performance and response quality
  • Mistral 7B Instruct v0.2: Superior multilingual capabilities
  • XGLM 7.5B: Specialized cross-lingual generation model (requires paid API access)

🚀 Technologies

• LangChain: Query processing chains and knowledge base management
• Hugging Face: Language model access and hosting
• FAISS: Efficient vector search
• Gradio: User interface creation
• BeautifulSoup: Web page information extraction
• PEFT: Efficient fine-tuning using LoRA
• SentencePiece: Tokenization

🏗️ Project Structure

status-law-gbot/
├── app.py                 # Main application file
├── requirements.txt       # Project dependencies
├── config/               # Configuration files
│   ├── settings.py       # Application and model settings
│   └── constants.py      # Constants and default values
├── src/                  # Source code
│   ├── analytics/        # Analytics module
│   │   └── chat_analyzer.py
│   ├── knowledge_base/   # Knowledge base management
│   │   ├── loader.py
│   │   └── vector_store.py
│   └── training/         # Training module
│       ├── fine_tuner.py
│       └── model_manager.py
└── data/               # Data storage
    ├── vector_store/   # FAISS vector storage
    │   ├── index.faiss
    │   └── index.pkl
    ├── chat_history/   # Conversation logs
    │   └── logs.json
    └── fine_tuned_models/  # Fine-tuned model storage
        └── model_registry.json

💾 Data Storage

Vector Store

• data/vector_store/index.faiss: FAISS vector store for document embeddings
• data/vector_store/index.pkl: Metadata and configuration for vector store

Chat History

• data/chat_history/logs.json: JSON file containing chat history and metadata

Models

• src/models/fine_tuned/: Directory for storing fine-tuned models
• src/models/registry.json: Model registry and configuration

🛠️ Setup

1. Clone the repository:

git clone https://github.com/PtOlga/status-law-gbot.git
cd status-law-gbot

2. Install dependencies:

pip install -r requirements.txt

3. Set up environment variables:

cp .env.example .env
# Edit .env with your configuration, including HUGGINGFACE_TOKEN

4. Run the application:

python app.py

🔧 Model Fine-tuning

To fine-tune the model on your chat history:

from src.training.fine_tuner import finetune_from_chat_history

success, message = finetune_from_chat_history(epochs=3)
print(message)

The fine-tuning process uses LoRA (Low-Rank Adaptation) for efficient training with minimal resource requirements.

🔄 Model Switching

The application supports multiple models with automatic fallback:

• Llama 2 7B Chat (default): Optimized for dialogues
• Zephyr 7B: Enhanced performance and response quality
• Mistral 7B Instruct v0.2: Superior multilingual capabilities
• XGLM 7.5B: Specialized cross-lingual generation model (requires paid API access)

Models can be switched dynamically through the interface or programmatically:

from src.training.model_manager import switch_to_model

switch_to_model("llama-7b")  # or "zephyr-7b", "mistral-7b", "xglm-7b"

🔗 Related Links

• Status Law Website
• Status Law Assistant on Hugging Face

📝 License

Public repository for Status Law Assistant.