A powerful Thai legal assistant with RAG techniques.
🚀 Installation
- Create an .env file with the following content:
OPENAI_API_KEY=YOUR_OPENAI_API_KEY
- Install the package using pip:
pip install -r requirements.txt git clone https://github.com/BetterACS/LawBuddy cd LawBuddy pip install -e .
💡 Quick Start Using OpenAI Model
from lawbuddy.rag import IterativeQueryChunking
pipeline = IterativeQueryChunking.from_openai_model(model="gpt-3.5-turbo")
pipeline.load_vector_store(path="spaces/iterative_query_chunking")
query = "โดนโกง 300 ล้านบาทไทย แต่คนโกงไม่โดนฟ้องควรทำยังไง"
response = pipeline.query(query)📚 Vector Store Management
-
Creating a New Vector Store
# Create vector store from CSV files pipeline.create_vector_store( csv_paths=["laws.csv"], save_dir="spaces/iterative_query_chunking" )
-
Loading Existing Vector Store
pipeline.load_vector_store(path="spaces/iterative_query_chunking")
🤖 Load local model
from transformers import AutoModelForCausalLM, AutoTokenizer
from lawbuddy.rag import IterativeQueryChunking
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("openthaigpt/openthaigpt1.5-7b-instruct")
tokenizer = AutoTokenizer.from_pretrained("openthaigpt/openthaigpt1.5-7b-instruct")
# Load specialized legal adapter
model.load_adapter("betteracs/lawbuddy-7b")
# Initialize pipeline with local model
pipeline = IterativeQueryChunking.from_local_model(
model_name="openthaigpt/openthaigpt1.5-7b-instruct",
model=model
)To evaluate the model performance on specific tasks or legal document types, use the following script. This example shows how to evaluate on the Civil (แพ่ง) law type.
import os
from dotenv import load_dotenv
from lawbuddy.eval import evaluate
from lawbuddy.rag import IterativeQueryChunking
# Load pipeline
pipeline = IterativeQueryChunking.from_openai_model(model="gpt-3.5-turbo")
# Load existing vector store
pipeline.load_vector_store(path="spaces/iterative_query_chunking")
# Get OpenAI API key
openai_key = os.getenv('OPENAI_API_KEY')
# Run evaluation
evaluate(pipeline, type_name='แพ่ง', model='gpt-3.5-turbo', openai_key=openai_key)The system supports various configurations for both OpenAI and local models. You can customize:
Chunk sizes for document processing Vector store parameters Model-specific settings Query processing parameters
🤝 Contributing Contributions are welcome! Please feel free to submit a Pull Request. For major changes, please open an issue first to discuss what you would like to change. 📝 License MIT License 📬 Contact For support or queries, please open an issue in the GitHub repository.
Made with ❤️ for the LawBuddy team.