English | 简体中文(Simplified Chinese)
ZeroPal is a demonstration project for a question-answering system for LightZero based on Retrieval-Augmented Generation (RAG). Zero represents LightZero, and Pal represents a companion.
- It utilizes large language models such as Kimi and GPT-4 in conjunction with a document retrieval vector database like Weaviate to respond to user queries by retrieving relevant document contexts and leveraging the generative capabilities of the language model.
- The project also includes a web-based interactive application built with Gradio and rag_demo.py.
- Supports loading OpenAI API keys via environment variables.
- Facilitates loading local documents and splitting them into chunks.
- Allows for the creation of a vector store and the conversion of document chunks into vectors for storage in Weaviate.
- Sets up a Retrieval-Augmented Generation process, combining document retrieval and language model generation to answer user questions.
- Executes queries and prints results, with the option to use the RAG process or not.
- Creates a Gradio application where users can input questions and the application employs the Retrieval-Augmented Generation (RAG) model to find answers, displaying results within the interface.
- Retrieved contexts are highlighted in the Markdown document to help users understand the source of the answers. The application interface is divided into two sections: the top for Q&A and the bottom to display the contexts referred to by the RAG model.
- Clone the project to your local machine.
- Install dependencies.
pip3 install -r requirements.txt
- Create a
.env
file in the project root directory and add your OpenAI API key:
OPENAI_API_KEY='your API key'
QUESTION_LANG='cn' # The language of the question, currently available option is 'cn'
- Ensure you have available documents as context or use the commented-out code snippet to download the documents you want to reference.
- Run the
python3 -u rag_demo.py
file to test ZeroPal on the local command line. - Run the
python3 -u app_mqa_database.py
file to test ZeroPal on a local web page.
if __name__ == "__main__":
# Assuming documents are already present locally
file_path = './documents/LightZero_README_zh.md'
# Load and split document
chunks = load_and_split_document(file_path, chunk_size=5000, chunk_overlap=500)
# Create vector store
vectorstore = create_vector_store(chunks, model=embedding_model)
retriever = get_retriever(vectorstore, k=5)
# Set up RAG process
rag_chain = setup_rag_chain(model_name=model_name, temperature=temperature)
# Pose a question and get an answer
query = "Does the AlphaZero algorithm implemented in LightZero support running in the Atari environment? Please explain in detail."
# Use RAG chain to get referenced documents and answer
retrieved_documents, result_with_rag = execute_query(retriever, rag_chain, query, model_name=model_name,
temperature=temperature)
# Get an answer without using RAG chain
result_without_rag = execute_query_no_rag(model_name=model_name, query=query, temperature=temperature)
# Details of data handling code are omitted here, please refer to the source files in this repository for specifics
# Print and compare results from both methods
print("=" * 40)
print(f"My question is:\n{query}")
print("=" * 40)
print(f"Result with RAG:\n{wrapped_result_with_rag}\nRetrieved context is: \n{context}")
print("=" * 40)
print(f"Result without RAG:\n{wrapped_result_without_rag}")
print("=" * 40)
RAG/
│
├── rag_demo.py # RAG demonstration script with support for outputting retrieved document chunks.
├── app_mqa.py # Web-based interactive application built with Gradio and rag_demo.py.
├── app_mqa_database.py # Web-based interactive application built with Gradio and rag_demo.py. Supports maintaining the database of conversation history.
├── .env # Environment variable configuration file
└── documents/ # Documents folder
└── your_document.txt # Context document
└── database/ # Database folder
└── conversation_history.db # Database for conversation history
If you would like to contribute code to ZeroPal, please follow these steps:
- Fork the project.
- Create a new branch.
- Commit your changes.
- Submit a Pull Request.
If you encounter any issues or require assistance, please submit a problem through the project's Issues page.
All code in this repository is compliant with Apache License 2.0.