Spaces:
Running
Running
title: LightZero RAG | |
emoji: ๐ | |
colorFrom: yellow | |
colorTo: blue | |
sdk: gradio | |
sdk_version: 4.1.1 | |
app_file: app.py | |
pinned: false | |
license: apache-2.0 | |
python_version: 3.8 | |
# RAG Demo | |
English | [็ฎไฝไธญๆ(Simplified Chinese)](https://github.com/puyuan1996/RAG/blob/main/README_zh.md) | |
## Introduction | |
RAG is a demonstration project for a question-answering system based on Retrieval-Augmented Generation (RAG). | |
- It utilizes large language models such as GPT-3.5 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. | |
## rag_demo.py Features | |
- 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. | |
## app.py Features | |
- 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. | |
## How to Use | |
1. Clone the project to your local machine. | |
2. Install dependencies. | |
```shell | |
pip3 install -r requirements.txt | |
``` | |
3. 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' | |
``` | |
4. Ensure you have available documents as context or use the commented-out code snippet to download the documents you want to reference. | |
5. Run the `python3 -u rag_demo.py` file to start using the application. | |
## Example | |
```python | |
# The difference between rag_demo.py and rag_demo_v0.py is that it can output the retrieved document chunks. | |
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) | |
# Create vector store | |
retriever = create_vector_store(chunks) | |
# Set up RAG process | |
rag_chain = setup_rag_chain() | |
# 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) | |
# Get an answer without using RAG chain | |
result_without_rag = execute_query_no_rag(query=query) | |
# 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) | |
``` | |
## Project Structure | |
``` | |
RAG/ | |
โ | |
โโโ rag_demo_v0.py # RAG demonstration script without support for outputting retrieved document chunks. | |
โโโ rag_demo.py # RAG demonstration script with support for outputting retrieved document chunks. | |
โโโ app.py # Web-based interactive application built with Gradio and rag_demo.py. | |
โโโ .env # Environment variable configuration file | |
โโโ documents/ # Documents folder | |
โโโ your_document.txt # Context document | |
``` | |
## Contribution Guide | |
If you would like to contribute code to RAG, please follow these steps: | |
1. Fork the project. | |
2. Create a new branch. | |
3. Commit your changes. | |
4. Submit a Pull Request. | |
## Issues and Support | |
If you encounter any issues or require assistance, please submit a problem through the project's Issues page. | |
## License | |
All code in this repository is compliant with [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0). | |