Introduction

If you have ever wondered how to create a robust Question-Answering system based on a local knowledge base by leveraging Large Language Models (LLMs), you are in the right place! This blog will guide you through the concept and the implementation of a project that aims to accomplish just this.

Why This Project?

• This project is modeled after Langchain-Chatchat, which serves as a foundation but lacks flexibility and seamless deployment.
• By taking insights from How to build a knowledge question answering system with a large language model, you can practice creating a user-friendly implementation.

Advantages of the Project

• Modular design: The project doesn’t depend on the Langchain library, making each component easily replaceable.
• Simple code: The simplicity aids in understanding and modifying the code.
• Deployment Flexibility: While LLMs will need separate deployment, other parts can effectively run on CPU.
• Multi-format Support: Supports a wide array of document formats including txt, md, pdf, docx, pptx, excel, etc.

Demo

If you have a Baidu Account, you can check out the online demo based on ERNIE Bot.

Documentation

For in-depth information, check out the comprehensive documentation (in Chinese).

Future Improvements (TODO)

• Support for hybrid search combining keywords and vectors.
• Create a Vue.js-based user interface.

How to Contribute

Your contributions are highly valued! Feel free to submit pull requests. Before making major changes, it is best to open an issue first to discuss your plans. Remember to update any relevant tests as well.

Troubleshooting

If you encounter issues during implementation, consider these strategies:

• Check your local environment for the correct Python version as specified (>= 3.8, <= 3.12).
• Ensure all necessary dependencies are installed and running well on your chosen OS (Linux, Win, or Mac).
• Review the project structure to ensure all modular components are properly integrated.
• For documentation-related questions, refer to the docs.

For more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.