PIKE-RAG addresses the limitations of standard Retrieval Augmented Generation (RAG) systems in industrial applications by incorporating domain-specific knowledge extraction and multi-step reasoning. It targets engineers and researchers needing to build more accurate and robust RAG solutions for complex tasks, offering improved accuracy and factual grounding.

How It Works

PIKE-RAG employs a modular framework that goes beyond simple embedding-based retrieval. It focuses on context-aware segmentation, automatic term alignment, and multi-granularity knowledge extraction to enhance the understanding and retrieval of domain-specific information. This approach aims to overcome challenges posed by specialized terminology and complex reasoning chains, leading to more reliable RAG performance.

Quick Start & Requirements

• Install: Clone the repository and set up the Python environment.
• Configuration: Create a .env file for endpoint information and modify YAML configuration files.
• Resources: Refer to documentation for reproducing experiments on MuSiQue.
• Links: 🌐Online Demo, 📊Technical Report, Documentation

Highlighted Details

• Achieved 87.6% accuracy on HotpotQA, 82.0% on 2WikiMultiHopQA, and 59.6% on MuSiQue.
• Demonstrated significant improvements in industrial manufacturing, mining, and pharmaceuticals.
• Supports flexible pipeline customization for diverse industrial needs.

Maintenance & Community

• Contributions are welcomed, requiring agreement with a Contributor License Agreement (CLA).
• Adheres to the Microsoft Open Source Code of Conduct.

Licensing & Compatibility

• The repository does not explicitly state a license in the provided README.

Limitations & Caveats

The README does not specify licensing details, which could impact commercial use or integration into closed-source projects. Further investigation into the project's licensing is recommended.