This project implements a recursive Retrieval-Augmented Generation (RAG) system that leverages DeepSeek's R1 reasoning capabilities for agentic information retrieval, filtering, and synthesis. It is designed for users needing to answer complex questions by intelligently navigating and processing information from a knowledge base.

How It Works

The system employs a recursive RAG approach, where an agent iteratively retrieves documents, evaluates their relevance using R1 reasoning, discards irrelevant information, and synthesizes the remaining content to construct a comprehensive answer. This method aims to improve answer accuracy and reduce noise by actively managing the retrieval and synthesis process.

Quick Start & Requirements

• Install via pip: pip install r1-reasoning-rag
• Requires Python 3.10+.
• Access to DeepSeek's R1 model is necessary.
• See official documentation for detailed setup.

Highlighted Details

• Agentic retrieval and discarding of information.
• Recursive RAG architecture for complex question answering.
• Utilizes DeepSeek's R1 reasoning for enhanced relevance assessment.

Maintenance & Community

• Project maintained by deansaco.
• Community channels and roadmap information are not explicitly detailed in the README.

Licensing & Compatibility

• The project is licensed under the MIT License.
• Compatible with commercial use and closed-source applications.

Limitations & Caveats

The effectiveness of the system is highly dependent on the performance and accessibility of the DeepSeek R1 model. The recursive nature may lead to increased computational cost and latency for very complex queries.