Peer-to-Peer Federated RAG Framework

Project Description

In today’s data-driven world, efficiently accessing and generating information across diverse domains is crucial. Our project proposes the development of a Federated Peer-to-Peer Retrieval-Augmented Generation (RAG) Network, a decentralized system where each node specializes in a specific domain. This architecture allows content hosts to join existing groups or establish new ones, creating a collaborative environment that enhances information retrieval and generation.

The network operates hierarchically, forwarding queries to the most relevant nodes based on their expertise. Responses are aggregated with a focus on confidence levels, ensuring reliable and accurate information delivery. This approach not only scales efficiently as more nodes participate but also fosters a federated knowledge graph capable of handling similarity queries, thereby improving the depth and relevance of generated content.

By leveraging this federated, peer-to-peer model, our project aims to revolutionize how information is retrieved and generated across various domains, promoting collaboration and specialization within a scalable and efficient network.

Target Audience

• Developers: Expertise in peer-to-peer networking, distributed systems, and RAG models.
• Data Scientists: Experience in knowledge graph construction and query optimization.
• Domain Experts: Specialists in various fields to contribute to node specialization.
• Infrastructure Support: Resources for hosting nodes and managing network operations.

Hackathon Goals

• Design and implement a peer-to-peer network architecture: Develop a decentralized framework that allows nodes to join, leave, and communicate efficiently within the network.
• Develop hierarchical query routing mechanisms: Create algorithms to forward queries to the most relevant specialized nodes or groups based on their domain expertise.
• Implement confidence-based result aggregation: Establish methods to aggregate responses from various nodes, prioritizing results with higher confidence scores to ensure reliability.
• Create a federated knowledge graph: Integrate data from participating nodes to form a comprehensive knowledge graph that supports similarity queries and enhances information retrieval.
• Ensure interoperability and scalability: Design the system to support various data formats and scale efficiently as more nodes and data are added.