Our current RAG (Retriever-Augmented Generation) pipeline, primarily designed for generic tasks, is facing significant challenges, particularly in its application to scientific and research workflows. There are notable issues in its stability and effectiveness, necessitating a complete overhaul.

A potential direction for this redesign is the integration of a framework like Llama Index, which could provide advanced capabilities in document retrieval and processing. However, the main task at hand is to fundamentally rethink our approach to constructing such a pipeline, especially tailored for scientific contexts.

Key considerations for this redesign include:

1. Document Management: We currently have a mix of uploaded documents and saved references from Semantic Scholar. A major part of this redesign will involve devising a strategy to unify these diverse sources into a cohesive system, ensuring seamless access and processing.
2. AI Accessibility: The pipeline should allow the AI to access the user’s documents directly. This implies building robust and secure pathways for AI-user document interaction.
3. Performance Optimization: Speed is crucial. The new pipeline should be optimized for rapid retrieval and processing without compromising on accuracy or reliability.
4. Citation and Referencing: The AI must be capable of properly citing answers, drawing from both the user’s documents and external references. This requires an intelligent and context-aware citation mechanism.
5. Integration of Llama Index: Explore the feasibility and benefits of integrating Llama Index or similar frameworks. This could potentially enhance the pipeline’s capabilities in handling complex scientific data and queries.

This project is critical for advancing our AI’s ability to interact with and process scientific documents effectively. We are looking for contributors with expertise in AI, RAG, and document management systems, particularly those who have a keen interest in applying these technologies in scientific research contexts. The goal is to build a RAG pipeline that is not only bug-free and stable but also sophisticated in its handling of scientific data and user interaction.