Local RAG Scientific Writing Assistant

A Retrieval-Augmented Generation (RAG) system that leverages existing scientific papers and research notes, and reference materials to support the drafting of scientific manuscripts.

Overview

This project implements a RAG-based pipeline that retrieves relevant information from curated scientific sources and uses it to generate context-aware assistance for academic writing tasks. It is designed to improve the quality, accuracy, and efficiency of scientific manuscript preparation, in combination with a local model.

Key Features

• Retrieval of relevant scientific literature and reference materials
• Context-aware generation for academic writing
• Support for literature review and synthesis
• Integration of curated knowledge sources for improved reliability

How It Works

1. User provides a query or writing prompt
2. The system retrieves relevant documents from a curated knowledge base
3. Retrieved context is passed to a (local) language model
4. The model generates a response grounded in the retrieved information