lfoppiano/document-insights-qa repository overview

⁠DocumentIQA: Scientific Document Insight QA

⁠Introduction

Question/Answering on scientific documents using LLMs (OpenAI, Mistral, LLama2, etc..). This application is the frontend for testing the RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS. Differently to most of the project, we focus on scientific articles. We target only the full-text using Grobid⁠ that provide and cleaner results than the raw PDF2Text converter (which is comparable with most of other solutions).

NER in LLM response: The responses from the LLMs are post-processed to extract physical quantities, measurements (with grobid-quantities⁠) and materials mentions (with grobid-superconductors⁠).

Demos:

• (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/⁠
• (on the Streamlit cloud): https://document-insights.streamlit.app/⁠

⁠Getting started

• Select the model+embedding combination you want ot use (for LLama2 you must acknowledge their licence both on meta.com and on huggingface. See here⁠)(Llama2 was removed due to API limitations).
• Enter your API Key (Open AI⁠ or Huggingface⁠).
• Upload a scientific article as PDF document. You will see a spinner or loading indicator while the processing is in progress.
• Once the spinner stops, you can proceed to ask your questions

⁠Run with docker

⁠Acknolwedgement

This project is developed at the National Institute for Materials Science⁠ (NIMS) in Japan in collaboration with the Lambard-ML-Team⁠.