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README.md
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## Introduction
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Question/Answering on scientific documents using LLMs
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Differently to most of the
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**Demos**:
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- (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/
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## Introduction
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Question/Answering on scientific documents using LLMs: ChatGPT-3.5-turbo, Mistral-7b-instruct and Zephyr-7b-beta.
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The streamlit application demonstrate the implementaiton of a RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS (National Institute for Materials Science), in Tsukuba, Japan.
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Differently to most of the projects, we focus on scientific articles.
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We target only the full-text using [Grobid](https://github.com/kermitt2/grobid) that provide and cleaner results than the raw PDF2Text converter (which is comparable with most of other solutions).
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Additionally, this frontend provides the visualisation of named entities on LLM responses to extract <span stype="color:yellow">physical quantities, measurements</span> (with [grobid-quantities](https://github.com/kermitt2/grobid-quantities)) and <span stype="color:blue">materials</span> mentions (with [grobid-superconductors](https://github.com/lfoppiano/grobid-superconductors)).
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The conversation is backed up by a sliding window memory (top 4 more recent messages) that help refers to information previously discussed in the chat.
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**Demos**:
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- (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/
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