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Browse files- README.md +4 -2
- streamlit_app.py +3 -2
README.md
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# DocumentIQA: Scientific Document Insight QA
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## Introduction
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Question/Answering on scientific documents using LLMs (OpenAI, Mistral, ~~LLama2,~~ etc..).
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This application is the frontend for testing the RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS.
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Differently to most of the project, we focus on scientific articles
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**
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**Demos**:
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- (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/
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# DocumentIQA: Scientific Document Insight QA
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**Work in progress** :construction_worker:
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## Introduction
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Question/Answering on scientific documents using LLMs (OpenAI, Mistral, ~~LLama2,~~ etc..).
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This application is the frontend for testing the RAG (Retrieval Augmented Generation) on scientific documents, that we are developing at NIMS.
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Differently to most of the project, we focus on scientific articles. 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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**NER in LLM response**: The responses from the LLMs are post-processed to extract <span stype="color:yellow">physical quantities, measurements</span> and <span stype="color:blue">materials</span> mentions.
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**Demos**:
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- (on HuggingFace spaces): https://lfoppiano-document-qa.hf.space/
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streamlit_app.py
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@@ -177,6 +177,7 @@ with st.sidebar:
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st.markdown(
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"""After entering 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.""")
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if st.session_state['git_rev'] != "unknown":
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st.markdown("**Revision number**: [" + st.session_state[
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'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")")
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# for entity in entities:
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# entity
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decorated_text = decorate_text_with_annotations(text_response.strip(), entities)
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decorated_text = decorated_text.replace('class="label material"', 'style="color:
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decorated_text = re.sub(r'class="label[^"]+"', 'style="color:
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st.markdown(decorated_text, unsafe_allow_html=True)
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text_response = decorated_text
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else:
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st.markdown(
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"""After entering 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.""")
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st.markdown('**NER on LLM responses**: The responses from the LLMs are post-processed to extract <span style="color:orange">physical quantities, measurements</span> and <span style="color:green">materials</span> mentions.', unsafe_allow_html=True)
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if st.session_state['git_rev'] != "unknown":
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st.markdown("**Revision number**: [" + st.session_state[
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'git_rev'] + "](https://github.com/lfoppiano/document-qa/commit/" + st.session_state['git_rev'] + ")")
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# for entity in entities:
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# entity
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decorated_text = decorate_text_with_annotations(text_response.strip(), entities)
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decorated_text = decorated_text.replace('class="label material"', 'style="color:green"')
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decorated_text = re.sub(r'class="label[^"]+"', 'style="color:orange"', decorated_text)
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st.markdown(decorated_text, unsafe_allow_html=True)
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text_response = decorated_text
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else:
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