paper-qa and document-qa-rag-system
These are **competitors** offering different sophistication levels for the same task—paper-qa targets production-grade scientific document QA with citation accuracy and robust performance, while document-qa-rag-system provides a lightweight, educational implementation suitable for quick prototyping or learning RAG fundamentals with LangChain and Streamlit.
About paper-qa
Future-House/paper-qa
High accuracy RAG for answering questions from scientific documents with citations
This tool helps researchers, scientists, and academics quickly find precise answers within a collection of scientific documents, such as PDFs or text files. You feed it your papers, and it provides accurate answers to your questions, complete with in-text citations to the original sources. This is ideal for anyone needing to extract specific information from a large volume of research literature.
About document-qa-rag-system
ZohaibCodez/document-qa-rag-system
A simple Retrieval-Augmented Generation (RAG) project built with LangChain and Streamlit. Upload documents (PDF/TXT) and interact with them using natural language questions powered by embeddings and vector search.
This tool helps you quickly get answers from your documents by turning any PDF or plain text file into an interactive Q&A experience. You upload your document, and then you can ask questions about its content in everyday language, getting direct answers back. It's ideal for professionals, researchers, or students who need to extract specific information or summarize key points from reports, articles, or books without manually sifting through pages.
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