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Future-House/paper-qa: High accuracy RAG for answering questions from scientific documents with citations

github.com · 6,606 words · saved by 1 readers

PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs or text files, with a focus on the scientific literature. See our recent 2024 paper to see examples of PaperQA2's superhuman performance in scientific tasks like question answering, summarization, and contradiction detection. In this example we take a folder of research paper PDFs, magically get their metadata - including citation counts and a retraction check, then parse and cache PDFs into a full-text search index, and finally answer the user question with an LLM agent. Question: Has anyone designed neural networks that compute with proteins or DNA? The claim that neural networks have been designed to compute with DNA is supported by multiple sources. The work by Qian, Winfree, and Bruck demonstrates the use of DNA strand displacement cascades to construct neural network components, such as artificial neurons and associative memories, using a DNA-based system (Qian2011Neural pages 1-2, Qian2011

PaperQA2 PaperQA2 is a package for doing high-accuracy retrieval augmented generation (RAG) on PDFs, text files, Microsoft Office documents, and source code files, with a focus on the scientific literature. See our recent 2024 paper to see examples of PaperQA2's superhuman performance in scientific tasks like question answering, summarization, and contradiction detection. Table of Contents Quickstart Example Output What is PaperQA2 PaperQA2 vs PaperQA PaperQA2 Goes CalVer in December 2025 What's New in Version 5 (aka PaperQA2)? What's New in December 2025? PaperQA2 Algorithm Installation CLI U

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