In simple English
This tool reads scientific research papers and answers questions about them. It extracts text, fetches citation details, and builds a searchable index from local PDF files. It uses artificial intelligence to find the right passages and write answers with exact citations. You need an API key from an artificial intelligence provider or a local server to run it. It saves time for researchers who need to search through many documents at once.
What it is
PaperQA2 is a Python library and command-line tool for agentic retrieval augmented generation (a way to let a language model search local files and synthesize grounded answers). It parses documents, retrieves relevant chunks using vector embeddings and keyword search, and generates responses with in-text citations.
What it is good for
Solo builders and researchers who need to query a large collection of scientific papers, technical documents, or source code files and get verified answers with source references.
- Automatically fetches paper metadata, citations, and retraction checks from services like Crossref and Semantic Scholar.
- Supports multiple document formats including PDF, text, Microsoft Office files, HTML, and source code.
- Works with many language model providers through LiteLLM, plus local models.
- Requires Python 3.11 or newer and familiarity with the command line.
- Consumes significant numbers of API tokens when indexing large sets of research papers.
- Index files built with older versions are incompatible and must be rebuilt.
Manual literature reviews and reading through hundreds of PDF files by hand.
LiteLLM or OpenAI API for language model access.
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pip install paper-qa>=5