paper-qa
High accuracy RAG for answering questions from scientific documents with citations
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Decision brief
PaperQA2 version 5 is a retrieval-augmented generation (RAG) system optimized for extracting information from scientific documents, enhancing user queries with citations.
Good fit when
- Your project specifically requires processing and querying scientific documents, as PaperQA2 offers specialized capabilities tuned for this domain.
- You are integrating high-fidelity search functionalities into a tool targeting researchers or students who need to query scientific literature with accuracy.
Avoid when
- If your use case does not involve scientific document processing, another RAG system better suited to your specific type of documents (e.g., legal, medical) might be more fitting.
- In scenarios where real-time performance is critical and extensive indexing or access to external APIs for large-scale paper handling becomes a bottleneck.
- Requirements:
- Min 4 GB RAM
Observed Jul 12, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Very active (5d since push)
- As of today
- Provenance
- Not a fork · Organization account
- As of today
- Security (OSV)
- No lockfile
- As of 1mo
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install paper-qaHow it fits your stack(9)
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
PaperQA2 version 5 offers a robust retrieval-augmented generation (RAG) approach specifically tailored for processing and querying scientific document content, using LLMs to enhance its capabilities.
Capability facts
- CLI
- CLI entrypoint
Source: pyproject.toml:[project.scripts] · Aug 18, 2026
- Languages
- python
Source: github.language+pyproject.toml · Aug 18, 2026
Categories
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Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Aug 18, 2026)
Note version 5 requires Python 3.11+.Source link
Tags
README
Installation
For a non-development setup, install PaperQA2 (aka version 5) from PyPI. Note version 5 requires Python 3.11+.
pip install paper-qa>=5
For development setup, please refer to the CONTRIBUTING.md file.
PaperQA2 uses an LLM to operate,
so you'll need to either set an appropriate [API key environment variable][LiteLLM providers]
(i.e. export OPENAI_API_KEY=sk-...)
or set up an open source LLM server (i.e. using llamafile.
Any LiteLLM compatible model can be configured to use with PaperQA2.
If you need to index a large set of papers (100+),
you will likely want an API key for both
Crossref
and Semantic Scholar,
which will allow you to avoid hitting public rate limits using these metadata services.
Those can be exported as CROSSREF_API_KEY and SEMANTIC_SCHOLAR_API_KEY variables.
For agents
This page has a .md twin and JSON over the API.