RAG et connaissances
autonomie
Open rag & knowledge for "autonomie" is a highly semantic, responsive, accessibl.
★ 103 étoilesMIT
Voir le guide →paper-qa est un projet de RAG et connaissances open source, créé par Future-House (Future-House/paper-qa sur GitHub). Il compte 9 329 étoiles sur GitHub et 934 forks, il est publié sous la licence Apache-2.0 et a été mis à jour pour la dernière fois sur GitHub le 25 septembre 2026.
PaperQA2 — high-accuracy RAG for answering questions from scientific papers, with citations you can check.
Gather the papers or documents you want to question (PDF, text, Markdown, HTML, Office and code files are supported) and choose your LLM and embedding model.
Clone Future-House/paper-qa from GitHub (or use the install path in the run steps below) and skim its README for the parts relevant to your goal.
Install paper-qa from PyPI — add the local extra if you want a local embedding model instead of a hosted one.
Add a small set of documents to a Docs object, ask one question you know the answer to, and verify the cited sources support it.
Designed specifically for answering questions from scientific literature with high accuracy, rather than generic chat over files.
Answers come with citations to the source documents, so claims can be checked against the papers.
The library can add and query documents agentically, or you can drive the Docs object manually for full control.
Use it as a Python library (sync or async) or from the bundled CLI, with bundled settings presets.
Configure the answering model and the embedding model separately, including a local embedding option via paper-qa[local].
Tune how many sources are retrieved per question to balance answer depth against cost and latency.
Get the source from the official GitHub repository for paper-qa.
git clone https://github.com/Future-House/paper-qa.git
cd paper-qaInstall the package from PyPI as the README's installation section shows.
pip install paper-qaUse the library quickstart pattern from the README: build a Docs object, add your papers, then query it.
# In Python: from paperqa import Docs; docs = Docs(); await docs.aadd('paper.pdf'); answer = await docs.aquery('Your question?') # see READMEFuture-House, through the Future-House/paper-qa repository, with a cookbook at futurehouse.gitbook.io.
Yes — the project is open source under the Apache-2.0 license as recorded for this listing. See the license terms for any conditions on redistribution.
By default it uses hosted LLM and embedding models, so you need your own API keys; a local embedding model is available via the paper-qa[local] extra.
General frameworks give you chunking and vector search to assemble yourself. PaperQA is an opinionated, science-focused system — agentic retrieval over papers and cited answers out of the box — which is faster to use for literature Q&A but less of a blank canvas.
Clone the repository (or use the documented installer), follow the install and configure steps on this page, and run the smallest real task first — add a small set of documents to a Docs object, ask one question you know the answer to, and verify the cited sources support it.
Open rag & knowledge for "autonomie" is a highly semantic, responsive, accessibl.
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