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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
High accuracy RAG for answering questions from scientific documents with citations
| Date | Stars |
|---|---|
| 2026-07-24 | 8930 |
| 2026-07-25 | 8933 |
| 2026-07-28 | 8933 |
| 2026-07-30 | 8933 |
| 2026-08-06 | 8933 |
Today
— stars today
This week
— stars this week
This month
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Momentum
5.0
growth rate 0.00%/day
# PaperQA2
<!-- pyml disable-num-lines 6 line-length -->
[](https://github.com/Future-House/paper-qa)
[](https://badge.fury.io/py/paper-qa)
[](https://github.com/Future-House/paper-qa)
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](https://paper.wikicrow.ai)
to see examples of PaperQA2's superhuman performance in scientific tasks like
question answering, summarization, and contradiction detection.
<!--TOC-->
---
**Table of Contents**
- [Quickstart](#quickstart)
- [Example Output](#example-output)
- [What is PaperQA2](#what-is-paperqa2)
- [PaperQA2 vs PaperQA](#paperqa2-vs-paperqa)
- [PaperQA2 Goes CalVer in December 2025](#paperqa2-goes-calver-in-december-2025)
- [What's New in Version 5 (aka PaperQA2)?](#whats-new-in-version-5-aka-paperqa2)
- [What's New in December 2025?](#whats-new-in-december-2025)
- [PaperQA2 Algorithm](#paperqa2-algorithm)
- [Installation](#installation)
- [CLI Usage](#cli-usage)
- [Bundled Settings](#bundled-settings)
- [Rate Limits](#rate-limits)
- [Library Usage](#library-usage)
- [Agentic Adding/Querying Documents](#agentic-addingquerying-documents)
- [Manual (No Agent) Adding/Querying Documents](#manual-no-agent-addingquerying-documents)
- [Async](#async)
- [Choosing Model](#choosing-model)
- [Locally Hosted](#locally-hosted)
- [Embedding Model](#embedding-model)
- [Specifying the Embedding Model](#specifying-the-embedding-model)
- [Local Embedding Models (Sentence Transformers)](#local-embedding-models-sentence-transformers)
- [Adjusting number of sources](#adjusting-number-of-sources)
- [Using Code or HTML](#using-code-or-html)
- [Multimodal Support](#multimodal-support)
- [Using External DB/Vector DB and Caching](#using-external-dbvector-db-and-caching)
- [Creating Index](#creating-index)
- [Manifest Files](#manifest-files)
- [Reusing Index](#reusing-index)
- [Using Clients Directly](#using-clients-directly)
- [Settings Cheatsheet](#settings-cheatsheet)
- [Where do I get papers?](#where-do-i-get-papers)
- [Callbacks](#callbacks)
- [Caching Embeddings](#caching-embeddings)
- [Customizing Prompts](#customizing-prompts)
- [Pre and Post Prompts](#pre-and-post-prompts)
- [FAQ](#faq)
- [How come I get different results than your papers?](#how-come-i-get-different-results-than-your-papers)
- [How is this different from LlamaIndex or LangChain?](#how-is-this-different-from-llamaindex-or-langchain)
- [Can I save or load?](#can-i-save-or-load)
- [Reproduction](#reproduction)
- [Citation](#citation)
---
<!--TOC-->
## Quickstart
In this example we take a folder of research paper PDFs,
magically get their metadata - including citation counts with a retraction check,
then parse and cache PDFs into a full-text search index,
and finally answer the user question with an LLM agent.
```bash
pip install paper-qa
mkdir my_papers
curl -o my_papers/PaperQA2.pdf https://arxiv.org/pdf/2409.13740
cd my_papers
pqa ask 'What is PaperQA2?'
```
### Example Output
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, Qian2011Neural pages 15-16,Excerpt of 60,462 characters
Read on GitHub
James Braza · EECS PhD Student @ MIT · United States
476
78
16
12
Geemi Wellawatte
12
8
Miles Cranmer · University of Cambridge · United Kingdom
5
5
2
2
2
2
Thomas Rochefort-Beaudoin · Canada
2
Tobias Diez
2
takeru.fukushima.
2
Anush · @qdrant
1
1
B. Ogan Mancarcı · @PavlidisLab, UBC · Canada
1
1
Devin Stein · Dosu
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:72eea82591ef7d95, topic:rag, readme:retrieval-augmented generation, readme:retrieval augmented