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Future-House
paper-qa
Python2024–2025
Future-House / paper-qa

paper-qa

High accuracy RAG for answering questions from scientific documents with citations

Science & EngineeringAI & Machine LearningProductivity
Explore on GitHubRead Hacker News discussions
4Distinct people
10Mentions
0Explicit recommenders
9.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

High accuracy RAG for answering questions from scientific documents with citations

GitHub

Where it is used

No documented product use has been verified yet.

What people are saying

No recommendation phrase was found in the indexed comments.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

No other repository is repeatedly linked in the same discussions.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 3:56:07 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by week10 dated mentions

Observed mentions: 2024-09-11 16:28:00 UTC to 2025-03-05 15:59:20 UTC. Weeks start Monday, UTC.

0122024-09-09 00:00:00 UTC to 2024-09-16 00:00:00 UTC (exclusive): 2 mentions; 1 submissions, 1 commentsSep 92024-09-16 00:00:00 UTC to 2024-09-23 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-09-23 00:00:00 UTC to 2024-09-30 00:00:00 UTC (exclusive): 1 mentions; 1 submissions, 0 comments2024-09-30 00:00:00 UTC to 2024-10-07 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-07 00:00:00 UTC to 2024-10-14 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-14 00:00:00 UTC to 2024-10-21 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-10-21 00:00:00 UTC to 2024-10-28 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-10-28 00:00:00 UTC to 2024-11-04 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-04 00:00:00 UTC to 2024-11-11 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-11 00:00:00 UTC to 2024-11-18 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-11-18 00:00:00 UTC to 2024-11-25 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-11-25 00:00:00 UTC to 2024-12-02 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-02 00:00:00 UTC to 2024-12-09 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 commentsDec 22024-12-09 00:00:00 UTC to 2024-12-16 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-16 00:00:00 UTC to 2024-12-23 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2024-12-23 00:00:00 UTC to 2024-12-30 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2024-12-30 00:00:00 UTC to 2025-01-06 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-06 00:00:00 UTC to 2025-01-13 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-13 00:00:00 UTC to 2025-01-20 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-20 00:00:00 UTC to 2025-01-27 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-01-27 00:00:00 UTC to 2025-02-03 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-03 00:00:00 UTC to 2025-02-10 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-10 00:00:00 UTC to 2025-02-17 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-02-17 00:00:00 UTC to 2025-02-24 00:00:00 UTC (exclusive): 0 mentions; 0 submissions, 0 comments2025-02-24 00:00:00 UTC to 2025-03-03 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments2025-03-03 00:00:00 UTC to 2025-03-10 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 commentsMar 3

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
Sep 92024-09-09 00:00:00 UTC2024-09-16 00:00:00 UTC211
Sep 162024-09-16 00:00:00 UTC2024-09-23 00:00:00 UTC101
Sep 232024-09-23 00:00:00 UTC2024-09-30 00:00:00 UTC110
Sep 302024-09-30 00:00:00 UTC2024-10-07 00:00:00 UTC000
Oct 72024-10-07 00:00:00 UTC2024-10-14 00:00:00 UTC000
Oct 142024-10-14 00:00:00 UTC2024-10-21 00:00:00 UTC000
Oct 212024-10-21 00:00:00 UTC2024-10-28 00:00:00 UTC101
Oct 282024-10-28 00:00:00 UTC2024-11-04 00:00:00 UTC000
Nov 42024-11-04 00:00:00 UTC2024-11-11 00:00:00 UTC000
Nov 112024-11-11 00:00:00 UTC2024-11-18 00:00:00 UTC101
Nov 182024-11-18 00:00:00 UTC2024-11-25 00:00:00 UTC000
Nov 252024-11-25 00:00:00 UTC2024-12-02 00:00:00 UTC000
Dec 22024-12-02 00:00:00 UTC2024-12-09 00:00:00 UTC000
Dec 92024-12-09 00:00:00 UTC2024-12-16 00:00:00 UTC000
Dec 162024-12-16 00:00:00 UTC2024-12-23 00:00:00 UTC000
Dec 232024-12-23 00:00:00 UTC2024-12-30 00:00:00 UTC101
Dec 302024-12-30 00:00:00 UTC2025-01-06 00:00:00 UTC000
Jan 62025-01-06 00:00:00 UTC2025-01-13 00:00:00 UTC000
Jan 132025-01-13 00:00:00 UTC2025-01-20 00:00:00 UTC000
Jan 202025-01-20 00:00:00 UTC2025-01-27 00:00:00 UTC000
Jan 272025-01-27 00:00:00 UTC2025-02-03 00:00:00 UTC000
Feb 32025-02-03 00:00:00 UTC2025-02-10 00:00:00 UTC000
Feb 102025-02-10 00:00:00 UTC2025-02-17 00:00:00 UTC101
Feb 172025-02-17 00:00:00 UTC2025-02-24 00:00:00 UTC000
Feb 242025-02-24 00:00:00 UTC2025-03-03 00:00:00 UTC101
Mar 32025-03-03 00:00:00 UTC2025-03-10 00:00:00 UTC101

What Hacker News says

10 sources
mskar·Mar 5, 2025
We measured PaperQA2 (https://github.com/Future-House/paper-qa) against the science portion of the RAG-Arena benchmark (https://arxiv.org/abs/2407.13998), it's the first time we've compared PaperQA2 against other systems based on Cohere or Contextual.ai. PaperQA2 achieves a 12.4% higher score than Contextual.ai on the same dataset (1,404 questions and 1.7M documents). We're thrilled about this because it's open source, and getting better every day -- check out the code to reproduce this result in our cookbook here: https://futurehouse.gitbook.io/futurehouse-cookbook/paperqa/....
In PaperQA2 tops the RAG-QA Arena science benchmark
mskar·Oct 23, 2024
We used an open-source AI RAG library, PaperQA2 (https://github.com/Future-House/paper-qa), to generate well cited articles for every gene in the human genome, ~15k of which had no existing prior articles. In terms of factuality, we tested our generated claims against the same gene's human written Wikipedia article in a blinded study evaluated by PhD biologists. Our system's articles were more precise on average than cited claims from existing articles. (https://paper.wikicrow.ai) The system is scalable in that we can comfortably generate all 19.2k gene articles once per week, building a repository of cited articles that automatically syncs with all published literature.
In WikiCrow: An article for every gene in the human genome
mskar·Sep 19, 2024
We're sharing some experiments in designing RAG systems via the open source PaperQA2 system (https://github.com/Future-House/paper-qa). PaperQA2's design is interesting because it isn't concerned with cost, so it uses expensive operations like agentic tool calling and LLM based re-ranking and contextual summarization for each query. Even though the costs are higher, we see that the RAG accuracy gains (in question-answering tasks) are worth it. Including LLM chunk re-ranking and contextual summaries in your RAG flow also makes the system robust to changes in chunk sizes, parsing oddities and embedding model shortcomings. It's one of the largest drivers of performance we could find.
In Exchanging more frontier LLM compute for higher accuracy in RAG systems
mskar·Sep 11, 2024
We are announcing PaperQA2 (https://github.com/Future-House/paper-qa), the first AI agent to achieve superhuman performance on a variety of different scientific literature search tasks. PaperQA2 is an agent optimized for retrieving and summarizing information over the scientific literature. PaperQA2 has access to a variety of tools that allow it to find papers, extract useful information from those papers, explore the citation graph, and formulate answers. PaperQA2 achieves higher accuracy than PhD and postdoc-level biology researchers at retrieving information from the scientific literature, as measured using LitQA2, a piece of the LAB-Bench evals set that we released earlier this summer. In addition, when applied to produce wikipedia-style summaries of scientific information, WikiCrow, an agent built on top of PaperQA2, produces summari
Read the rest
es that are more accurate on average than actual articles on Wikipedia that have been written and curated by humans, as judged by blinded PhD and postdoc-level biology researchers. To get a better feel for how it works, try out the repo or check this tweet thread here (https://x.com/SGRodriques/status/1833908643856818443). It's got some videos of the workflow live. PaperQA2 allows us to perform analyses over the literature at a scale that are currently unavailable to scientists. At FutureHouse, we previously showed that we could use an older version (PaperQA) to generate a Wikipedia article for all 20,000 genes in the human genome, by combining information from 1 million distinct scientific papers. However, those articles were less accurate on average than existing articles on Wikipedia. Now that the articles we can generate are significantly more accurate than Wikipedia articles, one can imagine generating Wikipedia-style summaries on demand, or even regenerating Wikipeda from scratch with more comprehensive and recent information. In the coming weeks, we will use WikiCrow to generate Wikipedia articles for all 20,000 genes in the human genome, and will release them at wikicrow.ai. In the meantime, wikicrow.ai contains a preview of 240 articles used in the paper. In addition, we are very interested in how PaperQA2 could allow us to generate new hypotheses. One approach to that problem is to identify contradictions between published scientific papers, which can point the way to new discoveries. In our paper, we describe how ContraCrow, an agent built on top of PaperQA2, can evaluate every claim in a scientific paper to identify any other papers in the literature that disagree with it. We can grade these contradictions on a Likert scale to remove trivial contradictions. We find 2.34 statements per paper on average in a random subset of biology papers that are contradicted by other papers from anywhere else in the literature. Exploring these contradictions in detail may allow agents like PaperQA2 and ContraCrow to generate new hypotheses and propose new pivotal experiments.
In PaperQA2: Superhuman Scientific Literature Search
mskar·Sep 11, 2024Submission

Show HN: PaperQA2, Agentic RAG for Science

We're excited to release PaperQA2, an open source RAG library specialized to work with the scientific literature. We've seen some really compelling results with it (https://paper.wikicrow.ai), like superhuman performance at question answering and summarization when compared with expert scientists. PaperQA2 is a major overhaul of our prior PaperQA system, it includes automatically obtained rich metadata for each paper, a CLI to work with local papers directly, a local full-text search engine for keywords searches over PDF files, a state-of-the-art algorithm for LLM-based re-ranking and contextual summarization (RCS), the ability to agentic RAG, and support for all LiteLLM compatible LLMs. We hope that folks are able to use PaperQA2 to help improve their scientific workflows and the way they interact with the literature. It's enabled many c
Read the rest
ool projects for us like (WikiCrow and ContraCrow), and we hope to see many more in the future.
8 points · Open discussion

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