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PaperQA2, Agentic RAG for Science

Hacker News

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 cool projects for us like (WikiCrow and ContraCrow), and we hope to see many more in the future.

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Launch Intel predictions

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Product HuntOn track for Day 1 leaderboard · Strong signals: agent, agentic, context · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Hacker NewsStrong engagement from HN community · Strong signals: excited, open source, io · Missing: https docs, just released, exist
77%77% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
Indie HackersFits the IH revenue-focused audience · Strong signals: compatible · Missing: supports, reddit linkedin, podcasting
72%72% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: way · Missing: mobile apps, ios, personal
43%43% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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