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SDKs, agent skills, IDE extensions, and reference pipelines from Backblaze Labs. All open source.
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Where it stands
Rank, growth pace and release cadence relative to everything GStars tracks.
1 release in the last 90 days, the latest 85 days ago, with 39 contributors on GitHub.
Use Cases & Benefits
- Implements high-accuracy retrieval augmented generation (RAG) to answer questions from scientific documents with in-text citations.
- Delivers superhuman performance on scientific tasks by combining document metadata-aware embeddings, LLM-based re-ranking, and contextual summarization.
- Use for building a local full-text search and question answering system over a repository of scientific PDFs and text files.
- Use for automating evidence gathering and summarization workflows in research environments requiring citation-backed answers.
- Use for integrating with various LLM providers and embedding models to customize scientific document QA pipelines asynchronously or via CLI.
Should you depend on it?
License terms, project hygiene and risks, from GitHub metadata and GStars' snapshots.
License
Apache 2.0
Permissive: commercial use, modification and redistribution are allowed with attribution; includes an explicit patent grant from contributors.
Community files
- README: present
- Contributing guide: present
Watch-outs
No watch-outs from GStars' checks.
General guidance from repository metadata, not legal advice. How we score
From the README
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 to see examples of PaperQA2's superhuman performance in scientific tasks like question answering, summarization, and contradiction detection.
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.
Question: Has anyone designed neural networks that compute with proteins or DNA?
Daily snapshots
Stars gained per day, and how forks and open issues moved, from GStars' daily snapshots
Forks
952+48
Open issues
158+16
Compared with peers in AI
Fastest-growing repositories GStars tracks in this cohort for the week of Nov 8, by 7-day star growth
| Repository | Stars | +7d | Health | Last release |
|---|---|---|---|---|
| D4Vinci/Scrapling | 101.1K | +6.3K | 75 | 6 days ago |
| thedotmack/claude-mem | 115.8K | +4.7K | 75 | today |
| heygen-com/hyperframes | 82.3K | +4.0K | 75 | today |
| harry0703/MoneyPrinterTurbo | 142.0K | +3.8K | 75 | 3 days ago |
| github/spec-kit | 149.2K | +2.6K | 75 | today |
| Future-House/paper-qa | 9.6K | — | 49 | 85 days ago |
| Category median | 115.8K | +4.0K | 75 | — |
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