Hi HN, recently our team at https://github.com/Cinnamon/kotaemon/ has been working on a public demo to showcase the new advanced citation features in our open-source RAG application:
We’re excited to share a free web app that lets users explore top daily machine learning (ML) papers on Arxiv (via the HuggingFace API) and upload their own Arxiv papers to get LLM-assisted summaries, mind maps, and answers to questions based on the content.
Some notable features:
- Instant Summaries & Mind Maps: Generate concise summaries and visual mind maps for any Arxiv paper.
- Transparent Citations: Verify LLM-generated answers with clear, evidence-backed citations. Citations can be highlighted directly in the in-browser PDF viewer.
- Flexible Citation Options: Choose between highlights and inline citations. Plus, select any sentence in the AI-generated response to see its supporting source from the original paper.
- Multi-Paper Analysis: Compare, contrast, and compose summaries from multiple papers simultaneously.
- Complex Question Solving: Use Chain-of-Thought (CoT) reasoning mode to break down and solve complex questions step-by-step.
- (And most important of all) Customizable & Self-hosted: Easily self-host your private app via the free HuggingFace Space hosting feature. You can securely configure your own LLM and upload your private document collections.
We’d love to hear your thoughts, feedback, and recommendations as we continue improving this tool.
Hi HN, recently our team at https://github.com/Cinnamon/kotaemon/ has been working on a public demo to showcase the new advanced citation features in our open-source RAG application:
https://cin-model-kotaemon.hf.space/
We’re excited to share a free web app that lets users explore top daily machine learning (ML) papers on Arxiv (via the HuggingFace API) and upload their own Arxiv papers to get LLM-assisted summaries, mind maps, and answers to questions based on the content.
Some notable features:
- Instant Summaries & Mind Maps: Generate concise summaries and visual mind maps for any Arxiv paper.
- Transparent Citations: Verify LLM-generated answers with clear, evidence-backed citations. Citations can be highlighted directly in the in-browser PDF viewer.
- Flexible Citation Options: Choose between highlights and inline citations. Plus, select any sentence in the AI-generated response to see its supporting source from the original paper.
- Multi-Paper Analysis: Compare, contrast, and compose summaries from multiple papers simultaneously.
- Complex Question Solving: Use Chain-of-Thought (CoT) reasoning mode to break down and solve complex questions step-by-step.
- (And most important of all) Customizable & Self-hosted: Easily self-host your private app via the free HuggingFace Space hosting feature. You can securely configure your own LLM and upload your private document collections.
We’d love to hear your thoughts, feedback, and recommendations as we continue improving this tool.
Check out the demo and happy hacking!