added fetch-paper function so we can fetch single papers if needed
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kpcto
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16
check_db.py
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16
check_db.py
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import asyncio
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from src.storage.paper_store import PaperStore
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async def check_papers():
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store = PaperStore()
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await store.initialize()
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papers = await store.get_all_papers()
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print('Papers in PostgreSQL:', len(papers))
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if papers:
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print('\nPaper IDs:')
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for paper in papers:
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print(f"- {paper['id']}")
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await store.close()
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if __name__ == "__main__":
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asyncio.run(check_papers())
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213
papers/2501_12948v1.json
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213
papers/2501_12948v1.json
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{
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"entry_id": "http://arxiv.org/abs/2501.12948v1",
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"title": "DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning",
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"authors": [
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"DeepSeek-AI",
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"Daya Guo",
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"Dejian Yang",
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"Haowei Zhang",
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"Junxiao Song",
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"Ruoyu Zhang",
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"Runxin Xu",
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"Qihao Zhu",
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"Shirong Ma",
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"Peiyi Wang",
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"Xiao Bi",
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"Xiaokang Zhang",
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"Xingkai Yu",
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"Yu Wu",
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"Z. F. Wu",
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"Zhibin Gou",
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"Zhihong Shao",
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"Zhuoshu Li",
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"Ziyi Gao",
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"Aixin Liu",
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"Bing Xue",
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"Bingxuan Wang",
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"Bochao Wu",
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"Bei Feng",
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"Chengda Lu",
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"Chenggang Zhao",
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"Chengqi Deng",
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"Chenyu Zhang",
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"Chong Ruan",
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"Damai Dai",
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"Deli Chen",
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"Dongjie Ji",
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"Erhang Li",
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"Fangyun Lin",
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"Fucong Dai",
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"Fuli Luo",
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"Guangbo Hao",
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"Guanting Chen",
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"Guowei Li",
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"H. Zhang",
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"Han Bao",
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"Hanwei Xu",
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"Haocheng Wang",
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"Honghui Ding",
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"Huajian Xin",
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"Huazuo Gao",
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"Hui Qu",
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"Hui Li",
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"Jianzhong Guo",
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"Jiashi Li",
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"Jiawei Wang",
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"Jingchang Chen",
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"Jingyang Yuan",
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"Junjie Qiu",
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"Junlong Li",
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"J. L. Cai",
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"Jiaqi Ni",
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"Jian Liang",
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"Jin Chen",
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"Kai Dong",
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"Kai Hu",
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"Kaige Gao",
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"Kang Guan",
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"Kexin Huang",
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"Kuai Yu",
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"Lean Wang",
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"Lecong Zhang",
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"Liang Zhao",
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"Litong Wang",
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"Liyue Zhang",
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"Lei Xu",
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"Leyi Xia",
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"Mingchuan Zhang",
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"Minghua Zhang",
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"Minghui Tang",
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"Meng Li",
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"Miaojun Wang",
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"Mingming Li",
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"Ning Tian",
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"Panpan Huang",
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"Peng Zhang",
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"Qiancheng Wang",
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"Qinyu Chen",
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"Qiushi Du",
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"Ruiqi Ge",
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"Ruisong Zhang",
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"Ruizhe Pan",
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"Runji Wang",
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"R. J. Chen",
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"R. L. Jin",
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"Ruyi Chen",
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"Shanghao Lu",
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"Shangyan Zhou",
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"Shanhuang Chen",
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"Shengfeng Ye",
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"Shiyu Wang",
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"Shuiping Yu",
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"Shunfeng Zhou",
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"Shuting Pan",
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"S. S. Li",
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"Shuang Zhou",
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"Shaoqing Wu",
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"Shengfeng Ye",
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"Tao Yun",
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"Tian Pei",
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"Tianyu Sun",
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"T. Wang",
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"Wangding Zeng",
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"Wanjia Zhao",
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"Wen Liu",
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"Wenfeng Liang",
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"Wenjun Gao",
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"Wenqin Yu",
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"Wentao Zhang",
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"W. L. Xiao",
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"Wei An",
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"Xiaodong Liu",
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"Xiaohan Wang",
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"Xiaokang Chen",
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"Xiaotao Nie",
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"Xin Cheng",
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"Xin Liu",
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"Xin Xie",
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"Xingchao Liu",
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"Xinyu Yang",
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"Xinyuan Li",
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"Xuecheng Su",
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"Xuheng Lin",
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"X. Q. Li",
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"Xiangyue Jin",
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"Xiaojin Shen",
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"Xiaosha Chen",
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"Xiaowen Sun",
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"Xiaoxiang Wang",
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"Xinnan Song",
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"Xinyi Zhou",
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"Xianzu Wang",
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"Xinxia Shan",
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"Y. K. Li",
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"Y. Q. Wang",
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"Y. X. Wei",
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"Yang Zhang",
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"Yanhong Xu",
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"Yao Li",
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"Yao Zhao",
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"Yaofeng Sun",
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"Yaohui Wang",
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"Yi Yu",
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"Yichao Zhang",
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"Yifan Shi",
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"Yiliang Xiong",
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"Ying He",
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"Yishi Piao",
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"Yisong Wang",
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"Yixuan Tan",
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"Yiyang Ma",
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"Yiyuan Liu",
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"Yongqiang Guo",
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"Yuan Ou",
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"Yuduan Wang",
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"Yue Gong",
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"Yuheng Zou",
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"Yujia He",
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"Yunfan Xiong",
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"Yuxiang Luo",
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"Yuxiang You",
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"Yuxuan Liu",
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"Yuyang Zhou",
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"Y. X. Zhu",
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"Yanhong Xu",
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"Yanping Huang",
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"Yaohui Li",
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"Yi Zheng",
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"Yuchen Zhu",
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"Yunxian Ma",
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"Ying Tang",
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"Yukun Zha",
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"Yuting Yan",
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"Z. Z. Ren",
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"Zehui Ren",
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"Zhangli Sha",
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"Zhe Fu",
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"Zhean Xu",
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"Zhenda Xie",
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"Zhengyan Zhang",
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"Zhewen Hao",
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"Zhicheng Ma",
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"Zhigang Yan",
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"Zhiyu Wu",
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"Zihui Gu",
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"Zijia Zhu",
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"Zijun Liu",
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"Zilin Li",
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"Ziwei Xie",
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"Ziyang Song",
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"Zizheng Pan",
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"Zhen Huang",
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"Zhipeng Xu",
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"Zhongyu Zhang",
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"Zhen Zhang"
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],
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"summary": "We introduce our first-generation reasoning models, DeepSeek-R1-Zero and\nDeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement\nlearning (RL) without supervised fine-tuning (SFT) as a preliminary step,\ndemonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero\nnaturally emerges with numerous powerful and intriguing reasoning behaviors.\nHowever, it encounters challenges such as poor readability, and language\nmixing. To address these issues and further enhance reasoning performance, we\nintroduce DeepSeek-R1, which incorporates multi-stage training and cold-start\ndata before RL. DeepSeek-R1 achieves performance comparable to OpenAI-o1-1217\non reasoning tasks. To support the research community, we open-source\nDeepSeek-R1-Zero, DeepSeek-R1, and six dense models (1.5B, 7B, 8B, 14B, 32B,\n70B) distilled from DeepSeek-R1 based on Qwen and Llama.",
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"pdf_url": "http://arxiv.org/pdf/2501.12948v1",
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"categories": [
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"cs.CL",
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"cs.AI",
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"cs.LG"
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]
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}
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92
src/main.py
92
src/main.py
@ -109,6 +109,47 @@ async def fetch_papers(days: int = 7, categories: Optional[List[str]] = None) ->
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logger.error(f"Error fetching papers: {e}")
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raise
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async def fetch_single_paper(paper_id: str) -> None:
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"""Fetch and analyze a single paper by ID."""
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print(f"\nFetching paper: {paper_id}")
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async with ArxivClient() as client, AgentController() as agent:
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try:
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# Get paper from arXiv
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paper = await client.get_paper_by_id(paper_id)
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if not paper:
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print(f"\nPaper {paper_id} not found on arXiv.")
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return
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print(f"\nFound paper: {paper['title']}")
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print(f"Authors: {', '.join(paper['authors'])}")
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# Analyze the paper
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analysis = await agent.analyze_paper(paper)
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if analysis:
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print("\nAnalysis:")
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print("=" * 80)
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print("\nSummary:")
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print(analysis.get('summary', 'No summary available'))
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print("\nTechnical Concepts:")
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print(analysis.get('technical_concepts', 'No technical concepts available'))
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fluff = analysis.get('fluff', {})
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score = fluff.get('score')
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if score is not None:
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color = '\033[92m' if score < 30 else '\033[93m' if score < 70 else '\033[91m'
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reset = '\033[0m'
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print(f"\nFluff Score: {color}{score}/100{reset}")
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print("Analysis:")
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print(fluff.get('explanation', 'No explanation available'))
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else:
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print("\nPaper was already analyzed or an error occurred during analysis.")
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except Exception as e:
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logger.error(f"Error processing paper: {e}")
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raise
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async def process_query(query: str) -> None:
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"""Process a search query and display results."""
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async with AgentController() as agent:
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@ -148,9 +189,39 @@ async def process_query(query: str) -> None:
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logger.error(f"Error processing query: {e}")
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raise
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async def search_arxiv(query: str, category: Optional[str] = None, max_results: int = 10) -> None:
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"""Search papers directly on arXiv."""
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print(f"\nSearching arXiv for: {query}")
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if category:
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print(f"Category: {category}")
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async with ArxivClient() as client:
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try:
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papers = await client.fetch_papers(query=query, category=category, max_results=max_results)
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if not papers:
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print("\nNo papers found matching your query.")
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return
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print(f"\nFound {len(papers)} papers:")
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print("=" * 80)
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for i, paper in enumerate(papers, 1):
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print(f"\n{i}. {paper['title']}")
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print(f" Authors: {', '.join(paper['authors'])}")
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print(f" arXiv ID: {paper['entry_id']}")
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print(f" PDF: {paper['pdf_url']}")
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print("\nAbstract:")
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print(paper.get('abstract', 'No abstract available'))
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print("-" * 80)
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except Exception as e:
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logger.error(f"Error searching arXiv: {e}")
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raise
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async def main():
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"""Main application entry point."""
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parser = argparse.ArgumentParser(description='Fetch and analyze arXiv papers')
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parser = argparse.ArgumentParser(description='AI Paper Analysis System')
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subparsers = parser.add_subparsers(dest='command', help='Command to run')
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# Fetch papers command
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@ -160,16 +231,31 @@ async def main():
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fetch_parser.add_argument('--categories', nargs='+', default=['cs.AI'],
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help='arXiv categories to fetch')
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# Search papers command
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search_parser = subparsers.add_parser('search', help='Search papers')
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# Fetch single paper command
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fetch_one_parser = subparsers.add_parser('fetch-paper', help='Fetch and analyze a single paper')
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fetch_one_parser.add_argument('paper_id', help='arXiv paper ID (e.g., 2502.06788v1)')
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# Search local papers command
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search_parser = subparsers.add_parser('search', help='Search papers in local database')
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search_parser.add_argument('query', help='Search query')
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# Search arXiv directly command
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arxiv_parser = subparsers.add_parser('arxiv-search', help='Search papers directly on arXiv')
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arxiv_parser.add_argument('query', help='Search query')
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arxiv_parser.add_argument('--category', help='arXiv category (e.g., cs.AI)')
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arxiv_parser.add_argument('--max-results', type=int, default=10,
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help='Maximum number of results to return')
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args = parser.parse_args()
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if args.command == 'fetch':
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await fetch_papers(days=args.days, categories=args.categories)
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elif args.command == 'fetch-paper':
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await fetch_single_paper(args.paper_id)
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elif args.command == 'search':
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await process_query(args.query)
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elif args.command == 'arxiv-search':
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await search_arxiv(args.query, args.category, args.max_results)
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else:
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parser.print_help()
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