镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-05 22:16:49 +00:00
8042750d41
* stage academic conversation * stage document conversation * fix buggy gradio version * file dynamic load * merge more academic plugins * accelerate nltk * feat: 为predict函数添加文件和URL读取功能 - 添加URL检测和网页内容提取功能，支持自动提取网页文本 - 添加文件路径识别和文件内容读取功能，支持private_upload路径格式 - 集成WebTextExtractor处理网页内容提取 - 集成TextContentLoader处理本地文件读取 - 支持文件路径与问题组合的智能处理 * back * block unstable --------- Co-authored-by: XiaoBoAI <liuboyin2019@ia.ac.cn>
636 行
23 KiB
Python
636 行
23 KiB
Python
import arxiv
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from typing import List, Optional, Union, Literal, Dict
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from datetime import datetime
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from .base_source import DataSource, PaperMetadata
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import os
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from urllib.request import urlretrieve
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import feedparser
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from tqdm import tqdm
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class ArxivSource(DataSource):
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"""arXiv API实现"""
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CATEGORIES = {
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# 物理学
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"Physics": {
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"astro-ph": "天体物理学",
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"cond-mat": "凝聚态物理",
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"gr-qc": "广义相对论与量子宇宙学",
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"hep-ex": "高能物理实验",
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"hep-lat": "格点场论",
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"hep-ph": "高能物理理论",
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"hep-th": "高能物理理论",
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"math-ph": "数学物理",
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"nlin": "非线性科学",
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"nucl-ex": "核实验",
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"nucl-th": "核理论",
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"physics": "物理学",
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"quant-ph": "量子物理",
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},
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# 数学
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"Mathematics": {
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"math.AG": "代数几何",
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"math.AT": "代数拓扑",
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"math.AP": "分析与偏微分方程",
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"math.CT": "范畴论",
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"math.CA": "复分析",
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"math.CO": "组合数学",
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"math.AC": "交换代数",
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"math.CV": "复变函数",
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"math.DG": "微分几何",
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"math.DS": "动力系统",
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"math.FA": "泛函分析",
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"math.GM": "一般数学",
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"math.GN": "一般拓扑",
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"math.GT": "几何拓扑",
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"math.GR": "群论",
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"math.HO": "数学史与数学概述",
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"math.IT": "信息论",
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"math.KT": "K理论与同调",
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"math.LO": "逻辑",
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"math.MP": "数学物理",
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"math.MG": "度量几何",
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"math.NT": "数论",
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"math.NA": "数值分析",
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"math.OA": "算子代数",
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"math.OC": "最优化与控制",
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"math.PR": "概率论",
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"math.QA": "量子代数",
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"math.RT": "表示论",
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"math.RA": "环与代数",
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"math.SP": "谱理论",
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"math.ST": "统计理论",
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"math.SG": "辛几何",
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},
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# 计算机科学
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"Computer Science": {
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"cs.AI": "人工智能",
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"cs.CL": "计算语言学",
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"cs.CC": "计算复杂性",
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"cs.CE": "计算工程",
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"cs.CG": "计算几何",
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"cs.GT": "计算机博弈论",
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"cs.CV": "计算机视觉",
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"cs.CY": "计算机与社会",
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"cs.CR": "密码学与安全",
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"cs.DS": "数据结构与算法",
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"cs.DB": "数据库",
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"cs.DL": "数字图书馆",
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"cs.DM": "离散数学",
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"cs.DC": "分布式计算",
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"cs.ET": "新兴技术",
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"cs.FL": "形式语言与自动机理论",
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"cs.GL": "一般文献",
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"cs.GR": "图形学",
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"cs.AR": "硬件架构",
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"cs.HC": "人机交互",
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"cs.IR": "信息检索",
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"cs.IT": "信息论",
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"cs.LG": "机器学习",
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"cs.LO": "逻辑与计算机",
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"cs.MS": "数学软件",
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"cs.MA": "多智能体系统",
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"cs.MM": "多媒体",
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"cs.NI": "网络与互联网架构",
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"cs.NE": "神经与进化计算",
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"cs.NA": "数值分析",
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"cs.OS": "操作系统",
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"cs.OH": "其他计算机科学",
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"cs.PF": "性能评估",
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"cs.PL": "编程语言",
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"cs.RO": "机器人学",
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"cs.SI": "社会与信息网络",
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"cs.SE": "软件工程",
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"cs.SD": "声音",
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"cs.SC": "符号计算",
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"cs.SY": "系统与控制",
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},
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# 定量生物学
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"Quantitative Biology": {
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"q-bio.BM": "生物分子",
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"q-bio.CB": "细胞行为",
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"q-bio.GN": "基因组学",
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"q-bio.MN": "分子网络",
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"q-bio.NC": "神经计算",
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"q-bio.OT": "其他",
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"q-bio.PE": "群体与进化",
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"q-bio.QM": "定量方法",
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"q-bio.SC": "亚细胞过程",
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"q-bio.TO": "组织与器官",
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},
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# 定量金融
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"Quantitative Finance": {
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"q-fin.CP": "计算金融",
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"q-fin.EC": "经济学",
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"q-fin.GN": "一般金融",
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"q-fin.MF": "数学金融",
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"q-fin.PM": "投资组合管理",
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"q-fin.PR": "定价理论",
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"q-fin.RM": "风险管理",
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"q-fin.ST": "统计金融",
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"q-fin.TR": "交易与市场微观结构",
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},
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# 统计学
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"Statistics": {
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"stat.AP": "应用统计",
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"stat.CO": "计算统计",
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"stat.ML": "机器学习",
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"stat.ME": "方法论",
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"stat.OT": "其他统计",
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"stat.TH": "统计理论",
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},
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# 电气工程与系统科学
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"Electrical Engineering and Systems Science": {
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"eess.AS": "音频与语音处理",
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"eess.IV": "图像与视频处理",
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"eess.SP": "信号处理",
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"eess.SY": "系统与控制",
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},
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# 经济学
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"Economics": {
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"econ.EM": "计量经济学",
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"econ.GN": "一般经济学",
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"econ.TH": "理论经济学",
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}
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}
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def __init__(self):
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"""初始化"""
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self._initialize() # 调用初始化方法
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# 修改排序选项映射
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self.sort_options = {
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'relevance': arxiv.SortCriterion.Relevance, # arXiv的相关性排序
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'lastUpdatedDate': arxiv.SortCriterion.LastUpdatedDate, # 最后更新日期
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'submittedDate': arxiv.SortCriterion.SubmittedDate, # 提交日期
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}
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self.sort_order_options = {
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'ascending': arxiv.SortOrder.Ascending,
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'descending': arxiv.SortOrder.Descending
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}
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self.default_sort = 'lastUpdatedDate'
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self.default_order = 'descending'
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def _initialize(self) -> None:
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"""初始化客户端，设置默认参数"""
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self.client = arxiv.Client()
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async def search(
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self,
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query: str,
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limit: int = 10,
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sort_by: str = None,
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sort_order: str = None,
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start_year: int = None
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) -> List[Dict]:
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"""搜索论文"""
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try:
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# 使用默认排序如果提供的排序选项无效
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if not sort_by or sort_by not in self.sort_options:
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sort_by = self.default_sort
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# 使用默认排序顺序如果提供的顺序无效
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if not sort_order or sort_order not in self.sort_order_options:
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sort_order = self.default_order
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# 如果指定了起始年份，添加到查询中
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if start_year:
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query = f"{query} AND submittedDate:[{start_year}0101 TO 99991231]"
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search = arxiv.Search(
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query=query,
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max_results=limit,
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sort_by=self.sort_options[sort_by],
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sort_order=self.sort_order_options[sort_order]
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)
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results = list(self.client.results(search))
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return [self._parse_paper_data(result) for result in results]
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except Exception as e:
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print(f"搜索论文时发生错误: {str(e)}")
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return []
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async def search_by_id(self, paper_id: Union[str, List[str]]) -> List[PaperMetadata]:
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"""按ID搜索论文
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Args:
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paper_id: 单个arXiv ID或ID列表，例如：'2005.14165' 或 ['2005.14165', '2103.14030']
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"""
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if isinstance(paper_id, str):
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paper_id = [paper_id]
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search = arxiv.Search(
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id_list=paper_id,
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max_results=len(paper_id)
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)
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results = list(self.client.results(search))
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return [self._parse_paper_data(result) for result in results]
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async def search_by_category(
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self,
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category: str,
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limit: int = 100,
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sort_by: str = 'relevance',
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sort_order: str = 'descending',
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start_year: int = None
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) -> List[PaperMetadata]:
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"""按类别搜索论文"""
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query = f"cat:{category}"
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# 如果指定了起始年份，添加到查询中
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if start_year:
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query = f"{query} AND submittedDate:[{start_year}0101 TO 99991231]"
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return await self.search(
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query=query,
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limit=limit,
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sort_by=sort_by,
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sort_order=sort_order
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)
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async def search_by_authors(
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self,
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authors: List[str],
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limit: int = 100,
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sort_by: str = 'relevance',
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start_year: int = None
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) -> List[PaperMetadata]:
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"""按作者搜索论文"""
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query = " AND ".join([f"au:\"{author}\"" for author in authors])
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# 如果指定了起始年份，添加到查询中
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if start_year:
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query = f"{query} AND submittedDate:[{start_year}0101 TO 99991231]"
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return await self.search(
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query=query,
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limit=limit,
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sort_by=sort_by
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)
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async def search_by_date_range(
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self,
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start_date: datetime,
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end_date: datetime,
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limit: int = 100,
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sort_by: Literal['relevance', 'updated', 'submitted'] = 'submitted',
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sort_order: Literal['ascending', 'descending'] = 'descending'
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) -> List[PaperMetadata]:
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"""按日期范围搜索论文"""
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query = f"submittedDate:[{start_date.strftime('%Y%m%d')} TO {end_date.strftime('%Y%m%d')}]"
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return await self.search(
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query,
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limit=limit,
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sort_by=sort_by,
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sort_order=sort_order
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)
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async def download_pdf(self, paper_id: str, dirpath: str = "./", filename: str = "") -> str:
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"""下载论文PDF
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Args:
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paper_id: arXiv ID
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dirpath: 保存目录
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filename: 文件名，如果为空则使用默认格式：{paper_id}_{标题}.pdf
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Returns:
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保存的文件路径
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"""
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papers = await self.search_by_id(paper_id)
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if not papers:
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raise ValueError(f"未找到ID为 {paper_id} 的论文")
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paper = papers[0]
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if not filename:
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# 清理标题中的非法字符
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safe_title = "".join(c if c.isalnum() else "_" for c in paper.title)
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filename = f"{paper_id}_{safe_title}.pdf"
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filepath = os.path.join(dirpath, filename)
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urlretrieve(paper.url, filepath)
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return filepath
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async def download_source(self, paper_id: str, dirpath: str = "./", filename: str = "") -> str:
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"""下载论文源文件（通常是LaTeX源码）
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Args:
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paper_id: arXiv ID
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dirpath: 保存目录
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filename: 文件名，如果为空则使用默认格式：{paper_id}_{标题}.tar.gz
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Returns:
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保存的文件路径
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"""
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papers = await self.search_by_id(paper_id)
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if not papers:
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raise ValueError(f"未找到ID为 {paper_id} 的论文")
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paper = papers[0]
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if not filename:
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safe_title = "".join(c if c.isalnum() else "_" for c in paper.title)
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filename = f"{paper_id}_{safe_title}.tar.gz"
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filepath = os.path.join(dirpath, filename)
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source_url = paper.url.replace("/pdf/", "/src/")
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urlretrieve(source_url, filepath)
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return filepath
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async def get_citations(self, paper_id: str) -> List[PaperMetadata]:
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# arXiv API不直接提供引用信息
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return []
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async def get_references(self, paper_id: str) -> List[PaperMetadata]:
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# arXiv API不直接提供引用信息
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return []
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async def get_paper_details(self, paper_id: str) -> Optional[PaperMetadata]:
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"""获取论文详情
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Args:
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paper_id: arXiv ID 或 DOI
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Returns:
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论文详细信息，如果未找到返回 None
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"""
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try:
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# 如果是完整的 arXiv URL，提取 ID
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if "arxiv.org" in paper_id:
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paper_id = paper_id.split("/")[-1]
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# 如果是 DOI 格式且是 arXiv 论文，提取 ID
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elif paper_id.startswith("10.48550/arXiv."):
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paper_id = paper_id.split(".")[-1]
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papers = await self.search_by_id(paper_id)
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return papers[0] if papers else None
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except Exception as e:
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print(f"获取论文详情时发生错误: {str(e)}")
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return None
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def _parse_paper_data(self, result: arxiv.Result) -> PaperMetadata:
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"""解析arXiv API返回的数据"""
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# 解析主要类别和次要类别
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primary_category = result.primary_category
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categories = result.categories
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# 构建venue信息
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venue_info = {
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'primary_category': primary_category,
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'categories': categories,
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'comments': getattr(result, 'comment', None),
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'journal_ref': getattr(result, 'journal_ref', None)
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}
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return PaperMetadata(
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title=result.title,
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authors=[author.name for author in result.authors],
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abstract=result.summary,
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year=result.published.year,
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doi=result.entry_id,
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url=result.pdf_url,
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citations=None,
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venue=f"arXiv:{primary_category}",
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institutions=[],
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venue_type='preprint', # arXiv论文都是预印本
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venue_name='arXiv',
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venue_info=venue_info,
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source='arxiv' # 添加来源标记
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)
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async def get_latest_papers(
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self,
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category: str,
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debug: bool = False,
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batch_size: int = 50
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) -> List[PaperMetadata]:
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"""获取指定类别的最新论文
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通过 RSS feed 获取最新发布的论文，然后批量获取详细信息
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Args:
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category: arXiv类别，例如：
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- 整个领域: 'cs'
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- 具体方向: 'cs.AI'
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- 多个类别: 'cs.AI+q-bio.NC'
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debug: 是否为调试模式，如果为True则只返回5篇最新论文
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batch_size: 批量获取论文的数量，默认50
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Returns:
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论文列表
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Raises:
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ValueError: 如果类别无效
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"""
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try:
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# 处理类别格式
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# 1. 转换为小写
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# 2. 确保多个类别之间使用+连接
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category = category.lower().replace(' ', '+')
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# 构建RSS feed URL
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feed_url = f"https://rss.arxiv.org/rss/{category}"
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print(f"正在获取RSS feed: {feed_url}") # 添加调试信息
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feed = feedparser.parse(feed_url)
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# 检查feed是否有效
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if hasattr(feed, 'status') and feed.status != 200:
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raise ValueError(f"获取RSS feed失败，状态码: {feed.status}")
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if not feed.entries:
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print(f"警告：未在feed中找到任何条目") # 添加调试信息
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print(f"Feed标题: {feed.feed.title if hasattr(feed, 'feed') else '无标题'}")
|
||
raise ValueError(f"无效的arXiv类别或未找到论文: {category}")
|
||
|
||
if debug:
|
||
# 调试模式：只获取5篇最新论文
|
||
search = arxiv.Search(
|
||
query=f'cat:{category}',
|
||
sort_by=arxiv.SortCriterion.SubmittedDate,
|
||
sort_order=arxiv.SortOrder.Descending,
|
||
max_results=5
|
||
)
|
||
results = list(self.client.results(search))
|
||
return [self._parse_paper_data(result) for result in results]
|
||
|
||
# 正常模式：获取所有新论文
|
||
# 从RSS条目中提取arXiv ID
|
||
paper_ids = []
|
||
for entry in feed.entries:
|
||
try:
|
||
# RSS链接格式可能是以下几种：
|
||
# - http://arxiv.org/abs/2403.xxxxx
|
||
# - http://arxiv.org/pdf/2403.xxxxx
|
||
# - https://arxiv.org/abs/2403.xxxxx
|
||
link = entry.link or entry.id
|
||
arxiv_id = link.split('/')[-1].replace('.pdf', '')
|
||
if arxiv_id:
|
||
paper_ids.append(arxiv_id)
|
||
except Exception as e:
|
||
print(f"警告：处理条目时出错: {str(e)}") # 添加调试信息
|
||
continue
|
||
|
||
if not paper_ids:
|
||
print("未能从feed中提取到任何论文ID") # 添加调试信息
|
||
return []
|
||
|
||
print(f"成功提取到 {len(paper_ids)} 个论文ID") # 添加调试信息
|
||
|
||
# 批量获取论文详情
|
||
papers = []
|
||
with tqdm(total=len(paper_ids), desc="获取arXiv论文") as pbar:
|
||
for i in range(0, len(paper_ids), batch_size):
|
||
batch_ids = paper_ids[i:i + batch_size]
|
||
search = arxiv.Search(
|
||
id_list=batch_ids,
|
||
max_results=len(batch_ids)
|
||
)
|
||
batch_results = list(self.client.results(search))
|
||
papers.extend([self._parse_paper_data(result) for result in batch_results])
|
||
pbar.update(len(batch_results))
|
||
|
||
return papers
|
||
|
||
except Exception as e:
|
||
print(f"获取最新论文时发生错误: {str(e)}")
|
||
import traceback
|
||
print(traceback.format_exc()) # 添加完整的错误追踪
|
||
return []
|
||
|
||
async def example_usage():
|
||
"""ArxivSource使用示例"""
|
||
arxiv_source = ArxivSource()
|
||
|
||
try:
|
||
# 示例1：基本搜索，使用不同的排序方式
|
||
# print("\n=== 示例1：搜索最新的机器学习论文（按提交时间排序）===")
|
||
# papers = await arxiv_source.search(
|
||
# "ti:\"machine learning\"",
|
||
# limit=3,
|
||
# sort_by='submitted',
|
||
# sort_order='descending'
|
||
# )
|
||
# print(f"找到 {len(papers)} 篇论文")
|
||
|
||
# for i, paper in enumerate(papers, 1):
|
||
# print(f"\n--- 论文 {i} ---")
|
||
# print(f"标题: {paper.title}")
|
||
# print(f"作者: {', '.join(paper.authors)}")
|
||
# print(f"发表年份: {paper.year}")
|
||
# print(f"arXiv ID: {paper.doi}")
|
||
# print(f"PDF URL: {paper.url}")
|
||
# if paper.abstract:
|
||
# print(f"\n摘要:")
|
||
# print(paper.abstract)
|
||
# print(f"发表venue: {paper.venue}")
|
||
|
||
# # 示例2：按ID搜索
|
||
# print("\n=== 示例2：按ID搜索论文 ===")
|
||
# paper_id = "2005.14165" # GPT-3论文
|
||
# papers = await arxiv_source.search_by_id(paper_id)
|
||
# if papers:
|
||
# paper = papers[0]
|
||
# print(f"标题: {paper.title}")
|
||
# print(f"作者: {', '.join(paper.authors)}")
|
||
# print(f"发表年份: {paper.year}")
|
||
|
||
# # 示例3：按类别搜索
|
||
# print("\n=== 示例3：搜索人工智能领域最新论文 ===")
|
||
# ai_papers = await arxiv_source.search_by_category(
|
||
# "cs.AI",
|
||
# limit=2,
|
||
# sort_by='updated',
|
||
# sort_order='descending'
|
||
# )
|
||
# for i, paper in enumerate(ai_papers, 1):
|
||
# print(f"\n--- AI论文 {i} ---")
|
||
# print(f"标题: {paper.title}")
|
||
# print(f"作者: {', '.join(paper.authors)}")
|
||
# print(f"发表venue: {paper.venue}")
|
||
|
||
# # 示例4：按作者搜索
|
||
# print("\n=== 示例4：搜索特定作者的论文 ===")
|
||
# author_papers = await arxiv_source.search_by_authors(
|
||
# ["Bengio"],
|
||
# limit=2,
|
||
# sort_by='relevance'
|
||
# )
|
||
# for i, paper in enumerate(author_papers, 1):
|
||
# print(f"\n--- Bengio的论文 {i} ---")
|
||
# print(f"标题: {paper.title}")
|
||
# print(f"作者: {', '.join(paper.authors)}")
|
||
# print(f"发表venue: {paper.venue}")
|
||
|
||
# # 示例5：按日期范围搜索
|
||
# print("\n=== 示例5：搜索特定日期范围的论文 ===")
|
||
# from datetime import datetime, timedelta
|
||
# end_date = datetime.now()
|
||
# start_date = end_date - timedelta(days=7) # 最近一周
|
||
# recent_papers = await arxiv_source.search_by_date_range(
|
||
# start_date,
|
||
# end_date,
|
||
# limit=2
|
||
# )
|
||
# for i, paper in enumerate(recent_papers, 1):
|
||
# print(f"\n--- 最近论文 {i} ---")
|
||
# print(f"标题: {paper.title}")
|
||
# print(f"作者: {', '.join(paper.authors)}")
|
||
# print(f"发表年份: {paper.year}")
|
||
|
||
# # 示例6：下载PDF
|
||
# print("\n=== 示例6：下载论文PDF ===")
|
||
# if papers: # 使用之前搜索到的GPT-3论文
|
||
# pdf_path = await arxiv_source.download_pdf(paper_id)
|
||
# print(f"PDF已下载到: {pdf_path}")
|
||
|
||
# # 示例7：下载源文件
|
||
# print("\n=== 示例7：下载论文源文件 ===")
|
||
# if papers:
|
||
# source_path = await arxiv_source.download_source(paper_id)
|
||
# print(f"源文件已下载到: {source_path}")
|
||
|
||
# 示例6：获取最新论文
|
||
print("\n=== 示例8：获取最新论文 ===")
|
||
|
||
# 获取CS.AI领域的最新论文
|
||
print("\n--- 获取AI领域最新论文 ---")
|
||
ai_latest = await arxiv_source.get_latest_papers("cs.AI", debug=True)
|
||
for i, paper in enumerate(ai_latest, 1):
|
||
print(f"\n论文 {i}:")
|
||
print(f"标题: {paper.title}")
|
||
print(f"作者: {', '.join(paper.authors)}")
|
||
print(f"发表年份: {paper.year}")
|
||
|
||
# 获取整个计算机科学领域的最新论文
|
||
print("\n--- 获取整个CS领域最新论文 ---")
|
||
cs_latest = await arxiv_source.get_latest_papers("cs", debug=True)
|
||
for i, paper in enumerate(cs_latest, 1):
|
||
print(f"\n论文 {i}:")
|
||
print(f"标题: {paper.title}")
|
||
print(f"作者: {', '.join(paper.authors)}")
|
||
print(f"发表年份: {paper.year}")
|
||
|
||
# 获取多个类别的最新论文
|
||
print("\n--- 获取AI和机器学习领域最新论文 ---")
|
||
multi_latest = await arxiv_source.get_latest_papers("cs.AI+cs.LG", debug=True)
|
||
for i, paper in enumerate(multi_latest, 1):
|
||
print(f"\n论文 {i}:")
|
||
print(f"标题: {paper.title}")
|
||
print(f"作者: {', '.join(paper.authors)}")
|
||
print(f"发表年份: {paper.year}")
|
||
|
||
except Exception as e:
|
||
print(f"发生错误: {str(e)}")
|
||
import traceback
|
||
print(traceback.format_exc())
|
||
|
||
if __name__ == "__main__":
|
||
import asyncio
|
||
asyncio.run(example_usage()) |