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lbykkkk
772
crazy_functions/rag_fns/arxiv_fns/arxiv_splitter.py
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772
crazy_functions/rag_fns/arxiv_fns/arxiv_splitter.py
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import os
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import re
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import time
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import aiohttp
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import asyncio
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import requests
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import tarfile
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import logging
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from pathlib import Path
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from copy import deepcopy
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from typing import Generator, List, Tuple, Optional, Dict, Set
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from crazy_functions.rag_fns.arxiv_fns.tex_utils import TexUtils
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from crazy_functions.rag_fns.arxiv_fns.section_fragment import SectionFragment
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from crazy_functions.rag_fns.arxiv_fns.essay_structure import EssayStructureParser, DocumentStructure, read_tex_file
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from crazy_functions.rag_fns.arxiv_fns.section_extractor import Section
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def save_fragments_to_file(fragments: List[SectionFragment], output_dir: str = "fragment_outputs") -> Path:
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"""
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Save all fragments to a single structured markdown file.
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Args:
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fragments: List of SectionFragment objects
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output_dir: Output directory path
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Returns:
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Path: Path to the generated markdown file
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"""
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from datetime import datetime
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from pathlib import Path
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import re
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# Create output directory
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timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
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output_path = Path(output_dir)
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output_path.mkdir(parents=True, exist_ok=True)
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# Generate filename
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filename = f"fragments_{timestamp}.md"
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file_path = output_path / filename
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# Group fragments by section
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sections = {}
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for fragment in fragments:
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section = fragment.current_section or "Uncategorized"
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if section not in sections:
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sections[section] = []
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sections[section].append(fragment)
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with open(file_path, "w", encoding="utf-8") as f:
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# Write document header
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f.write("# Document Fragments Analysis\n\n")
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f.write("## Overview\n")
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f.write(f"- Total Fragments: {len(fragments)}\n")
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f.write(f"- Generated Time: {datetime.now().strftime('%Y-%m-%d %H:%M:%S')}\n")
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# Add paper information if available
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if fragments and (fragments[0].title or fragments[0].abstract):
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f.write("\n## Paper Information\n")
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if fragments[0].title:
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f.write(f"### Title\n{fragments[0].title}\n")
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if fragments[0].abstract:
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f.write(f"\n### Abstract\n{fragments[0].abstract}\n")
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# Write section tree if available
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if fragments and fragments[0].section_tree:
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f.write("\n## Section Tree\n")
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f.write(fragments[0].section_tree)
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# Generate table of contents
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f.write("\n## Table of Contents\n")
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for section in sections:
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clean_section = section.strip() or "Uncategorized"
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fragment_count = len(sections[section])
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f.write(f"- [{clean_section}](#{clean_section.lower().replace(' ', '-')}) "
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f"({fragment_count} fragments)\n")
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# Write content sections
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f.write("\n## Content\n")
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for section, section_fragments in sections.items():
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clean_section = section.strip() or "Uncategorized"
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f.write(f"\n### {clean_section}\n")
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# Write each fragment
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for i, fragment in enumerate(section_fragments, 1):
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f.write(f"\n#### Fragment {i}\n")
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# Metadata
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f.write("**Metadata:**\n")
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metadata = [
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f"- Section: {fragment.current_section}",
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f"- Length: {len(fragment.content)} chars",
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f"- ArXiv ID: {fragment.arxiv_id}" if fragment.arxiv_id else None
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]
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f.write("\n".join(filter(None, metadata)) + "\n")
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# Content
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f.write("\n**Content:**\n")
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f.write("```tex\n")
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f.write(fragment.content)
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f.write("\n```\n")
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# Bibliography if exists
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if fragment.bibliography:
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f.write("\n**Bibliography:**\n")
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f.write("```bibtex\n")
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f.write(fragment.bibliography)
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f.write("\n```\n")
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# Add separator
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if i < len(section_fragments):
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f.write("\n---\n")
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# Add statistics
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f.write("\n## Statistics\n")
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# Length distribution
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lengths = [len(f.content) for f in fragments]
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f.write("\n### Length Distribution\n")
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f.write(f"- Minimum: {min(lengths)} chars\n")
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f.write(f"- Maximum: {max(lengths)} chars\n")
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f.write(f"- Average: {sum(lengths) / len(lengths):.1f} chars\n")
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# Section distribution
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f.write("\n### Section Distribution\n")
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for section, section_fragments in sections.items():
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percentage = (len(section_fragments) / len(fragments)) * 100
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f.write(f"- {section}: {len(section_fragments)} ({percentage:.1f}%)\n")
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print(f"Fragments saved to: {file_path}")
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return file_path
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# 定义各种引用命令的模式
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CITATION_PATTERNS = [
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# 基本的 \cite{} 格式
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r'\\cite(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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# natbib 格式
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r'\\citep(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\citet(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\citeauthor(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\citeyear(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\citealt(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\citealp(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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# biblatex 格式
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r'\\textcite(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\parencite(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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r'\\autocite(?:\*)?(?:\[[^\]]*\])?{([^}]+)}',
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# 自定义 [cite:...] 格式
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r'\[cite:([^\]]+)\]',
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]
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# 编译所有模式
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COMPILED_PATTERNS = [re.compile(pattern) for pattern in CITATION_PATTERNS]
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class ArxivSplitter:
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"""Arxiv论文智能分割器"""
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def __init__(self,
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root_dir: str = "gpt_log/arxiv_cache",
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proxies: Optional[Dict[str, str]] = None,
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cache_ttl: int = 7 * 24 * 60 * 60):
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"""
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初始化分割器
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Args:
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char_range: 字符数范围(最小值, 最大值)
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root_dir: 缓存根目录
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proxies: 代理设置
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cache_ttl: 缓存过期时间(秒)
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"""
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self.root_dir = Path(root_dir)
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self.root_dir.mkdir(parents=True, exist_ok=True)
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self.proxies = proxies or {}
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self.cache_ttl = cache_ttl
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# 动态计算最优线程数
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import multiprocessing
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cpu_count = multiprocessing.cpu_count()
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# 根据CPU核心数动态设置，但设置上限防止过度并发
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self.document_structure = DocumentStructure()
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self.document_parser = EssayStructureParser()
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self.max_workers = min(32, cpu_count * 2)
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# 初始化TeX处理器
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self.tex_processor = TexUtils()
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# 配置日志
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self._setup_logging()
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def _setup_logging(self):
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"""配置日志"""
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logging.basicConfig(
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level=logging.INFO,
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format='%(asctime)s - %(levelname)s - %(message)s'
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)
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self.logger = logging.getLogger(__name__)
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def _normalize_arxiv_id(self, input_str: str) -> str:
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"""规范化ArXiv ID"""
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if 'arxiv.org/' in input_str.lower():
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# 处理URL格式
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if '/pdf/' in input_str:
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arxiv_id = input_str.split('/pdf/')[-1]
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else:
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arxiv_id = input_str.split('/abs/')[-1]
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# 移除版本号和其他后缀
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return arxiv_id.split('v')[0].strip()
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return input_str.split('v')[0].strip()
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def _check_cache(self, paper_dir: Path) -> bool:
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"""
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检查缓存是否有效，包括文件完整性检查
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Args:
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paper_dir: 论文目录路径
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Returns:
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bool: 如果缓存有效返回True，否则返回False
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"""
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if not paper_dir.exists():
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return False
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# 检查目录中是否存在必要文件
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has_tex_files = False
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has_main_tex = False
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for file_path in paper_dir.rglob("*"):
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if file_path.suffix == '.tex':
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has_tex_files = True
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content = self.tex_processor.read_file(str(file_path))
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if content and r'\documentclass' in content:
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has_main_tex = True
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break
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if not (has_tex_files and has_main_tex):
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return False
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# 检查缓存时间
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cache_time = paper_dir.stat().st_mtime
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if (time.time() - cache_time) < self.cache_ttl:
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self.logger.info(f"Using valid cache for {paper_dir.name}")
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return True
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return False
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async def download_paper(self, arxiv_id: str, paper_dir: Path) -> bool:
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"""
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异步下载论文，包含重试机制和临时文件处理
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Args:
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arxiv_id: ArXiv论文ID
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paper_dir: 目标目录路径
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Returns:
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bool: 下载成功返回True，否则返回False
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"""
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from crazy_functions.rag_fns.arxiv_fns.arxiv_downloader import ArxivDownloader
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temp_tar_path = paper_dir / f"{arxiv_id}_temp.tar.gz"
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final_tar_path = paper_dir / f"{arxiv_id}.tar.gz"
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# 确保目录存在
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paper_dir.mkdir(parents=True, exist_ok=True)
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# 尝试使用 ArxivDownloader 下载
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try:
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downloader = ArxivDownloader(root_dir=str(paper_dir), proxies=self.proxies)
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downloaded_dir = downloader.download_paper(arxiv_id)
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if downloaded_dir:
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self.logger.info(f"Successfully downloaded using ArxivDownloader to {downloaded_dir}")
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return True
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except Exception as e:
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self.logger.warning(f"ArxivDownloader failed: {str(e)}. Falling back to direct download.")
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# 如果 ArxivDownloader 失败，使用原有的下载方式作为备选
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urls = [
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f"https://arxiv.org/src/{arxiv_id}",
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f"https://arxiv.org/e-print/{arxiv_id}"
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]
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max_retries = 3
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retry_delay = 1 # 初始重试延迟（秒）
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for url in urls:
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for attempt in range(max_retries):
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try:
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self.logger.info(f"Downloading from {url} (attempt {attempt + 1}/{max_retries})")
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async with aiohttp.ClientSession() as session:
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async with session.get(url, proxy=self.proxies.get('http')) as response:
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if response.status == 200:
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content = await response.read()
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# 写入临时文件
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temp_tar_path.write_bytes(content)
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try:
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# 验证tar文件完整性并解压
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loop = asyncio.get_event_loop()
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await loop.run_in_executor(None, self._process_tar_file, temp_tar_path, paper_dir)
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# 下载成功后移动临时文件到最终位置
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temp_tar_path.rename(final_tar_path)
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return True
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except Exception as e:
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self.logger.warning(f"Invalid tar file: {str(e)}")
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if temp_tar_path.exists():
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temp_tar_path.unlink()
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except Exception as e:
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self.logger.warning(f"Download attempt {attempt + 1} failed from {url}: {str(e)}")
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await asyncio.sleep(retry_delay * (attempt + 1)) # 指数退避
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continue
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return False
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def _process_tar_file(self, tar_path: Path, extract_path: Path):
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"""处理tar文件的同步操作"""
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with tarfile.open(tar_path, 'r:gz') as tar:
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tar.testall() # 验证文件完整性
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tar.extractall(path=extract_path) # 解压文件
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def process_references(self, doc_structure: DocumentStructure, ref_bib: str) -> DocumentStructure:
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"""
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Process citations in document structure and add referenced literature for each section
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Args:
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doc_structure: DocumentStructure object
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ref_bib: String containing references separated by newlines
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Returns:
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Updated DocumentStructure object
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"""
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try:
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# Create a copy to avoid modifying the original
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doc = deepcopy(doc_structure)
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# Parse references into a mapping
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ref_map = self._parse_references(ref_bib)
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if not ref_map:
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self.logger.warning("No valid references found in ref_bib")
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return doc
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# Process all sections recursively
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self._process_section_references(doc.toc, ref_map)
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return doc
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except Exception as e:
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self.logger.error(f"Error processing references: {str(e)}")
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return doc_structure # Return original if processing fails
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def _process_section_references(self, sections: List[Section], ref_map: Dict[str, str]) -> None:
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"""
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Recursively process sections to add references
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Args:
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sections: List of Section objects
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ref_map: Mapping of citation keys to full references
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"""
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for section in sections:
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if section.content:
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# Find citations in current section
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cited_refs = self.find_citations(section.content)
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if cited_refs:
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# Get full references for citations
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full_refs = []
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for ref_key in cited_refs:
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ref_text = ref_map.get(ref_key)
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if ref_text:
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full_refs.append(ref_text)
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else:
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self.logger.warning(f"Reference not found for citation key: {ref_key}")
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# Add references to section content
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if full_refs:
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section.bibliography = "\n\n".join(full_refs)
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# Process subsections recursively
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if section.subsections:
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self._process_section_references(section.subsections, ref_map)
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def _parse_references(self, ref_bib: str) -> Dict[str, str]:
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"""
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Parse reference string into a mapping of citation keys to full references
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Args:
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ref_bib: Reference string with references separated by newlines
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Returns:
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Dict mapping citation keys to full reference text
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"""
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ref_map = {}
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current_ref = []
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current_key = None
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try:
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for line in ref_bib.split('\n'):
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line = line.strip()
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if not line:
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continue
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# New reference entry
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if line.startswith('@'):
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# Save previous reference if exists
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if current_key and current_ref:
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ref_map[current_key] = '\n'.join(current_ref)
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current_ref = []
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# Extract key from new reference
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key_match = re.search(r'{(.*?),', line)
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if key_match:
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current_key = key_match.group(1)
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current_ref.append(line)
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else:
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if current_ref is not None:
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current_ref.append(line)
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# Save last reference
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if current_key and current_ref:
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ref_map[current_key] = '\n'.join(current_ref)
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except Exception as e:
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self.logger.error(f"Error parsing references: {str(e)}")
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return ref_map
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# 编译一次正则表达式以提高效率
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@staticmethod
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def _clean_citation_key(key: str) -> str:
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"""Clean individual citation key."""
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return key.strip().strip(',').strip()
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def _extract_keys_from_group(self, keys_str: str) -> Set[str]:
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"""Extract and clean individual citation keys from a group."""
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try:
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# 分割多个引用键（支持逗号和分号分隔）
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||||
separators = '[,;]'
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keys = re.split(separators, keys_str)
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# 清理并过滤空键
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return {self._clean_citation_key(k) for k in keys if self._clean_citation_key(k)}
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except Exception as e:
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||||
self.logger.warning(f"Error processing citation group '{keys_str}': {e}")
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return set()
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def find_citations(self, content: str) -> Set[str]:
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"""
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Find citation keys in text content in various formats.
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||||
|
||||
Args:
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||||
content: Text content to search for citations
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||||
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||||
Returns:
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||||
Set of unique citation keys
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||||
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||||
Examples:
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Supported formats include:
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||||
- \cite{key1,key2}
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||||
- \cite[p. 1]{key}
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||||
- \citep{key}
|
||||
- \citet{key}
|
||||
- [cite:key1, key2]
|
||||
- And many other variants
|
||||
"""
|
||||
citations = set()
|
||||
|
||||
if not content:
|
||||
return citations
|
||||
|
||||
try:
|
||||
# 对每个编译好的模式进行搜索
|
||||
for pattern in COMPILED_PATTERNS:
|
||||
matches = pattern.finditer(content)
|
||||
for match in matches:
|
||||
# 获取捕获组中的引用键
|
||||
keys_str = match.group(1)
|
||||
if keys_str:
|
||||
# 提取并添加所有引用键
|
||||
new_keys = self._extract_keys_from_group(keys_str)
|
||||
citations.update(new_keys)
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error finding citations: {str(e)}")
|
||||
|
||||
# 移除明显无效的键
|
||||
citations = {key for key in citations
|
||||
if key and not key.startswith(('\\', '{', '}', '[', ']'))}
|
||||
|
||||
return citations
|
||||
|
||||
def get_citation_contexts(self, content: str, context_chars: int = 100) -> dict:
|
||||
"""
|
||||
Find citations and their surrounding context.
|
||||
|
||||
Args:
|
||||
content: Text content to search for citations
|
||||
context_chars: Number of characters of context to include before/after
|
||||
|
||||
Returns:
|
||||
Dict mapping citation keys to lists of context strings
|
||||
"""
|
||||
contexts = {}
|
||||
|
||||
if not content:
|
||||
return contexts
|
||||
|
||||
try:
|
||||
for pattern in COMPILED_PATTERNS:
|
||||
matches = pattern.finditer(content)
|
||||
for match in matches:
|
||||
# 获取匹配的位置
|
||||
start = max(0, match.start() - context_chars)
|
||||
end = min(len(content), match.end() + context_chars)
|
||||
|
||||
# 获取上下文
|
||||
context = content[start:end]
|
||||
|
||||
# 获取并处理引用键
|
||||
keys_str = match.group(1)
|
||||
keys = self._extract_keys_from_group(keys_str)
|
||||
|
||||
# 为每个键添加上下文
|
||||
for key in keys:
|
||||
if key not in contexts:
|
||||
contexts[key] = []
|
||||
contexts[key].append(context)
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Error finding citation contexts: {str(e)}")
|
||||
|
||||
return contexts
|
||||
async def process(self, arxiv_id_or_url: str) -> List[SectionFragment]:
|
||||
"""
|
||||
Process ArXiv paper and convert to list of SectionFragments.
|
||||
Each fragment represents the smallest section unit.
|
||||
|
||||
Args:
|
||||
arxiv_id_or_url: ArXiv paper ID or URL
|
||||
|
||||
Returns:
|
||||
List[SectionFragment]: List of processed paper fragments
|
||||
"""
|
||||
try:
|
||||
arxiv_id = self._normalize_arxiv_id(arxiv_id_or_url)
|
||||
paper_dir = self.root_dir / arxiv_id
|
||||
|
||||
# Check if paper directory exists, if not, try to download
|
||||
if not paper_dir.exists():
|
||||
self.logger.info(f"Downloading paper {arxiv_id}")
|
||||
await self.download_paper(arxiv_id, paper_dir)
|
||||
|
||||
# Find main TeX file
|
||||
main_tex = self.tex_processor.find_main_tex_file(str(paper_dir))
|
||||
if not main_tex:
|
||||
raise RuntimeError(f"No main TeX file found in {paper_dir}")
|
||||
|
||||
# Get all related TeX files and references
|
||||
tex_files = self.tex_processor.resolve_includes(main_tex)
|
||||
ref_bib = self.tex_processor.resolve_references(main_tex, paper_dir)
|
||||
|
||||
if not tex_files:
|
||||
raise RuntimeError(f"No valid TeX files found for {arxiv_id}")
|
||||
|
||||
# Reset document structure for new processing
|
||||
self.document_structure = DocumentStructure()
|
||||
|
||||
# Process each TeX file
|
||||
for file_path in tex_files:
|
||||
self.logger.info(f"Processing TeX file: {file_path}")
|
||||
tex_content = read_tex_file(file_path)
|
||||
if tex_content:
|
||||
additional_doc = self.document_parser.parse(tex_content)
|
||||
self.document_structure = self.document_structure.merge(additional_doc)
|
||||
|
||||
# Process references if available
|
||||
if ref_bib:
|
||||
self.document_structure = self.process_references(self.document_structure, ref_bib)
|
||||
self.logger.info("Successfully processed references")
|
||||
else:
|
||||
self.logger.info("No references found to process")
|
||||
|
||||
# Generate table of contents once
|
||||
section_tree = self.document_structure.generate_toc_tree()
|
||||
|
||||
# Convert DocumentStructure to SectionFragments
|
||||
fragments = self._convert_to_fragments(
|
||||
doc_structure=self.document_structure,
|
||||
arxiv_id=arxiv_id,
|
||||
section_tree=section_tree
|
||||
)
|
||||
|
||||
return fragments
|
||||
|
||||
except Exception as e:
|
||||
self.logger.error(f"Failed to process {arxiv_id_or_url}: {str(e)}")
|
||||
raise
|
||||
|
||||
def _convert_to_fragments(self,
|
||||
doc_structure: DocumentStructure,
|
||||
arxiv_id: str,
|
||||
section_tree: str) -> List[SectionFragment]:
|
||||
"""
|
||||
Convert DocumentStructure to list of SectionFragments.
|
||||
Creates a fragment for each leaf section in the document hierarchy.
|
||||
|
||||
Args:
|
||||
doc_structure: Source DocumentStructure
|
||||
arxiv_id: ArXiv paper ID
|
||||
section_tree: Pre-generated table of contents tree
|
||||
|
||||
Returns:
|
||||
List[SectionFragment]: List of paper fragments
|
||||
"""
|
||||
fragments = []
|
||||
|
||||
# Create a base template for all fragments to avoid repetitive assignments
|
||||
base_fragment_template = {
|
||||
'title': doc_structure.title,
|
||||
'abstract': doc_structure.abstract,
|
||||
'section_tree': section_tree,
|
||||
'arxiv_id': arxiv_id
|
||||
}
|
||||
|
||||
def get_leaf_sections(section: Section, path: List[str] = None) -> None:
|
||||
"""
|
||||
Recursively find all leaf sections and create fragments.
|
||||
A leaf section is one that has content but no subsections, or has neither.
|
||||
|
||||
Args:
|
||||
section: Current section being processed
|
||||
path: List of section titles forming the path to current section
|
||||
"""
|
||||
if path is None:
|
||||
path = []
|
||||
|
||||
current_path = path + [section.title]
|
||||
|
||||
if not section.subsections:
|
||||
# This is a leaf section, create a fragment if it has content
|
||||
if section.content or section.bibliography:
|
||||
fragment = SectionFragment(
|
||||
**base_fragment_template,
|
||||
current_section="/".join(current_path),
|
||||
content=self._clean_content(section.content),
|
||||
bibliography=section.bibliography
|
||||
)
|
||||
if self._validate_fragment(fragment):
|
||||
fragments.append(fragment)
|
||||
else:
|
||||
# Process each subsection
|
||||
for subsection in section.subsections:
|
||||
get_leaf_sections(subsection, current_path)
|
||||
|
||||
# Process all top-level sections
|
||||
for section in doc_structure.toc:
|
||||
get_leaf_sections(section)
|
||||
|
||||
# Add a fragment for the abstract if it exists
|
||||
if doc_structure.abstract:
|
||||
abstract_fragment = SectionFragment(
|
||||
**base_fragment_template,
|
||||
current_section="Abstract",
|
||||
content=self._clean_content(doc_structure.abstract)
|
||||
)
|
||||
if self._validate_fragment(abstract_fragment):
|
||||
fragments.insert(0, abstract_fragment)
|
||||
|
||||
self.logger.info(f"Created {len(fragments)} fragments")
|
||||
return fragments
|
||||
|
||||
def _validate_fragment(self, fragment: SectionFragment) -> bool:
|
||||
"""
|
||||
Validate if the fragment has all required fields with meaningful content.
|
||||
|
||||
Args:
|
||||
fragment: SectionFragment to validate
|
||||
|
||||
Returns:
|
||||
bool: True if fragment is valid, False otherwise
|
||||
"""
|
||||
try:
|
||||
return all([
|
||||
fragment.title.strip(),
|
||||
fragment.section_tree.strip(),
|
||||
fragment.current_section.strip(),
|
||||
fragment.content.strip() or fragment.bibliography.strip()
|
||||
])
|
||||
except AttributeError:
|
||||
return False
|
||||
|
||||
def _clean_content(self, content: str) -> str:
|
||||
"""
|
||||
Clean and normalize content text.
|
||||
|
||||
Args:
|
||||
content: Raw content text
|
||||
|
||||
Returns:
|
||||
str: Cleaned content text
|
||||
"""
|
||||
if not content:
|
||||
return ""
|
||||
|
||||
# Remove excessive whitespace
|
||||
content = re.sub(r'\s+', ' ', content)
|
||||
|
||||
# Remove remaining LaTeX artifacts
|
||||
content = re.sub(r'\\item\s*', '• ', content) # Convert \item to bullet points
|
||||
content = re.sub(r'\\[a-zA-Z]+\{([^}]*)\}', r'\1', content) # Remove simple LaTeX commands
|
||||
|
||||
# Clean special characters
|
||||
content = content.replace('\\\\', '\n') # Convert LaTeX newlines to actual newlines
|
||||
content = re.sub(r'\s*\n\s*', '\n', content) # Clean up newlines
|
||||
|
||||
return content.strip()
|
||||
|
||||
|
||||
async def test_arxiv_splitter():
|
||||
"""测试ArXiv分割器的功能"""
|
||||
|
||||
# 测试配置
|
||||
test_cases = [
|
||||
{
|
||||
"arxiv_id": "2411.03663",
|
||||
"expected_title": "Large Language Models and Simple Scripts",
|
||||
"min_fragments": 10,
|
||||
},
|
||||
# {
|
||||
# "arxiv_id": "1805.10988",
|
||||
# "expected_title": "RAG vs Fine-tuning",
|
||||
# "min_fragments": 15,
|
||||
# }
|
||||
]
|
||||
|
||||
# 创建分割器实例
|
||||
splitter = ArxivSplitter(
|
||||
root_dir="test_cache"
|
||||
)
|
||||
|
||||
|
||||
for case in test_cases:
|
||||
print(f"\nTesting paper: {case['arxiv_id']}")
|
||||
try:
|
||||
fragments = await splitter.process(case['arxiv_id'])
|
||||
|
||||
# 保存fragments
|
||||
output_dir = save_fragments_to_file(fragments,output_dir="crazy_functions/rag_fns/arxiv_fns/gpt_log")
|
||||
print(f"Output saved to: {output_dir}")
|
||||
# 内容检查
|
||||
for fragment in fragments:
|
||||
# 长度检查
|
||||
|
||||
print((fragment.content))
|
||||
print(len(fragment.content))
|
||||
# 类型检查
|
||||
|
||||
|
||||
except Exception as e:
|
||||
print(f"✗ Test failed for {case['arxiv_id']}: {str(e)}")
|
||||
raise
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_arxiv_splitter())
|
||||
Reference in New Issue
Block a user