380 lines
15 KiB
Python
380 lines
15 KiB
Python
"""pi CLI 调用与 JSON 提取 — 调用 pi 生成总结，从输出中提取结构化 JSON。"""
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from __future__ import annotations
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import asyncio
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import json
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import logging
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import re
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from pathlib import Path
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from app.config import settings
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logger = logging.getLogger(__name__)
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# ── 自定义异常 ──────────────────────────────────────────────────────────
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class PiTimeoutError(Exception):
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pass
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class PiProcessError(Exception):
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def __init__(self, returncode: int, stderr: str):
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self.returncode = returncode
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self.stderr = stderr
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super().__init__(f"pi exited with code {returncode}: {stderr[:500]}")
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class JsonNotFoundError(Exception):
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pass
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# ── meta.json ───────────────────────────────────────────────────────────
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def write_meta_json(paper) -> Path:
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"""写入 data/papers/{arxiv_id}/meta.json，返回路径。"""
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from app.services.pdf_downloader import paper_dir
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d = paper_dir(paper.arxiv_id)
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d.mkdir(parents=True, exist_ok=True)
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meta_path = d / "meta.json"
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authors = [a.name for a in paper.authors]
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tags = [t.tag for t in paper.tags]
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meta = {
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"arxiv_id": paper.arxiv_id,
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"title_en": paper.title_en,
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"abstract": paper.abstract or "",
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"published_at": paper.published_at.isoformat() if paper.published_at else None,
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"authors": authors,
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"tags": tags,
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"upvotes": paper.upvotes,
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}
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meta_path.write_text(
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json.dumps(meta, ensure_ascii=False, indent=2), encoding="utf-8"
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)
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return meta_path
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# ── PDF 文本提取 ────────────────────────────────────────────────────────
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def _trim_body(text: str, max_chars: int | None = None) -> str:
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"""去除参考文献，保留正文+附录，超长时从末尾截断。
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策略：
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1. 去掉 References/Bibliography 段落（纯引用列表，对解读无用）
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2. 正文 + 附录全部保留
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3. 如果指定了 max_chars 且总长超过，从末尾截断（附录靠后，优先保留正文）
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"""
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import re
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# 找 References 段落的位置（在 Appendix 之后的那个）
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# 简单策略：找到 References 标题，如果后面没有 Appendix 就全删
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# 如果后面还有 Appendix，只删 References 到 Appendix 之间的内容
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ref_match = re.search(r"(?m)^(?:References|Bibliography|参考文献)\s*$", text)
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if ref_match:
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ref_start = ref_match.start()
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# 看 References 之后有没有 Appendix
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after_ref = text[ref_start:]
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app_match = re.search(
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r"(?m)^(?:A\s+(?:Appendix|Supplementary)|Appendix|附录)\s*$", after_ref
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)
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if app_match:
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# References 之后有 Appendix：只删 References 段
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ref_end = ref_start + app_match.start()
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text = text[:ref_start] + text[ref_end:]
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else:
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# References 之后没有 Appendix：删掉从 References 到结尾
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text = text[:ref_start].rstrip()
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# 去掉 Acknowledgments（对解读无用）
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ack_match = re.search(r"(?m)^(?:Acknowledgments?\s*|致谢\s*)$", text)
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if ack_match:
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# 只删 Acknowledgments 本身，不删后面的内容
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next_section = re.search(r"(?m)^(?:A\s|Appendix|Supplementary|附录)\s*$", text[ack_match.start():])
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if next_section:
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text = text[:ack_match.start()] + text[ack_match.start() + next_section.start():]
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else:
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text = text[:ack_match.start()].rstrip()
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# 最后：如果指定了上限且超长，从末尾截断（附录在后面，正文在前面，优先保留正文）
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if max_chars is not None and len(text) > max_chars:
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text = text[:max_chars].rstrip()
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return text
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def extract_pdf_text(pdf_path: Path, max_chars: int | None = None) -> Path:
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"""用 pymupdf 提取 PDF 正文文本，保存为 .txt。
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max_chars=None 时不截断，给 search/auto 模式保留完整内容。
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"""
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import pymupdf
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txt_path = pdf_path.with_suffix(".txt")
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if txt_path.exists():
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# 缓存优先；如果需重新提取（不同 max_chars），先删旧文件
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return txt_path
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doc = pymupdf.open(str(pdf_path))
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raw_text = "\n\n".join(page.get_text() for page in doc)
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doc.close()
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body = _trim_body(raw_text, max_chars=max_chars)
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txt_path.write_text(body, encoding="utf-8")
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logger.info(
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"Extracted PDF text: %s (%d -> %d chars, -%d%%)",
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txt_path,
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len(raw_text),
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len(body),
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(1 - len(body) / len(raw_text)) * 100 if raw_text else 0,
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)
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return txt_path
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# ── Prompt 构建 ─────────────────────────────────────────────────────────
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def _build_prompt(
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arxiv_id: str,
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meta_path: Path,
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txt_path: Path,
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pdf_mode: str,
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fix_errors: list[str] | None = None,
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) -> str:
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"""根据模式构建 pi prompt。
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inject: 全量注入，prompt 末尾包含论文全文内容
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search: pi 自主 read 文件，prompt 只包含工作流指令
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"""
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json_schema = (
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"## 必须包含以下字段（不要自创字段名）：\n"
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'{"arxiv_id": "...", '
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'"title_zh": "中文标题", '
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'"one_line": "一句话概括(≤50字)", '
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'"tags": ["标签1","标签2"], '
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'"difficulty": "入门/进阶/前沿", '
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'"prerequisites": {"concepts": [{"term":"术语","explanation":"详细解释这个概念是什么、怎么工作的（50-150字）","why_matters":"为什么读懂本文需要它"}]}, '
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'"motivation": {"problem": "详细段落：现有方法的具体问题（包含具体场景和数据）", '
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'"goal": "详细段落：本文的具体目标", '
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'"gap": "详细段落：本文的独特切入角度"}, '
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'"method": {"overview": "详细段落：方法整体思路（先直觉再技术路线）", '
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'"key_idea": "详细段落：核心创新点（和已有方法的本质区别）", '
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'"steps": "详细段落：方法步骤的完整描述（每步的输入输出和具体操作）", '
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'"novelty": "详细段落：技术新颖性分析"}, '
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'"results": {"main_findings": "详细段落：核心发现（带具体数字和指标，逐一分析每个实验）", '
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'"benchmarks": [{"task":"任务","metric":"指标","this_work":"本文结果","baseline":"基线","improvement":"提升"}], '
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'"limitations": "详细段落：局限性分析（作者承认的+你自己的观察")}, '
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'"improvements": {"weaknesses": "详细段落：独立分析的弱点（具体场景，每个弱点给改进方向）", '
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'"future_work": "详细段落：未来研究方向（作者提出的+基于成果可延伸的）", '
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'"reproducibility": "详细段落：复现评估（开源情况、数据、算力、难度")}, '
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'"figures": [{"id":"Figure 1","caption":"原图标题","description":"文字描述图展示了什么","reason":"为什么这张图对理解论文重要"},'
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'{"id":"Table 1","caption":"表格标题","description":"文字描述表格包含的数据和结论","reason":"为什么这个表格对理解论文重要"}]'
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"\n注意：figures 必须包含论文中的所有重要图表，包括 Figure 和 Table，id 严格使用 \"Figure N\" 或 \"Table N\" 格式。"
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"}"
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)
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writing_requirements = (
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"## 写作要求\n"
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"- 每个字符串字段必须写成详细段落（200-500字），不要用列表或数组\n"
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"- 必须包含论文中的具体数据、数字、实验指标\n"
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"- 像资深同事给同事讲论文一样，专业但易懂\n"
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"- 数学公式、符号、变量必须使用 LaTeX 格式：行内公式用 $...$，独立公式用 $$...$$\n"
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" 例如：损失函数 $\\mathcal{L} = -\\sum_{i} \\log p(y_i | x_i)$，学习率 $\\eta$\n"
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)
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if fix_errors:
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error_list = "\n".join(f"- {e}" for e in fix_errors)
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return (
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"你之前生成的 JSON 存在以下问题，请修正后重新用 write_file 保存到 "
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f"data/papers/{arxiv_id}/summary.json：\n\n"
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f"{error_list}\n\n"
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"注意：所有字符串字段必须是详细段落（≥50字），不能是数组或列表。"
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"修正后请用 bash 运行 python scripts/validate_summary.py 验证。"
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)
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if pdf_mode == "search":
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return (
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"请深度解读以下论文，严格按下面的 JSON schema 输出结果。\n\n"
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"## 工作流程\n"
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f"1. 先用 read 工具读取 {meta_path} 了解论文元信息（标题、作者、摘要）\n"
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f"2. 再用 read 工具阅读 {txt_path}（论文正文全文），可以多次读取定位关键段落\n"
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f"3. 充分理解后，用 write_file 将结果保存到 data/papers/{arxiv_id}/summary.json\n\n"
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+ writing_requirements
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+ "\n"
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+ json_schema
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)
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else:
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return (
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"请深度解读以下论文，严格按下面的 JSON schema 输出结果。\n\n"
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"## 工作流程\n"
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"论文元信息和正文全文已在上文提供，请仔细阅读。\n"
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f"1. 充分理解论文后，用 write_file 将结果保存到 data/papers/{arxiv_id}/summary.json\n"
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"2. 用 bash 运行 python scripts/validate_summary.py 验证\n\n"
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+ writing_requirements
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+ "\n"
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+ json_schema
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)
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# ── pi CLI 调用 ────────────────────────────────────────────────────────
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async def call_pi(
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meta_path: Path,
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pdf_path: Path,
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fix_errors: list[str] | None = None,
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session_id: str | None = None,
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pdf_mode: str = "inject",
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) -> tuple[str, str]:
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"""调用 pi CLI 非交互模式，返回 (stdout 文本, session_id)。
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fix_errors: 如果非空，表示上一次验证失败的错误列表，pi 需要修正这些问题。
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session_id: 如果非空，用 --continue 延续该 session；否则创建新 session。
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pdf_mode: "inject" = 全量注入 prompt（@file），"search" = pi 自主 read 文件。
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"""
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arxiv_id = meta_path.parent.name
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# 提取 PDF 全文（不截断），根据实际大小自动选择模式
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txt_path = extract_pdf_text(pdf_path, max_chars=None)
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txt_size = len(txt_path.read_text(encoding="utf-8"))
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actual_mode = pdf_mode
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if pdf_mode == "auto":
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if txt_size > 80_000:
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actual_mode = "search"
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logger.info(
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"Auto mode: %s text=%d chars > 80k → search", arxiv_id, txt_size
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)
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else:
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actual_mode = "inject"
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logger.info(
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"Auto mode: %s text=%d chars ≤ 80k → inject", arxiv_id, txt_size
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)
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# inject 模式需要截断过长的文本（避免撑爆 context）
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if actual_mode == "inject" and txt_size > 80_000:
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body = txt_path.read_text(encoding="utf-8")
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trimmed = body[:80_000].rstrip()
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txt_path.write_text(trimmed, encoding="utf-8")
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logger.info("Truncated %s for inject: %d → %d chars", arxiv_id, txt_size, len(trimmed))
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prompt_text = _build_prompt(arxiv_id, meta_path, txt_path, actual_mode, fix_errors)
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# 构建 session ID（每篇论文一个独立 session）
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if session_id is None:
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import uuid
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session_id = f"summary-{arxiv_id}-{uuid.uuid4().hex[:8]}"
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# 工具列表：search 模式需要 read 工具
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tools = "bash,write_file" if actual_mode != "search" else "bash,write_file,read"
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cmd = [
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settings.PI_BIN,
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"-p",
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"--tools", tools,
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]
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if fix_errors:
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cmd += ["--session", session_id, "--continue"]
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else:
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cmd += ["--session-id", session_id]
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cmd += [
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"--skill",
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settings.SUMMARY_SKILL,
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prompt_text,
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]
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if not fix_errors and actual_mode != "search":
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# inject 模式：首次调用传 @file；search 模式 pi 自己 read，不注入
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cmd += [f"@{meta_path}", f"@{txt_path}"]
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logger.info(
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"Calling pi for %s (fix=%s, session=%s, mode=%s)",
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arxiv_id, bool(fix_errors), session_id, actual_mode,
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)
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proc = await asyncio.create_subprocess_exec(
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*cmd,
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stdout=asyncio.subprocess.PIPE,
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stderr=asyncio.subprocess.PIPE,
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)
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try:
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stdout, stderr = await asyncio.wait_for(
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proc.communicate(),
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timeout=settings.SUMMARY_TIMEOUT_SECONDS,
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)
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except asyncio.TimeoutError:
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proc.kill()
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await proc.wait()
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raise PiTimeoutError(f"pi timed out after {settings.SUMMARY_TIMEOUT_SECONDS}s")
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if proc.returncode != 0:
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raise PiProcessError(proc.returncode, stderr.decode("utf-8", errors="replace"))
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return stdout.decode("utf-8", errors="replace"), session_id
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# ── JSON 提取 ──────────────────────────────────────────────────────────
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def extract_json(raw_output: str) -> dict:
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"""从 pi 输出中提取 JSON dict。三步策略：直接解析 → 代码块 → 最大花括号块。"""
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# 策略 1：整体直接解析
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stripped = raw_output.strip()
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try:
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result = json.loads(stripped)
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if isinstance(result, dict) and "title_zh" in result:
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return result
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except json.JSONDecodeError:
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pass
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# 策略 2：提取 ```json ... ``` 代码块
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fence_pattern = re.compile(r"```(?:json)?\s*\n(.*?)```", re.DOTALL)
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for match in fence_pattern.finditer(raw_output):
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try:
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result = json.loads(match.group(1).strip())
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if isinstance(result, dict) and "title_zh" in result:
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return result
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except json.JSONDecodeError:
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continue
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# 策略 3：匹配包含 title_zh 的最大 {...} 块
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brace_pattern = re.compile(r"\{[^{}]*\"title_zh\"[^{}]*\}", re.DOTALL)
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for match in brace_pattern.finditer(raw_output):
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try:
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return json.loads(match.group(0))
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except json.JSONDecodeError:
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continue
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# 更宽松：找到最大的 { ... } 平衡块
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best = None
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best_len = 0
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for i, ch in enumerate(raw_output):
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if ch != "{":
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continue
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depth = 0
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for j in range(i, len(raw_output)):
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if raw_output[j] == "{":
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depth += 1
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elif raw_output[j] == "}":
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depth -= 1
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if depth == 0:
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candidate = raw_output[i : j + 1]
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if len(candidate) > best_len:
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try:
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parsed = json.loads(candidate)
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if isinstance(parsed, dict):
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best = parsed
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best_len = len(candidate)
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except json.JSONDecodeError:
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pass
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break
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if best is not None:
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return best
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raise JsonNotFoundError("no JSON object found in pi output")
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