重构简历导入
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zk
@@ -1,13 +1,12 @@
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"""简历两阶段并行提取
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第一阶段：5路并行提取主表短字段 + 各子表标识名（极快，输出极短）。
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第二阶段：N+1路并行提取每条子表记录的短字段 + description/summary 的「行号区间」。
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description/summary 不再由 AI 照抄原文，AI 只返回行号区间字符串，由代码从原文单元数组切片还原。
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最终组装为与原方案完全一致的 dict 结构（description 为 list[str]），上下游无感知。
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第二阶段：N+1路并行提取每条子表记录的短字段 + description，以及 profile 补充（skills/certificates/summary）。
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description/summary 由 AI 直接按原文结构输出为字符串数组（不再返回行号区间），代码只做类型兜底与清理。
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最终组装为与原方案一致的 dict 结构（description 为 list[str]，summary 为 str），上下游无感知。
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"""
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import asyncio
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import re
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import time
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from langchain_core.output_parsers import StrOutputParser
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@@ -43,78 +42,11 @@ async def _safe_invoke(chain, inp: dict, label: str):
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return None
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def _number_segments(segments: list[str]) -> str:
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"""将单元数组构造为带行号文本：每行 `[行号] 内容`，行号 0 开始"""
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return "\n".join(f"[{i}] {seg}" for i, seg in enumerate(segments))
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# 条目/序号行的起始特征：● ○ • · ▪ ■ ‣ ◆ 等项目符号，或 "1." "2、" "3，" "(4)" "①" 等序号。
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# 视觉行解析下，一条 bullet 常被折成多行；只有「条目起始行」才另起段落，其余行视为折行续接。
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_LIST_ITEM_RE = re.compile(
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r"^\s*(?:"
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r"[●○◦•·∙▪■□‣◆◇►▶*]" # 项目符号
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r"|[-–—]\s" # 连字符 + 空格（markdown 风格）
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r"|\d+\s*[.．、，)）]" # 阿拉伯数字 + 标点：1. / 2、 / 3，/ 4)
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r"|[（(]\s*\d+\s*[）)]" # 括号数字：(1) （2）
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r"|[①②③④⑤⑥⑦⑧⑨⑩⑪⑫⑬⑭⑮⑯⑰⑱⑲⑳]" # 圈数字
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r")"
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)
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def _split_into_paragraphs(lines: list[str]) -> list[str]:
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"""将一个行号区间内的多行拆分为段落列表：
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- 以项目符号/序号开头的行 → 另起一个新段落；
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- 其余行 → 视为上一段落的折行续接，直连拼接（无分隔符）。
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这样即使 AI 把多条 bullet 塞进同一个区间，也能按条目正确换行。
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"""
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paragraphs: list[str] = []
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current = ""
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for line in lines:
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if not current:
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current = line
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elif _LIST_ITEM_RE.match(line):
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paragraphs.append(current)
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current = line
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else:
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current += line
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if current:
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paragraphs.append(current)
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return [p for p in paragraphs if p.strip()]
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def _slice_ranges(range_str, segments: list[str]) -> list[str]:
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"""按行号区间字符串切片还原段落：逗号分段，段内按条目符号/折行拆分。
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range_str 形如 "3-6,7-9" / "5"；非法或越界 token 静默跳过/clamp。
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返回段落数组（每个逗号段可能因含多条 bullet 而进一步拆成多个段落）。
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"""
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if not range_str or not isinstance(range_str, str):
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def _clean_str_list(value) -> list[str]:
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"""将 AI 返回值规整为字符串数组：过滤非字符串与空白元素，去除首尾空白"""
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if not isinstance(value, list):
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return []
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n = len(segments)
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paragraphs: list[str] = []
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for token in range_str.split(","):
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token = token.strip()
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if not token:
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continue
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if "-" in token:
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a, _, b = token.partition("-")
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a, b = a.strip(), b.strip()
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if not a.isdigit() or not b.isdigit():
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continue
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start, end = int(a), int(b)
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else:
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if not token.isdigit():
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continue
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start = end = int(token)
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if start > end:
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start, end = end, start
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start = max(0, start)
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end = min(n - 1, end)
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if start > n - 1:
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continue
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paragraphs.extend(_split_into_paragraphs(segments[start:end + 1]))
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return paragraphs
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return [s.strip() for s in value if isinstance(s, str) and s.strip()]
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# ==================== 第一阶段：概览 ====================
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@@ -126,9 +58,9 @@ _overview_project_chain = _build_chain(OVERVIEW_PROJECT_PROMPT)
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_overview_competition_chain = _build_chain(OVERVIEW_COMPETITION_PROMPT)
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async def _extract_overview(numbered_text: str) -> dict:
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async def _extract_overview(text: str) -> dict:
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"""第一阶段：5路并行提取概览信息"""
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inp = {"text": numbered_text}
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inp = {"text": text}
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profile, edu_names, work_names, proj_names, comp_names = await asyncio.gather(
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_safe_invoke(_overview_profile_chain, inp, "概览-个人信息"),
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_safe_invoke(_overview_education_chain, inp, "概览-教育"),
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@@ -160,20 +92,20 @@ _DETAIL_MODULES: tuple[tuple[str, str, str], ...] = (
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_SUB_MODULES: tuple[str, ...] = tuple(m[0] for m in _DETAIL_MODULES)
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async def _extract_detail(prompt_tpl: str, name: str, numbered_text: str, label: str) -> dict | None:
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async def _extract_detail(prompt_tpl: str, name: str, text: str, label: str) -> dict | None:
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"""单条子表记录详情提取：用 name 替换 prompt 中的 {name}"""
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chain = _build_chain(prompt_tpl.replace("{name}", name))
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return await _safe_invoke(chain, {"text": numbered_text}, label)
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return await _safe_invoke(chain, {"text": text}, label)
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async def _extract_all_details(overview: dict, numbered_text: str) -> dict:
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async def _extract_all_details(overview: dict, text: str) -> dict:
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"""第二阶段：根据概览结果，N+1路并行提取所有子表记录详情 + 个人信息补充"""
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# 第 0 路固定为 profile 补充（skills/certificates/summaryRange），其余按子表记录展开
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tasks = [_extract_detail(DETAIL_PROFILE_PROMPT, "", numbered_text, "详情-个人信息补充")]
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# 第 0 路固定为 profile 补充（skills/certificates/summary），其余按子表记录展开
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tasks = [_extract_detail(DETAIL_PROFILE_PROMPT, "", text, "详情-个人信息补充")]
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task_modules = ["profile_extra"]
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for module, prompt_tpl, label in _DETAIL_MODULES:
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for name in overview[module]:
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tasks.append(_extract_detail(prompt_tpl, name, numbered_text, f"详情-{label}-{name}"))
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tasks.append(_extract_detail(prompt_tpl, name, text, f"详情-{label}-{name}"))
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task_modules.append(module)
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results = await asyncio.gather(*tasks)
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@@ -185,21 +117,20 @@ async def _extract_all_details(overview: dict, numbered_text: str) -> dict:
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# ==================== 组装 ====================
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def _assemble(overview: dict, details: dict, segments: list[str]) -> dict:
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"""将两阶段结果组装为与原方案一致的 dict 结构（description 还原为 list[str]）"""
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def _assemble(overview: dict, details: dict) -> dict:
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"""将两阶段结果组装为与原方案一致的 dict 结构（description 为 list[str]，summary 为 str）"""
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profile = overview["profile"]
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profile_extra = details.get("profile_extra", [{}])[0] if details.get("profile_extra") else {}
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profile["skills"] = (profile_extra.get("skills") or [])[:5]
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profile["certificates"] = profile_extra.get("certificates") or []
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summary_paras = _slice_ranges(profile_extra.get("summaryRange"), segments)
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profile["skills"] = _clean_str_list(profile_extra.get("skills"))[:5]
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profile["certificates"] = _clean_str_list(profile_extra.get("certificates"))
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summary_paras = _clean_str_list(profile_extra.get("summary"))
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profile["summary"] = "\n".join(summary_paras) if summary_paras else None
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result = dict(profile)
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for module in _SUB_MODULES:
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items = []
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for item in details.get(module, []):
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item["description"] = _slice_ranges(item.get("descriptionRange"), segments)
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item.pop("descriptionRange", None)
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item["description"] = _clean_str_list(item.get("description"))
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items.append(item)
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result[module] = items
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return result
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@@ -207,23 +138,21 @@ def _assemble(overview: dict, details: dict, segments: list[str]) -> dict:
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# ==================== 入口 ====================
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async def extract_all(segments: list[str]) -> dict:
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async def extract_all(text: str) -> dict:
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"""两阶段并行提取简历，返回与原方案完全一致的结构化数据
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segments: 文件解析后的「文本单元数组」（PDF按块/docx·txt按行）。
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text: 简历纯文本全文（PyMuPDF/docx/txt 提取，保留原始换行）。
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"""
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numbered_text = _number_segments(segments)
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log.info("第一阶段：5路并行概览提取")
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overview = await _extract_overview(numbered_text)
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overview = await _extract_overview(text)
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log.info(
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"概览完成 - " + " ".join(f"{label}:{len(overview[module])}" for module, _, label in _DETAIL_MODULES)
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)
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total = sum(len(overview[m]) for m in _SUB_MODULES)
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log.info(f"第二阶段：{total + 1}路并行详情提取")
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details = await _extract_all_details(overview, numbered_text)
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details = await _extract_all_details(overview, text)
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result = _assemble(overview, details, segments)
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result = _assemble(overview, details)
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log.info("两阶段提取完成，数据组装完毕")
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return result
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