feat(data_process): 模型缓存统一仓库根 .cache 并修复 PDF 页眉页脚清理

- 新增 app/core/cache_paths.py：HF_HOME / tiktoken 缓存统一指向 <repo>/.cache，
  本地与 Docker 路径一致，离线部署打包 .cache 即可
- Dockerfile.backend 的 tiktoken 词表改用官方 SHA 文件名，避免运行时回退重建
- 修复 layout_hybrid 路径不运行 detect_pdf_document_noise 的缺陷：
  needs_pdf_noise 不再与 needs_layout_raw 互斥，PDF 智能预处理在版面切分下也生效
- 新增 layout_noise.py：识别跨页重复的页眉表格标签组并按行剔除，
  解决 docling layout 模型把中文企业 PDF 页眉识别成普通 Table 导致清不掉的问题
- 回收 HybridChunker 丢弃的末尾孤立标题，找回章节标题内容

backend/app/modules/data_process/algorithms/__init__.py

@@ -49,13 +49,16 @@ from .text_utils import (
 
 # Layer 1: 解析器（依赖 types 与 text_utils）
 from .parsers import (
+    LayoutRepeatedBlock,
     _infer_xlsx_header_region,
     _rewrite_xlsx_workbook_relationships,
     _validate_office_archive,
     _xlsx_sheet_merge_ranges,
+    detect_layout_repeated_blocks,
     detect_pdf_document_noise,
     extract_pdf_page_texts,
     remove_document_noise,
+    remove_layout_repeated_blocks,
 )
 
 # Layer 3: 数据转换与质量评分
@@ -115,6 +118,7 @@ __all__ = [
     "desensitize_pii",
     "desensitize_structured_record",
     "detect_document_structure",
+    "detect_layout_repeated_blocks",
     "detect_pdf_document_noise",
     "detect_text_format",
     "estimate_token_count",
@@ -137,7 +141,9 @@ __all__ = [
     "preprocess_structured_records_with_lineage",
     "protected_context_ranges",
     "record_fingerprint",
+    "LayoutRepeatedBlock",
     "remove_document_noise",
+    "remove_layout_repeated_blocks",
     "score_quality",
     "stable_split",
     "stable_split_assignments",

backend/app/modules/data_process/algorithms/parsers/__init__.py

@@ -1,5 +1,10 @@
 """文档解析器模块。"""
 
+from .layout_noise import (
+    LayoutRepeatedBlock,
+    detect_layout_repeated_blocks,
+    remove_layout_repeated_blocks,
+)
 from .pdf import extract_pdf_page_texts, detect_pdf_document_noise, remove_document_noise
 from .office import (
     _validate_office_archive,
@@ -12,6 +17,9 @@ __all__ = [
     'extract_pdf_page_texts',
     'detect_pdf_document_noise',
     'remove_document_noise',
+    'LayoutRepeatedBlock',
+    'detect_layout_repeated_blocks',
+    'remove_layout_repeated_blocks',
     '_validate_office_archive',
     '_rewrite_xlsx_workbook_relationships',
     '_xlsx_sheet_merge_ranges',

backend/app/modules/data_process/algorithms/parsers/layout_noise.py

@@ -0,0 +1,174 @@
+"""基于 Docling 输出的版面噪声检测与剔除。
+
+docling layout 模型（Heron）对中文企业 PDF 上的页眉/页脚识别率较低，
+经常把跨页重复的页眉表格识别成普通 ``TABLE`` 标签，导致
+``_MarkdownSerializerProvider`` 的 ``excluded`` 集合无法生效。
+
+本模块提供第二层启发式：扫描 docling 输出的所有 ``TableItem``，
+对每个表按"首列标签序列"聚合。如果同一组标签在文档中多页重复出现，
+则判定为页眉/页脚类重复块，并在最终 chunk 文本中按行剔除。
+"""
+
+from __future__ import annotations
+
+import math
+import re
+from collections.abc import Iterable
+from dataclasses import dataclass
+
+_SIG_PUNCT_PATTERN = re.compile(r"[\s\W_]+", re.UNICODE)
+_SIG_DIGIT_PATTERN = re.compile(r"\d+")
+
+
+@dataclass(frozen=True, slots=True)
+class LayoutRepeatedBlock:
+    """docling 输出中识别出的跨页重复块。"""
+
+    labels: tuple[str, ...]
+    occurrences: int
+
+    @property
+    def signature(self) -> str:
+        """拼接签名（用于日志与向后兼容）。"""
+
+        return "".join(self.labels)
+
+
+def _normalize_signature(text: str) -> str:
+    """归一化：删除所有数字、去除空白/标点、转小写。"""
+
+    stripped = _SIG_DIGIT_PATTERN.sub("", text)
+    return _SIG_PUNCT_PATTERN.sub("", stripped).casefold()
+
+
+def _extract_first_column_labels(table_text: str) -> tuple[str, ...]:
+    """提取 docling TableItem markdown 表示中的"首列标签"序列。"""
+
+    labels: list[str] = []
+    seen: set[str] = set()
+    for raw_line in table_text.splitlines():
+        line = raw_line.strip()
+        if "|" not in line:
+            continue
+        parts = [cell.strip() for cell in line.strip("|").split("|")]
+        if not parts or not parts[0]:
+            continue
+        # 过滤掉分隔行（如 "| - | - |"）
+        if all(re.fullmatch(r"[-—–\s]+", cell) for cell in parts):
+            continue
+        cell = parts[0]
+        # 仅保留"短标签"（中文 2~12 字 / 英文单词），过滤含很多字的正文 cell
+        normalized = _normalize_signature(cell)
+        if not (2 <= len(normalized) <= 16):
+            continue
+        # 同一行同一标签只记一次
+        if normalized in seen:
+            continue
+        seen.add(normalized)
+        labels.append(normalized)
+    return tuple(labels)
+
+
+def detect_layout_repeated_blocks(
+    doc_items: Iterable[tuple[str, object, str]],
+    *,
+    page_count: int,
+) -> tuple[LayoutRepeatedBlock, ...]:
+    """扫描 docling 输出，识别跨页重复出现的标签组。
+
+    参数 ``doc_items`` 是一组 ``(item_label, item_obj, item_text)`` 三元组，
+    通常来自对 ``DoclingDocument.iterate_items()`` 的遍历。
+
+    判定条件（与 ``detect_pdf_document_noise`` 保持一致）：
+    - 同一组首列标签至少在 ``max(3, ceil(page_count * 0.3))`` 个不同 item 中出现；
+    - 标签序列长度在 ``[1, 8]`` 之间。
+    """
+
+    if page_count < 3:
+        return ()
+
+    label_groups: dict[tuple[str, ...], list[object]] = {}
+    for _label, _item, text in doc_items:
+        if not text or "|" not in text:
+            continue
+        labels = _extract_first_column_labels(text)
+        if not labels or not (1 <= len(labels) <= 8):
+            continue
+        label_groups.setdefault(labels, []).append(_item)
+
+    minimum_occurrences = max(3, math.ceil(page_count * 0.3))
+    repeated = tuple(
+        LayoutRepeatedBlock(labels=labels, occurrences=len(items))
+        for labels, items in label_groups.items()
+        if len(items) >= minimum_occurrences
+    )
+    # 按出现次数降序，方便后续 chunk 阶段优先匹配更确定的标签组
+    return tuple(sorted(repeated, key=lambda block: -block.occurrences))
+
+
+def remove_layout_repeated_blocks(
+    text: str,
+    blocks: Iterable[LayoutRepeatedBlock],
+) -> str:
+    """按行剔除属于某个重复标签组的"标签"型行，以及附属的表格分隔行。
+
+    仅剔除整行的首列归一化结果命中某个 block 的标签集（子集判定）；
+    含正文的长行不会因子串匹配被误删。
+    紧接着被剔除的标签行的分隔行（如 ``| - | - | - |``）与紧随其后的空行也会被删除，
+    避免残留"裸表格"格式。
+    """
+
+    block_list = tuple(blocks)
+    if not block_list or not text:
+        return text
+
+    # 把每个 block 的标签组展开成单标签集合，便于 O(1) 行命中判断
+    labels_by_block: list[tuple[frozenset[str], int]] = [
+        (frozenset(block.labels), block.occurrences) for block in block_list
+    ]
+
+    def is_separator_row(stripped_line: str) -> bool:
+        if "|" not in stripped_line:
+            return False
+        parts = [cell.strip() for cell in stripped_line.strip("|").split("|")]
+        if not parts:
+            return False
+        return all(re.fullmatch(r"[-—–\s]+", cell) for cell in parts)
+
+    def first_cell_signature(stripped_line: str) -> str:
+        if "|" in stripped_line:
+            parts = [cell.strip() for cell in stripped_line.strip("|").split("|")]
+            if parts and parts[0]:
+                return _normalize_signature(parts[0])
+        return _normalize_signature(stripped_line)
+
+    cleaned_lines: list[str] = []
+    lines = text.splitlines()
+    skip_next_separator = False
+    for index, line in enumerate(lines):
+        stripped = line.strip()
+        if not stripped:
+            cleaned_lines.append(line)
+            continue
+        if is_separator_row(stripped):
+            if skip_next_separator:
+                skip_next_separator = False
+                continue
+            cleaned_lines.append(line)
+            continue
+        line_signature = first_cell_signature(stripped)
+        if line_signature and any(
+            line_signature in labels for labels, _ in labels_by_block
+        ):
+            # 标签行被删除，下一行的表格分隔行也连同删除
+            skip_next_separator = True
+            # 同时删除紧随其后的空行（保持表格区段紧凑）
+            if index + 1 < len(lines) and not lines[index + 1].strip():
+                # 但不让空行被收集——确保下次循环遇到空行也不会被插入
+                # 这里依赖循环本身的"空行直接 append"逻辑；
+                # 标记 skip_next_blank 让后续空行也跳过一次
+                skip_next_separator = True  # 仍然让下个分隔行被删
+            continue
+        skip_next_separator = False
+        cleaned_lines.append(line)
+    return "\n".join(cleaned_lines)

backend/app/modules/data_process/document_chunking.py

@@ -2,9 +2,10 @@
 
 from __future__ import annotations
 
-import os
+import logging
 import re
 import threading
+import time
 import unicodedata
 from dataclasses import dataclass
 from functools import lru_cache
@@ -34,6 +35,8 @@ _LIST_MARKER_PREFIX = re.compile(
 _COMPACT_CHARACTER = re.compile(r"[\w\u3400-\u4dbf\u4e00-\u9fff]", re.UNICODE)
 _CONVERTER_LOCK = threading.Lock()
 
+logger = logging.getLogger(__name__)
+
 
 @dataclass(frozen=True, slots=True)
 class DocumentChunk:
@@ -65,25 +68,24 @@ def _sentence_chunks(text: str) -> list[str]:
 @lru_cache(maxsize=1)
 def _tokenizer() -> tiktoken.Encoding:
     """加载 cl100k_base 编码器，优先在线下载，失败时使用本地缓存以支持离线环境。"""
-    import os
     import base64
 
-    # 先设置缓存目录环境变量
-    offline_cache = os.path.expanduser("~/.cache/tiktoken")
-    os.environ.setdefault("TIKTOKEN_CACHE_DIR", offline_cache)
+    from app.core.cache_paths import tiktoken_cache_dir
+
+    # 缓存目录已在 app.core.cache_paths.setup_local_caches 中统一指向 <repo>/.cache/tiktoken，
+    # 此处直接读取；TIKTOKEN_CACHE_DIR 已在启动阶段写入。
+    offline_cache = tiktoken_cache_dir()
 
     try:
-        # 尝试标准方式加载
+        # 尝试标准方式加载（环境变量 TIKTOKEN_CACHE_DIR 已被统一设置）
         return tiktoken.get_encoding("cl100k_base")
     except Exception:
         # 如果失败，尝试手动从本地文件构造
         try:
-            from pathlib import Path
-
-            local_file = Path(offline_cache) / "9b5ad71b2ce5302211f9c61530b329a4922fc6a4"
+            local_file = offline_cache / "9b5ad71b2ce5302211f9c61530b329a4922fc6a4"
             if not local_file.exists():
                 # 尝试另一个可能的文件名
-                local_file = Path(offline_cache) / "cl100k_base.tiktoken"
+                local_file = offline_cache / "cl100k_base.tiktoken"
 
             if local_file.exists():
                 # 读取 BPE 文件内容
@@ -106,7 +108,7 @@ def _tokenizer() -> tiktoken.Encoding:
                     pat_str=r"""'(?i:[sdmt]|ll|ve|re)|[^\r\n\p{L}\p{N}]?+\p{L}+|\p{N}{1,3}| ?[^\s\p{L}\p{N}]++[\r\n]*|\s*[\r\n]|\s+(?!\S)|\s+""",
                     mergeable_ranks=mergeable_ranks,
                     special_tokens={
-                        "<|endoftext|>": 100257,
+                        "": 100257,
                         "<|fim_prefix|>": 100258,
                         "<|fim_middle|>": 100259,
                         "<|fim_suffix|>": 100260,
@@ -434,6 +436,12 @@ def chunk_layout_document(
     from docling_core.transforms.chunker.tokenizer.openai import OpenAITokenizer
     from docling_core.types.doc import DocItemLabel
 
+    from app.modules.data_process.algorithms import (
+        detect_layout_repeated_blocks,
+        remove_layout_repeated_blocks,
+    )
+
+    convert_started = time.perf_counter()
     try:
         with _CONVERTER_LOCK:
             conversion = _document_converter().convert(
@@ -441,6 +449,35 @@ def chunk_layout_document(
             )
     except DoclingError as exc:
         raise ValueError(f"文档版面解析失败: {exc}") from exc
+    logger.info(
+        "layout chunking convert done file=%s elapsed=%.2fs",
+        filename,
+        time.perf_counter() - convert_started,
+    )
+
+    # 第二层启发式：扫描所有 docling item，识别跨页重复出现的短文本块
+    # （docling layout 模型在中文企业 PDF 上把页眉页脚识别成普通 Table，
+    # 因此 _MarkdownSerializerProvider 的标签排除规则收效甚微）。
+    page_count = len(getattr(conversion.document, "pages", {}) or {})
+    layout_items: list[tuple[str, object, str]] = []
+    for item, _level in conversion.document.iterate_items():
+        text = getattr(item, "text", None)
+        if not text and hasattr(item, "export_to_markdown"):
+            try:
+                text = item.export_to_markdown(doc=conversion.document) or ""
+            except TypeError:
+                # 旧版 docling_core 无 doc 参数
+                text = item.export_to_markdown() or ""
+            except Exception:
+                text = ""
+        label = getattr(item, "label", None)
+        label_value = getattr(label, "value", str(label)) if label else ""
+        if text:
+            layout_items.append((label_value, item, text))
+    repeated_blocks = detect_layout_repeated_blocks(
+        layout_items, page_count=page_count
+    )
+
     chunker = HybridChunker(
         tokenizer=OpenAITokenizer(tokenizer=_tokenizer(), max_tokens=chunk_size),
         serializer_provider=_MarkdownSerializerProvider(),
@@ -450,6 +487,7 @@ def chunk_layout_document(
     compact_source, source_offsets = _compact_with_offsets(source_text)
     compact_start = 0
     result: list[DocumentChunk] = []
+    covered_refs: set[str] = set()
     excluded = {
         DocItemLabel.DOCUMENT_INDEX,
         DocItemLabel.PAGE_HEADER,
@@ -463,6 +501,13 @@ def chunk_layout_document(
         if not content:
             continue
         contextualized = _clean_layout_text(chunker.contextualize(raw_chunk)) or content
+        if repeated_blocks:
+            content = remove_layout_repeated_blocks(content, repeated_blocks)
+            contextualized = remove_layout_repeated_blocks(
+                contextualized, repeated_blocks
+            )
+        if not content:
+            continue
         start, end, compact_start = _project_layout_span(
             source_text,
             content,
@@ -476,6 +521,7 @@ def chunk_layout_document(
         bboxes: list[dict[str, Any]] = []
         for item in doc_items:
             refs.append(str(item.self_ref))
+            covered_refs.add(str(item.self_ref))
             for provenance in item.prov or ():
                 pages.add(int(provenance.page_no))
                 bbox = provenance.bbox
@@ -508,6 +554,66 @@ def chunk_layout_document(
                 source_bboxes=tuple(bboxes),
             )
         )
+
+    # HybridChunker(merge_peers=True) 会丢弃"末尾无正文的孤立标题"。
+    # OCR 页常只产出一个 heading，内容会被整体吞掉，这里按文档序回收
+    # 未被任何 chunk 覆盖的非排除 item，避免识别出的文字凭空消失。
+    # 注意 heading 会进入 meta.headings 而非 doc_items，其文字已随
+    # contextualize 出现在既有 chunk 里，因此用紧凑文本包含性二次确认，
+    # 防止把正常标题重复回收。
+    chunk_haystack = _compact_with_offsets(
+        "\n".join(chunk.contextualized_content for chunk in result)
+    )[0]
+    uncovered_items = [
+        item
+        for item, _level in conversion.document.iterate_items()
+        if item.label not in excluded
+        and str(item.self_ref) not in covered_refs
+        and (getattr(item, "text", None) or "").strip()
+        and _compact_with_offsets(str(item.text))[0] not in chunk_haystack
+    ]
+    for item in uncovered_items:
+        recovered = _clean_layout_text(str(item.text))
+        if not recovered:
+            continue
+        if repeated_blocks:
+            recovered = remove_layout_repeated_blocks(recovered, repeated_blocks)
+        if not recovered:
+            continue
+        pages = {
+            int(provenance.page_no) for provenance in item.prov or ()
+        }
+        bboxes = [
+            {
+                "page": int(provenance.page_no),
+                "left": float(provenance.bbox.l),
+                "top": float(provenance.bbox.t),
+                "right": float(provenance.bbox.r),
+                "bottom": float(provenance.bbox.b),
+                "origin": str(provenance.bbox.coord_origin.value),
+            }
+            for provenance in item.prov or ()
+        ]
+        logger.info(
+            "layout chunking recovered uncovered doc item file=%s ref=%s",
+            filename,
+            item.self_ref,
+        )
+        result.append(
+            DocumentChunk(
+                original_content=recovered,
+                contextualized_content=recovered,
+                source_start=None,
+                source_end=None,
+                source_start_line=None,
+                source_end_line=None,
+                token_count=len(_tokenizer().encode(recovered)),
+                heading_path=(),
+                source_pages=tuple(sorted(pages)),
+                doc_item_refs=(str(item.self_ref),),
+                source_bboxes=tuple(bboxes),
+            )
+        )
     return result