Files
ragflow/rag/app/laws.py
euvre 2846a93998 Fix: Remove hardcoded page limits causing parsing failures on large PDFs (>300 pages) (#14382)
### What problem does this PR solve?

Fixes #14196

## Problem

When using DeepDOC to parse large PDFs (over 1000 pages), the parser
silently truncated processing at 300 pages due to a hardcoded default
`page_to=299` in `RAGFlowPdfParser.__images__()`. This caused:

- **Errors** on pages beyond the limit
- **Poor image quality** as the parser attempted to compensate with
missing page data
- **Inconsistent chunk splitting** between full PDF imports and partial
imports

Additionally, the codebase scattered magic numbers (`299`, `600`,
`10000`, `100000`, `100000000`, `10000000000`, `10**9`) across 22 files
as sentinel values for "parse all pages", making future maintenance
error-prone.

## Root Cause

```python
# deepdoc/parser/pdf_parser.py (before)
def __images__(self, fnm, zoomin=3, page_from=0, page_to=299, callback=None):
    # Only the first 300 pages were rendered; everything beyond was silently dropped
```

While most callers in `rag/app/*.py` correctly passed `to_page=100000`,
the base class `RAGFlowPdfParser.__call__()` and `parse_into_bboxes()`
invoked `__images__` **without** forwarding `page_from`/`page_to`,
falling back to the restrictive default of 299.

## Solution

### 1. Define constants in `common/constants.py`

```python
MAXIMUM_PAGE_NUMBER = 100000                        # Used by the parsing layer
MAXIMUM_TASK_PAGE_NUMBER = MAXIMUM_PAGE_NUMBER * 1000  # Used by the task/DB layer
```

### 2. Replace all hardcoded sentinel values

| Layer | Files Changed | Old Values | New Value |
|---|---|---|---|
| **Deepdoc parsers** | `pdf_parser.py`, `mineru_parser.py`,
`docling_parser.py`, `opendataloader_parser.py`, `paddleocr_parser.py`,
`docx_parser.py` | `299`, `600`, `10**9`, `100000000` |
`MAXIMUM_PAGE_NUMBER` |
| **Chunk parsers** | `naive.py`, `book.py`, `qa.py`, `one.py`,
`manual.py`, `paper.py`, `presentation.py`, `laws.py`, `resume.py`,
`email.py`, `table.py` | `100000`, `10000`, `10000000000` |
`MAXIMUM_PAGE_NUMBER` |
| **Task/DB layer** | `db_models.py`, `task_service.py`,
`document_service.py`, `file_service.py` | `100000000` |
`MAXIMUM_TASK_PAGE_NUMBER` |

### 3. Fix `parse_into_bboxes()` missing parameters

Added `from_page`/`to_page` parameters to `parse_into_bboxes()` so that
the `rag/flow/parser/parser.py` DeepDOC path no longer falls back to the
restrictive default.

## Files Changed (22)

- `common/constants.py`
- `deepdoc/parser/pdf_parser.py`
- `deepdoc/parser/mineru_parser.py`
- `deepdoc/parser/docling_parser.py`
- `deepdoc/parser/opendataloader_parser.py`
- `deepdoc/parser/paddleocr_parser.py`
- `deepdoc/parser/docx_parser.py`
- `rag/app/naive.py`
- `rag/app/book.py`
- `rag/app/qa.py`
- `rag/app/one.py`
- `rag/app/manual.py`
- `rag/app/paper.py`
- `rag/app/presentation.py`
- `rag/app/laws.py`
- `rag/app/resume.py`
- `rag/app/email.py`
- `rag/app/table.py`
- `api/db/db_models.py`
- `api/db/services/task_service.py`
- `api/db/services/document_service.py`
- `api/db/services/file_service.py`

### Type of change

- [x] Bug Fix (non-breaking change which fixes an issue)
- [x] Refactoring

---------

Signed-off-by: noob <yixiao121314@outlook.com>
2026-04-27 14:57:20 +08:00

230 lines
8.3 KiB
Python

#
# Copyright 2025 The InfiniFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
import logging
import re
from io import BytesIO
from docx import Document
from common.constants import ParserType, MAXIMUM_PAGE_NUMBER
from deepdoc.parser.utils import get_text
from rag.nlp import bullets_category, remove_contents_table, make_colon_as_title, tokenize_chunks, docx_question_level, tree_merge
from rag.nlp import rag_tokenizer, Node
from deepdoc.parser import PdfParser, DocxParser, HtmlParser
from rag.app.naive import by_plaintext, PARSERS
from common.parser_config_utils import normalize_layout_recognizer
class Docx(DocxParser):
def __init__(self):
pass
def __clean(self, line):
line = re.sub(r"\u3000", " ", line).strip()
return line
def old_call(self, filename, binary=None, from_page=0, to_page=MAXIMUM_PAGE_NUMBER):
self.doc = Document(filename) if not binary else Document(BytesIO(binary))
pn = 0
lines = []
for p in self.doc.paragraphs:
if pn > to_page:
break
if from_page <= pn < to_page and p.text.strip():
lines.append(self.__clean(p.text))
for run in p.runs:
if "lastRenderedPageBreak" in run._element.xml:
pn += 1
continue
if "w:br" in run._element.xml and 'type="page"' in run._element.xml:
pn += 1
return [line for line in lines if line]
def __call__(self, filename, binary=None, from_page=0, to_page=MAXIMUM_PAGE_NUMBER):
self.doc = Document(filename) if not binary else Document(BytesIO(binary))
pn = 0
lines = []
level_set = set()
bull = bullets_category([p.text for p in self.doc.paragraphs])
for p in self.doc.paragraphs:
if pn > to_page:
break
question_level, p_text = docx_question_level(p, bull)
if not p_text.strip("\n"):
continue
lines.append((question_level, p_text))
level_set.add(question_level)
for run in p.runs:
if "lastRenderedPageBreak" in run._element.xml:
pn += 1
continue
if "w:br" in run._element.xml and 'type="page"' in run._element.xml:
pn += 1
sorted_levels = sorted(level_set)
h2_level = sorted_levels[1] if len(sorted_levels) > 1 else 1
h2_level = sorted_levels[-2] if h2_level == sorted_levels[-1] and len(sorted_levels) > 2 else h2_level
root = Node(level=0, depth=h2_level, texts=[])
root.build_tree(lines)
return [element for element in root.get_tree() if element]
def __str__(self) -> str:
return f"""
question:{self.question},
answer:{self.answer},
level:{self.level},
childs:{self.childs}
"""
class Pdf(PdfParser):
def __init__(self):
self.model_speciess = ParserType.LAWS.value
super().__init__()
def __call__(self, filename, binary=None, from_page=0, to_page=MAXIMUM_PAGE_NUMBER, zoomin=3, callback=None):
from timeit import default_timer as timer
start = timer()
callback(msg="OCR started")
self.__images__(filename if not binary else binary, zoomin, from_page, to_page, callback)
callback(msg="OCR finished ({:.2f}s)".format(timer() - start))
start = timer()
self._layouts_rec(zoomin)
callback(0.67, "Layout analysis ({:.2f}s)".format(timer() - start))
logging.debug("layouts: {}".format((timer() - start)))
self._naive_vertical_merge()
callback(0.8, "Text extraction ({:.2f}s)".format(timer() - start))
return [(b["text"], self._line_tag(b, zoomin)) for b in self.boxes], None
def chunk(filename, binary=None, from_page=0, to_page=MAXIMUM_PAGE_NUMBER, lang="Chinese", callback=None, **kwargs):
"""
Supported file formats are docx, pdf, txt.
"""
parser_config = kwargs.get("parser_config", {"chunk_token_num": 512, "delimiter": "\n!?。;!?", "layout_recognize": "DeepDOC"})
doc = {"docnm_kwd": filename, "title_tks": rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", filename))}
doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
pdf_parser = None
sections = []
# is it English
eng = lang.lower() == "english" # is_english(sections)
if re.search(r"\.docx$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
chunks = Docx()(filename, binary)
callback(0.7, "Finish parsing.")
return tokenize_chunks(chunks, doc, eng, None)
elif re.search(r"\.pdf$", filename, re.IGNORECASE):
layout_recognizer, parser_model_name = normalize_layout_recognizer(parser_config.get("layout_recognize", "DeepDOC"))
if isinstance(layout_recognizer, bool):
layout_recognizer = "DeepDOC" if layout_recognizer else "Plain Text"
name = layout_recognizer.strip().lower()
parser = PARSERS.get(name, by_plaintext)
callback(0.1, "Start to parse.")
raw_sections, tables, pdf_parser = parser(
filename=filename,
binary=binary,
from_page=from_page,
to_page=to_page,
lang=lang,
callback=callback,
pdf_cls=Pdf,
layout_recognizer=layout_recognizer,
mineru_llm_name=parser_model_name,
paddleocr_llm_name=parser_model_name,
**kwargs,
)
if not raw_sections and not tables:
return []
if name in ["tcadp", "docling", "mineru", "paddleocr"]:
parser_config["chunk_token_num"] = 0
for txt, poss in raw_sections:
sections.append(txt + poss)
callback(0.8, "Finish parsing.")
elif re.search(r"\.(txt|md|markdown|mdx)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
txt = get_text(filename, binary)
sections = txt.split("\n")
sections = [s for s in sections if s]
callback(0.8, "Finish parsing.")
elif re.search(r"\.(htm|html)$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
sections = HtmlParser()(filename, binary)
sections = [s for s in sections if s]
callback(0.8, "Finish parsing.")
elif re.search(r"\.doc$", filename, re.IGNORECASE):
callback(0.1, "Start to parse.")
try:
from tika import parser as tika_parser
except Exception as e:
callback(0.8, f"tika not available: {e}. Unsupported .doc parsing.")
logging.warning(f"tika not available: {e}. Unsupported .doc parsing for {filename}.")
return []
binary = BytesIO(binary)
doc_parsed = tika_parser.from_buffer(binary)
if doc_parsed.get("content", None) is not None:
sections = doc_parsed["content"].split("\n")
sections = [s for s in sections if s]
callback(0.8, "Finish parsing.")
else:
callback(0.8, f"tika.parser got empty content from {filename}.")
logging.warning(f"tika.parser got empty content from {filename}.")
return []
else:
raise NotImplementedError("file type not supported yet(doc, docx, pdf, txt supported)")
# Remove 'Contents' part
remove_contents_table(sections, eng)
make_colon_as_title(sections)
bull = bullets_category(sections)
res = tree_merge(bull, sections, 2)
if not res:
callback(0.99, "No chunk parsed out.")
return tokenize_chunks(res, doc, eng, pdf_parser)
# chunks = hierarchical_merge(bull, sections, 5)
# return tokenize_chunks(["\n".join(ck)for ck in chunks], doc, eng, pdf_parser)
if __name__ == "__main__":
import sys
def dummy(prog=None, msg=""):
pass
chunk(sys.argv[1], callback=dummy)