Add pdf support for QA parser (infiniflow#1155)

### What problem does this PR solve?

Support extracting questions and answers from PDF files

### Type of change

- [x] New Feature (non-breaking change which adds functionality)

rag/app/qa.py

@@ -13,13 +13,13 @@
 import re
 from copy import deepcopy
 from io import BytesIO
+from timeit import default_timer as timer
 from nltk import word_tokenize
 from openpyxl import load_workbook
-from rag.nlp import is_english, random_choices, find_codec
-from rag.nlp import rag_tokenizer
-from deepdoc.parser import ExcelParser
-
-
+from rag.nlp import is_english, random_choices, find_codec, qbullets_category, add_positions, has_qbullet
+from rag.nlp import rag_tokenizer, tokenize_table
+from rag.settings import cron_logger
+from deepdoc.parser import PdfParser, ExcelParser
 class Excel(ExcelParser):
     def __call__(self, fnm, binary=None, callback=None):
         if not binary:
@@ -62,12 +62,80 @@ def __call__(self, fnm, binary=None, callback=None):
             [rmPrefix(q) for q, _ in random_choices(res, k=30) if len(q) > 1])
         return res
 
-
+class Pdf(PdfParser):
+    def __call__(self, filename, binary=None, from_page=0,
+                 to_page=100000, zoomin=3, callback=None):
+        start = timer()
+        callback(msg="OCR is running...")
+        self.__images__(
+            filename if not binary else binary,
+            zoomin,
+            from_page,
+            to_page,
+            callback
+        )
+        callback(msg="OCR finished")
+        cron_logger.info("OCR({}~{}): {}".format(from_page, to_page, timer() - start))
+        start = timer()
+        self._layouts_rec(zoomin, drop=False)
+        callback(0.63, "Layout analysis finished.")
+        self._table_transformer_job(zoomin)
+        callback(0.65, "Table analysis finished.")
+        self._text_merge()
+        callback(0.67, "Text merging finished")
+        tbls = self._extract_table_figure(True, zoomin, True, True)
+        #self._naive_vertical_merge()
+        # self._concat_downward()
+        #self._filter_forpages()
+        cron_logger.info("layouts: {}".format(timer() - start))
+        sections = [b["text"] for b in self.boxes]
+        bull_x0_list = []
+        q_bull, reg = qbullets_category(sections)
+        if q_bull == -1:
+            raise ValueError("Unable to recognize Q&A structure.")
+        qai_list = []
+        last_q, last_a, last_tag = '', '', ''
+        last_index = -1
+        last_box = {'text':''}
+        last_bull = None
+        for box in self.boxes:
+            section, line_tag = box['text'], self._line_tag(box, zoomin)
+            has_bull, index = has_qbullet(reg, box, last_box, last_index, last_bull, bull_x0_list)
+            last_box, last_index, last_bull = box, index, has_bull
+            if not has_bull:  # No question bullet
+                if not last_q:
+                    continue
+                else:
+                    last_a = f'{last_a}{section}'
+                    last_tag = f'{last_tag}{line_tag}'
+            else:
+                if last_q:
+                    qai_list.append((last_q, last_a, *self.crop(last_tag, need_position=True)))
+                    last_q, last_a, last_tag = '', '', ''
+                last_q = has_bull.group()
+                _, end = has_bull.span()
+                last_a = section[end:]
+                last_tag = line_tag
+        if last_q:
+            qai_list.append((last_q, last_a, *self.crop(last_tag, need_position=True)))
+        return qai_list, tbls
+
 def rmPrefix(txt):
     return re.sub(
         r"^(问题|答案|回答|user|assistant|Q|A|Question|Answer|问|答)[\t:： ]+", "", txt.strip(), flags=re.IGNORECASE)
 
 
+def beAdocPdf(d, q, a, eng, image, poss):
+    qprefix = "Question: " if eng else "问题："
+    aprefix = "Answer: " if eng else "回答："
+    d["content_with_weight"] = "\t".join(
+        [qprefix + rmPrefix(q), aprefix + rmPrefix(a)])
+    d["content_ltks"] = rag_tokenizer.tokenize(q)
+    d["content_sm_ltks"] = rag_tokenizer.fine_grained_tokenize(d["content_ltks"])
+    d["image"] = image
+    add_positions(d, poss)
+    return d
+
 def beAdoc(d, q, a, eng):
     qprefix = "Question: " if eng else "问题："
     aprefix = "Answer: " if eng else "回答："
@@ -145,6 +213,19 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
             f"{len(fails)} failure, line: %s..." % (",".join(fails[:3])) if fails else "")))
 
         return res
+    elif re.search(r"\.pdf$", filename, re.IGNORECASE):
+        pdf_parser = Pdf()
+        count = 0
+        qai_list, tbls = pdf_parser(filename if not binary else binary,
+                                    from_page=0, to_page=10000, callback=callback)
+
+        res = tokenize_table(tbls, doc, eng)
+
+        for q, a, image, poss in qai_list:
+            count += 1
+            res.append(beAdocPdf(deepcopy(doc), q, a, eng, image, poss))
+        return res
+
 
     raise NotImplementedError(
         "Excel and csv(txt) format files are supported.")
@@ -153,6 +234,8 @@ def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
 if __name__ == "__main__":
     import sys
 
-    def dummy(a, b):
+    def dummy(prog=None, msg=""):
         pass
-    chunk(sys.argv[1], callback=dummy)
+    import json
+
+    chunk(sys.argv[1], from_page=0, to_page=10, callback=dummy)

rag/nlp/__init__.py

@@ -21,6 +21,9 @@
 from . import rag_tokenizer
 import re
 import copy
+import roman_numbers as r
+from word2number import w2n
+from cn2an import cn2an
 
 all_codecs = [
     'utf-8', 'gb2312', 'gbk', 'utf_16', 'ascii', 'big5', 'big5hkscs',
@@ -57,6 +60,95 @@ def find_codec(blob):
 
     return "utf-8"
 
+QUESTION_PATTERN = [
+    r"第([零一二三四五六七八九十百0-9]+)问",
+    r"第([零一二三四五六七八九十百0-9]+)条",
+    r"[\(（]([零一二三四五六七八九十百]+)[\)）]",
+    r"第([0-9]+)问",
+    r"第([0-9]+)条",
+    r"([0-9]{1,2})[\. 、]",
+    r"([零一二三四五六七八九十百]+)[ 、]",
+    r"[\(（]([0-9]{1,2})[\)）]",
+    r"QUESTION (ONE|TWO|THREE|FOUR|FIVE|SIX|SEVEN|EIGHT|NINE|TEN)",
+    r"QUESTION (I+V?|VI*|XI|IX|X)",
+    r"QUESTION ([0-9]+)",
+]
+
+def has_qbullet(reg, box, last_box, last_index, last_bull, bull_x0_list):
+    section, last_section = box['text'], last_box['text']
+    q_reg = r'(\w|\W)*?(?:？|\?|\n|$)+'
+    full_reg = reg + q_reg
+    has_bull = re.match(full_reg, section)
+    index_str = None
+    if has_bull:
+        if 'x0' not in last_box:
+            last_box['x0'] = box['x0']
+        if 'top' not in last_box:
+            last_box['top'] = box['top']
+        if last_bull and box['x0']-last_box['x0']>10:
+            return None, last_index
+        if not last_bull and box['x0'] >= last_box['x0'] and box['top'] - last_box['top'] < 20:
+            return None, last_index
+        avg_bull_x0 = 0
+        if bull_x0_list:
+            avg_bull_x0 = sum(bull_x0_list) / len(bull_x0_list)
+        else:
+            avg_bull_x0 = box['x0']
+        if box['x0'] - avg_bull_x0 > 10:
+            return None, last_index
+        index_str = has_bull.group(1)
+        index = index_int(index_str)
+        if last_section[-1] == ':' or last_section[-1] == '：':
+            return None, last_index
+        if not last_index or index >= last_index:
+            bull_x0_list.append(box['x0'])
+            return has_bull, index
+        if section[-1] == '?' or section[-1] == '？':
+            bull_x0_list.append(box['x0'])
+            return has_bull, index
+        if box['layout_type'] == 'title':
+            bull_x0_list.append(box['x0'])
+            return has_bull, index
+        pure_section = section.lstrip(re.match(reg, section).group()).lower()
+        ask_reg = r'(what|when|where|how|why|which|who|whose|为什么|为啥|哪)'
+        if re.match(ask_reg, pure_section):
+            bull_x0_list.append(box['x0'])
+            return has_bull, index
+    return None, last_index
+
+def index_int(index_str):
+    res = -1
+    try:
+        res=int(index_str)
+    except ValueError:
+        try:
+            res=w2n.word_to_num(index_str)
+        except ValueError:
+            try:
+                res = cn2an(index_str)
+            except ValueError:
+                try:
+                    res = r.number(index_str)
+                except ValueError:
+                    return -1
+    return res
+
+def qbullets_category(sections):
+    global QUESTION_PATTERN
+    hits = [0] * len(QUESTION_PATTERN)
+    for i, pro in enumerate(QUESTION_PATTERN):
+        for sec in sections:
+            if re.match(pro, sec) and not not_bullet(sec):
+                hits[i] += 1
+                break
+    maxium = 0
+    res = -1
+    for i, h in enumerate(hits):
+        if h <= maxium:
+            continue
+        res = i
+        maxium = h
+    return res, QUESTION_PATTERN[res]
 
 BULLET_PATTERN = [[
     r"第[零一二三四五六七八九十百0-9]+(分?编|部分)",

requirements.txt

@@ -141,3 +141,6 @@ readability-lxml==0.8.1
 html_text==0.6.2
 selenium==4.21.0
 webdriver-manager==4.0.1
+cn2an==0.5.22
+roman-numbers==1.0.2
+word2number==1.1

requirements_arm.txt

@@ -139,4 +139,7 @@ fasttext==0.9.2
 volcengine==1.0.141
 opencv-python-headless==4.9.0.80
 readability-lxml==0.8.1
-html_text==0.6.2
+html_text==0.6.2
+cn2an==0.5.22
+roman-numbers==1.0.2
+word2number==1.1

requirements_dev.txt

@@ -126,4 +126,7 @@ fasttext==0.9.2
 umap-learn
 volcengine==1.0.141
 readability-lxml==0.8.1
-html_text==0.6.2
+html_text==0.6.2
+cn2an==0.5.22
+roman-numbers==1.0.2
+word2number==1.1