Add docx support for manual parser (#1227)

### What problem does this PR solve?

Add docx support for manual parser

### Type of change

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

rag/app/manual.py

@@ -18,10 +18,13 @@ import copy
 import re
 
 from api.db import ParserType
-from rag.nlp import rag_tokenizer, tokenize, tokenize_table, add_positions, bullets_category, title_frequency, tokenize_chunks
+from io import BytesIO
+from rag.nlp import rag_tokenizer, tokenize, tokenize_table, add_positions, bullets_category, title_frequency, tokenize_chunks, docx_question_level
 from deepdoc.parser import PdfParser, PlainParser
 from rag.utils import num_tokens_from_string
-
+from deepdoc.parser import PdfParser, ExcelParser, DocxParser
+from docx import Document
+from PIL import Image
 
 class Pdf(PdfParser):
     def __init__(self):
@@ -64,6 +67,98 @@ class Pdf(PdfParser):
         return [(b["text"], b.get("layout_no", ""), self.get_position(b, zoomin))
                 for i, b in enumerate(self.boxes)], tbls
 
+class Docx(DocxParser):
+    def __init__(self):
+        pass
+    def get_picture(self, document, paragraph):
+        img = paragraph._element.xpath('.//pic:pic')
+        if not img:
+            return None
+        img = img[0]
+        embed = img.xpath('.//a:blip/@r:embed')[0]
+        related_part = document.part.related_parts[embed]
+        image = related_part.image
+        image = Image.open(BytesIO(image.blob))
+        return image
+    def concat_img(self, img1, img2):
+        if img1 and not img2:
+            return img1
+        if not img1 and img2:
+            return img2
+        if not img1 and not img2:
+            return None
+        width1, height1 = img1.size
+        width2, height2 = img2.size
+
+        new_width = max(width1, width2)
+        new_height = height1 + height2
+        new_image = Image.new('RGB', (new_width, new_height))
+
+        new_image.paste(img1, (0, 0))
+        new_image.paste(img2, (0, height1))
+
+        return new_image
+
+    def __call__(self, filename, binary=None, from_page=0, to_page=100000, callback=None):
+        self.doc = Document(
+            filename) if not binary else Document(BytesIO(binary))
+        pn = 0
+        last_answer, last_image = "", None
+        question_stack, level_stack = [], []
+        ti_list = []
+        for p in self.doc.paragraphs:
+            if pn > to_page:
+                break
+            question_level, p_text = 0, ''
+            if from_page <= pn < to_page and p.text.strip():
+                question_level, p_text = docx_question_level(p)
+            if not question_level or question_level > 6: # not a question
+                last_answer = f'{last_answer}\n{p_text}'
+                current_image = self.get_picture(self.doc, p)
+                last_image = self.concat_img(last_image, current_image)
+            else:   # is a question
+                if last_answer or last_image:
+                    sum_question = '\n'.join(question_stack)
+                    if sum_question:
+                        ti_list.append((f'{sum_question}\n{last_answer}', last_image))
+                    last_answer, last_image = '', None
+
+                i = question_level
+                while question_stack and i <= level_stack[-1]:
+                    question_stack.pop()
+                    level_stack.pop()
+                question_stack.append(p_text)
+                level_stack.append(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
+        if last_answer:
+            sum_question = '\n'.join(question_stack)
+            if sum_question:
+                ti_list.append((f'{sum_question}\n{last_answer}', last_image))
+                
+        tbls = []
+        for tb in self.doc.tables:
+            html= "<table>"
+            for r in tb.rows:
+                html += "<tr>"
+                i = 0
+                while i < len(r.cells):
+                    span = 1
+                    c = r.cells[i]
+                    for j in range(i+1, len(r.cells)):
+                        if c.text == r.cells[j].text:
+                            span += 1
+                            i = j
+                    i += 1
+                    html += f"<td>{c.text}</td>" if span == 1 else f"<td colspan='{span}'>{c.text}</td>"
+                html += "</tr>"
+            html += "</table>"
+            tbls.append(((None, html), ""))
+        return ti_list, tbls
 
 def chunk(filename, binary=None, from_page=0, to_page=100000,
           lang="Chinese", callback=None, **kwargs):
@@ -71,7 +166,13 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         Only pdf is supported.
     """
     pdf_parser = None
-
+    doc = {
+        "docnm_kwd": filename
+    }
+    doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"]))
+    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
+    # is it English
+    eng = lang.lower() == "english"  # pdf_parser.is_english
     if re.search(r"\.pdf$", filename, re.IGNORECASE):
         pdf_parser = Pdf() if kwargs.get(
             "parser_config", {}).get(
@@ -80,17 +181,6 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
                                     from_page=from_page, to_page=to_page, callback=callback)
         if sections and len(sections[0]) < 3:
             sections = [(t, l, [[0] * 5]) for t, l in sections]
-
-    else:
-        raise NotImplementedError("file type not supported yet(pdf supported)")
-    doc = {
-        "docnm_kwd": filename
-    }
-    doc["title_tks"] = rag_tokenizer.tokenize(re.sub(r"\.[a-zA-Z]+$", "", doc["docnm_kwd"]))
-    doc["title_sm_tks"] = rag_tokenizer.fine_grained_tokenize(doc["title_tks"])
-    # is it English
-    eng = lang.lower() == "english"  # pdf_parser.is_english
-
         # set pivot using the most frequent type of title,
         # then merge between 2 pivot
         if len(sections) > 0 and len(pdf_parser.outlines) / len(sections) > 0.1:
@@ -154,6 +244,21 @@ def chunk(filename, binary=None, from_page=0, to_page=100000,
         res = tokenize_table(tbls, doc, eng)
         res.extend(tokenize_chunks(chunks, doc, eng, pdf_parser))
         return res
+    if re.search(r"\.docx$", filename, re.IGNORECASE):
+        docx_parser = Docx()
+        ti_list, tbls = docx_parser(filename, binary,
+                                    from_page=0, to_page=10000, callback=callback)
+        res = tokenize_table(tbls, doc, eng)
+        for text, image in ti_list:
+            d = copy.deepcopy(doc)
+            d['image'] = image
+            tokenize(d, text, eng)
+            res.append(d)
+        return res
+    else:
+        raise NotImplementedError("file type not supported yet(pdf and docx supported)")
+    
+
 
 
 if __name__ == "__main__":

rag/app/qa.py

@@ -16,7 +16,7 @@ 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, qbullets_category, add_positions, has_qbullet
+from rag.nlp import is_english, random_choices, find_codec, qbullets_category, add_positions, has_qbullet, docx_question_level
 from rag.nlp import rag_tokenizer, tokenize_table
 from rag.settings import cron_logger
 from deepdoc.parser import PdfParser, ExcelParser, DocxParser
@@ -165,7 +165,7 @@ class Docx(DocxParser):
                 break
             question_level, p_text = 0, ''
             if from_page <= pn < to_page and p.text.strip():
-                question_level, p_text = docxQuestionLevel(p)
+                question_level, p_text = docx_question_level(p)
             if not question_level or question_level > 6: # not a question
                 last_answer = f'{last_answer}\n{p_text}'
                 current_image = self.get_picture(self.doc, p)
@@ -254,12 +254,6 @@ def mdQuestionLevel(s):
     match = re.match(r'#*', s)
     return (len(match.group(0)), s.lstrip('#').lstrip()) if match else (0, s)
 
-def docxQuestionLevel(p):
-    if p.style.name.startswith('Heading'):
-        return int(p.style.name.split(' ')[-1]), re.sub(r"\u3000", " ", p.text).strip()
-    else:
-        return 0, re.sub(r"\u3000", " ", p.text).strip()
-
 def chunk(filename, binary=None, lang="Chinese", callback=None, **kwargs):
     """
         Excel and csv(txt) format files are supported.

rag/nlp/__init__.py

@@ -497,3 +497,9 @@ def naive_merge(sections, chunk_token_num=128, delimiter="\n。；！？"):
             add_chunk(sec[s: e], pos)
 
     return cks
+
+def docx_question_level(p):
+    if p.style.name.startswith('Heading'):
+        return int(p.style.name.split(' ')[-1]), re.sub(r"\u3000", " ", p.text).strip()
+    else:
+        return 0, re.sub(r"\u3000", " ", p.text).strip()