Add clean_chunk_pol177.py - Clean and chunk parsed PDF data

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2026-07-16 15:06:35 +07:00
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#!/usr/bin/env python3
"""
Clean and chunk parsed PDF data from unstructured-api.
Filters noise, preserves structure, chunks to 1500-1800 chars.
"""
import json
import re
def load_parsed_data(path):
with open(path, 'r', encoding='utf-8') as f:
return json.load(f)
def clean_text(text):
if not text:
return ""
text = re.sub(r'\s+', ' ', text.strip())
return text.strip()
def is_noise(text):
"""Check if text is noise."""
text = clean_text(text)
if not text:
return True
# Company name header/footer
if text == "АО «ХК «Сибцем»":
return True
# Page numbers: "Стр. 2 из 10"
if re.match(r'^Стр\.\s+\d+\s+из\s+\d+$', text):
return True
# Standalone numbers: "4."
if re.match(r'^\d+\.$', text):
return True
# Table of contents
lines = text.split('\n')
if len(lines) > 3:
page_pattern = re.compile(r'\d+\s*$')
page_lines = sum(1 for l in lines if page_pattern.search(l.strip()))
if page_lines > len(lines) * 0.5:
return True
# Specific noise
noise_patterns = [
r'^Оглавление$',
r'^Содержание$',
r'^Редакция \d+$',
r'^Тип документа:',
r'^Наименование процесса:',
r'^Ведущее подразделение:',
r'^Дата утверждения:',
]
for pattern in noise_patterns:
if re.search(pattern, text):
return True
return False
def is_section_title(text):
"""Check if text is a section title like '4. Термины, определения и сокращения'."""
text = clean_text(text)
return bool(re.match(r'^\d+\.\s+\w', text))
def is_section_subtitle(text):
"""Check if text is a section subtitle like '6.7. Электронные...'."""
text = clean_text(text)
return bool(re.match(r'^\d+\.\d+\.\s+\w', text))
def is_section_header(text):
"""Check if text starts with a section/subsection number."""
text = clean_text(text)
return bool(re.match(r'^\d+(?:\.\d+)*[\.\s]', text))
def merge_elements(elements):
"""
Merge short fragments with context.
Strategy: merge consecutive non-title elements into text blocks.
Keep titles and tables as separate blocks.
"""
if not elements:
return []
# Filter noise first
filtered = [el for el in elements if not is_noise(el.get('text', ''))]
# Separate into blocks: text blocks, title blocks, table blocks
blocks = []
current_text_block = []
for el in filtered:
text = el.get('text', '')
el_type = el.get('type', '')
# Skip Image (OCR noise from cover page)
if el_type == 'Image':
continue
# If this is a title/section header, flush text block and add title
if el_type in ('Title',) and is_section_header(text):
if current_text_block:
blocks.append({
'text': ' '.join(clean_text(e['text']) for e in current_text_block),
'type': 'NarrativeText',
'metadata': current_text_block[-1].get('metadata', {})
})
current_text_block = []
blocks.append({
'text': text,
'type': 'Title',
'metadata': el.get('metadata', {})
})
continue
# If this is a table, flush text block and add table
if el_type == 'Table':
if current_text_block:
blocks.append({
'text': ' '.join(clean_text(e['text']) for e in current_text_block),
'type': 'NarrativeText',
'metadata': current_text_block[-1].get('metadata', {})
})
current_text_block = []
blocks.append({
'text': text,
'type': 'Table',
'metadata': el.get('metadata', {})
})
continue
# Otherwise, accumulate into text block
current_text_block.append(el)
# Flush remaining text block
if current_text_block:
blocks.append({
'text': ' '.join(clean_text(e['text']) for e in current_text_block),
'type': 'NarrativeText',
'metadata': current_text_block[-1].get('metadata', {})
})
# Now merge short text blocks with adjacent content
# Specifically: merge short blocks that are continuations of previous sections
result = []
for i, block in enumerate(blocks):
text = block['text']
el_type = block['type']
clean = clean_text(text)
# If this is a short text block, try to merge with previous
if len(clean) < 60 and el_type == 'NarrativeText' and result:
# Check if it looks like a continuation (section subtitle or fragment)
if is_section_subtitle(clean) or not clean[0].isdigit():
# Merge with previous block
result[-1]['text'] = f"{result[-1]['text']} {clean}"
continue
result.append(block)
return result
def chunk_text(text, max_chunk_size=1800):
"""Split text into chunks of ~1500-1800 characters."""
if len(text) <= max_chunk_size:
return [text]
# Try to split by paragraphs first
paragraphs = re.split(r'\n+', text)
if len(paragraphs) > 1:
chunks = []
current = ""
for para in paragraphs:
para = para.strip()
if not para:
continue
if len(current) + len(para) + 1 <= max_chunk_size:
current = f"{current}\n{para}"
else:
if current:
chunks.append(current)
current = para
if current:
chunks.append(current)
return chunks
# Split by sentences
sentences = re.split(r'(?<=[.!?])\s+', text)
chunks = []
current = ""
for sentence in sentences:
if len(current) + len(sentence) + 1 <= max_chunk_size:
current = f"{current} {sentence}"
else:
if current:
chunks.append(current)
current = sentence
if current:
chunks.append(current)
return chunks
def process_elements(elements, max_chunk_size=1800):
"""Process elements into chunks."""
# Merge blocks
merged = merge_elements(elements)
# Create chunks
chunks = []
chunk_index = 0
for el in merged:
text = el.get('text', '')
el_type = el.get('type', 'Unknown')
page = el.get('metadata', {}).get('page_number', '?')
# Skip very short chunks
if len(text.strip()) < 10:
continue
# Split long texts
parts = chunk_text(text, max_chunk_size)
for part in parts:
chunk_index += 1
chunks.append({
'index': chunk_index,
'type': el_type,
'page': page,
'text': part,
'size': len(part)
})
return chunks, merged
def main():
parsed_path = '/tmp/pol177_parsed.json'
output_path = '/tmp/pol177_clean_chunk.json'
print("Loading parsed data...")
elements = load_parsed_data(parsed_path)
print(f"Loaded {len(elements)} elements")
print("Processing...")
chunks, merged = process_elements(elements)
print(f"Generated {len(chunks)} chunks")
# Stats
if chunks:
sizes = [c['size'] for c in chunks]
print(f"Chunk sizes: min={min(sizes)}, max={max(sizes)}, avg={sum(sizes)/len(sizes):.0f}")
# Type distribution
types = {}
for c in chunks:
types[c['type']] = types.get(c['type'], 0) + 1
print(f"Chunk types: {types}")
# Show all chunks
print("\n=== All chunks ===")
for c in chunks:
print(f"[{c['index']}] {c['type']} (page {c['page']}, {c['size']} chars)")
print(f" {c['text'][:120]}")
print()
if __name__ == '__main__':
main()