| name | specification-extractor |
| description | Extract structured data from construction specifications. Parse CSI sections, requirements, submittals, and product data from spec documents. |
| homepage | https://datadrivenconstruction.io |
| metadata | {"openclaw":{"emoji":"📑","os":["darwin","linux","win32"],"homepage":"https://datadrivenconstruction.io","requires":{"bins":["python3"]}}} |
Specification Extractor for Construction
Overview
Extract structured data from construction specification documents. Parse CSI MasterFormat sections, identify requirements, submittals, product standards, and compile actionable data for estimating and procurement.
Business Case
Automated spec extraction enables:
- Faster Estimating: Quickly identify scope and requirements
- Procurement Accuracy: Extract exact product specifications
- Submittal Tracking: Identify all required submittals
- Compliance Checking: Verify specs against standards
Technical Implementation
from dataclasses import dataclass, field
from typing import List, Dict, Any, Optional
import re
import pdfplumber
from pathlib import Path
@dataclass
class SpecSection:
number: str
title: str
part1_general: Dict[str, Any]
part2_products: Dict[str, Any]
part3_execution: Dict[str, Any]
raw_text: str
@dataclass
class ProductRequirement:
section: str
manufacturer: str
product_name: str
model: str
standards: List[str]
properties: Dict[str, str]
@dataclass
class SubmittalRequirement:
section: str
submittal_type: str
description: str
timing: str
copies: int
@dataclass
class SpecExtractionResult:
document_name: str
total_pages: int
sections: List[SpecSection]
products: List[ProductRequirement]
submittals: List[SubmittalRequirement]
standards_referenced: List[str]
class SpecificationExtractor:
"""Extract structured data from construction specifications."""
CSI_SECTION_PATTERN = r'^(\d{2}\s?\d{2}\s?\d{2})\s*[-–]\s*(.+?)$'
PART_PATTERN = r'^PART\s+(\d+)\s*[-–]\s*(.+?)$'
ARTICLE_PATTERN = r'^(\d+\.\d+)\s+([A-Z][A-Z\s]+)$'
SUBMITTAL_TYPES = {
'shop drawings': 'Shop Drawings',
'product data': 'Product Data',
'samples': 'Samples',
'certificates': 'Certificates',
'test reports': 'Test Reports',
'manufacturer instructions': 'Manufacturer Instructions',
'warranty': 'Warranty',
'maintenance data': 'Maintenance Data',
'mock-ups': 'Mock-ups',
}
STANDARD_PATTERNS = [
r'ASTM\s+[A-Z]\d+',
r'ANSI\s+[A-Z]?\d+',
r'ACI\s+\d+',
r'AISC\s+\d+',
r'AWS\s+[A-Z]\d+',
r'ASCE\s+\d+',
r'UL\s+\d+',
r'FM\s+\d+',
r'NFPA\s+\d+',
r'IBC\s+\d+',
]
def __init__(self):
self.sections: Dict[str, SpecSection] = {}
def extract_from_pdf(self, pdf_path: str) -> SpecExtractionResult:
"""Extract specification data from PDF."""
path = Path(pdf_path)
all_text = ""
page_count = 0
with pdfplumber.open(pdf_path) as pdf:
page_count = len(pdf.pages)
for page in pdf.pages:
text = page.extract_text() or ""
all_text += text + "\n\n"
sections = self._parse_sections(all_text)
products = self._extract_products(sections)
submittals = self._extract_submittals(sections)
standards = self._extract_standards(all_text)
return SpecExtractionResult(
document_name=path.name,
total_pages=page_count,
sections=sections,
products=products,
submittals=submittals,
standards_referenced=standards
)
def _parse_sections(self, text: str) -> List[SpecSection]:
"""Parse CSI sections from specification text."""
sections = []
lines = text.split('\n')
current_section = None
current_part = None
current_content = []
for line in lines:
line = line.strip()
if not line:
continue
section_match = re.match(self.CSI_SECTION_PATTERN, line, re.IGNORECASE)
if section_match:
if current_section:
sections.append(self._finalize_section(current_section, current_content))
current_section = {
'number': section_match.group(1).replace(' ', ''),
'title': section_match.group(2).strip(),
'parts': {}
}
current_content = []
current_part = None
continue
part_match = re.match(self.PART_PATTERN, line, re.IGNORECASE)
if part_match and current_section:
part_num = part_match.group(1)
part_name = part_match.group(2).strip()
current_part = f"part{part_num}"
current_section['parts'][current_part] = {
'name': part_name,
'content': []
}
continue
if current_section and current_part:
current_section['parts'][current_part]['content'].append(line)
elif current_section:
current_content.append(line)
if current_section:
sections.append(self._finalize_section(current_section, current_content))
return sections
def _finalize_section(self, section_data: Dict, general_content: List[str]) -> SpecSection:
"""Finalize a section with parsed parts."""
parts = section_data.get('parts', {})
part1 = self._parse_part_content(parts.get('part1', {}).get('content', []))
part2 = self._parse_part_content(parts.get('part2', {}).get('content', []))
part3 = self._parse_part_content(parts.get('part3', {}).get('content', []))
return SpecSection(
number=section_data['number'],
title=section_data['title'],
part1_general=part1,
part2_products=part2,
part3_execution=part3,
raw_text='\n'.join(general_content)
)
def _parse_part_content(self, content: List[str]) -> Dict[str, Any]:
"""Parse part content into structured data."""
result = {
'articles': {},
'items': []
}
current_article = None
for line in content:
article_match = re.match(self.ARTICLE_PATTERN, line)
if article_match:
current_article = article_match.group(1)
result['articles'][current_article] = {
'title': article_match.group(2),
'items': []
}
continue
if current_article and current_article in result['articles']:
result['articles'][current_article]['items'].append(line)
else:
result['items'].append(line)
return result
def _extract_products(self, sections: List[SpecSection]) -> List[ProductRequirement]:
"""Extract product requirements from Part 2."""
products = []
for section in sections:
part2 = section.part2_products
for article_num, article in part2.get('articles', {}).items():
if 'MANUFACTURERS' in article['title'].upper():
for item in article['items']:
if item.strip().startswith(('A.', 'B.', 'C.', '1.', '2.', '3.')):
mfr_name = re.sub(r'^[A-Z\d]+\.\s*', '', item).strip()
products.append(ProductRequirement(
section=section.number,
manufacturer=mfr_name,
product_name='',
model='',
standards=[],
properties={}
))
elif 'MATERIALS' in article['title'].upper() or 'PRODUCTS' in article['title'].upper():
for item in article['items']:
standards = self._extract_standards(item)
if standards:
products.append(ProductRequirement(
section=section.number,
manufacturer='',
product_name=item[:100],
model='',
standards=standards,
properties={}
))
return products
def _extract_submittals(self, sections: List[SpecSection]) -> List[SubmittalRequirement]:
"""Extract submittal requirements from Part 1."""
submittals = []
for section in sections:
part1 = section.part1_general
for article_num, article in part1.get('articles', {}).items():
if 'SUBMITTAL' in article['title'].upper():
for item in article['items']:
item_lower = item.lower()
for keyword, submittal_type in self.SUBMITTAL_TYPES.items():
if keyword in item_lower:
submittals.append(SubmittalRequirement(
section=section.number,
submittal_type=submittal_type,
description=item.strip(),
timing='Prior to fabrication',
copies=3
))
break
return submittals
def _extract_standards(self, text: str) -> List[str]:
"""Extract referenced standards from text."""
standards = []
for pattern in self.STANDARD_PATTERNS:
matches = re.findall(pattern, text, re.IGNORECASE)
standards.extend(matches)
return list(set(standards))
def generate_submittal_log(self, result: SpecExtractionResult) -> str:
"""Generate submittal log from extraction results."""
lines = ["# Submittal Log", ""]
lines.append(f"**Project Specs:** {result.document_name}")
lines.append(f"**Total Submittals:** {len(result.submittals)}")
lines.append("")
lines.append("| # | Section | Type | Description | Status |")
lines.append("|---|---------|------|-------------|--------|")
for i, sub in enumerate(result.submittals, 1):
desc = sub.description[:50] + "..." if len(sub.description) > 50 else sub.description
lines.append(f"| {i} | {sub.section} | {sub.submittal_type} | {desc} | Pending |")
return "\n".join(lines)
def generate_product_schedule(self, result: SpecExtractionResult) -> str:
"""Generate product schedule from extraction results."""
lines = ["# Product Schedule", ""]
by_section = {}
for prod in result.products:
if prod.section not in by_section:
by_section[prod.section] = []
by_section[prod.section].append(prod)
for section, products in sorted(by_section.items()):
lines.append(f"## Section {section}")
lines.append("")
for prod in products:
if prod.manufacturer:
lines.append(f"- **Manufacturer:** {prod.manufacturer}")
if prod.product_name:
lines.append(f"- **Product:** {prod.product_name}")
if prod.standards:
lines.append(f"- **Standards:** {', '.join(prod.standards)}")
lines.append("")
return "\n".join(lines)
def generate_report(self, result: SpecExtractionResult) -> str:
"""Generate comprehensive extraction report."""
lines = ["# Specification Extraction Report", ""]
lines.append(f"**Document:** {result.document_name}")
lines.append(f"**Pages:** {result.total_pages}")
lines.append(f"**Sections Found:** {len(result.sections)}")
lines.append("")
lines.append("## Sections Extracted")
for section in result.sections:
lines.append(f"- **{section.number}** - {section.title}")
lines.append("")
if result.standards_referenced:
lines.append("## Standards Referenced")
for std in sorted(set(result.standards_referenced)):
lines.append(f"- {std}")
lines.append("")
lines.append("## Submittals Required")
lines.append(f"Total: {len(result.submittals)}")
by_type = {}
for sub in result.submittals:
by_type[sub.submittal_type] = by_type.get(sub.submittal_type, 0) + 1
for t, count in sorted(by_type.items()):
lines.append(f"- {t}: {count}")
lines.append("")
lines.append("## Products/Manufacturers")
lines.append(f"Total: {len(result.products)}")
return "\n".join(lines)
Quick Start
extractor = SpecificationExtractor()
result = extractor.extract_from_pdf("Project_Specifications.pdf")
print(f"Found {len(result.sections)} sections")
print(f"Found {len(result.submittals)} submittals")
print(f"Found {len(result.products)} product requirements")
submittal_log = extractor.generate_submittal_log(result)
print(submittal_log)
product_schedule = extractor.generate_product_schedule(result)
print(product_schedule)
report = extractor.generate_report(result)
print(report)
Dependencies
pip install pdfplumber