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from typing import List, Dict, Any, Optional, Tuple
from pilotcore.chunking.base import BaseChunker
from pilotcore.chunking.recursive import RecursiveCharacterChunker
# Comprehensive catalog of semantic section categories
SEMANTIC_SECTIONS: List[Tuple[str, re.Pattern]] = [
# Resumes & CVs
("experience", re.compile(r"^(?:work\s+|professional\s+|relevant\s+|industry\s+)?experience|employment(?:\s+history)?|work\s+history|career\s+history", re.IGNORECASE)),
("education", re.compile(r"^education(?:al\s+background)?|academic(?:\s+background|\s+qualifications)?|qualifications|degrees", re.IGNORECASE)),
("projects", re.compile(r"^(?:featured\s+|key\s+|technical\s+|academic\s+|personal\s+)?projects", re.IGNORECASE)),
("skills", re.compile(r"^(?:technical\s+|core\s+|key\s+)?skills|skills\s*&?\s*(?:expertise|competencies|tools)|technical\s+expertise|technologies", re.IGNORECASE)),
("certifications", re.compile(r"^certifications?|licenses(?:\s*&?\s*certifications?)?|courses|accreditations", re.IGNORECASE)),
("achievements", re.compile(r"^achievements?|awards(?:\s*&?\s*honors)?|honors|accomplishments", re.IGNORECASE)),
("publications", re.compile(r"^publications?|research(?:\s+papers?)?|patents", re.IGNORECASE)),
("leadership", re.compile(r"^positions?\s+of\s+responsibility|leadership(?:\s+experience)?|volunteering|extracurricular(?:\s+activities)?", re.IGNORECASE)),
("summary", re.compile(r"^(?:executive\s+|professional\s+|career\s+)?summary|about(?:\s+me)?|profile|objective", re.IGNORECASE)),
("contact", re.compile(r"^contact(?:\s+info(?:rmation)?)?|personal\s+details|portfolio", re.IGNORECASE)),
# Technical Reports, Papers & Documentation
("abstract", re.compile(r"^abstract", re.IGNORECASE)),
("introduction", re.compile(r"^introduction|overview|background(?:\s+and\s+motivation)?", re.IGNORECASE)),
("architecture", re.compile(r"^(?:system\s+)?architecture|system\s+design|framework\s+overview|architectural\s+overview", re.IGNORECASE)),
("methodology", re.compile(r"^methodology|methods|proposed\s+(?:method|approach|system)|experimental\s+setup", re.IGNORECASE)),
("implementation", re.compile(r"^implementation(?:\s+details)?|technical\s+implementation|pipeline\s+design", re.IGNORECASE)),
("experiments", re.compile(r"^experiments|benchmarks?|evaluations?|performance\s+analysis", re.IGNORECASE)),
("results", re.compile(r"^results(?:\s+and\s+discussion)?|findings|key\s+metrics", re.IGNORECASE)),
("discussion", re.compile(r"^discussion|analysis|tradeoffs|limitations", re.IGNORECASE)),
("related_work", re.compile(r"^related\s+work|literature\s+review|prior\s+art", re.IGNORECASE)),
("conclusion", re.compile(r"^conclusion(?:\s+and\s+future\s+work)?|summary\s+and\s+conclusions?|future\s+work", re.IGNORECASE)),
("references", re.compile(r"^references|bibliography", re.IGNORECASE)),
("appendix", re.compile(r"^appendix(?:\s+[A-Z0-9]+)?|supplementary\s+material", re.IGNORECASE)),
# Business & Financial
("financials", re.compile(r"^(?:financial\s+performance|revenue(?:\s+breakdown)?|q[1-4]\s+(?:financials|results|performance))", re.IGNORECASE)),
("recommendations", re.compile(r"^(?:strategic\s+)?recommendations?|action\s+items|next\s+steps", re.IGNORECASE)),
]
# Patterns for sub-items: Bullet projects/roles (e.g. "• DevDiscuss GitHub | Live" or "OCR Engineer Sep 2023 - Aug 2024")
DATE_RANGE_PATTERN = re.compile(r"(?:jan|feb|mar|apr|may|jun|jul|aug|sep|sept|oct|nov|dec|20\d\d)\b.*?(?:present|\d{4})", re.IGNORECASE)
BULLET_SUBHEADER_PATTERN = re.compile(r"^[\s•\-\*]+([A-Za-z0-9_\-\s]+?)(?:\s+(?:GitHub|Live|Coursera|\d{4}))?$", re.MULTILINE)
class StructureAwareChunker(BaseChunker):
"""
Intelligent Structure-Aware Chunker.
Extracts semantic document hierarchies across:
1. Markdown `#` to `######` headers & HTML `<h1>`-`<h6>` tags
2. Resume & CV structural sections (Experience, Projects, Education, Skills, etc.)
3. Technical paper / report structures (Abstract, Methods, Results, Conclusion, etc.)
4. Individual subsection blocks (Projects, Roles with dates, and Companies)
Generates rich contextual breadcrumb paths:
[Section > Subsection / Role (Details)]
{chunk_content}
"""
MD_HEADER_PATTERN = re.compile(r"^(#{1,6})\s+(.+)$", re.MULTILINE)
HTML_HEADER_PATTERN = re.compile(r"<h([1-6])(?:\s+[^>]*)?>(.*?)</h\1>", re.IGNORECASE)
def chunk(
self,
text: str,
chunk_size: int = 500,
overlap: int = 80,
**kwargs: Any,
) -> List[Dict[str, Any]]:
if not text or not text.strip():
return []
# 1. Parse document into structural blocks
sections = self._parse_document_structure(text)
if not sections:
fallback = RecursiveCharacterChunker()
return fallback.chunk(text, chunk_size=chunk_size, overlap=overlap)
# 2. Process sections and sub-split oversized sections while preserving breadcrumbs
recursive_splitter = RecursiveCharacterChunker()
final_chunks = []
for sec in sections:
content = sec["content"].strip()
if not content:
continue
header_path = sec["header_path"]
header_level = sec["level"]
section_title = sec["title"]
section_type = sec["type"]
subsection = sec.get("subsection")
# Format chunk header: [Path]
breadcrumb_header = f"[{header_path}]" if header_path else ""
if len(content) <= chunk_size:
formatted_text = (
f"{breadcrumb_header}\n\n{content}" if breadcrumb_header else content
)
final_chunks.append({
"text": formatted_text,
"metadata": {
"section_title": section_title,
"section_type": section_type,
"subsection": subsection,
"header_path": header_path,
"structure_type": sec.get("structure_type", "section"),
"level": header_level,
},
})
else:
# Sub-split oversized section content
sub_splits = recursive_splitter.chunk(
content,
chunk_size=chunk_size,
overlap=overlap,
)
for sub_idx, sub in enumerate(sub_splits):
sub_text = sub["text"].strip()
if not sub_text:
continue
part_header = (
f"[{header_path} - Part {sub_idx + 1}]"
if header_path
else ""
)
formatted_text = (
f"{part_header}\n\n{sub_text}" if part_header else sub_text
)
final_chunks.append({
"text": formatted_text,
"metadata": {
"section_title": section_title,
"section_type": section_type,
"subsection": subsection,
"header_path": header_path,
"structure_type": sec.get("structure_type", "section"),
"level": header_level,
"sub_chunk_index": sub_idx,
},
})
return final_chunks
def _parse_document_structure(self, text: str) -> List[Dict[str, Any]]:
lines = text.splitlines(keepends=True)
sections = []
current_major_title: Optional[str] = None
current_major_type: str = "general"
current_major_level: int = 1
current_sub_title: Optional[str] = None
current_sub_level: int = 2
current_lines: List[str] = []
def flush_current_section():
nonlocal current_lines, current_major_title, current_sub_title, current_major_type, current_major_level, current_sub_level
if not current_lines:
return
content_str = "".join(current_lines).strip()
if not content_str:
current_lines = []
return
# Determine title & path
if current_major_title and current_sub_title:
path = f"{current_major_title} > {current_sub_title}"
title = current_major_title
level = current_sub_level
elif current_major_title:
path = current_major_title
title = current_major_title
level = current_major_level
else:
path = ""
title = "Overview"
level = 1
sections.append({
"title": title,
"type": current_major_type,
"subsection": current_sub_title,
"header_path": path,
"level": level,
"structure_type": "semantic_section",
"content": content_str,
})
current_lines = []
for line in lines:
trimmed = line.strip()
if not trimmed:
current_lines.append(line)
continue
# 1. Check for Markdown Header
md_match = self.MD_HEADER_PATTERN.match(trimmed)
if md_match:
flush_current_section()
level = len(md_match.group(1))
title = md_match.group(2).strip()
if level <= 2:
current_major_title = title
current_major_type = self._classify_title(title)
current_major_level = level
current_sub_title = None
else:
current_sub_title = title
current_sub_level = level
current_lines.append(line)
continue
# 2. Check for HTML Header
html_match = self.HTML_HEADER_PATTERN.search(trimmed)
if html_match:
flush_current_section()
level = int(html_match.group(1))
title = re.sub(r"<[^>]+>", "", html_match.group(2)).strip()
if level <= 2:
current_major_title = title
current_major_type = self._classify_title(title)
current_major_level = level
current_sub_title = None
else:
current_sub_title = title
current_sub_level = level
current_lines.append(line)
continue
# 3. Check for Semantic Domain Heading (Resume / Paper / Document Sections)
sem_title, sem_type = self._detect_semantic_heading(trimmed)
if sem_title:
flush_current_section()
current_major_title = sem_title
current_major_type = sem_type
current_major_level = 1
current_sub_title = None
current_lines.append(line)
continue
# 4. Check for Sub-item / Role / Project within major section
if current_major_title and current_major_type in ["projects", "experience", "leadership", "education"]:
sub_header = self._detect_sub_item(trimmed)
if sub_header:
flush_current_section()
current_sub_title = sub_header
current_sub_level = 2
current_lines.append(line)
continue
current_lines.append(line)
flush_current_section()
return sections
@staticmethod
def _detect_semantic_heading(line: str) -> Tuple[Optional[str], str]:
"""
Detect standalone section headers like 'Experience', 'Education', 'Projects', 'Technical Skills'.
"""
clean = line.rstrip(":").strip()
words = clean.split()
if not words or len(words) > 6:
return None, "general"
# Check against semantic catalog
for sec_type, pattern in SEMANTIC_SECTIONS:
if pattern.fullmatch(clean) or (len(words) <= 3 and pattern.match(clean)):
# Return normalized Title Case
return clean.title() if not clean.isupper() else clean.title(), sec_type
# Check for standalone ALL-CAPS headers (e.g. 'TECHNICAL SKILLS', 'WORK EXPERIENCE')
if len(clean) >= 3 and clean.isupper() and clean.replace(" ", "").isalpha() and len(words) <= 4:
return clean.title(), "section"
return None, "general"
@staticmethod
def _detect_sub_item(line: str) -> Optional[str]:
"""
Detect specific project names or role headings like:
- '• DevDiscuss GitHub | Live'
- '• OCR Engineer Sep 2023 - Aug 2024'
- 'Intern Feb 2022 - Mar 2022'
- 'Heart Disease RAG Assistant'
"""
clean = line.strip().lstrip("•-*").strip()
if not clean or len(clean) > 80:
return None
# Check if line contains a role with date range: "OCR Engineer Sep 2023 - Aug 2024"
if DATE_RANGE_PATTERN.search(clean) and len(clean.split()) <= 10:
return clean
# Check bullet project headers: "• DevDiscuss" or "• DevDiscuss GitHub | Live"
if line.strip().startswith(("•", "-", "*")) and len(clean.split()) <= 8:
# Clean off trailing links like GitHub | Live
cleaned_name = re.sub(r"\s+(?:GitHub|Live|Demo|Link|Coursera)(?:\s*\|\s*(?:GitHub|Live|Demo|Link))?", "", clean, flags=re.IGNORECASE).strip()
if cleaned_name and len(cleaned_name) >= 3:
return cleaned_name
return None
@staticmethod
def _classify_title(title: str) -> str:
clean = title.lower().strip()
for sec_type, pattern in SEMANTIC_SECTIONS:
if pattern.search(clean):
return sec_type
return "general"
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