Regex Vs Llm Structured Text

import refrom dataclasses import dataclass
@dataclass(frozen=True)class ParsedItem:    id: str    text: str    choices: tuple[str, ...]    answer: str    confidence: float = 1.0
def parse_structured_text(content: str) -> list[ParsedItem]:    """Parse structured text using regex patterns."""    pattern = re.compile(        r"(?P<id>\d+)\.\s*(?P<text>.+?)\n"        r"(?P<choices>(?:[A-D]\..+?\n)+)"        r"Answer:\s*(?P<answer>[A-D])",        re.MULTILINE | re.DOTALL,    )    items = []    for match in pattern.finditer(content):        choices = tuple(            c.strip() for c in re.findall(r"[A-D]\.\s*(.+)", match.group("choices"))        )        items.append(ParsedItem(            id=match.group("id"),            text=match.group("text").strip(),            choices=choices,            answer=match.group("answer"),        ))    return items

@dataclass(frozen=True)class ConfidenceFlag:    item_id: str    score: float    reasons: tuple[str, ...]
def score_confidence(item: ParsedItem) -> ConfidenceFlag:    """Score extraction confidence and flag issues."""    reasons = []    score = 1.0
    if len(item.choices) < 3:        reasons.append("few_choices")        score -= 0.3
    if not item.answer:        reasons.append("missing_answer")        score -= 0.5
    if len(item.text) < 10:        reasons.append("short_text")        score -= 0.2
    return ConfidenceFlag(        item_id=item.id,        score=max(0.0, score),        reasons=tuple(reasons),    )
def identify_low_confidence(    items: list[ParsedItem],    threshold: float = 0.95,) -> list[ConfidenceFlag]:    """Return items below confidence threshold."""    flags = [score_confidence(item) for item in items]    return [f for f in flags if f.score < threshold]

def validate_with_llm(    item: ParsedItem,    original_text: str,    client,) -> ParsedItem:    """Use LLM to fix low-confidence extractions."""    response = client.messages.create(        model="claude-haiku-4-5-20251001",  # Cheapest model for validation        max_tokens=500,        messages=[{            "role": "user",            "content": (                f"Extract the question, choices, and answer from this text.\n\n"                f"Text: {original_text}\n\n"                f"Current extraction: {item}\n\n"                f"Return corrected JSON if needed, or 'CORRECT' if accurate."            ),        }],    )    # Parse LLM response and return corrected item...    return corrected_item

def process_document(    content: str,    *,    llm_client=None,    confidence_threshold: float = 0.95,) -> list[ParsedItem]:    """Full pipeline: regex -> confidence check -> LLM for edge cases."""    # Step 1: Regex extraction (handles 95-98%)    items = parse_structured_text(content)
    # Step 2: Confidence scoring    low_confidence = identify_low_confidence(items, confidence_threshold)
    if not low_confidence or llm_client is None:        return items
    # Step 3: LLM validation (only for flagged items)    low_conf_ids = {f.item_id for f in low_confidence}    result = []    for item in items:        if item.id in low_conf_ids:            result.append(validate_with_llm(item, content, llm_client))        else:            result.append(item)
    return result