Mevzuat MCP

""" Article-level keyword search for legislation. Splits legislation into articles and searches for keywords within them. """ import re from typing import List, Dict from pydantic import BaseModel class MaddeMatch(BaseModel): """A single article match result.""" madde_no: str # e.g., "1", "15", "142" madde_title: str # e.g., "Amaç", "Tanımlar" madde_content: str # Full article text match_count: int # Number of keyword occurrences preview: str # Short preview showing keyword in context class ArticleSearchResult(BaseModel): """Search results within a legislation.""" mevzuat_no: str mevzuat_tur: int keyword: str total_matches: int matching_articles: List[MaddeMatch] def split_into_articles(markdown_content: str) -> List[Dict[str, str]]: """ Split markdown content into individual articles. Returns list of dicts with keys: madde_no, madde_title, madde_content """ articles = [] # Split by article headers: **MADDE X –** or **MADDE X**- # Regex to match both formats: # - **MADDE 1 –** (dash inside **) - used in some laws # - **MADDE 1**- (dash outside **) - used in regulations pattern = r'\*\*MADDE\s+(\d+)(?:\s*[–-])?\*\*\s*-?' # Find all article positions matches = list(re.finditer(pattern, markdown_content)) if not matches: return [] for i, match in enumerate(matches): madde_no = match.group(1) start_pos = match.start() # Find end position (start of next article or end of content) if i < len(matches) - 1: end_pos = matches[i + 1].start() else: end_pos = len(markdown_content) # Extract full article content article_text = markdown_content[start_pos:end_pos].strip() # Try to extract title (usually follows the article number) # Pattern: **MADDE X –** (1) or **Title** after article number title = "" lines = article_text.split('\n', 3) if len(lines) > 1: # Check if second line is a title (surrounded by **) second_line = lines[1].strip() if second_line.startswith('**') and second_line.endswith('**'): title = second_line.strip('*').strip() articles.append({ 'madde_no': madde_no, 'madde_title': title, 'madde_content': article_text }) return articles def _matches_query(content: str, query: str, case_sensitive: bool = False) -> tuple[bool, int]: """ Check if content matches query with support for AND, OR, NOT, and exact match. Query syntax: - "exact phrase" - Exact match with quotes - word1 AND word2 - Both words must be present (AND must be uppercase) - word1 OR word2 - At least one word must be present (OR must be uppercase) - word1 NOT word2 - word1 present but word2 must not be (NOT must be uppercase) - Combinations: "exact phrase" AND word1 OR word2 NOT word3 Returns: (matches: bool, score: int) - Whether content matches and relevance score """ # Parse exact phrases (quoted) - before any case conversion exact_phrases = re.findall(r'"([^"]*)"', query) # Remove exact phrases from query for further parsing temp_query = query for phrase in exact_phrases: temp_query = temp_query.replace(f'"{phrase}"', '') # Apply case sensitivity search_content = content if case_sensitive else content.lower() # Split by logical operators while preserving them (operators are case sensitive - must be uppercase) tokens = re.split(r'\s+(AND|OR|NOT)\s+', temp_query) tokens = [t.strip() for t in tokens if t.strip()] # Build evaluation stack # Start with True (neutral for AND chains) result = None current_op = 'AND' # Default operator score = 0 # Check exact phrases first for phrase in exact_phrases: phrase_search = phrase if case_sensitive else phrase.lower() if phrase_search in search_content: score += search_content.count(phrase_search) * 2 # Exact matches worth more if result is None: result = True else: if current_op == 'AND' or result is None: return False, 0 # Required phrase not found # Process remaining tokens i = 0 while i < len(tokens): token = tokens[i] if token in ('AND', 'OR', 'NOT'): current_op = token i += 1 continue # Token is a search term - apply case sensitivity search_token = token if case_sensitive else token.lower() term_found = search_token in search_content term_count = search_content.count(search_token) if term_found else 0 if current_op == 'AND': if result is None: result = term_found if term_found: score += term_count else: result = result and term_found if term_found: score += term_count else: return False, 0 # AND condition failed elif current_op == 'OR': if result is None: result = term_found else: result = result or term_found if term_found: score += term_count elif current_op == 'NOT': if term_found: return False, 0 # NOT condition failed # NOT doesn't affect score i += 1 # If no terms were processed, default to False if result is None: result = False return result, score def search_articles_by_keyword( markdown_content: str, keyword: str, case_sensitive: bool = False, max_results: int = 50 ) -> List[MaddeMatch]: """ Search for keyword within articles with support for advanced operators. Query syntax: - Simple keyword: "yatırımcı" - Exact phrase: "mali sıkıntı" - AND operator: yatırımcı AND tazmin - OR operator: yatırımcı OR müşteri - NOT operator: yatırımcı NOT kurum - Combinations: "mali sıkıntı" AND yatırımcı NOT kurum Args: markdown_content: Full legislation content in markdown keyword: Search query with optional operators (AND, OR, NOT, "exact phrase") case_sensitive: Whether to match case max_results: Maximum number of matching articles to return Returns: List of matching articles sorted by relevance (score based on match count) """ articles = split_into_articles(markdown_content) matches = [] for article in articles: content = article['madde_content'] # Check if article matches query matches_query, score = _matches_query(content, keyword, case_sensitive) if matches_query and score > 0: # Generate preview (first occurrence of a search term) search_content = content if case_sensitive else content.lower() search_keyword = keyword if case_sensitive else keyword.lower() # Try to find first quoted phrase or first word preview_terms = re.findall(r'"([^"]*)"', search_keyword) if not preview_terms: # Use first word (excluding operators) words = re.split(r'\s+(?:AND|OR|NOT)\s+', search_keyword) preview_terms = [w.strip() for w in words if w.strip() and w.strip() not in ('AND', 'OR', 'NOT')] preview = "" if preview_terms: first_term = preview_terms[0] if case_sensitive else preview_terms[0].lower() if first_term in search_content: keyword_pos = search_content.find(first_term) start = max(0, keyword_pos - 100) end = min(len(content), keyword_pos + len(first_term) + 100) preview = content[start:end] if start > 0: preview = "..." + preview if end < len(content): preview = preview + "..." if not preview: preview = content[:200] + "..." matches.append(MaddeMatch( madde_no=article['madde_no'], madde_title=article['madde_title'], madde_content=content, match_count=score, preview=preview )) # Sort by score (most relevant first) matches.sort(key=lambda x: x.match_count, reverse=True) return matches[:max_results] def format_search_results(result: ArticleSearchResult) -> str: """Format search results as readable text.""" output = [] output.append(f"Keyword: '{result.keyword}'") output.append(f"Total matching articles: {result.total_matches}") output.append("") for i, match in enumerate(result.matching_articles, 1): output.append(f"=== MADDE {match.madde_no} ===") if match.madde_title: output.append(f"Title: {match.madde_title}") output.append(f"Matches: {match.match_count}") output.append("") output.append("Full content:") output.append(match.madde_content) output.append("") return "\n".join(output)