@siva-sub/mcp-public-transport
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A Model Context Protocol server for Singapore transport data with real-time information and routing
230 lines (229 loc) • 10.1 kB
JavaScript
;
Object.defineProperty(exports, "__esModule", { value: true });
exports.FuzzySearchService = void 0;
const logger_js_1 = require("../utils/logger.js");
class FuzzySearchService {
constructor() {
this.singaporeAbbreviations = new Map([
// Common Singapore abbreviations
['blk', ['block', 'building']],
['opp', ['opposite', 'across from']],
['bef', ['before', 'in front of']],
['aft', ['after', 'behind']],
['stn', ['station', 'terminal']],
['cp', ['car park', 'carpark', 'parking']],
['int', ['interchange', 'terminal']],
['ctr', ['center', 'centre']],
['rd', ['road', 'street']],
['ave', ['avenue']],
['st', ['street']],
['dr', ['drive']],
['cres', ['crescent']],
['ter', ['terrace']],
['pk', ['park']],
['gdns', ['gardens']],
['hts', ['heights']],
['vw', ['view']],
['wk', ['walk']],
['cl', ['close']],
['pl', ['place']],
['sq', ['square']],
['cir', ['circle']],
['ct', ['court']],
['ln', ['lane']],
['mrt', ['mass rapid transit', 'train station']],
['lrt', ['light rail transit', 'light rail']],
['sch', ['school']],
['pri', ['primary']],
['sec', ['secondary']],
['jc', ['junior college']],
['poly', ['polytechnic']],
['univ', ['university']],
['hosp', ['hospital']],
['clin', ['clinic']],
['cc', ['community centre', 'community center']],
['rc', ['residents committee', 'residents centre']],
['temp', ['temporary']],
['perm', ['permanent']],
['hdb', ['housing development board', 'public housing']],
['condo', ['condominium']],
['apt', ['apartment']],
['twr', ['tower']],
['bldg', ['building']],
['shp', ['shop', 'shopping']],
['mkt', ['market']],
['fc', ['food court']],
['hc', ['hawker centre', 'hawker center']],
['mall', ['shopping mall', 'shopping centre']],
['plz', ['plaza']],
['pt', ['point']],
['jn', ['junction']],
['flyover', ['bridge', 'overpass']],
['underpass', ['subway', 'tunnel']],
]);
this.commonPatterns = [
// HDB block patterns
{ pattern: /\bblk\s*(\d+[a-z]?)\b/gi, replacement: 'block $1', weight: 1.2 },
{ pattern: /\bblock\s*(\d+[a-z]?)\b/gi, replacement: 'blk $1', weight: 1.2 },
// Opposite patterns
{ pattern: /\bopp\s+(.+)/gi, replacement: 'opposite $1', weight: 1.1 },
{ pattern: /\bopposite\s+(.+)/gi, replacement: 'opp $1', weight: 1.1 },
// Before/After patterns
{ pattern: /\bbef\s+(.+)/gi, replacement: 'before $1', weight: 1.1 },
{ pattern: /\baft\s+(.+)/gi, replacement: 'after $1', weight: 1.1 },
// Car park patterns
{ pattern: /\bcp\b/gi, replacement: 'car park', weight: 1.1 },
{ pattern: /\bcar\s*park\b/gi, replacement: 'cp', weight: 1.1 },
// Station patterns
{ pattern: /\bstn\b/gi, replacement: 'station', weight: 1.2 },
{ pattern: /\bmrt\s*stn\b/gi, replacement: 'mrt station', weight: 1.3 },
{ pattern: /\blrt\s*stn\b/gi, replacement: 'lrt station', weight: 1.3 },
// Road patterns
{ pattern: /\brd\b/gi, replacement: 'road', weight: 1.0 },
{ pattern: /\bave\b/gi, replacement: 'avenue', weight: 1.0 },
{ pattern: /\bst\b/gi, replacement: 'street', weight: 1.0 },
];
}
/**
* Expand abbreviations in a search query
*/
expandAbbreviations(query) {
const variations = [query.toLowerCase()];
const words = query.toLowerCase().split(/\s+/);
// Generate variations by expanding abbreviations
const expandedVariations = [];
for (const word of words) {
const expansions = this.singaporeAbbreviations.get(word);
if (expansions) {
for (const expansion of expansions) {
const expandedQuery = query.toLowerCase().replace(new RegExp(`\\b${word}\\b`, 'gi'), expansion);
expandedVariations.push(expandedQuery);
}
}
}
variations.push(...expandedVariations);
// Apply common patterns
for (const pattern of this.commonPatterns) {
const patternVariations = variations.map(v => v.replace(pattern.pattern, pattern.replacement)).filter(v => v !== query.toLowerCase());
variations.push(...patternVariations);
}
return [...new Set(variations)]; // Remove duplicates
}
/**
* Calculate Levenshtein distance between two strings
*/
levenshteinDistance(str1, str2) {
const matrix = Array(str2.length + 1).fill(null).map(() => Array(str1.length + 1).fill(null));
for (let i = 0; i <= str1.length; i++)
matrix[0][i] = i;
for (let j = 0; j <= str2.length; j++)
matrix[j][0] = j;
for (let j = 1; j <= str2.length; j++) {
for (let i = 1; i <= str1.length; i++) {
const indicator = str1[i - 1] === str2[j - 1] ? 0 : 1;
matrix[j][i] = Math.min(matrix[j][i - 1] + 1, // deletion
matrix[j - 1][i] + 1, // insertion
matrix[j - 1][i - 1] + indicator // substitution
);
}
}
return matrix[str2.length][str1.length];
}
/**
* Calculate similarity score between query and target string
*/
calculateSimilarity(query, target) {
const queryLower = query.toLowerCase().trim();
const targetLower = target.toLowerCase().trim();
// Exact match gets highest score
if (queryLower === targetLower)
return 1.0;
// Check if target contains query (substring match)
if (targetLower.includes(queryLower)) {
const ratio = queryLower.length / targetLower.length;
return 0.8 + (ratio * 0.2); // 0.8 to 1.0 based on length ratio
}
// Check if query contains target
if (queryLower.includes(targetLower)) {
const ratio = targetLower.length / queryLower.length;
return 0.7 + (ratio * 0.2); // 0.7 to 0.9 based on length ratio
}
// Word-based matching
const queryWords = queryLower.split(/\s+/);
const targetWords = targetLower.split(/\s+/);
let matchingWords = 0;
for (const qWord of queryWords) {
for (const tWord of targetWords) {
if (qWord === tWord || qWord.includes(tWord) || tWord.includes(qWord)) {
matchingWords++;
break;
}
}
}
const wordMatchRatio = matchingWords / Math.max(queryWords.length, targetWords.length);
if (wordMatchRatio > 0.5) {
return 0.5 + (wordMatchRatio * 0.3); // 0.5 to 0.8 based on word matches
}
// Levenshtein distance for fuzzy matching
const maxLength = Math.max(queryLower.length, targetLower.length);
if (maxLength === 0)
return 0;
const distance = this.levenshteinDistance(queryLower, targetLower);
const similarity = 1 - (distance / maxLength);
// Only return meaningful similarities
return similarity > 0.3 ? similarity * 0.6 : 0; // Scale down fuzzy matches
}
/**
* Search through items with fuzzy matching
*/
search(query, items, extractText, minScore = 0.3, maxResults = 10) {
if (!query.trim())
return [];
logger_js_1.logger.debug(`Fuzzy search for: "${query}"`);
const queryVariations = this.expandAbbreviations(query);
const results = [];
for (const item of items) {
const textFields = extractText(item);
let bestScore = 0;
const matches = [];
// Test each text field against each query variation
for (const text of textFields) {
for (const queryVar of queryVariations) {
const score = this.calculateSimilarity(queryVar, text);
if (score > bestScore) {
bestScore = score;
matches.length = 0; // Clear previous matches
matches.push(text);
}
else if (score === bestScore && score > minScore) {
matches.push(text);
}
}
}
if (bestScore >= minScore) {
results.push({
item,
score: bestScore,
matches: [...new Set(matches)] // Remove duplicates
});
}
}
// Sort by score (descending) and limit results
results.sort((a, b) => b.score - a.score);
const limitedResults = results.slice(0, maxResults);
logger_js_1.logger.debug(`Fuzzy search found ${limitedResults.length} results (${results.length} total)`);
return limitedResults;
}
/**
* Extract common Singapore location patterns from text
*/
extractLocationPatterns(text) {
const patterns = {
blockNumber: /(?:blk|block)\s*(\d+[a-z]?)/i.exec(text)?.[1],
direction: /\b(opp|opposite|bef|before|aft|after|nr|near)\s+/i.exec(text)?.[1],
amenityType: /\b(cp|car\s*park|stn|station|mrt|lrt|sch|school|hosp|hospital|cc|community\s*centre?|mall|market|hawker)\b/i.exec(text)?.[0],
};
return Object.fromEntries(Object.entries(patterns).filter(([_, value]) => value !== undefined));
}
}
exports.FuzzySearchService = FuzzySearchService;