/* eslint no-console: 0 */ // Load wink-bm25-text-search var bm25 = require( 'wink-bm25-text-search' ); // Create search engine's instance var engine = bm25(); // Load sample data (load any other JSON data instead of sample) var docs = require( 'wink-bm25-text-search/sample-data/demo-data-for-wink-bm25.json' ); // Load wink nlp and its model const winkNLP = require( 'wink-nlp' ); // Use web model const model = require( 'wink-eng-lite-web-model' ); const nlp = winkNLP( model ); const its = nlp.its; const prepTask = function ( text ) { const tokens = []; nlp.readDoc(text) .tokens() // Use only words ignoring punctuations etc and from them remove stop words .filter( (t) => ( t.out(its.type) === 'word' && !t.out(its.stopWordFlag) ) ) // Handle negation and extract stem of the word .each( (t) => tokens.push( (t.out(its.negationFlag)) ? '!' + t.out(its.stem) : t.out(its.stem) ) ); return tokens; }; // Contains search query. var query; // Step I: Define config // Only field weights are required in this example. engine.defineConfig( { fldWeights: { title: 1, body: 2 } } ); // Step II: Define PrepTasks pipe. // Set up 'default' preparatory tasks i.e. for everything else engine.definePrepTasks( [ prepTask ] ); // Step III: Add Docs // Add documents now... docs.forEach( function ( doc, i ) { // Note, 'i' becomes the unique id for 'doc' engine.addDoc( doc, i ); } ); // Step IV: Consolidate // Consolidate before searching engine.consolidate(); // All set, start searching! query = 'not studied law'; // `results` is an array of [ doc-id, score ], sorted by score var results = engine.search( query ); // Print number of results. console.log( '%d entries found.', results.length ); // -> 1 entries found. // results[ 0 ][ 0 ] i.e. the top result is: console.log( docs[ results[ 0 ][ 0 ] ].body ); // -> George Walker Bush (born July 6, 1946) is an... // -> ... He never studied Law... // Whereas if you search for `law` then multiple entries will be // found except the above entry!