return out;
};

// Will hold unique terms and their counts and frequencies
var _terms = {};

// docObj will eventually be added to the documents database
var docObj = {id: doc.id,tokens: tokens,body: doc.body};

// Count number of terms
docObj.termCount = tokens.length;

// Increment totalDocuments
this.totalDocuments++;

// Readjust averageDocumentLength
this.totalDocumentTermLength += docObj.termCount;
this.averageDocumentLength = this.totalDocumentTermLength / this.totalDocuments;

// Calculate term frequency
// First get terms count
for (var i = 0,len = tokens.length; i < len; i++) {
var term = tokens[i];
if (!_terms[term]) {
_terms[term] = {
count: 0,freq: 0
};
};
_terms[term].count++;
}

// Then re-loop to calculate term frequency.
// We'll also update inverse document frequencies here.
var keys = Object.keys(_terms);
for (var i = 0,len = keys.length; i < len; i++) {
var term = keys[i];
// Term Frequency for this document.
_terms[term].freq = _terms[term].count / docObj.termCount;

// Inverse Document Frequency initialization
if (!this.terms[term]) {
  this.terms[term] = {
    n: 0,// Number of docs this term appears in,uniquely
    idf: 0
  };
}

this.terms[term].n++;

};

// Calculate inverse document frequencies
// This is SLOWish so if you want to index a big batch of documents,// comment this out and run it once at the end of your addDocuments run
// If you're only indexing a document or two at a time you can leave this in.
// this.updateIdf();

// Add docObj to docs db
docObj.terms = _terms;
this.documents[docObj.id] = docObj;
};

// Look at each document in turn. There are better ways to do this with inverted indices.
var keys = Object.keys(this.documents);
for (var j = 0,nDocs = keys.length; j < nDocs; j++) {
var id = keys[j];
// The relevance score for a document is the sum of a tf-idf-like
// calculation for each query term.
this.documents[id]._score = 0;

// Calculate the score for each query term
for (var i = 0,len = queryTerms.length; i < len; i++) {
  var queryTerm = queryTerms[i];

  // We've never seen this term before so IDF will be 0.
  // Means we can skip the whole term,it adds nothing to the score
  // and isn't in any document.
  if (typeof this.terms[queryTerm] === 'undefined') {
    continue;
  }

  // This term isn't in the document,so the TF portion is 0 and this
  // term contributes nothing to the search score.
  if (typeof this.documents[id].terms[queryTerm] === 'undefined') {
    continue;
  }

  // The term is in the document,let's go.
  // The whole term is :
  // IDF * (TF * (k1 + 1)) / (TF + k1 * (1 - b + b * docLength / avgDocLength))

  // IDF is pre-calculated for the whole docset.
  var idf = this.terms[queryTerm].idf;
  // Numerator of the TF portion.
  var num = this.documents[id].terms[queryTerm].count * (this.k1 + 1);
  // Denomerator of the TF portion.
  var denom = this.documents[id].terms[queryTerm].count 
    + (this.k1 * (1 - this.b + (this.b * this.documents[id].termCount / this.averageDocumentLength)));

  // Add this query term to the score
  this.documents[id]._score += idf * num / denom;
}

if (!isNaN(this.documents[id]._score) && this.documents[id]._score > 0) {
  results.push(this.documents[id]);
}

}

results.sort(function(a,b) { return b._score - a._score; });
return results.slice(0,10);
};