def add(self, text, doc_id):
# Convert text to dictionary of words mapping to their term frequency
wf = word_freq(clean_text(text))
# Get doc length by summing all term frequencies
doc_length = reduce(lambda x, y: x + y, wf.values())
self.db.insert(create_document_words(wf, doc_id))
self.db.insert([Document(id=doc_id, length=doc_length)])
def search(self, query):
"""Searches the index for the query.
Returns a list of dictiories containing keys "document_id" and
"score" sorted in descending order.
"""
words = word_tokenize(clean_text(query))
results = self.index_reader.search(words)
doc_scores_sum = defaultdict(float)
for word, doc_scores in results.items():
for doc_id, score in doc_scores.items():
doc_scores_sum[doc_id] += score
# Sort all the doc_ids by the score descending
sorted_doc_ids = sorted(doc_scores_sum, key=doc_scores_sum.get,
reverse=True)
return [{"document_id" : doc_id, "score" : doc_scores_sum[doc_id]}
for doc_id in sorted_doc_ids]
