def text_to_docs(text_id):
"""
Query a text against the OSP corpus.
Args:
text_id (int): A text row id.
"""
row = Text.get(Text.id==text_id)
doc_ids = set()
for tokens in row.queries:
# Execute the query.
results = config.es.search(
index='document',
request_timeout=90,
body={
'fields': [],
'size': 1000000,
'filter': {
'query': {
'match_phrase': {
'body': {
'query': ' '.join(tokens),
'slop': 5,
}
}
}
}
}
)
# Fail the job if the result is incomplete.
if results['timed_out']:
raise TimeoutError()
# Register the doc ids.
if results['hits']['total'] > 0:
for hit in results['hits']['hits']:
doc_ids.add(int(hit['_id']))
# Build doc -> text links.
citations = []
for doc_id in doc_ids:
citations.append({
'document': doc_id,
'text': row.id,
'tokens': row.hash_tokens,
})
# Bulk-insert the results.
if citations:
Citation.insert_many(citations).execute()
