def setUp(self):
Role.create_defaults()
self.config = self.app.loom_config
if not BA_FIXTURES['resolver']:
schema_dir = os.path.join(FIXTURES, 'schema')
load_local_schema(self.config.resolver, schema_dir=schema_dir)
BA_FIXTURES['resolver'] = self.config.resolver
self.config._resolver = BA_FIXTURES['resolver']
self.es = get_es()
get_loom_indexer().configure()
def setUp(self):
Role.create_defaults()
self.config = self.app.loom_config
if not BA_FIXTURES['resolver']:
schema_dir = os.path.join(FIXTURES, 'schema')
load_local_schema(self.config.resolver, schema_dir=schema_dir)
BA_FIXTURES['resolver'] = self.config.resolver
self.config._resolver = BA_FIXTURES['resolver']
self.es = get_es()
get_loom_indexer().configure()
def suggest_entity(args):
""" Auto-complete API. """
config = get_loom_config()
text = args.get('text')
options = []
if text is not None and len(text.strip()):
q = authz_filter({'match': {'$suggest': text}})
schema = args.get('$schema')
if schema is not None:
# Find matching sub-schemas as well.
schemas = config.implied_schemas(schema)
q = add_filter(q, {"terms": {"$schema": schemas}})
boost_collection = args.get('boost_collection')
if boost_collection is not None:
q = {
'function_score': {
'query': q,
'functions': [{
'boost_factor': 2,
'filter': {
'term': {
'$collections': boost_collection
}
}
}]
}
}
exclude_collection = args.get('exclude_collection')
if exclude_collection is not None:
q = add_filter(q, {'not': {
'term': {'$collections': exclude_collection}
}})
q = {
'size': 5,
'query': q,
'_source': ['name', 'id', '$schema']
}
result = get_es().search(index=get_es_index(), body=q)
for res in result.get('hits', {}).get('hits', []):
options.append(res.get('_source'))
return {
'text': text,
'options': options
}
def execute_query(args, q, facets):
""" Execute the query and return a set of results. """
result = get_es().search(index=get_es_index(), body=q)
hits = result.get('hits', {})
output = {
'status': 'ok',
'results': [],
'offset': q['from'],
'limit': q['size'],
'took': result.get('took'),
'total': hits.get('total'),
'next': None,
'facets': {}
}
next_offset = output['offset'] + output['limit']
if output['total'] > next_offset:
params = {'offset': next_offset}
for k, v in args.iterlists():
if k in ['facet', 'offset']:
continue
params[k] = v
output['next'] = url_for('base.search', **params)
for doc in hits.get('hits', []):
hlt = doc.get('highlight', {}).get('$text', None)
doc = result_entity(doc)
if hlt is not None:
doc['$highlight'] = hlt
output['results'].append(doc)
# traverse and get all facets.
aggs = result.get('aggregations')
for facet in facets:
scoped = aggs.get('scoped').get(facet, {})
value = aggs.get(facet, scoped.get(facet, {}))
data = {
'total': scoped.get('doc_count', hits.get('total')),
'values': value.get('buckets', [])
}
output['facets'][facet] = data
return output
def execute_query(args, q, facets):
""" Execute the query and return a set of results. """
result = get_es().search(index=get_es_index(), body=q)
hits = result.get('hits', {})
output = {
'status': 'ok',
'results': [],
'offset': q['from'],
'limit': q['size'],
'took': result.get('took'),
'total': hits.get('total'),
'next': None,
'facets': {}
}
next_offset = output['offset'] + output['limit']
if output['total'] > next_offset:
params = {'offset': next_offset}
for k, v in args.iterlists():
if k in ['facet', 'offset']:
continue
params[k] = v
output['next'] = url_for('base.search', **params)
for doc in hits.get('hits', []):
hlt = doc.get('highlight', {}).get('$text', None)
doc = result_entity(doc)
if hlt is not None:
doc['$highlight'] = hlt
output['results'].append(doc)
# traverse and get all facets.
aggs = result.get('aggregations')
for facet in facets:
scoped = aggs.get('scoped').get(facet, {})
value = aggs.get(facet, scoped.get(facet, {}))
data = {
'total': scoped.get('doc_count', hits.get('total')),
'values': value.get('buckets', [])
}
output['facets'][facet] = data
return output
def more_like_this(entity):
query = {
'query': {
'more_like_this': {
'fields': ['name', '$text', '$latin'],
'docs': [
{'_id': entity['_id'], '_type': entity['_type']}
],
},
},
'_source': {
'excludes': ['$text', '$latin', '*.*']
}
}
query = authz_filter(query)
results = get_es().search(index=get_es_index(), body=query)
similar = {'status': 'ok', 'results': []}
for result in results.get('hits', {}).get('hits', []):
similar['results'].append(result_entity(result))
return similar
def suggest_entity(args):
""" Auto-complete API. """
config = get_loom_config()
text = args.get('text')
options = []
if text is not None and len(text.strip()):
q = authz_filter({'match': {'$suggest': text}})
schema = args.get('$schema')
if schema is not None:
# Find matching sub-schemas as well.
schemas = config.implied_schemas(schema)
q = add_filter(q, {"terms": {"$schema": schemas}})
boost_collection = args.get('boost_collection')
if boost_collection is not None:
q = {
'function_score': {
'query':
q,
'functions': [{
'boost_factor': 2,
'filter': {
'term': {
'$collections': boost_collection
}
}
}]
}
}
exclude_collection = args.get('exclude_collection')
if exclude_collection is not None:
q = add_filter(
q, {'not': {
'term': {
'$collections': exclude_collection
}
}})
q = {'size': 5, 'query': q, '_source': ['name', 'id', '$schema']}
result = get_es().search(index=get_es_index(), body=q)
for res in result.get('hits', {}).get('hits', []):
options.append(res.get('_source'))
return {'text': text, 'options': options}
def like(id):
ent = get_es().get(index=get_es_index(), id=id, _source=False)
return jsonify(more_like_this(ent))
def like(id):
ent = get_es().get(index=get_es_index(), id=id, _source=False)
return jsonify(more_like_this(ent))