def index_single(obj, data, texts):
"""Indexing aspects common to entities and documents."""
data['bulk'] = False
data['roles'] = obj.collection.roles
data['collection_id'] = obj.collection.id
data['created_at'] = obj.created_at
data['updated_at'] = obj.updated_at
data = finalize_index(data, obj.model, texts)
data = clean_dict(data)
return index_doc(entity_index(), obj.id, data)
def index_collection(collection):
"""Index a collection."""
if collection.deleted_at is not None:
return delete_collection(collection.id)
data = {
'foreign_id': collection.foreign_id,
'created_at': collection.created_at,
'updated_at': collection.updated_at,
'label': collection.label,
'kind': collection.kind,
'summary': collection.summary,
'category': collection.category,
'publisher': collection.publisher,
'publisher_url': collection.publisher_url,
'info_url': collection.info_url,
'data_url': collection.data_url,
'casefile': collection.casefile,
'roles': collection.roles,
'schemata': {},
'team': []
}
texts = [v for v in data.values() if isinstance(v, str)]
if collection.creator is not None:
data['creator'] = {
'id': collection.creator.id,
'type': collection.creator.type,
'name': collection.creator.name
}
texts.append(collection.creator.name)
for role in collection.team:
data['team'].append({
'id': role.id,
'type': role.type,
'name': role.name
})
# texts.append(role.name)
# Compute some statistics on the content of a collection.
query = {
'size': 0,
'query': {
'bool': {
'filter': [{
'term': {
'collection_id': collection.id
}
}, {
'term': {
'schemata': Entity.THING
}
}]
}
},
'aggs': {
'schema': {
'terms': {
'field': 'schema',
'size': 1000
}
},
'countries': {
'terms': {
'field': 'countries',
'size': 500
}
},
'languages': {
'terms': {
'field': 'languages',
'size': 100
}
},
}
}
result = es.search(index=entities_index(), body=query)
aggregations = result.get('aggregations')
data['count'] = result['hits']['total']
# expose entities by schema count.
for schema in aggregations['schema']['buckets']:
data['schemata'][schema['key']] = schema['doc_count']
# if no countries or langs are given, take the most common from the data.
countries = collection.countries
if countries is None or not len(countries):
countries = aggregations['countries']['buckets']
countries = [c['key'] for c in countries]
data['countries'] = exactitude.countries.normalize_set(countries)
languages = collection.languages
if languages is None or not len(languages):
languages = aggregations['languages']['buckets']
languages = [c['key'] for c in languages]
data['languages'] = exactitude.languages.normalize_set(languages)
texts.extend([normalize(t, ascii=True) for t in texts])
data['text'] = index_form(texts)
return index_doc(collections_index(), collection.id, data)