def post(self, request, pk: int, index: str, document_id: str):
validate_index_and_project_perms(request, pk, index)
serializer = self.get_serializer(data=request.data)
if serializer.is_valid(raise_exception=True):
ed = ElasticDocument(index)
document = ed.get(document_id, fields=[TEXTA_TAGS_KEY])
if not document:
raise NotFound(
f"Could not find document with ID '{document_id}' from index '{index}'!"
)
document = document.get("_source")
target_facts = serializer.validated_data.get("facts", [])
existing_facts = document.get(TEXTA_TAGS_KEY, [])
new_facts = []
for index_count, existing_fact in enumerate(existing_facts):
for fact in target_facts:
if not (fact.items() <= existing_fact.items()):
new_facts.append(existing_fact)
document[TEXTA_TAGS_KEY] = new_facts
ed.update(index, document_id, doc=document)
return Response({
"message":
f"Removed given facts from document with the ID of {document_id}!"
})
def apply_mlp_on_es_docs(self, source_and_meta_docs: List[str], mlp_id: int):
"""
Applies MLP on documents received by previous tasks and updates them in Elasticsearch.
:param self: Reference to the Celery Task object of this task, courtesy of the bind parameter in the decorator.
:param source_and_meta_docs: List of Elasticsearch document ID's to pull from Elasticsearch.
:param mlp_id: ID of the MLPObject which contains progress.
"""
mlp_object = get_mlp_object(mlp_id)
task_object = mlp_object.task
# Get the necessary fields.
field_data: List[str] = json.loads(mlp_object.fields)
if TEXTA_TAGS_KEY not in field_data:
# Add in existing facts so that proper duplicate filtering would be applied.
field_data.append(TEXTA_TAGS_KEY)
analyzers: List[str] = json.loads(mlp_object.analyzers)
# retrieve document from ES
document_wrapper = ElasticDocument(index=None)
source_and_meta_docs = document_wrapper.get_bulk(
doc_ids=source_and_meta_docs, fields=field_data)
source_documents = [doc["_source"] for doc in source_and_meta_docs]
mlp_docs = apply_mlp_on_documents(source_documents, analyzers, field_data,
mlp_id)
es_documents = unite_source_with_meta(source_and_meta_docs, mlp_docs)
update_documents_in_es(es_documents)
# Update progress
task_object.update_progress_iter(len(source_and_meta_docs))
return True
def post(self, request, pk: int):
ed = ElasticDocument(index=None)
# Validate payload and project permissions.
serializer: InsertDocumentsSerializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
project = get_object_or_404(Project, pk=pk)
if project.users.filter(pk=request.user.pk).exists() is False:
raise PermissionDenied("You do not have permissions for this project!")
# Split indices on whether they have index access or lack any index details at all.
documents = serializer.validated_data["documents"]
split_fields = serializer.validated_data["split_text_in_fields"]
indices = project.get_indices()
correct_actions, failed_actions, missing_actions = self._split_documents_per_index(allowed_indices=indices, documents=documents)
missing_actions, index_name, has_new_index = self._normalize_missing_index_values(missing_actions, project.pk, indices)
split_actions = self._split_text(correct_actions + missing_actions, split_fields)
if has_new_index:
ed.core.create_index(index_name)
ed.core.add_texta_facts_mapping(index_name)
index, is_created = Index.objects.get_or_create(name=index_name, is_open=True)
project.indices.add(index)
# Send the documents to Elasticsearch.
success_count, errors = ed.bulk_add_generator(actions=split_actions, stats_only=False)
return Response(
{
"successfully_indexed": success_count,
"errors": errors,
"failed_index_permissions": len(failed_actions)
}
)
def get(self, request, pk: int, index: str, document_id: str):
validate_index_and_project_perms(request, pk, index)
ed = ElasticDocument(index)
document = ed.get(document_id)
if not document:
raise NotFound(f"Could not find document with ID '{document_id}' from index '{index}'!")
return Response(document)
def fact_delete_query_task(self, worker_id: int):
worker_object = DeleteFactsByQueryTask.objects.get(pk=worker_id)
try:
show_progress = ShowProgress(worker_object.task, multiplier=1)
show_progress.update_step(
'Scrolling through the indices to delete the facts.')
# Get the necessary fields.
indices: List[str] = worker_object.get_indices()
target_facts = json.loads(worker_object.facts)
scroll_size = worker_object.scroll_size
searcher = ElasticSearcher(
query=json.loads(worker_object.query),
indices=indices,
field_data=[TEXTA_TAGS_KEY],
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout=f"{worker_object.es_timeout}m")
ed = ElasticDocument(index=None)
actions = query_delete_actions_generator(searcher, target_facts)
ed.bulk_update(actions)
worker_object.task.complete()
worker_object.save()
return worker_id
except Exception as e:
worker_object.task.handle_failed_task(e)
raise e
def get_significant_words(indices: List[str], fields: List[str], document_ids: List[str], stop_words: List = None, exclude=""):
"""
This is a helper function to parse all the given fields and use the document_ids
as input to make a significant_words aggregation.
Args:
exclude: Regex compatible string for which words to exclude, uses the exclude parameter of Elasticsearch aggregations.
stop_words: Optional parameter to remove stopwords from the results.
indices: Indices from which to perform the aggregation.
fields: From which fields can you get the text content needed for comparison.
document_ids: IDs of the documents you want to use as baseline for the aggregation.
Returns: List of dictionaries with the signifcant word and how many times it occurs in the documents.
"""
ed = ElasticDocument("*")
ea = ElasticAggregator(indices=indices)
stop_words = StopWords._get_stop_words(custom_stop_words=stop_words)
# Validate that those documents exist.
validated_docs: List[dict] = ed.get_bulk(document_ids)
if validated_docs:
unique_ids = list(set([index["_id"] for index in validated_docs]))
significant_words = []
for field in fields:
sw = ea.get_significant_words(document_ids=unique_ids, field=field, stop_words=stop_words, exclude=exclude)
significant_words += sw
return significant_words
else:
return []
def update_documents_in_es(documents: List[dict]):
"""
Updates the documents inside Elasticsearch, either with the MLP results or the
error messages.
:param documents: Full Elasticsearch documents..
"""
ed = ElasticDocument(index=None)
ed.bulk_update(actions=documents)
def delete(self, request, pk: int, index: str, document_id: str):
validate_index_and_project_perms(request, pk, index)
try:
ed = ElasticDocument(index)
document = ed.delete(doc_id=document_id)
return Response(document)
except texta_elastic.exceptions.NotFoundError:
return Response(status=status.HTTP_404_NOT_FOUND)
def tag_cluster(self, cluster_pk: int, clustering_object_pk: int, fact: dict):
ed = ElasticDocument("")
cluster = Cluster.objects.get(pk=cluster_pk)
clustering_object = ClusteringResult.objects.get(pk=clustering_object_pk)
doc_ids = json.loads(cluster.document_ids)
ignored_ids = json.loads(clustering_object.ignored_ids)
ed.add_fact_to_documents(fact=fact, doc_ids=doc_ids)
clustering_object.ignored_ids = json.dumps(doc_ids + ignored_ids)
clustering_object.save()
return True
def patch(self, request, pk: int, index: str, document_id: str):
validate_index_and_project_perms(request, pk, index)
try:
ed = ElasticDocument(index)
document = ed.update(index=index, doc_id=document_id, doc=request.data)
return Response(document)
except elasticsearch.exceptions.RequestError as e:
if e.error == "mapper_parsing_exception": # TODO Extend the decorator with different variants of the request error instead.
return Response(e.info["error"]["root_cause"], status=status.HTTP_400_BAD_REQUEST)
except texta_elastic.exceptions.NotFoundError:
return Response(status=status.HTTP_404_NOT_FOUND)
def apply_rakun_extractor_to_index(self, object_id: int, indices: List[str],
fields: List[str], query: dict,
es_timeout: int, bulk_size: int,
fact_name: str, add_spans: bool):
"""Apply Rakun Keyword Extractor to index."""
logging.getLogger(INFO_LOGGER).info(
f"Starting task 'apply_rakun_extractor_to_index' with ID: {object_id}!"
)
rakun_extractor_object = RakunExtractor.objects.get(id=object_id)
try:
progress = ShowProgress(rakun_extractor_object.task)
# retrieve fields
field_data = fields
ec = ElasticCore()
[ec.add_texta_facts_mapping(index) for index in indices]
searcher = ElasticSearcher(
indices=indices,
field_data=field_data +
["texta_facts"], # Get facts to add upon existing ones.
query=query,
timeout=f"{es_timeout}m",
output=ElasticSearcher.OUT_RAW,
callback_progress=progress,
scroll_size=bulk_size)
keyword_detector = rakun_extractor_object.load_rakun_keyword_detector()
actions = update_generator(
keyword_detector=keyword_detector,
generator=searcher,
ec=ec,
fields=field_data,
rakun_extractor_object=rakun_extractor_object,
fact_name=fact_name,
fact_value="",
add_spans=add_spans)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
rakun_extractor_object.task.complete()
return True
except Exception as e:
logging.getLogger(ERROR_LOGGER).exception(e)
error_message = f"{str(e)[:100]}..." # Take first 100 characters in case the error message is massive.
rakun_extractor_object.task.add_error(error_message)
rakun_extractor_object.task.update_status(Task.STATUS_FAILED)
def bulk_add_documents(
elastic_search: ElasticSearcher,
elastic_doc: ElasticDocument,
index: str,
chunk_size: int,
field_data: List[dict],
flatten_doc=False,
):
new_docs = apply_custom_processing(elastic_search, flatten_doc)
actions = apply_field_changes_generator(new_docs, index, field_data)
# No need to wait for indexing to actualize, hence refresh is False.
elastic_doc.bulk_add_generator(actions=actions,
chunk_size=chunk_size,
refresh="wait_for")
def pull_document_by_id(self, request, pk=None, project_pk=None):
annotator: Annotator = self.get_object()
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
ed = ElasticDocument(index=annotator.get_indices())
document_id = serializer.validated_data["document_id"]
document = ed.get(document_id)
if document:
document = self._process_document_output(document, annotator)
return Response(document)
else:
return Response({"message": "No such document!"},
status=status.HTTP_404_NOT_FOUND)
def apply_search_fields_tagger_on_index(object_id: int):
search_fields_tagger = SearchFieldsTagger.objects.get(pk=object_id)
task_object = search_fields_tagger.task
"""Apply Search Fields Tagger to index."""
try:
progress = ShowProgress(task_object)
progress.update_step('scrolling search fields')
# Get the necessary fields.
indices: List[str] = search_fields_tagger.get_indices()
fields: List[str] = json.loads(search_fields_tagger.fields)
fact_name: str = search_fields_tagger.fact_name
scroll_timeout = search_fields_tagger.es_timeout
scroll_size = search_fields_tagger.bulk_size
use_breakup = search_fields_tagger.use_breakup
breakup_character = search_fields_tagger.breakup_character
ec = ElasticCore()
[ec.add_texta_facts_mapping(index) for index in indices]
searcher = ElasticSearcher(
indices=indices,
field_data=fields +
["texta_facts"], # Get facts to add upon existing ones.
query=json.loads(search_fields_tagger.query),
output=ElasticSearcher.OUT_RAW,
scroll_timeout=f"{scroll_timeout}m",
callback_progress=progress,
scroll_size=scroll_size)
actions = update_search_fields_generator(
generator=searcher,
ec=ec,
fields=fields,
fact_name=fact_name,
search_field_tagger_object=search_fields_tagger,
use_breakup=use_breakup,
breakup_character=breakup_character)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
return object_id
except Exception as e:
logging.getLogger(ERROR_LOGGER).exception(e)
task_object.add_error(str(e))
task_object.update_status(Task.STATUS_FAILED)
raise e
def apply_analyzers_on_indices(self, worker_id: int):
worker_object = ApplyESAnalyzerWorker.objects.get(pk=worker_id)
task_object = worker_object.task
try:
show_progress = ShowProgress(task_object, multiplier=1)
show_progress.update_step(
'scrolling through the indices to apply lang')
# Get the necessary fields.
indices: List[str] = worker_object.get_indices()
fields = json.loads(worker_object.fields)
detect_lang = worker_object.detect_lang
snowball_language = worker_object.stemmer_lang
scroll_timeout = f"{worker_object.es_timeout}m"
scroll_size = worker_object.bulk_size
analyzers = json.loads(worker_object.analyzers)
tokenizer = worker_object.tokenizer
strip_html = worker_object.strip_html
searcher = ElasticSearcher(query=json.loads(worker_object.query),
indices=indices,
field_data=fields,
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout=scroll_timeout)
task_object.set_total(searcher.count())
actions = process_analyzer_actions(generator=searcher,
worker=worker_object,
detect_lang=detect_lang,
snowball_language=snowball_language,
fields_to_parse=fields,
analyzers=analyzers,
tokenizer=tokenizer,
strip_html=strip_html)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
ed.bulk_update(actions=actions, chunk_size=scroll_size)
worker_object.task.complete()
return worker_id
except Exception as e:
task_object.handle_failed_task(e)
raise e
def import_dataset(self) -> list:
error_container = []
# retrieve content from file
success, file_content = self._get_file_content()
file_content = file_content.dropna(how="all")
# check if file was parsed
if not success:
error_container.append('unknown file type')
return error_container
# convert content to list of records (dicts)
records = file_content.to_dict(orient='records')
# set num_records
self.num_records = len(records)
# set total number of documents for progress
if self.show_progress:
self.show_progress.set_total(self.num_records)
# add documents to ES
es_doc = ElasticDocument(self.index)
# create index
es_doc.core.create_index(self.index)
# add mapping for texta facts
es_doc.core.add_texta_facts_mapping(self.index)
# get records
chunk_size = 500
records = [{
k: v
for k, v in record.items() if pd.Series(v).notna().all()
} for record in records]
record_chunks = list(chunks(records, chunk_size))
for documents in record_chunks:
success, errors = es_doc.bulk_add(documents,
chunk_size=chunk_size,
stats_only=False,
raise_on_error=False)
self.num_records_success += success
if self.show_progress:
self.show_progress.update(success)
for error in list(errors):
message = error["index"]["error"]["reason"] if isinstance(
error, dict) else str(error)
error_container.append(message)
return error_container
def multitag_docs(self, request, pk=None, project_pk=None):
serializer = self.get_serializer(data=request.data)
serializer.is_valid(raise_exception=True)
# filter tagger groups present in project
project_object = Project.objects.get(id=project_pk)
regex_taggers_groups = RegexTaggerGroup.objects.filter(
project=project_object)
# filter again based on serializer
if serializer.validated_data['tagger_groups']:
regex_taggers_groups = regex_taggers_groups.filter(
pk__in=serializer.validated_data['tagger_groups'])
docs = serializer.validated_data["docs"]
fields = serializer.validated_data["fields"]
# apply taggers
result = []
for regex_tagger_group in regex_taggers_groups:
matches = regex_tagger_group.tag_docs(fields, docs)
result.extend(matches)
result = ElasticDocument.remove_duplicate_facts(result)
return Response(result, status=status.HTTP_200_OK)
def _initialize_es(self, project_pk, text_processor, callback_progress,
prediction_to_match):
# create es doc
es_doc = ElasticDocument(self.feedback_index)
# if no model objects, return nones for query and search
if not self.model_object:
return es_doc, None, None
# create mathing query
query = Query()
query.add_string_filter(query_string=self.model_object.MODEL_TYPE,
fields=["model_type"])
if self.model_object:
query.add_string_filter(query_string=str(self.model_object.pk),
fields=["model_id"])
if prediction_to_match:
query.add_string_filter(query_string=prediction_to_match,
fields=["correct_result"])
# if no index, don't create searcher object
if not self.check_index_exists():
return es_doc, None, query.query
# create es search
es_search = ElasticSearcher(indices=self.feedback_index,
query=query.query,
text_processor=text_processor,
output=ElasticSearcher.OUT_DOC_WITH_ID,
callback_progress=callback_progress)
# return objects
return es_doc, es_search, query.query
def _get_sample_document(self, id_field: str, id_value: str, index: str):
query = Search().query(Q("term", **{f"{id_field}.keyword": id_value})).to_dict()
es = ElasticSearcher(query=query, output=ElasticSearcher.OUT_RAW)
ed = ElasticDocument(index=index)
response = es.search()["hits"]["hits"]
document = response[0] if response else None
return ed, document
def setUp(self):
self.user = create_test_user('user', '*****@*****.**', 'pw')
self.new_test_index_name = f"ttk_test_query_tagger_{uuid.uuid4().hex[:5]}"
self.project = project_creation("SearchQueryTaggerTestProject", self.new_test_index_name, self.user)
self.project.users.add(self.user)
self.url = reverse("v2:search_query_tagger-list", kwargs={"project_pk": self.project.pk})
self.uuid = "adasda-5874856a-das4das98f5"
self.document = {
"Field_1": "This is sentence1. This is sentence2. This is sentence3. This is sentence4. This is sentence5.",
"Field_2": "This is a different sentence.",
"Field_3": "This is test data.",
"uuid": self.uuid}
self.ed = ElasticDocument(index=self.new_test_index_name)
self.ed.add(self.document)
self.client.login(username='******', password='******')
def count_indices(self, request, pk=None, project_pk=None):
serializer = self.get_serializer(data=request.data)
serializer.is_valid()
indices = [{"name": name} for name in serializer.validated_data.get("indices", [])]
serializer = IndexSerializer(data=indices, many=True)
serializer.is_valid(raise_exception=True)
project: Project = self.get_object()
ed = ElasticDocument(None)
indices = [index["name"] for index in indices]
if indices:
# We check for indices before to prevent the default behaviour of picking all the indices in project.
indices = project.get_available_or_all_project_indices(indices)
count = ed.count(indices=indices)
return Response(count)
else:
return Response(0)
def post(self, request, project_pk: int):
project: Project = get_object_or_404(Project, pk=project_pk)
self.check_object_permissions(request, project)
serializer = ProjectDocumentSerializer(data=request.data)
if not serializer.is_valid():
raise SerializerNotValid(detail=serializer.errors)
indices = project.get_available_or_all_project_indices(serializer.validated_data["indices"])
if not indices:
raise ProjectValidationFailed(detail="No indices supplied and project has no indices")
doc_id = serializer.validated_data["doc_id"]
if not doc_id:
raise InvalidInputDocument(detail="No doc_id supplied")
es = ElasticDocument(index=indices)
results = es.get(doc_id)
return Response(results, status=status.HTTP_200_OK)
def process_mlp_actions(generator: ElasticSearcher, analyzers: List[str],
field_data: List[str], mlp_class: MLP, mlp_id: int):
"""
ElasticSearcher returns a list of 100 RAW elastic documents.
Since MLP needs a raw document to process, we need to memorize the index of the document in question
so that we could later fetch it's metadata for the Bulk generator.
"""
counter = 0
info_logger = logging.getLogger(INFO_LOGGER)
info_logger.info(
f"Starting the processing of indices for MLP worker with ID of {mlp_id}!"
)
for document_batch in generator:
document_sources = [dict(hit["_source"]) for hit in document_batch]
mlp_processed = mlp_class.process_docs(document_sources,
analyzers=analyzers,
doc_paths=field_data)
for index, mlp_processed_document in enumerate(mlp_processed):
original_elastic_document = document_batch[index]
# Make sure that existing facts in the document and new ones don't overlap.
original_facts = original_elastic_document["_source"].get(
"texta_facts", [])
new_facts = mlp_processed_document.get("texta_facts", [])
total_facts = [fact for fact in original_facts + new_facts if fact]
unique_facts = ElasticDocument.remove_duplicate_facts(total_facts)
elastic_update_body = {
"_id": original_elastic_document["_id"],
"_index": original_elastic_document["_index"],
"_type": original_elastic_document.get("_type", "_doc"),
"_op_type": "update",
"doc": {
**mlp_processed_document,
**{
"texta_facts": unique_facts
}
}
}
yield elastic_update_body
counter += 1
progress = generator.callback_progress
if counter % generator.scroll_size == 0:
info_logger.info(
f"Progress on applying MLP for worker with id: {mlp_id} at {counter} out of {progress.n_total} documents!"
)
elif counter == progress.n_total:
info_logger.info(
f"Finished applying MLP for worker with id: {mlp_id} at {counter}/{progress.n_total} documents!"
)
def apply_lang_on_indices(self, apply_worker_id: int):
worker_object = ApplyLangWorker.objects.get(pk=apply_worker_id)
task_object = worker_object.task
try:
load_mlp()
show_progress = ShowProgress(task_object, multiplier=1)
show_progress.update_step(
'scrolling through the indices to apply lang')
# Get the necessary fields.
indices: List[str] = worker_object.get_indices()
field = worker_object.field
scroll_size = 100
searcher = ElasticSearcher(query=json.loads(worker_object.query),
indices=indices,
field_data=[field],
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout="15m")
for index in indices:
searcher.core.add_texta_facts_mapping(index=index)
actions = process_lang_actions(generator=searcher,
field=field,
worker_id=apply_worker_id,
mlp_class=mlp)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
worker_object.task.complete()
return apply_worker_id
except Exception as e:
task_object.handle_failed_task(e)
raise e
def apply_summarizer_on_index(self, summarizer_id: int):
summarizer_object = Summarizer.objects.get(pk=summarizer_id)
task_object = summarizer_object.task
try:
load_sumy()
show_progress = ShowProgress(task_object, multiplier=1)
show_progress.update_step('scrolling summarizer')
# Get the necessary fields.
indices: List[str] = summarizer_object.get_indices()
field_data: List[str] = json.loads(summarizer_object.fields)
ratio_data: float[str] = summarizer_object.ratio
algorithm_data: List[str] = summarizer_object.algorithm
scroll_size = 100
searcher = ElasticSearcher(query=json.loads(summarizer_object.query),
indices=indices,
field_data=field_data,
output=ElasticSearcher.OUT_RAW,
callback_progress=show_progress,
scroll_size=scroll_size,
scroll_timeout="30m")
actions = process_actions(searcher,
field_data,
ratio_data,
algorithm=algorithm_data,
summarizer_class=sumy,
summarizer_id=summarizer_id)
# Send the data towards Elasticsearch
ed = ElasticDocument("_all")
elastic_response = ed.bulk_update(actions=actions)
return summarizer_id
except Exception as e:
logging.getLogger(ERROR_LOGGER).exception(e)
task_object.add_error(str(e))
task_object.update_status(Task.STATUS_FAILED)
raise e
def update_generator(generator: ElasticSearcher,
ec: ElasticCore,
fields: List[str],
fact_name: str,
fact_value: str,
tagger_object: BertTaggerObject,
tagger: BertTagger = None):
for i, scroll_batch in enumerate(generator):
logging.getLogger(INFO_LOGGER).info(
f"Appyling BERT Tagger with ID {tagger_object.id} to batch {i + 1}..."
)
for raw_doc in scroll_batch:
hit = raw_doc["_source"]
flat_hit = ec.flatten(hit)
existing_facts = hit.get("texta_facts", [])
for field in fields:
text = flat_hit.get(field, None)
if text and isinstance(text, str):
result = tagger_object.apply_loaded_tagger(
tagger, text, input_type="text", feedback=False)
# If tagger is binary and fact value is not specified by the user, use tagger description as fact value
if result["result"] in ["true", "false"]:
if not fact_value:
fact_value = tagger_object.description
# For multitag, use the prediction as fact value
else:
fact_value = result["result"]
new_facts = to_texta_facts(result, field, fact_name,
fact_value)
existing_facts.extend(new_facts)
if existing_facts:
# Remove duplicates to avoid adding the same facts with repetitive use.
existing_facts = ElasticDocument.remove_duplicate_facts(
existing_facts)
yield {
"_index": raw_doc["_index"],
"_id": raw_doc["_id"],
"_type": raw_doc.get("_type", "_doc"),
"_op_type": "update",
"_source": {
"doc": {
"texta_facts": existing_facts
}
}
}
def retrieve(self, request, *args, **kwargs):
# API v1 to v2 compliance
if "clustering_pk" in self.kwargs:
topic_analyzer_pk = self.kwargs["clustering_pk"]
elif "topic_analyzer_pk" in self.kwargs:
topic_analyzer_pk = self.kwargs["topic_analyzer_pk"]
queryset = Cluster.objects.filter(
clusteringresult__project__pk=self.kwargs["project_pk"],
clusteringresult__pk=topic_analyzer_pk)
cluster = get_object_or_404(queryset, pk=self.kwargs["pk"])
doc_ids = json.loads(cluster.document_ids)
fields = json.loads(cluster.fields)
indices = json.loads(cluster.indices)
significant_words = json.loads(cluster.significant_words)
display_fields = json.loads(cluster.display_fields)
if display_fields:
fields += display_fields
ed = ElasticDocument(index=",".join(indices))
documents = ed.get_bulk(doc_ids, flatten=True)
documents = documents if documents else []
documents = [{
"id": doc["_id"],
"index": doc["_index"],
"content": doc["_source"]
} for doc in documents]
formated_cluster = {
"id": cluster.pk,
"intracluster_similarity": cluster.intracluster_similarity,
"document_count": cluster.get_document_count(),
"significant_words": significant_words,
"documents": documents
}
return Response(formated_cluster)
def update(self, request, *args, **kwargs):
serializer = ClusterSerializer(data=request.data, partial=True)
serializer.is_valid()
cluster = Cluster.objects.get(pk=kwargs["pk"])
clustering_object = ClusteringResult.objects.get(
pk=kwargs["topic_analyzer_pk"])
fields = json.loads(cluster.fields)
stop_words = json.loads(clustering_object.stop_words)
indices = json.loads(cluster.indices)
if "document_ids" in serializer.validated_data:
document_ids = serializer.validated_data["document_ids"]
ed = ElasticDocument("*")
# Validate that those documents exist.
validated_docs = ed.get_bulk(document_ids)
if validated_docs:
unique_ids = list(
set([index["_id"] for index in validated_docs]))
cluster.document_ids = json.dumps(unique_ids)
sw = Cluster.get_significant_words(indices=indices,
fields=fields,
document_ids=unique_ids,
stop_words=stop_words)
cluster.significant_words = json.dumps(sw)
cluster_content = ClusterContent(
unique_ids,
vectors_filepath=clustering_object.vector_model.name)
cluster.intracluster_similarity = cluster_content.get_intracluster_similarity(
)
else:
cluster.document_ids = json.dumps([])
cluster.save()
return Response({"message": "Cluster has been updated successfully!"})
def skip_document(self, request, pk=None, project_pk=None):
serializer: DocumentIDSerializer = self.get_serializer(
data=request.data)
serializer.is_valid(raise_exception=True)
annotator: Annotator = self.get_object()
ed = ElasticDocument(index=annotator.get_indices())
document_id = serializer.validated_data["document_id"]
document = ed.get(document_id)
texta_annotations = document["_source"].get("texta_annotator", [])
processed_timestamp = None
if texta_annotations:
for texta_annotation in texta_annotations:
processed_timestamp = texta_annotation.get(
"processed_timestamp_utc", None)
if processed_timestamp:
return Response({
"detail":
f"Document with ID: {serializer.validated_data['document_id']} is already annotated"
})
annotator.skip_document(serializer.validated_data["document_id"],
serializer.validated_data["index"],
user=request.user)
return Response({
"detail":
f"Skipped document with ID: {serializer.validated_data['document_id']}"
})
else:
annotator.skip_document(serializer.validated_data["document_id"],
serializer.validated_data["index"],
user=request.user)
return Response({
"detail":
f"Skipped document with ID: {serializer.validated_data['document_id']}"
})
def setUp(self):
self.user = create_test_user('user', '*****@*****.**', 'pw')
self.index_uuid = uuid.uuid4().hex[:5]
self.new_test_index_name = f"ttk_test_fields_tagger_{self.index_uuid}"
self.ed = ElasticDocument(index=self.new_test_index_name)
self.ed.core.es.indices.create(index=self.new_test_index_name, ignore=[400, 404])
self.project = project_creation("SearchFieldsTaggerTestProject", self.new_test_index_name, self.user)
self.project.users.add(self.user)
self.url = reverse(f"{VERSION_NAMESPACE}:search_fields_tagger-list", kwargs={"project_pk": self.project.pk})
self.uuid = uuid.uuid4().hex[:10]
self.document = {
"Field_1": "This is sentence1. This is sentence2. This is sentence3. This is sentence4. This is sentence5.",
"Field_2": "This is a different sentence.",
"Field_3": "This is test data.",
"newline_break": "olgu\nõnnistatud\npüha\nkäsikranaat",
"array_break": ["olgu", "õnnistatud", "püha", "käsikranaat"],
"uuid": self.uuid
}
self.ed.add(self.document)
self.client.login(username='******', password='******')