def query(self,
vector_query,
filter_query=None,
collection_name=None,
guarantee_timestamp=None,
timeout=300):
""" This method corresponds to the search method of milvus """
tmp_collection_name = self._collection_name if collection_name is None else collection_name
params = util.search_param_analysis(vector_query, filter_query)
params.update({"timeout": timeout})
if guarantee_timestamp is not None:
params.update({"guarantee_timestamp": guarantee_timestamp})
# logger.debug("Params of search : %s" % str(params))
result = self._milvus.search(tmp_collection_name, **params)
# must_params = [vector_query]
# if filter_query:
# must_params.extend(filter_query)
# query = {
# "bool": {"must": must_params}
# }
# result = self._milvus.search(tmp_collection_name, query, timeout=timeout)
return result
def warm_query(self, index_field_name, search_param, metric_type, times=2):
query_vectors = [[random.random() for _ in range(self._dimension)]
for _ in range(DEFAULT_WARM_QUERY_NQ)]
# index_info = self.describe_index(index_field_name)
vector_query = {
"vector": {
index_field_name: {
"topk": DEFAULT_WARM_QUERY_TOPK,
"query": query_vectors,
"metric_type": metric_type,
"params": search_param
}
}
}
must_params = [vector_query]
# query = {
# "bool": {"must": must_params}
# }
logger.debug("Start warm up query")
for i in range(times):
params = util.search_param_analysis(vector_query, None)
self._milvus.search(self._collection_name, **params)
# self._milvus.search(self._collection_name, query)
logger.debug("End warm up query")
def load_and_query(self, vector_query, filter_query=None, collection_name=None, timeout=120):
tmp_collection_name = self._collection_name if collection_name is None else collection_name
must_params = [vector_query]
if filter_query:
must_params.extend(filter_query)
query = {
"bool": {"must": must_params}
}
self.load_collection(tmp_collection_name)
params = util.search_param_analysis(vector_query, filter_query)
params.update({"timeout": timeout})
result = self._milvus.search(tmp_collection_name, **params)
# result = self._milvus.search(tmp_collection_name, query, timeout=timeout)
return result
def query(self, vector_query, filter_query=None, collection_name=None, timeout=300):
""" This method corresponds to the search method of milvus """
tmp_collection_name = self._collection_name if collection_name is None else collection_name
params = util.search_param_analysis(vector_query, filter_query)
params.update({"timeout": timeout})
result = self._milvus.search(tmp_collection_name, **params)
# must_params = [vector_query]
# if filter_query:
# must_params.extend(filter_query)
# query = {
# "bool": {"must": must_params}
# }
# result = self._milvus.search(tmp_collection_name, query, timeout=timeout)
return result
