def del_vectors(collection_name, ids):
try:
milvus = Milvus(host=MILVUS_ADDR, port=MILVUS_PORT)
milvus.delete_entity_by_id(collection_name=collection_name, id_array=ids)
except Exception as e:
err_msg = "There was some error when delete vectors"
logger.error(f"{err_msg} : {str(e)}", exc_info=True)
raise MilvusError(err_msg, e)
class MilvusClient(object):
def __init__(self,
collection_name=None,
host=None,
port=None,
timeout=180):
self._collection_name = collection_name
start_time = time.time()
if not host:
host = SERVER_HOST_DEFAULT
if not port:
port = SERVER_PORT_DEFAULT
logger.debug(host)
logger.debug(port)
# retry connect remote server
i = 0
while time.time() < start_time + timeout:
try:
self._milvus = Milvus(host=host,
port=port,
try_connect=False,
pre_ping=False)
break
except Exception as e:
logger.error(str(e))
logger.error("Milvus connect failed: %d times" % i)
i = i + 1
time.sleep(i)
if time.time() > start_time + timeout:
raise Exception("Server connect timeout")
# self._metric_type = None
def __str__(self):
return 'Milvus collection %s' % self._collection_name
def check_status(self, status):
if not status.OK():
logger.error(status.message)
logger.error(self._milvus.server_status())
logger.error(self.count())
raise Exception("Status not ok")
def check_result_ids(self, result):
for index, item in enumerate(result):
if item[0].distance >= epsilon:
logger.error(index)
logger.error(item[0].distance)
raise Exception("Distance wrong")
# only support the given field name
def create_collection(self,
dimension,
data_type=DataType.FLOAT_VECTOR,
auto_id=False,
collection_name=None,
other_fields=None):
self._dimension = dimension
if not collection_name:
collection_name = self._collection_name
vec_field_name = utils.get_default_field_name(data_type)
fields = [{
"name": vec_field_name,
"type": data_type,
"params": {
"dim": dimension
}
}]
if other_fields:
other_fields = other_fields.split(",")
if "int" in other_fields:
fields.append({
"name": utils.DEFAULT_INT_FIELD_NAME,
"type": DataType.INT64
})
if "float" in other_fields:
fields.append({
"name": utils.DEFAULT_FLOAT_FIELD_NAME,
"type": DataType.FLOAT
})
create_param = {"fields": fields, "auto_id": auto_id}
try:
self._milvus.create_collection(collection_name, create_param)
logger.info("Create collection: <%s> successfully" %
collection_name)
except Exception as e:
logger.error(str(e))
raise
def create_partition(self, tag, collection_name=None):
if not collection_name:
collection_name = self._collection_name
self._milvus.create_partition(collection_name, tag)
def generate_values(self, data_type, vectors, ids):
values = None
if data_type in [DataType.INT32, DataType.INT64]:
values = ids
elif data_type in [DataType.FLOAT, DataType.DOUBLE]:
values = [(i + 0.0) for i in ids]
elif data_type in [DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR]:
values = vectors
return values
def generate_entities(self, vectors, ids=None, collection_name=None):
entities = []
if collection_name is None:
collection_name = self._collection_name
info = self.get_info(collection_name)
for field in info["fields"]:
field_type = field["type"]
entities.append({
"name":
field["name"],
"type":
field_type,
"values":
self.generate_values(field_type, vectors, ids)
})
return entities
@time_wrapper
def insert(self, entities, ids=None, collection_name=None):
tmp_collection_name = self._collection_name if collection_name is None else collection_name
try:
insert_ids = self._milvus.insert(tmp_collection_name,
entities,
ids=ids)
return insert_ids
except Exception as e:
logger.error(str(e))
def get_dimension(self):
info = self.get_info()
for field in info["fields"]:
if field["type"] in [
DataType.FLOAT_VECTOR, DataType.BINARY_VECTOR
]:
return field["params"]["dim"]
def get_rand_ids(self, length):
segment_ids = []
while True:
stats = self.get_stats()
segments = stats["partitions"][0]["segments"]
# random choice one segment
segment = random.choice(segments)
try:
segment_ids = self._milvus.list_id_in_segment(
self._collection_name, segment["id"])
except Exception as e:
logger.error(str(e))
if not len(segment_ids):
continue
elif len(segment_ids) > length:
return random.sample(segment_ids, length)
else:
logger.debug("Reset length: %d" % len(segment_ids))
return segment_ids
# def get_rand_ids_each_segment(self, length):
# res = []
# status, stats = self._milvus.get_collection_stats(self._collection_name)
# self.check_status(status)
# segments = stats["partitions"][0]["segments"]
# segments_num = len(segments)
# # random choice from each segment
# for segment in segments:
# status, segment_ids = self._milvus.list_id_in_segment(self._collection_name, segment["name"])
# self.check_status(status)
# res.extend(segment_ids[:length])
# return segments_num, res
# def get_rand_entities(self, length):
# ids = self.get_rand_ids(length)
# status, get_res = self._milvus.get_entity_by_id(self._collection_name, ids)
# self.check_status(status)
# return ids, get_res
def get(self):
get_ids = random.randint(1, 1000000)
self._milvus.get_entity_by_id(self._collection_name, [get_ids])
@time_wrapper
def get_entities(self, get_ids):
get_res = self._milvus.get_entity_by_id(self._collection_name, get_ids)
return get_res
@time_wrapper
def delete(self, ids, collection_name=None):
tmp_collection_name = self._collection_name if collection_name is None else collection_name
self._milvus.delete_entity_by_id(tmp_collection_name, ids)
def delete_rand(self):
delete_id_length = random.randint(1, 100)
count_before = self.count()
logger.debug("%s: length to delete: %d" %
(self._collection_name, delete_id_length))
delete_ids = self.get_rand_ids(delete_id_length)
self.delete(delete_ids)
self.flush()
logger.info("%s: count after delete: %d" %
(self._collection_name, self.count()))
get_res = self._milvus.get_entity_by_id(self._collection_name,
delete_ids)
for item in get_res:
assert not item
# if count_before - len(delete_ids) < self.count():
# logger.error(delete_ids)
# raise Exception("Error occured")
@time_wrapper
def flush(self, _async=False, collection_name=None):
tmp_collection_name = self._collection_name if collection_name is None else collection_name
self._milvus.flush([tmp_collection_name], _async=_async)
@time_wrapper
def compact(self, collection_name=None):
tmp_collection_name = self._collection_name if collection_name is None else collection_name
status = self._milvus.compact(tmp_collection_name)
self.check_status(status)
@time_wrapper
def create_index(self,
field_name,
index_type,
metric_type,
_async=False,
index_param=None):
index_type = INDEX_MAP[index_type]
metric_type = utils.metric_type_trans(metric_type)
logger.info(
"Building index start, collection_name: %s, index_type: %s, metric_type: %s"
% (self._collection_name, index_type, metric_type))
if index_param:
logger.info(index_param)
index_params = {
"index_type": index_type,
"metric_type": metric_type,
"params": index_param
}
self._milvus.create_index(self._collection_name,
field_name,
index_params,
_async=_async)
# TODO: need to check
def describe_index(self, field_name):
# stats = self.get_stats()
info = self._milvus.describe_index(self._collection_name, field_name)
index_info = {"index_type": "flat", "index_param": None}
for field in info["fields"]:
for index in field['indexes']:
if not index or "index_type" not in index:
continue
else:
for k, v in INDEX_MAP.items():
if index['index_type'] == v:
index_info['index_type'] = k
index_info['index_param'] = index['params']
return index_info
return index_info
def drop_index(self, field_name):
logger.info("Drop index: %s" % self._collection_name)
return self._milvus.drop_index(self._collection_name, field_name)
@time_wrapper
def query(self, vector_query, filter_query=None, collection_name=None):
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}}
result = self._milvus.search(tmp_collection_name, query)
return result
@time_wrapper
def load_and_query(self,
vector_query,
filter_query=None,
collection_name=None):
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)
result = self._milvus.search(tmp_collection_name, query)
return result
def get_ids(self, result):
idss = result._entities.ids
ids = []
len_idss = len(idss)
len_r = len(result)
top_k = len_idss // len_r
for offset in range(0, len_idss, top_k):
ids.append(idss[offset:min(offset + top_k, len_idss)])
return ids
def query_rand(self, nq_max=100):
# for ivf search
dimension = 128
top_k = random.randint(1, 100)
nq = random.randint(1, nq_max)
nprobe = random.randint(1, 100)
search_param = {"nprobe": nprobe}
query_vectors = [[random.random() for _ in range(dimension)]
for _ in range(nq)]
metric_type = random.choice(["l2", "ip"])
logger.info("%s, Search nq: %d, top_k: %d, nprobe: %d" %
(self._collection_name, nq, top_k, nprobe))
vec_field_name = utils.get_default_field_name()
vector_query = {
"vector": {
vec_field_name: {
"topk": top_k,
"query": query_vectors,
"metric_type": utils.metric_type_trans(metric_type),
"params": search_param
}
}
}
self.query(vector_query)
def load_query_rand(self, nq_max=100):
# for ivf search
dimension = 128
top_k = random.randint(1, 100)
nq = random.randint(1, nq_max)
nprobe = random.randint(1, 100)
search_param = {"nprobe": nprobe}
query_vectors = [[random.random() for _ in range(dimension)]
for _ in range(nq)]
metric_type = random.choice(["l2", "ip"])
logger.info("%s, Search nq: %d, top_k: %d, nprobe: %d" %
(self._collection_name, nq, top_k, nprobe))
vec_field_name = utils.get_default_field_name()
vector_query = {
"vector": {
vec_field_name: {
"topk": top_k,
"query": query_vectors,
"metric_type": utils.metric_type_trans(metric_type),
"params": search_param
}
}
}
self.load_and_query(vector_query)
# TODO: need to check
def count(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
row_count = self._milvus.get_collection_stats(
collection_name)["row_count"]
logger.debug("Row count: %d in collection: <%s>" %
(row_count, collection_name))
return row_count
def drop(self, timeout=120, collection_name=None):
timeout = int(timeout)
if collection_name is None:
collection_name = self._collection_name
logger.info("Start delete collection: %s" % collection_name)
self._milvus.drop_collection(collection_name)
i = 0
while i < timeout:
try:
row_count = self.count(collection_name=collection_name)
if row_count:
time.sleep(1)
i = i + 1
continue
else:
break
except Exception as e:
logger.debug(str(e))
break
if i >= timeout:
logger.error("Delete collection timeout")
def get_stats(self):
return self._milvus.get_collection_stats(self._collection_name)
def get_info(self, collection_name=None):
# pdb.set_trace()
if collection_name is None:
collection_name = self._collection_name
return self._milvus.get_collection_info(collection_name)
def show_collections(self):
return self._milvus.list_collections()
def exists_collection(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
res = self._milvus.has_collection(collection_name)
return res
def clean_db(self):
collection_names = self.show_collections()
for name in collection_names:
self.drop(collection_name=name)
@time_wrapper
def load_collection(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
return self._milvus.load_collection(collection_name, timeout=3000)
@time_wrapper
def release_collection(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
return self._milvus.release_collection(collection_name, timeout=3000)
@time_wrapper
def load_partitions(self, tag_names, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
return self._milvus.load_partitions(collection_name,
tag_names,
timeout=3000)
@time_wrapper
def release_partitions(self, tag_names, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
return self._milvus.release_partitions(collection_name,
tag_names,
timeout=3000)
def main():
milvus = Milvus(_HOST, _PORT)
# num = random.randint(1, 100000)
num = 100000
# Create collection demo_collection if it dosen't exist.
collection_name = 'example_hybrid_collections_{}'.format(num)
if milvus.has_collection(collection_name):
milvus.drop_collection(collection_name)
collection_param = {
"fields": [{
"field": "A",
"type": DataType.INT32
}, {
"field": "B",
"type": DataType.INT32
}, {
"field": "C",
"type": DataType.INT64
}, {
"field": "Vec",
"type": DataType.FLOAT_VECTOR,
"params": {
"dim": 128,
"metric_type": "L2"
}
}],
"segment_size":
100
}
milvus.create_collection(collection_name, collection_param)
milvus.compact(collection_name)
# milvus.create_partition(collection_name, "p_01", timeout=1800)
# pars = milvus.list_partitions(collection_name)
# ok = milvus.has_partition(collection_name, "p_01", timeout=1800)
# assert ok
# ok = milvus.has_partition(collection_name, "p_02")
# assert not ok
# for p in pars:
# if p == "_default":
# continue
# milvus.drop_partition(collection_name, p)
# milvus.drop_collection(collection_name)
# sys.exit(0)
A_list = [random.randint(0, 255) for _ in range(num)]
vec = [[random.random() for _ in range(128)] for _ in range(num)]
hybrid_entities = [{
"field": "A",
"values": A_list,
"type": DataType.INT32
}, {
"field": "B",
"values": A_list,
"type": DataType.INT32
}, {
"field": "C",
"values": A_list,
"type": DataType.INT64
}, {
"field": "Vec",
"values": vec,
"type": DataType.FLOAT_VECTOR,
"params": {
"dim": 128
}
}]
for slice_e in utils.entities_slice(hybrid_entities):
ids = milvus.insert(collection_name, slice_e)
milvus.flush([collection_name])
print("Flush ... ")
# time.sleep(3)
count = milvus.count_entities(collection_name)
milvus.delete_entity_by_id(collection_name, ids[:1])
milvus.flush([collection_name])
print("Get entity be id start ...... ")
entities = milvus.get_entity_by_id(collection_name, ids[:1])
et = entities.dict()
milvus.delete_entity_by_id(collection_name, ids[1:2])
milvus.flush([collection_name])
print("Create index ......")
milvus.create_index(collection_name, "Vec", {
"index_type": "IVF_FLAT",
"metric_type": "L2",
"params": {
"nlist": 100
}
})
print("Create index done.")
info = milvus.get_collection_info(collection_name)
print(info)
stats = milvus.get_collection_stats(collection_name)
print("\nstats\n")
print(stats)
query_hybrid = \
{
"bool": {
"must": [
{
"term": {
"A": [1, 2, 5]
}
},
{
"range": {
"B": {"GT": 1, "LT": 100}
}
},
{
"vector": {
"Vec": {
"topk": 10, "query": vec[: 10000], "params": {"nprobe": 10}
}
}
}
],
},
}
# print("Start searach ..", flush=True)
# results = milvus.search(collection_name, query_hybrid)
# print(results)
#
# for r in list(results):
# print("ids", r.ids)
# print("distances", r.distances)
t0 = time.time()
count = 0
results = milvus.search(collection_name, query_hybrid, fields=["B"])
for r in list(results):
# print("ids", r.ids)
# print("distances", r.distances)
for rr in r:
count += 1
# print(rr.entity.get("B"))
print("Search cost {} s".format(time.time() - t0))
# for result in results:
# for r in result:
# print(f"{r}")
# itertor entity id
# for result in results:
# for r in result:
# # get distance
# dis = r.distance
# id_ = r.id
# # obtain all field name
# fields = r.entity.fields
# for f in fields:
# # get field value by field name
# # fv = r.entity.
# fv = r.entity.value_of_field(f)
# print(fv)
milvus.drop_collection(collection_name)
for entities in results:
for topk_film in entities:
current_entity = topk_film.entity
print("- id: {}".format(topk_film.id))
print("- distance: {}".format(topk_film.distance))
print("- release_year: {}".format(current_entity.release_year))
print("- duration: {}".format(current_entity.duration))
print("- embedding: {}".format(current_entity.embedding))
# ------
# Basic delete:
# Now let's see how to delete things in Milvus.
# You can simply delete entities by their ids.
# ------
client.delete_entity_by_id(collection_name, ids=[1, 4])
client.flush() # flush is important
result = client.get_entity_by_id(collection_name, ids=[1, 4])
counts_delete = sum([1 for entity in result if entity is not None])
counts_in_collection = client.count_entities(collection_name)
print("\n----------delete id = 1, id = 4----------")
print("Get {} entities by id 1, 4".format(counts_delete))
print("There are {} entities after delete films with 1, 4".format(counts_in_collection))
# ------
# Basic delete:
# You can drop partitions we create, and drop the collection we create.
# ------
client.drop_partition(collection_name, partition_tag='American')
if collection_name in client.list_collections():
class MilvusHelper(BaseVectorSimilarityHelper):
def __init__(self, _server_url, _server_port, _timeout=10):
super().__init__()
self.server_url = _server_url
self.server_port = _server_port
self.timeout = _timeout
self.client = None
self.metric_type_mapper = {
VectorMetricType.L2: MetricType.L2,
VectorMetricType.IP: MetricType.IP,
VectorMetricType.JACCARD: MetricType.JACCARD,
VectorMetricType.HAMMING: MetricType.HAMMING,
}
self.index_type_mapper = {
VectorIndexType.FLAT: IndexType.FLAT,
VectorIndexType.IVFLAT: IndexType.IVFLAT,
VectorIndexType.IVF_SQ8: IndexType.IVF_SQ8,
VectorIndexType.RNSG: IndexType.RNSG,
VectorIndexType.IVF_SQ8H: IndexType.IVF_SQ8H,
VectorIndexType.IVF_PQ: IndexType.IVF_PQ,
VectorIndexType.HNSW: IndexType.HNSW,
VectorIndexType.ANNOY: IndexType.ANNOY,
}
def init(self):
if self.client is None:
if not (self.server_url is None or self.server_url is None):
try:
self.client = Milvus(host=self.server_url, port=self.server_port)
except:
raise MilvusRuntimeException(f'cannot connect to {self.server_url}:{self.server_port}')
else:
raise MilvusRuntimeException('Milvus config is not correct')
def insert(self, _database_name, _to_insert_vector, _partition_tag=None, _params=None):
"""
向数据库中插入一系列的特征向量
notes：如果用户有自己的id，建议使用insert_with_id函数
ATTENTION!!!
一个库中不能既调用insert_with_id还调用insert，只能调用一种，否则会报错
Args:
_database_name: 数据库名称
_to_insert_vector: 待插入的特征向量的列表
_partition_tag: 分区标签
_params: 插入参数
Returns: 插入后的id
"""
self.init()
status, ids = self.client.insert(_database_name, _to_insert_vector, partition_tag=_partition_tag,
params=_params,
timeout=self.timeout)
self.flush(_database_name)
if status.OK():
return ids
else:
raise MilvusRuntimeException(status.message)
def insert_with_id(self, _database_name, _to_insert_vector, _to_insert_ids, _partition_tag=None, _params=None):
"""
向数据库中插入一系列的有固定id的特征向量
ATTENTION!!!
一个库中不能既调用insert_with_id还调用insert，只能调用一种，否则会报错
Args:
_database_name: 数据库名称
_to_insert_vector: 待插入的特征向量的列表
_to_insert_ids: 待插入的特征向量的id的列表，每个元素必须为正整数，且不越界
_partition_tag: 分区标签
_params: 插入参数
Returns: 插入后的id
"""
self.init()
status, ids = self.client.insert(_database_name, _to_insert_vector,
ids=_to_insert_ids, partition_tag=_partition_tag,
params=_params,
timeout=self.timeout)
self.flush(_database_name)
if status.OK():
return ids
else:
raise MilvusRuntimeException(status.message)
def delete(self, _database_name, _to_delete_ids):
"""
删除特定id
Args:
_database_name: 数据库名称
_to_delete_ids: 待删除的id
Returns: 是否删除成功
"""
self.init()
status = self.client.delete_entity_by_id(_database_name, _to_delete_ids, self.timeout)
self.flush(_database_name)
if status.OK():
return True
else:
raise MilvusRuntimeException(status.message)
def database_exist(self, _database_name):
"""
数据库是否存在
Args:
_database_name: 数据库名称
Returns: 是否存在
"""
self.init()
status, is_exist = self.client.has_collection(_database_name, self.timeout)
if status.OK():
return is_exist
else:
raise MilvusRuntimeException(status.message)
def create_database(self, _database_name, _dimension, _index_file_size, _metric_type):
"""
创建数据库
Args:
_database_name: 数据库名称
_dimension: 特征向量维度
_index_file_size: index的文件大小
_metric_type: 度量类型
Returns: 是否创建成功
"""
self.init()
if not self.database_exist(_database_name):
assert _metric_type in self.metric_type_mapper, f'{_metric_type} not support in milvus'
status = self.client.create_collection({
'collection_name': _database_name,
'dimension': _dimension,
'index_file_size': _index_file_size,
'metric_type': self.metric_type_mapper[_metric_type]
})
if status.OK():
return True
else:
raise MilvusRuntimeException(status.message)
else:
return True
def create_index(self, _database_name, _index_type):
"""
创建index（索引）
Args:
_database_name: 数据库名称
_index_type: index类型
Returns: 是否创建成功
"""
self.init()
if self.database_exist(_database_name):
assert _index_type in self.index_type_mapper, f'{_index_type} not support in milvus'
status = self.client.create_index(_database_name, self.index_type_mapper[_index_type], timeout=self.timeout)
if status.OK():
return True
else:
raise MilvusRuntimeException(status.message)
def search(self, _database_name, _query_vector_list, _top_k, _partition_tag=None, _params=None):
"""
检索用参数
Args:
_database_name: 数据库名称
_query_vector_list: 检索用的特征向量列表
_top_k: top k
_partition_tag: 分区标签
_params: 检索参数
Returns: 检索的结果，包含id和distance
"""
self.init()
if self.database_exist(_database_name):
status, search_result = self.client.search(_database_name, _top_k, _query_vector_list,
partition_tags=_partition_tag,
params=_params,
timeout=self.timeout)
if status.OK():
return search_result
else:
raise MilvusRuntimeException(status.message)
else:
raise DatabaseNotExist(f'{_database_name} not exist')
def flush(self, _database_name):
"""
sink数据库
Args:
_database_name: 数据库名称
Returns: 是否flush成功
"""
self.init()
status = self.client.flush([_database_name, ], self.timeout)
if status.OK():
return True
else:
raise MilvusRuntimeException(status.message)
def __del__(self):
if self.client is not None:
self.client.close()
class Indexer:
'''
索引器。
'''
def __init__(self, name, host='127.0.0.1', port='19531'):
'''
初始化。
'''
self.client = Milvus(host=host, port=port)
self.collection = name
def init(self, lenient=False):
'''
创建集合。
'''
if lenient:
status, result = self.client.has_collection(
collection_name=self.collection)
if status.code != 0:
raise ExertMilvusException(status)
if result:
return
status = self.client.create_collection({
'collection_name': self.collection,
'dimension': 512,
'index_file_size': 1024,
'metric_type': MetricType.L2
})
if status.code != 0 and not (lenient and status.code == 9):
raise ExertMilvusException(status)
# 创建索引。
status = self.client.create_index(collection_name=self.collection,
index_type=IndexType.IVF_FLAT,
params={'nlist': 16384})
if status.code != 0:
raise ExertMilvusException(status)
return status
def drop(self):
'''
删除集合。
'''
status = self.client.drop_collection(collection_name=self.collection)
if status.code != 0:
raise ExertMilvusException(status)
def flush(self):
'''
写入到硬盘。
'''
status = self.client.flush([self.collection])
if status.code != 0:
raise ExertMilvusException(status)
def compact(self):
'''
压缩集合。
'''
status = self.client.compact(collection_name=self.collection)
if status.code != 0:
raise ExertMilvusException(status)
def close(self):
'''
关闭链接。
'''
self.client.close()
def new_tag(self, tag):
'''
建分块标签。
'''
status = self.client.create_partition(collection_name=self.collection,
partition_tag=tag)
if status.code != 0:
raise ExertMilvusException(status)
def list_tag(self):
'''
列举分块标签。
'''
status, result = self.client.list_partitions(
collection_name=self.collection)
if status.code != 0:
raise ExertMilvusException(status)
return result
def drop_tag(self, tag):
'''
删除分块标签。
'''
status = self.client.drop_partition(collection_name=self.collection,
partition_tag=tag)
if status.code != 0:
raise ExertMilvusException(status)
def index(self, vectors, tag=None, ids=None):
'''
添加索引
'''
params = {}
if tag != None:
params['tag'] = tag
if ids != None:
params['ids'] = ids
status, result = self.client.insert(collection_name=self.collection,
records=vectors,
**params)
if status.code != 0:
raise ExertMilvusException(status)
return result
def listing(self, ids):
'''
列举信息。
'''
status, result = self.client.get_entity_by_id(
collection_name=self.collection, ids=ids)
if status.code != 0:
raise ExertMilvusException(status)
return result
def counting(self):
'''
计算索引数。
'''
status, result = self.client.count_entities(
collection_name=self.collection)
if status.code != 0:
raise ExertMilvusException(status)
return result
def unindex(self, ids):
'''
去掉索引。
'''
status = self.client.delete_entity_by_id(
collection_name=self.collection, id_array=ids)
if status.code != 0:
raise ExertMilvusException(status)
def search(self, vectors, top_count=100, tags=None):
'''
搜索。
'''
params = {'params': {'nprobe': 16}}
if tags != None:
params['partition_tags'] = tags
status, results = self.client.search(collection_name=self.collection,
query_records=vectors,
top_k=top_count,
**params)
if status.code != 0:
raise ExertMilvusException(status)
return results
class MilvusClient(object):
def __init__(self, collection_name=None, host=None, port=None, timeout=60):
"""
Milvus client wrapper for python-sdk.
Default timeout set 60s
"""
self._collection_name = collection_name
try:
start_time = time.time()
if not host:
host = SERVER_HOST_DEFAULT
if not port:
port = SERVER_PORT_DEFAULT
logger.debug(host)
logger.debug(port)
# retry connect for remote server
i = 0
while time.time() < start_time + timeout:
try:
self._milvus = Milvus(host=host, port=port, try_connect=False, pre_ping=False)
if self._milvus.server_status():
logger.debug("Try connect times: %d, %s" % (i, round(time.time() - start_time, 2)))
break
except Exception as e:
logger.debug("Milvus connect failed: %d times" % i)
i = i + 1
if time.time() > start_time + timeout:
raise Exception("Server connect timeout")
except Exception as e:
raise e
self._metric_type = None
if self._collection_name and self.exists_collection():
self._metric_type = metric_type_to_str(self.describe()[1].metric_type)
self._dimension = self.describe()[1].dimension
def __str__(self):
return 'Milvus collection %s' % self._collection_name
def set_collection(self, name):
self._collection_name = name
def check_status(self, status):
if not status.OK():
logger.error(self._collection_name)
logger.error(status.message)
logger.error(self._milvus.server_status())
logger.error(self.count())
raise Exception("Status not ok")
def check_result_ids(self, result):
for index, item in enumerate(result):
if item[0].distance >= epsilon:
logger.error(index)
logger.error(item[0].distance)
raise Exception("Distance wrong")
def create_collection(self, collection_name, dimension, index_file_size, metric_type):
if not self._collection_name:
self._collection_name = collection_name
if metric_type not in METRIC_MAP.keys():
raise Exception("Not supported metric_type: %s" % metric_type)
metric_type = METRIC_MAP[metric_type]
create_param = {'collection_name': collection_name,
'dimension': dimension,
'index_file_size': index_file_size,
"metric_type": metric_type}
status = self._milvus.create_collection(create_param)
self.check_status(status)
def create_partition(self, tag_name):
status = self._milvus.create_partition(self._collection_name, tag_name)
self.check_status(status)
def drop_partition(self, tag_name):
status = self._milvus.drop_partition(self._collection_name, tag_name)
self.check_status(status)
def list_partitions(self):
status, tags = self._milvus.list_partitions(self._collection_name)
self.check_status(status)
return tags
@time_wrapper
def insert(self, X, ids=None, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
status, result = self._milvus.insert(collection_name, X, ids)
self.check_status(status)
return status, result
def insert_rand(self):
insert_xb = random.randint(1, 100)
X = [[random.random() for _ in range(self._dimension)] for _ in range(insert_xb)]
X = utils.normalize(self._metric_type, X)
count_before = self.count()
status, _ = self.insert(X)
self.check_status(status)
self.flush()
if count_before + insert_xb != self.count():
raise Exception("Assert failed after inserting")
def get_rand_ids(self, length):
while True:
status, stats = self._milvus.get_collection_stats(self._collection_name)
self.check_status(status)
segments = stats["partitions"][0]["segments"]
# random choice one segment
segment = random.choice(segments)
status, segment_ids = self._milvus.list_id_in_segment(self._collection_name, segment["name"])
if not status.OK():
logger.error(status.message)
continue
if len(segment_ids):
break
if length >= len(segment_ids):
logger.debug("Reset length: %d" % len(segment_ids))
return segment_ids
return random.sample(segment_ids, length)
def get_rand_ids_each_segment(self, length):
res = []
status, stats = self._milvus.get_collection_stats(self._collection_name)
self.check_status(status)
segments = stats["partitions"][0]["segments"]
segments_num = len(segments)
# random choice from each segment
for segment in segments:
status, segment_ids = self._milvus.list_id_in_segment(self._collection_name, segment["name"])
self.check_status(status)
res.extend(segment_ids[:length])
return segments_num, res
def get_rand_entities(self, length):
ids = self.get_rand_ids(length)
status, get_res = self._milvus.get_entity_by_id(self._collection_name, ids)
self.check_status(status)
return ids, get_res
@time_wrapper
def get_entities(self, get_ids):
status, get_res = self._milvus.get_entity_by_id(self._collection_name, get_ids)
self.check_status(status)
return get_res
@time_wrapper
def delete(self, ids, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
status = self._milvus.delete_entity_by_id(collection_name, ids)
self.check_status(status)
def delete_rand(self):
delete_id_length = random.randint(1, 100)
count_before = self.count()
logger.info("%s: length to delete: %d" % (self._collection_name, delete_id_length))
delete_ids = self.get_rand_ids(delete_id_length)
self.delete(delete_ids)
self.flush()
logger.info("%s: count after delete: %d" % (self._collection_name, self.count()))
status, get_res = self._milvus.get_entity_by_id(self._collection_name, delete_ids)
self.check_status(status)
for item in get_res:
if item:
raise Exception("Assert failed after delete")
if count_before - len(delete_ids) != self.count():
raise Exception("Assert failed after delete")
@time_wrapper
def flush(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
status = self._milvus.flush([collection_name])
self.check_status(status)
@time_wrapper
def compact(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
status = self._milvus.compact(collection_name)
self.check_status(status)
@time_wrapper
def create_index(self, index_type, index_param=None):
index_type = INDEX_MAP[index_type]
logger.info("Building index start, collection_name: %s, index_type: %s" % (self._collection_name, index_type))
if index_param:
logger.info(index_param)
status = self._milvus.create_index(self._collection_name, index_type, index_param)
self.check_status(status)
def describe_index(self):
status, result = self._milvus.get_index_info(self._collection_name)
self.check_status(status)
index_type = None
for k, v in INDEX_MAP.items():
if result._index_type == v:
index_type = k
break
return {"index_type": index_type, "index_param": result._params}
def drop_index(self):
logger.info("Drop index: %s" % self._collection_name)
return self._milvus.drop_index(self._collection_name)
def query(self, X, top_k, search_param=None, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
status, result = self._milvus.search(collection_name, top_k, query_records=X, params=search_param)
self.check_status(status)
return result
def query_rand(self):
top_k = random.randint(1, 100)
nq = random.randint(1, 100)
nprobe = random.randint(1, 100)
search_param = {"nprobe": nprobe}
_, X = self.get_rand_entities(nq)
logger.info("%s, Search nq: %d, top_k: %d, nprobe: %d" % (self._collection_name, nq, top_k, nprobe))
status, _ = self._milvus.search(self._collection_name, top_k, query_records=X, params=search_param)
self.check_status(status)
# for i, item in enumerate(search_res):
# if item[0].id != ids[i]:
# logger.warning("The index of search result: %d" % i)
# raise Exception("Query failed")
# @time_wrapper
# def query_ids(self, top_k, ids, search_param=None):
# status, result = self._milvus.search_by_id(self._collection_name, ids, top_k, params=search_param)
# self.check_result_ids(result)
# return result
def count(self, name=None):
if name is None:
name = self._collection_name
logger.debug(self._milvus.count_entities(name))
row_count = self._milvus.count_entities(name)[1]
if not row_count:
row_count = 0
logger.debug("Row count: %d in collection: <%s>" % (row_count, name))
return row_count
def drop(self, timeout=120, name=None):
timeout = int(timeout)
if name is None:
name = self._collection_name
logger.info("Start delete collection: %s" % name)
status = self._milvus.drop_collection(name)
self.check_status(status)
i = 0
while i < timeout:
if self.count(name=name):
time.sleep(1)
i = i + 1
continue
else:
break
if i >= timeout:
logger.error("Delete collection timeout")
def describe(self):
# logger.info(self._milvus.get_collection_info(self._collection_name))
return self._milvus.get_collection_info(self._collection_name)
def show_collections(self):
return self._milvus.list_collections()
def exists_collection(self, collection_name=None):
if collection_name is None:
collection_name = self._collection_name
_, res = self._milvus.has_collection(collection_name)
# self.check_status(status)
return res
def clean_db(self):
collection_names = self.show_collections()[1]
for name in collection_names:
logger.debug(name)
self.drop(name=name)
@time_wrapper
def preload_collection(self):
status = self._milvus.load_collection(self._collection_name, timeout=3000)
self.check_status(status)
return status
def get_server_version(self):
_, res = self._milvus.server_version()
return res
def get_server_mode(self):
return self.cmd("mode")
def get_server_commit(self):
return self.cmd("build_commit_id")
def get_server_config(self):
return json.loads(self.cmd("get_config *"))
def get_mem_info(self):
result = json.loads(self.cmd("get_system_info"))
result_human = {
# unit: Gb
"memory_used": round(int(result["memory_used"]) / (1024*1024*1024), 2)
}
return result_human
def cmd(self, command):
status, res = self._milvus._cmd(command)
logger.info("Server command: %s, result: %s" % (command, res))
self.check_status(status)
return res
class SearchEngine:
def __init__(self, host, port):
self.host = os.environ.get('MILVUS_HOST', host)
self.port = os.environ.get('MILVUS_PORT', str(port))
self.engine = Milvus(host=self.host, port=self.port)
self.collection_name = None
#################################################
# HANDLE COLLECTION
#################################################
def create_collection(self, collection_name, dimension):
# collection 생성
param = {
'collection_name': collection_name,
'dimension': dimension,
'index_file_size': 1000,
'metric_type': MetricType.IP
}
self.engine.create_collection(param)
print('[INFO] collection {}을 생성했습니다.'.format(collection_name))
def drop_collection(self, collection_name):
# collection 삭제
self.engine.drop_collection(collection_name=collection_name)
print('[INFO] collection {}을 삭제했습니다.'.format(collection_name))
def get_collection_stats(self, collection_name):
# collection 정보 출력
print(self.engine.get_collection_info(collection_name))
print(self.engine.get_collection_stats(collection_name))
def set_collection(self, collection_name):
# 쿼리 조작을 하기 위한 collection 지정
self.collection_name = collection_name
print('[INFO] setting collection {}'.format(self.collection_name))
#################################################
# UTILS
#################################################
def check_set_collection(self):
# 쿼리 조작을 위한 collection 지정이 되어있는 지 체크
assert self.collection_name is not None, '[ERROR] collection을 setting해 주십시오!!'
def check_exist_data_by_key(self, key):
# collection에 정해진 key가 존재하는 지 확인
self.check_set_collection()
_, vector = self.engine.get_entity_by_id(
collection_name=self.collection_name, ids=key)
vector = vector if vector else [vector]
return True if vector[0] else False
def convert_key_format(self, key):
return [key] if isinstance(key, int) else key
def convert_value_format(self, value):
rank = len(value.shape)
assert rank < 2, '[ERROR] value의 dim을 2 미만으로 입력해 주세요!!'
return value.reshape(1, -1) if rank == 1 else value
#################################################
# INSERT
#################################################
def insert_data(self, key, value):
# 데이터를 collection에 입력
key = self.convert_key_format(key)
value = self.convert_value_format(value)
if self.check_exist_data_by_key(key):
print("[ERROR] 이미 collection에 데이터가 존재합니다.")
return
self.engine.insert(collection_name=self.collection_name,
records=value,
ids=key)
self.engine.flush([self.collection_name])
print('[INFO] insert key {}'.format(key))
#################################################
# DELELTE
#################################################
def delete_data(self, key):
# 데이터를 collection에서 제거
key = self.convert_key_format(key)
if not self.check_exist_data_by_key(key):
print("[ERROR] collection에 데이터가 존재하지 않습니다.")
return
self.engine.delete_entity_by_id(self.collection_name, key)
self.engine.flush([self.collection_name])
print('[INFO] delete key {}'.format(key))
#################################################
# UPDATE
#################################################
def update_data(self, key, value):
# 데이터를 업데이트
key = self.convert_key_format(key)
value = self.convert_value_format(value)
if not self.check_exist_data_by_key(key):
print("[ERROR] collection에 데이터가 존재하지 않습니다.")
return
self.engine.delete_entity_by_id(self.collection_name, key)
self.engine.flush([self.collection_name])
self.engine.insert(collection_name=self.collection_name,
records=value,
ids=key)
self.engine.flush([self.collection_name])
print('[INFO] update key {}'.format(key))
#################################################
# SEARCH
#################################################
def search_by_feature(self, feature, top_k):
# feature를 이용해서 데이터를 검색
self.check_set_collection()
feature = self.convert_value_format(feature)
_, result = self.engine.search(collection_name=self.collection_name,
query_records=feature,
top_k=top_k)
li_id = [
list(map(lambda x: x.id, result[0])) for i in range(len(result))
]
li_dist = [
list(map(lambda x: x.distance, result[0]))
for i in range(len(result))
]
return li_id, li_dist
def search_by_key(self, key, top_k):
# key를 이용해서 데이터를 검색
self.check_set_collection()
key = self.convert_key_format(key)
if not self.check_exist_data_by_key(key):
print("[ERROR] collection에 데이터가 존재하지 않습니다.")
return
_, vector = self.engine.get_entity_by_id(
collection_name=self.collection_name, ids=key)
_, result = self.engine.search(collection_name=self.collection_name,
query_records=vector,
top_k=top_k + 1)
li_id = [
list(map(lambda x: x.id, result[0][1:]))
for i in range(len(result))
]
li_dist = [
list(map(lambda x: x.distance, result[0][1:]))
for i in range(len(result))
]
return li_id, li_dist
def main():
milvus = Milvus(uri=uri)
param = {
'collection_name': collection_name,
'dimension': _DIM,
'index_file_size': 32,
#'metric_type': MetricType.IP
'metric_type': MetricType.L2
}
# show collections in Milvus server
_, collections = milvus.list_collections()
# 创建 collection
milvus.create_collection(param)
# 创建 collection partion
milvus.create_partition(collection_name, partition_tag)
print(f'collections in Milvus: {collections}')
# Describe demo_collection
_, collection = milvus.get_collection_info(collection_name)
print(f'descript demo_collection: {collection}')
# build fake vectors
vectors = [[random.random() for _ in range(_DIM)] for _ in range(10)]
vectors1 = [[random.random() for _ in range(_DIM)] for _ in range(10)]
status, id = milvus.insert(collection_name=collection_name,
records=vectors,
ids=list(range(10)),
partition_tag=partition_tag)
print(f'status: {status} | id: {id}')
if not status.OK():
print(f"insert failded: {status}")
status1, id1 = milvus.insert(collection_name=collection_name,
records=vectors1,
ids=list(range(10, 20)),
partition_tag=partition_tag)
print(f'status1: {status1} | id1: {id1}')
ids_deleted = list(range(10))
status_delete = milvus.delete_entity_by_id(collection_name=collection_name,
id_array=ids_deleted)
if status_delete.OK():
print(f'delete successful')
# Flush collection insered data to disk
milvus.flush([collection_name])
# Get demo_collection row count
status, result = milvus.count_entities(collection_name)
print(f"demo_collection row count: {result}")
# Obtain raw vectors by providing vector ids
status, result_vectors = milvus.get_entity_by_id(collection_name,
list(range(10, 20)))
# create index of vectors, search more repidly
index_param = {'nlist': 2}
# create ivflat index in demo_collection
status = milvus.create_index(collection_name, IndexType.IVF_FLAT,
index_param)
if status.OK():
print(f"create index ivf_flat succeeed")
# use the top 10 vectors for similarity search
query_vectors = vectors1[0:2]
# execute vector similariy search
search_param = {"nprobe": 16}
param = {
'collection_name': collection_name,
'query_records': query_vectors,
'top_k': 1,
'params': search_param
}
status, results = milvus.search(**param)
if status.OK():
if results[0][0].distance == 0.0:
print('query result is correct')
else:
print('not correct')
print(results)
else:
print(f'search failed: {status}')
# 清除已经存在的collection
milvus.drop_collection(collection_name=collection_name)
milvus.close()
for seg in par["segments"]:
print(
"\t\tsegment name: {}, vector count: {}, index: {}, storage size {:.3f} MB"
.format(seg["name"], seg["row_count"], seg["index_name"],
seg["data_size"] / 1024 / 1024))
# obtain vector ids from segment, then
# get vector by specifying vector id
segment0 = info["partitions"][0]["segments"][0]
status, ids = client.list_id_in_segment(collection_name, segment0["name"])
if not status.OK():
print("Cannot obtain vector ids from segment {}. exiting ....".format(
segment0["name"]))
sys.exit(1)
# obtain top 5 vector
status, vectors = client.get_entity_by_id(collection_name, ids[:5])
if not status.OK():
print("Cannot obtain vector. exiting ....")
sys.exit(1)
# delete top 10 vectors
status = client.delete_entity_by_id(collection_name, ids[:10])
if status.OK():
print("Delete top 10 vectors successfully")
else:
print("Error occurred when try to delete top 10 vectors. Reason: ",
status.message)
client.drop_collection(collection_name)