![Introducing Enhanced Alert Actions and Triage Functionality](https://cdn.sanity.io/images/cgdhsj6q/production/fe71306d515f85de6139b46745ea7180362324f0-2530x946.png?w=800&fit=max&auto=format)
Product
Introducing Enhanced Alert Actions and Triage Functionality
Socket now supports four distinct alert actions instead of the previous two, and alert triaging allows users to override the actions taken for all individual alerts.
Readme
Use Hologres to store large amount of vector data and perform high speed k-nearest-neighbour search!
Table of Contents
pip install hologres-vector
from hologres_vector import HologresVector
import os
host = os.environ["HOLO_HOST"]
port = os.environ["HOLO_PORT"]
dbname = os.environ["HOLO_DBNAME"]
user = os.environ["HOLO_USER"]
password = os.environ["HOLO_PASSWORD"]
connection_string = HologresVector.connection_string_from_db_params(host, port, dbname, user, password)
建表时,需要指定向量的维数,以及表中的除向量数据、主键、json元数据以外的其他强schema列。
table_name = "test_table"
holo = HologresVector(
connection_string, # 连接信息
5, # 向量维度
table_name=table_name, # 表名
table_schema={"t": "text", "date": "timestamptz", "i": "int"},
distance_method="SquaredEuclidean", # 距离函数,推荐用默认值,也可以选择"Euclidean"或"InnerProduct"
pre_delete_table=False, # 若表已存在则先删除
)
支持强schema列 schema_datas
与一个json列 metadatas
。
该接口为批量导入,内部会将输入数据切分为512行的批进行插入。
vectors = [[0,0,0,0,0], [1,1,1,1,1], [2,2,2,2,2]]
ids = ['0', '1', '2'] # primary key
schema_datas = [
{'t': 'text 0', 'date': '2023-08-02 18:30:00', 'i': 0},
{'t': 'text 1', 'date': '2023-08-02 19:30:00', 'i': 1},
{'t': 'text 2', 'date': '2023-08-02 20:30:00', 'i': 2},
]
metadatas = [
{'a': "hello"},
{'b': 123},
{},
]
holo.upsert_vectors(vectors, ids, schema_datas=schema_datas, metadatas=metadatas)
holo.query(limit=1)
[{'id': '2', 'vector': [2.0, 2.0, 2.0, 2.0, 2.0], 'metadata': {}}]
2. 近邻查询:根据向量从数据库中取最近邻
holo.search([0.1, 0.1, 0.1, 0.1, 0.1], k=2, select_columns=['t'])
[{'id': '0', 'metadata': {'a': 'hello'}, 'distance': 0.05, 't': 'text 0'},
{'id': '1', 'metadata': {'b': 123}, 'distance': 4.05, 't': 'text 1'}]
3. 融合查询:根据向量从数据库中取最近邻,并用其他列查询条件约束
holo.search([0.1, 0.1, 0.1, 0.1, 0.1], k=2, schema_data_filters={'t': 'text 1'})
[{'id': '1', 'metadata': {'b': 123}, 'distance': 4.05}]
本SDK目前默认使用根据主键id
的一种插入替换策略:当插入的数据和已有数据主键相同时,用新插入的整行替换已有的行。
# 先插入一行id为3的数据
holo.upsert_vectors([[3, 3, 3, 3, 3]], [3], schema_datas=[{'t': 'old data'}])
# 再插入一行id为3的数据,下面这行会将上面的整行替换掉
holo.upsert_vectors([[-3, -3, -3, -3, -3]], [3], schema_datas=[{'t': 'new data'}])
holo.query(schema_data_filters={'id': '3'})
[{'id': '3', 'vector': [-3.0, -3.0, -3.0, -3.0, -3.0], 'metadata': {}}]
可使用与查询格式相同的filter条件来对数据进行部分删除。
holo.delete_vectors(schema_data_filters={'id': '2'})
holo.query(limit=10)
[{'id': '0', 'vector': [0.0, 0.0, 0.0, 0.0, 0.0], 'metadata': {'a': 'hello'}},
{'id': '1', 'vector': [1.0, 1.0, 1.0, 1.0, 1.0], 'metadata': {'b': 123}},
{'id': '3', 'vector': [-3.0, -3.0, -3.0, -3.0, -3.0], 'metadata': {}}]
holo.delete_vectors() # 删除全部数据
holo.query(limit=10)
hologres-vector
is distributed under the terms of the MIT license.
FAQs
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We found that hologres-vector demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 2 open source maintainers collaborating on the project.
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