我要评论前提条件
已创建cluster
已获得api-key
已安装最新版sdk
接口定义
python示例：
collection.query_group_by(
self,
vector: optional[union[list[union[int, float]], np.ndarray]] = none,
*,
group_by_field: str,
group_count: int = 10,
group_topk: int = 10,
id: optional[str] = none,
filter: optional[str] = none,
include_vector: bool = false,
partition: optional[str] = none,
output_fields: optional[list[str]] = none,
sparse_vector: optional[dict[int, float]] = none,
async_req: bool = false,
) -> dashvectorresponse:
使用示例
说明
需要使用您的api-key替换示例中的your_api_key、您的cluster endpoint替换示例中的your_cluster_endpoint，代码才能正常运行。
python示例：
import dashvector
import numpy as np
client = dashvector.client(
api_key='your_api_key',
endpoint='your_cluster_endpoint'
)
ret = client.create(
name='group_by_demo',
dimension=4,
fields_schema={'document_id': str, 'chunk_id': int}
)
assert ret
collection = client.get(name='group_by_demo')
ret = collection.insert([
('1', np.random.rand(4), {'document_id': 'paper-01', 'chunk_id': 1, 'content': 'xxxa'}),
('2', np.random.rand(4), {'document_id': 'paper-01', 'chunk_id': 2, 'content': 'xxxb'}),
('3', np.random.rand(4), {'document_id': 'paper-02', 'chunk_id': 1, 'content': 'xxxc'}),
('4', np.random.rand(4), {'document_id': 'paper-02', 'chunk_id': 2, 'content': 'xxxd'}),
('5', np.random.rand(4), {'document_id': 'paper-02', 'chunk_id': 3, 'content': 'xxxe'}),
('6', np.random.rand(4), {'document_id': 'paper-03', 'chunk_id': 1, 'content': 'xxxf'}),
])
assert ret
根据向量进行分组相似性检索
python示例：
ret = collection.query_group_by(
vector=[0.1, 0.2, 0.3, 0.4],
group_by_field='document_id', # 按document_id字段的值分组
group_count=2, # 返回2个分组
group_topk=2, # 每个分组最多返回2个doc
)
# 判断是否成功
if ret:
print('query_group_by success')
print(len(ret))
print('------------------------')
for group in ret:
print('group key:', group.group_id)
for doc in group.docs:
prefix = ' -'
print(prefix, doc)
参考输出如下
query_group_by success
4
------------------------
group key: paper-01
- {"id": "2", "fields": {"document_id": "paper-01", "chunk_id": 2, "content": "xxxb"}, "score": 0.6807}
- {"id": "1", "fields": {"document_id": "paper-01", "chunk_id": 1, "content": "xxxa"}, "score": 0.4289}
group key: paper-02
- {"id": "3", "fields": {"document_id": "paper-02", "chunk_id": 1, "content": "xxxc"}, "score": 0.6553}
- {"id": "5", "fields": {"document_id": "paper-02", "chunk_id": 3, "content": "xxxe"}, "score": 0.4401}
根据主键对应的向量进行分组相似性检索
python示例：
ret = collection.query_group_by(
id='1',
group_by_field='name',
)
# 判断query接口是否成功
if ret:
print('query_group_by success')
print(len(ret))
for group in ret:
print('group:', group.group_id)
for doc in group.docs:
print(doc)
print(doc.id)
print(doc.vector)
print(doc.fields)
带过滤条件的分组相似性检索
python示例：
# 根据向量或者主键进行分组相似性检索 + 条件过滤
ret = collection.query_group_by(
vector=[0.1, 0.2, 0.3, 0.4], # 向量检索，也可设置主键检索
group_by_field='name',
filter='age > 18', # 条件过滤，仅对age > 18的doc进行相似性检索
output_fields=['name', 'age'], # 仅返回name、age这2个field
include_vector=true
)
带有sparse vector的分组向量检索
python示例：
# 根据向量进行分组相似性检索 + 稀疏向量
ret = collection.query_group_by(
vector=[0.1, 0.2, 0.3, 0.4], # 向量检索
sparse_vector={1: 0.3, 20: 0.7},
group_by_field='name',
)
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