E-Learning是学习系统而不是教育系统

冈张忱鞘ython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',

)