本文介绍如何通过Python SDK在Collection中按分组进行相似性检索。
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', ) 
