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：
```python
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：
```python
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：
```python
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
```plaintext
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：
```python
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：
```python
# 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：
```python
# 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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