541 lines
20 KiB
Plaintext
541 lines
20 KiB
Plaintext
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Metadata-Version: 2.1
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Name: dashvector
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Version: 1.0.22
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Summary: DashVector Client Python Sdk Library
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Home-page: https://github.com/alibaba/proxima
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License: Apache-2.0
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Keywords: DashVector,vector,database,cloud
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Author: Alibaba
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Requires-Python: >=3.9,<4.0
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Classifier: Development Status :: 5 - Production/Stable
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Classifier: Intended Audience :: Developers
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Classifier: License :: OSI Approved :: Apache Software License
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Classifier: Programming Language :: Python
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Classifier: Programming Language :: Python :: 3
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Classifier: Programming Language :: Python :: 3.9
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Classifier: Programming Language :: Python :: 3.10
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Classifier: Programming Language :: Python :: 3.11
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Classifier: Programming Language :: Python :: 3.12
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Classifier: Topic :: Database
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Classifier: Topic :: Software Development
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Classifier: Topic :: Software Development :: Libraries
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Classifier: Topic :: Software Development :: Libraries :: Application Frameworks
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Classifier: Topic :: Software Development :: Libraries :: Python Modules
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Requires-Dist: aiohttp (>=3.1.0,<4.0.0)
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Requires-Dist: certifi (>=2023.7.22,<2024.0.0)
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Requires-Dist: grpcio (>=1.44.0) ; python_version >= "3.8" and python_version < "3.11"
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Requires-Dist: grpcio (>=1.59.0) ; python_version >= "3.11" and python_version < "4.0"
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Requires-Dist: importlib_metadata
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Requires-Dist: numpy
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Requires-Dist: protobuf (>=5.29,<6.0)
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Project-URL: Documentation, https://help.aliyun.com/document_detail/2510225.html
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Description-Content-Type: text/markdown
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# DashVector Client Python Library
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DashVector is a scalable and fully-managed vector-database service for building various machine learning applications. The DashVector client SDK is your gateway to access the DashVector service.
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For more information about DashVector, please visit: https://help.aliyun.com/document_detail/2510225.html
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## Installation
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To install the DashVector client Python SDK, simply run:
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```shell
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pip install dashvector
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```
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## QuickStart
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```python
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import numpy as np
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import dashvector
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# Use DashVector `Client` api to communicate with the backend vectorDB service.
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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# Create a collection named "quickstart" with dimension of 4, using the default Cosine distance metric
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rsp = client.create(name='quickstart', dimension=4)
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assert rsp
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# Get a collection by name
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collection = client.get(name='quickstart')
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# Operations on 'Collection' includes Inert/Query/Upsert/Update/Delete/Fetch of docs
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# Here we insert sample data (4-dimensional vectors) in batches of 16
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collection.insert(
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[
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dashvector.Doc(id=str(i), vector=np.random.rand(4), fields={'anykey': 'anyvalue'})
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for i in range(16)
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]
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)
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# Query a vector from the collection
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docs = collection.query([0.1, 0.2, 0.3, 0.4], topk=5)
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print(docs)
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# Get statistics about collection
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stats = collection.stats()
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print(stats)
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# Delete a collection by name
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client.delete(name='quickstart')
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```
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## Reference
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### Create a Client
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`Client` host various APIs for interacting with DashVector `Collection`.
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```python
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dashvector.Client(
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api_key: str,
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endpoint: str = 'dashvector.cn-hangzhou.aliyuncs.com',
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protocal: dashvector.DashVectorProtocol = dashvector.DashVectorProtocol.GRPC,
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timeout: float = 10.0
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) -> Client
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```
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| Parameters | Type | Required | Description |
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|------------|--------------------|----------|----------------------------------------------------------------------------------------------|
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| api_key | str | Yes | Your DashVector API-KEY |
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| endpoint | str | No | Service Endpoint. <br/>Default value: `dashvector.cn-hangzhou.aliyuncs.com` |
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| protocol | DashVectorProtocol | No | Communication protocol, support HTTP and GRPC. <br/>Default value: `DashVectorProtocol.GRPC` |
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| timeout | float | No | Timeout period (in seconds), -1 means no timeout. <br/>Default value: `10.0` |
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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assert client
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```
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### Create Collection
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```python
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Client.create(
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name: str,
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dimension: int,
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dtype: Union[Type[int], Type[float]] = float,
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fields_schema: Optional[Dict[str, Union[Type[str], Type[int], Type[float], Type[bool]]]] = None,
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metric: str = 'cosine',
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timeout: Optional[int] = None
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) -> DashVectorResponse
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```
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| Parameters | Type | Required | Description |
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|----------------|----------------------------------------------------------------------------|----------|------------------------------------------------------------------------------------------------------------------|
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| name | str | Yes | The name of the Collection to create. |
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| dimension | int | Yes | The dimensions of the Collection's vectors. Valid values: 1-20,000 |
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| dtype | Union[Type[int], Type[float]] | No | The date type of the Collection's vectors.<br/>Default value: `Type[float]` |
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| fields_schema | Optional[Dict[str, Union[Type[str], Type[int], Type[float], Type[bool]]]] | No | Fields schema of the Collection.<br/>Default value: `None`<br/>e.g. `{"name": str, "age": int}` |
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| metric | str | No | Vector similarity metric. For `cosine`, dtype must be `float`.<br/>Valid values:<br/> 1. (Default)`cosine`<br/>2. `dotproduct`<br/>3. `euclidean` |
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| timeout | Optional[int] | No | Timeout period (in seconds), -1 means asynchronous creation collection.<br/>Default value: `None` |
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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rsp = client.create('YOUR-COLLECTION-NAME', dimension=4)
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assert rsp
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```
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### List Collections
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`Client.list() -> DashVectorResponse`
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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collections = client.list()
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for collection in collections:
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print(collection)
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# outputs:
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# 'quickstart'
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```
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### Describe Collection
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`Client.describe(name: str) -> DashVectorResponse`
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| Parameters | Type | Required | Description |
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|------------|-------|----------|-----------------------------------------|
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| name | str | Yes | The name of the Collection to describe. |
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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rsp = client.describe('YOUR-COLLECTION-NAME')
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print(rsp)
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# example output:
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# {
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# "request_id": "8d3ac14e-5382-4736-b77c-4318761ddfab",
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# "code": 0,
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# "message": "",
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# "output": {
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# "name": "quickstart",
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# "dimension": 4,
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# "dtype": "FLOAT",
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# "metric": "dotproduct",
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# "fields_schema": {
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# "name": "STRING",
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# "age": "INT",
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# "height": "FLOAT"
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# },
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# "status": "SERVING",
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# "partitions": {
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# "default": "SERVING"
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# }
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# }
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# }
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```
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### Delete Collection
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`Client.delete(name: str) -> DashVectorResponse`
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| Parameters | Type | Required | Description |
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|------------|-------|----------|---------------------------------------|
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| name | str | Yes | The name of the Collection to delete. |
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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client.delete('YOUR-COLLECTION-NAME')
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```
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### Get a Collection Instance
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`Collection` provides APIs for accessing `Doc` and `Partition`
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`Client.get(name: str) -> Collection`
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| Parameters | Type | Required | Description |
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|------------|-------|----------|------------------------------------|
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| name | str | Yes | The name of the Collection to get. |
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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collection = client.get('YOUR-COLLECTION-NAME')
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assert collection
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```
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### Describe Collection Statistics
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`Collection.stats() -> DashVectorResponse`
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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collection = client.get('YOUR-COLLECTION-NAME')
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rsp = collection.stats()
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print(rsp)
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# example output:
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# {
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# "request_id": "14448bcb-c9a3-49a8-9152-0de3990bce59",
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# "code": 0,
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# "message": "Success",
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# "output": {
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# "total_doc_count": "26",
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# "index_completeness": 1.0,
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# "partitions": {
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# "default": {
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# "total_doc_count": "26"
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# }
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# }
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# }
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# }
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```
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### Insert/Update/Upsert Docs
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```python
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Collection.insert(
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docs: Union[Doc, List[Doc], Tuple, List[Tuple]],
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partition: Optional[str] = None,
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async_req: False
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) -> DashVectorResponse
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```
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| Parameters | Type | Required | Description |
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|------------|-------------------------------------------|----------|------------------------------------------------------------------------|
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| docs | Union[Doc, List[Doc], Tuple, List[Tuple]] | Yes | The docs to Insert/Update/Upsert. |
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| partition | Optional[str] | No | Name of the partition to Insert/Update/Upsert.<br/>Default value: `None` |
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| async_req | bool | No | Enable async request or not.<br/>Default value: `False` |
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Example:
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```python
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import dashvector
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import numpy as np
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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collection = client.get('YOUR-COLLECTION-NAME')
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# insert a doc with Tuple
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collection.insert(('YOUR-DOC-ID1', [0.1, 0.2, 0.3, 0.4]))
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collection.insert(('YOUR-DOC-ID2', [0.2, 0.3, 0.4, 0.5], {'age': 30, 'name': 'alice', 'anykey': 'anyvalue'}))
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# insert a doc with dashvector.Doc
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collection.insert(
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dashvector.Doc(
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id='YOUR-DOC-ID3',
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vector=[0.3, 0.4, 0.5, 0.6],
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fields={'foo': 'bar'}
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)
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)
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# insert in batches
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ret = collection.insert(
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[
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('YOUR-DOC-ID4', [0.2, 0.7, 0.8, 1.3], {'age': 1}),
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('YOUR-DOC-ID4', [0.3, 0.6, 0.9, 1.2], {'age': 2}),
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('YOUR-DOC-ID6', [0.4, 0.5, 1.0, 1.1], {'age': 3})
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]
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)
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# insert in batches
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ret = collection.insert(
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[
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dashvector.Doc(id=str(i), vector=np.random.rand(4)) for i in range(10)
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]
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)
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# async insert
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ret_funture = collection.insert(
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[
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dashvector.Doc(id=str(i+10), vector=np.random.rand(4)) for i in range(10)
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],
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async_req=True
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)
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ret = ret_funture.get()
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```
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### Query a Collection
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```python
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Collection.query(
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vector: Optional[Union[List[Union[int, float]], np.ndarray]] = None,
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id: Optional[str] = None,
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topk: int = 10,
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filter: Optional[str] = None,
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include_vector: bool = False,
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partition: Optional[str] = None,
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output_fields: Optional[List[str]] = None,
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async_req: False
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) -> DashVectorResponse
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```
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| Parameters | Type | Required | Description |
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|-----------------|------------------------------------------------------|----------|--------------------------------------------------------------------------------------------------------------|
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| vector | Optional[Union[List[Union[int, float]], np.ndarray]] | No | The vector to query |
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| id | Optional[str] | No | The doc id to query.<br/>Setting `id` means searching by vector corresponding to the id |
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| topk | Optional[str] | No | Number of similarity results to return.<br/>Default value: `10` |
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| filter | Optional[str] | No | Expression used to filter results <br/>Default value: None <br/>e.g. `age>20` |
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| include_vector | bool | No | Return vector details or not.<br/>Default value: `False` |
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| partition | Optional[str] | No | Name of the partition to Query.<br/>Default value: `None` |
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| output_fields | Optional[List[str]] | No | List of field names to return.<br/>Default value: `None`, means return all fields<br/>e.g. `['name', 'age']` |
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| async_req | bool | No | Enable async request or not.<br/>Default value: `False` |
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Example:
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```python
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import dashvector
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client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
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collection = client.get('YOUR-COLLECTION-NAME')
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match_docs = collection.query([0.1, 0.2, 0.3, 0.4], topk=100, filter='age>20', include_vector=True, output_fields=['age','name','foo'])
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if match_docs:
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for doc in match_docs:
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print(doc.id)
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print(doc.vector)
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print(doc.fields)
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print(doc.score)
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```
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### Delete Docs
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|
|
```python
|
||
|
|
collection.delete(
|
||
|
|
ids: Union[str, List[str]],
|
||
|
|
delete_all: bool = False,
|
||
|
|
partition: Optional[str] = None,
|
||
|
|
async_req: bool = False
|
||
|
|
) -> DashVectorResponse
|
||
|
|
```
|
||
|
|
|
||
|
|
| Parameters | Type | Required | Description |
|
||
|
|
|------------|-----------------------|----------|-----------------------------------------------------------------|
|
||
|
|
| ids | Union[str, List[str]] | Yes | The id (or list of ids) for the Doc(s) to Delete |
|
||
|
|
| delete_all | bool | No | Delete all vectors from partition.<br/>Default value: `False` |
|
||
|
|
| partition | Optional[str] | No | Name of the partition to Delete from.<br/>Default value: `None` |
|
||
|
|
| async_req | bool | No | Enable async request or not.<br/>Default value: `False` |
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
collection.delete(['YOUR-DOC-ID1','YOUR-DOC-ID2'])
|
||
|
|
```
|
||
|
|
|
||
|
|
### Fetch Docs
|
||
|
|
```python
|
||
|
|
Collection.fetch(
|
||
|
|
ids: Union[str, List[str]],
|
||
|
|
partition: Optional[str] = None,
|
||
|
|
async_req: bool = False
|
||
|
|
) -> DashVectorResponse
|
||
|
|
```
|
||
|
|
|
||
|
|
| Parameters | Type | Required | Description |
|
||
|
|
|------------|-----------------------|----------|----------------------------------------------------------------|
|
||
|
|
| ids | Union[str, List[str]] | Yes | The id (or list of ids) for the Doc(s) to Fetch |
|
||
|
|
| partition | Optional[str] | No | Name of the partition to Fetch from.<br/>Default value: `None` |
|
||
|
|
| async_req | bool | No | Enable async request or not.<br/>Default value: `False` |
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
fetch_docs = collection.fetch(['YOUR-DOC-ID1', 'YOUR-DOC-ID2'])
|
||
|
|
if fetch_docs:
|
||
|
|
for doc_id in fetch_docs:
|
||
|
|
doc = fetch_docs[doc_id]
|
||
|
|
print(doc.id)
|
||
|
|
print(doc.vector)
|
||
|
|
print(doc.fields)
|
||
|
|
```
|
||
|
|
|
||
|
|
### Create Collection Partition
|
||
|
|
`Collection.create_partition(name: str) -> DashVectorResponse`
|
||
|
|
|
||
|
|
| Parameters | Type | Required | Description |
|
||
|
|
|------------|----------------|----------|-------------------------------------------------------------------------------------------------------|
|
||
|
|
| name | str | Yes | The name of the Partition to Create. |
|
||
|
|
| timeout | Optional[int] | No | Timeout period (in seconds), -1 means asynchronous creation partition.<br/>Default value: `None` |
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
rsp = collection.create_partition('YOUR-PARTITION-NAME')
|
||
|
|
assert rsp
|
||
|
|
```
|
||
|
|
|
||
|
|
### Delete Collection Partition
|
||
|
|
`Collection.delete_partition(name: str) -> DashVectorResponse`
|
||
|
|
|
||
|
|
| Parameters | Type | Required | Description |
|
||
|
|
|------------|-------|----------|--------------------------------------|
|
||
|
|
| name | str | Yes | The name of the Partition to Delete. |
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
rsp = collection.delete_partition('YOUR-PARTITION-NAME')
|
||
|
|
assert rsp
|
||
|
|
```
|
||
|
|
|
||
|
|
### List Collection Partitions
|
||
|
|
`Collection.list_partitions() -> DashVectorResponse`
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
partitions = collection.list_partitions()
|
||
|
|
|
||
|
|
assert partitions
|
||
|
|
for pt in partitions:
|
||
|
|
print(pt)
|
||
|
|
```
|
||
|
|
|
||
|
|
### Describe Collection Partition
|
||
|
|
`Collection.describe_partition(name: str) -> DashVectorResponse`
|
||
|
|
|
||
|
|
| Parameters | Type | Required | Description |
|
||
|
|
|------------|-------|----------|----------------------------------------|
|
||
|
|
| name | str | Yes | The name of the Partition to Describe. |
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
|
||
|
|
rsp = collection.describe_partition('shoes')
|
||
|
|
print(rsp)
|
||
|
|
# example output:
|
||
|
|
# {"request_id":"296267a7-68e2-483a-87e6-5992d85a5806","code":0,"message":"","output":"SERVING"}
|
||
|
|
```
|
||
|
|
|
||
|
|
### Statistics for Collection Partition
|
||
|
|
`Collection.stats_partition(name: str) -> DashVectorResponse`
|
||
|
|
|
||
|
|
| Parameters | Type | Required | Description |
|
||
|
|
|------------|-------|----------|----------------------------------------------|
|
||
|
|
| name | str | Yes | The name of the Partition to get Statistics. |
|
||
|
|
|
||
|
|
Example:
|
||
|
|
```python
|
||
|
|
import dashvector
|
||
|
|
|
||
|
|
client = dashvector.Client(api_key='YOUR-DASHVECTOR-API-KEY')
|
||
|
|
collection = client.get('YOUR-COLLECTION-NAME')
|
||
|
|
|
||
|
|
rsp = collection.stats_partition('shoes')
|
||
|
|
print(rsp)
|
||
|
|
# example outptut:
|
||
|
|
# {
|
||
|
|
# "code":0,
|
||
|
|
# "message":"",
|
||
|
|
# "requests_id":"330a2bcb-e4a7-4fc6-a711-2fe5f8a24e8c",
|
||
|
|
# "output":{
|
||
|
|
# "total_doc_count":0
|
||
|
|
# }
|
||
|
|
# }
|
||
|
|
```
|
||
|
|
|
||
|
|
|
||
|
|
## Class
|
||
|
|
### dashvector.Doc
|
||
|
|
```python
|
||
|
|
@dataclass(frozen=True)
|
||
|
|
class Doc(object):
|
||
|
|
id: str
|
||
|
|
vector: Union[List[int], List[float], numpy.ndarray]
|
||
|
|
fields: Optional[Dict[str, Union[Type[str], Type[int], Type[float], Type[bool]]]] = None
|
||
|
|
score: float = 0.0
|
||
|
|
```
|
||
|
|
|
||
|
|
### dashvector.DashVectorResponse
|
||
|
|
|
||
|
|
```python
|
||
|
|
class DashVectorResponse(object):
|
||
|
|
code: DashVectorCode
|
||
|
|
message: str
|
||
|
|
request_id: str
|
||
|
|
output: Any
|
||
|
|
```
|
||
|
|
|
||
|
|
## License
|
||
|
|
This project is licensed under the Apache License (Version 2.0).
|