# python-dingodb
The DingoDB python sdk
First, you have prepared the DingoDB environment, see the docs at https://github.com/dingodb/dingo-deploy.git
For more information about DingoDB, see the docs at https://dingodb.readthedocs.io/en/latest/
## How to Contribute
### 1. Compile
```
pip install -r requirements.txt
git submodule update --init --recursive
```
### 2. Release
## Usage
### Installation
1. Install from pypi
```shell
pip3 install dingodb
```
2. Install from Source
```shell
pip install git+https://github.com/dingodb/pydingo.git
```
### Basic API
#### Creating an index
The following example creates an index without a metadata configuration.
```python
>>> import dingodb
>>> dingo_client = dingodb.DingoDB("user", "password", ["172.20.3.20:13000"])
>>> dingo_client.create_index("testdingo", 6, index_type="flat")
True
```
dingodb provides flexible indexing parameters.
```python
>>> help(dingo_client.create_index)
create_index(index_name, dimension, index_type='hnsw', metric_type='euclidean', replicas=3, index_config=None, metadata_config=None, partition_rule=None, auto_id=True)
```
#### Get index
The following example returns all indexes in your schema.
```python
>>> dingo_client.get_index()
['testdingo']
```
#### Get index info
The following example returns the info in specified index.
```python
>>> dingo_client.describe_index_info("testdingo")
{'name': 'testdingo', 'version': 0, 'replica': 3, 'autoIncrement': 1, 'indexParameter': {'indexType': 'INDEX_TYPE_VECTOR', 'vectorIndexParameter': {'vectorIndexType': 'VECTOR_INDEX_TYPE_FLAT', 'flatParam': {'dimension': 6, 'metricType': 'METRIC_TYPE_L2'}, 'ivfFlatParam': None, 'ivfPqParam': None, 'hnswParam': None, 'diskAnnParam': None}}}
```
#### Add vector
The following example add vector to database.
```python
>>> dingo_client.vector_add("testdingo", [{"a1":"b1", "aa1":"bb1"}, {"a1": "b1"}],[[0.19151945,0.62210876,0.43772775,0.7853586,0.77997583,0.2725926], [0.27746424078941345,0.801872193813324,0.9581393599510193,0.8759326338768005,0.35781726241111755,0.5009950995445251]])
[{'id': 1, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [0.19151945, 0.62210876, 0.43772775, 0.7853586, 0.77997583, 0.2725926], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}, 'aa1': {'fieldType': 'STRING', 'fields': [{'data': 'bb1'}]}}}, {'id': 2, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [0.27746424, 0.8018722, 0.95813936, 0.87593263, 0.35781726, 0.5009951], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}}]
```
#### Get MAX ID
you can use autoIncrement id, The following example get max id
```python
>>> dingo_client.get_max_index_row("testdingo")
2
```
#### Search Vector
The following example Basic Search without metata.
```python
>>> dingo_client.vector_search("testdingo", [[0.19151945,0.62210876,0.43772775,0.7853586,0.77997583,0.2725926]], 10)
[{'vectorWithDistances': [{'id': 1, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}, 'aa1': {'fieldType': 'STRING', 'fields': [{'data': 'bb1'}]}}, 'distance': 0.0}, {'id': 2, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}, 'distance': 0.5491189}]}]
```
The following example Search with metata.
```python
>>> dingo_client.vector_search("testdingo", [0.19151945,0.62210876,0.43772775,0.7853586,0.77997583,0.2725926],10, {"meta_expr": {"aa1": "bb1"}})
{'vectorWithDistances': [{'id': 1, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [], 'binaryValues': []}, 'scalarData': {'aa1': {'fieldType': 'STRING', 'fields': [{'data': 'bb1'}]}, 'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}, 'distance': 0.0}]}
```
#### Query vector with ids
The following example Query vector with ids.
```python
>>> dingo_client.vector_get("testdingo", [2])
[{'id': 2, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [0.27746424, 0.8018722, 0.95813936, 0.87593263, 0.35781726, 0.5009951], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}}]
```
#### Detele vector with ids
The following example Detele vector with ids.
```python
>>> dingo_client.vector_delete("testdingo", [2])
[True]
```
#### Drop index
The following example Drop one index.
```python
>>> dingo_client.delete_index("testdingo")
True
```
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"description": "# python-dingodb\nThe DingoDB python sdk\n\nFirst, you have prepared the DingoDB environment, see the docs at https://github.com/dingodb/dingo-deploy.git\n\nFor more information about DingoDB, see the docs at https://dingodb.readthedocs.io/en/latest/\n\n\n## How to Contribute\n\n### 1. Compile\n\n```\npip install -r requirements.txt\ngit submodule update --init --recursive\n```\n\n### 2. Release\n\n\n\n## Usage\n\n### Installation\n\n1. Install from pypi\n\n```shell\npip3 install dingodb\n```\n\n2. Install from Source\n\n```shell\npip install git+https://github.com/dingodb/pydingo.git\n```\n\n### Basic API\n\n#### Creating an index\n\nThe following example creates an index without a metadata configuration. \n```python\n>>> import dingodb\n>>> dingo_client = dingodb.DingoDB(\"user\", \"password\", [\"172.20.3.20:13000\"])\n>>> dingo_client.create_index(\"testdingo\", 6, index_type=\"flat\")\nTrue\n```\ndingodb provides flexible indexing parameters.\n```python\n>>> help(dingo_client.create_index)\ncreate_index(index_name, dimension, index_type='hnsw', metric_type='euclidean', replicas=3, index_config=None, metadata_config=None, partition_rule=None, auto_id=True)\n```\n\n#### Get index\nThe following example returns all indexes in your schema.\n```python\n>>> dingo_client.get_index()\n['testdingo']\n```\n\n#### Get index info\n\nThe following example returns the info in specified index.\n```python\n>>> dingo_client.describe_index_info(\"testdingo\")\n{'name': 'testdingo', 'version': 0, 'replica': 3, 'autoIncrement': 1, 'indexParameter': {'indexType': 'INDEX_TYPE_VECTOR', 'vectorIndexParameter': {'vectorIndexType': 'VECTOR_INDEX_TYPE_FLAT', 'flatParam': {'dimension': 6, 'metricType': 'METRIC_TYPE_L2'}, 'ivfFlatParam': None, 'ivfPqParam': None, 'hnswParam': None, 'diskAnnParam': None}}}\n```\n\n#### Add vector\n\nThe following example add vector to database.\n```python\n>>> dingo_client.vector_add(\"testdingo\", [{\"a1\":\"b1\", \"aa1\":\"bb1\"}, {\"a1\": \"b1\"}],[[0.19151945,0.62210876,0.43772775,0.7853586,0.77997583,0.2725926], [0.27746424078941345,0.801872193813324,0.9581393599510193,0.8759326338768005,0.35781726241111755,0.5009950995445251]])\n[{'id': 1, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [0.19151945, 0.62210876, 0.43772775, 0.7853586, 0.77997583, 0.2725926], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}, 'aa1': {'fieldType': 'STRING', 'fields': [{'data': 'bb1'}]}}}, {'id': 2, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [0.27746424, 0.8018722, 0.95813936, 0.87593263, 0.35781726, 0.5009951], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}}]\n```\n\n#### Get MAX ID\n\nyou can use autoIncrement id, The following example get max id\n```python\n>>> dingo_client.get_max_index_row(\"testdingo\")\n2\n```\n\n#### Search Vector\n\nThe following example Basic Search without metata.\n```python\n>>> dingo_client.vector_search(\"testdingo\", [[0.19151945,0.62210876,0.43772775,0.7853586,0.77997583,0.2725926]], 10)\n[{'vectorWithDistances': [{'id': 1, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}, 'aa1': {'fieldType': 'STRING', 'fields': [{'data': 'bb1'}]}}, 'distance': 0.0}, {'id': 2, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}, 'distance': 0.5491189}]}]\n```\n\nThe following example Search with metata.\n```python\n>>> dingo_client.vector_search(\"testdingo\", [0.19151945,0.62210876,0.43772775,0.7853586,0.77997583,0.2725926],10, {\"meta_expr\": {\"aa1\": \"bb1\"}})\n{'vectorWithDistances': [{'id': 1, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [], 'binaryValues': []}, 'scalarData': {'aa1': {'fieldType': 'STRING', 'fields': [{'data': 'bb1'}]}, 'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}, 'distance': 0.0}]}\n```\n\n#### Query vector with ids\n\nThe following example Query vector with ids.\n```python\n>>> dingo_client.vector_get(\"testdingo\", [2])\n[{'id': 2, 'vector': {'dimension': 6, 'valueType': 'FLOAT', 'floatValues': [0.27746424, 0.8018722, 0.95813936, 0.87593263, 0.35781726, 0.5009951], 'binaryValues': []}, 'scalarData': {'a1': {'fieldType': 'STRING', 'fields': [{'data': 'b1'}]}}}]\n```\n\n#### Detele vector with ids\n\nThe following example Detele vector with ids.\n```python\n>>> dingo_client.vector_delete(\"testdingo\", [2])\n[True]\n```\n\n#### Drop index\n\nThe following example Drop one index.\n```python\n>>> dingo_client.delete_index(\"testdingo\")\nTrue\n```\n",
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