Pre-release
This release is a pre-release and may not be stable for production use.
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
- Install from pypi
pip3 install dingodb
- Install from Source
pip install git+https://github.com/dingodb/pydingo.git
Basic API
Creating an index
The following example creates an index without a metadata configuration.
>>> 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.
>>> 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.
>>> dingo_client.get_index()
['testdingo']
Get index info
The following example returns the info in specified index.
>>> 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.
>>> 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
>>> dingo_client.get_max_index_row("testdingo")
2
Search Vector
The following example Basic Search without metata.
>>> 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.
>>> 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.
>>> 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.
>>> dingo_client.vector_delete("testdingo", [2])
[True]
Drop index
The following example Drop one index.
>>> dingo_client.delete_index("testdingo")
True
1 maintainer
Metadata
1 maintainer
Release files for dingodb 0.0.17rc14
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| dingodb-0.0.17rc14.tar.gz | 55.3 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| dingodb-0.0.17rc14-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 119.8 kB
Release files / dingodb-0.0.17rc14.tar.gz
| Download URL | dingodb-0.0.17rc14.tar.gz |
|---|---|
| Size | 55.3 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
054f91364af85bc23a5ee3f8ec414f4d1704c856095a36fd839ef28f9cbc5e29
|
|
BLAKE2b-256 checksum How to use checksums |
ddc7d8e7eaedbd960ae0f4978d76f62afbeff3698ab7d5cf0c1eb910ac662057
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.0 CPython/3.12.2
|
Release files / dingodb-0.0.17rc14-py3-none-any.whl
| Download URL | dingodb-0.0.17rc14-py3-none-any.whl |
|---|---|
| Size | 64.4 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
c6c3d097e38be64a1455cb8a8a73e9b7a10de80cda0b252ad727dbf719102555
|
|
BLAKE2b-256 checksum How to use checksums |
805e5ba0529f4f50bee8ae5327ab7f7c3952552b0ed1f1685d0254670c467f84
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
No |
| Uploaded via |
twine/5.1.0 CPython/3.12.2
|
