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docs/api/python/client/index.html

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md-docs/api/python/client.md

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## ML3PlatformClient
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```python
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ML3PlatformClient(
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url: str, api_key: str
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url: str, api_key: str, timeout: int = 60
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)
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```
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---
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Client class is the single point of interaction with ML cube
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Platform APIs, it is initialized providing the `url` and the User
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`api_key`.
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`api_key`. Optionally, you can specify the `timeout` for the
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operations.
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Every operation is performed verifying the API Key and the
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permissions associated to the User that own that key.
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* **job_id** : `str` job identifier of the pipeline in execution
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**Raises**
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`UpdateModelVersionException`
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### .update_model_version
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```python
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.update_model_version(
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model_id: str, new_model_version: str
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)
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```
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---
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Update model version with empty reference data.
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It is similar to create model but it does not actually create
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a different Model but a new version of the existent one.
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After this request, it is possible to upload data with
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add_historical_data. To start monitoring is required to set the
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reference of this new model with set_reference request.
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**Allowed Roles:**
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- At least `PROJECT_EDIT` for that project
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- `COMPANY_OWNER`
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- `COMPANY_ADMIN`
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**Args**
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* **model_id** : the identifier of the model
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* **new_model_version** : the new version of the model
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**Returns**
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* **model_id** : `str` job identifier of new model id
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**Raises**
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### .add_historical_data
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```python
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.add_historical_data(
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task_id: str, inputs: Data, target: (Data|None) = None
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task_id: str, inputs: Data, target: (Data|None) = None,
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predictions: (list[tuple[str, Data]]|None) = None
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)
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```
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It can be None if you upload other kinds of data
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* **target** : data object that contains target data.
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It can be None if you upload other kinds of data
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* **predictions** : list of data objects that contain prediction data.
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Each element is a tuple with model_id and data object.
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It can be None if you upload other kinds of data.
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Predictions are mandatory for RAG tasks while not
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permitted for the other TaskType.
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**Returns**

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