opengate_data.ai_models.ai_models

AIModelsBuilder

AIModelsBuilder Objects

class AIModelsBuilder()

AI Model Builder

with_organization_name

def with_organization_name(organization_name: str) -> "AIModelsBuilder"

Specify the organization name.

Arguments:

  • organization_name str - The name of the organization.

Returns:

  • AIModelsBuilder - Returns self for chaining.

Example:

builder.with_organization_name('organization_name')

with_identifier

def with_identifier(identifier: str) -> "AIModelsBuilder"

Specify the identifier for the pipeline.

Arguments:

  • identifier str - The identifier for the pipeline.

Returns:

  • AIModelsBuilder - Returns self for chaining.

Example:

builder.with_identifier('identifier')

with_env

def with_env(data_env: str) -> "AIModelsBuilder"

Specify the environment variable.

Arguments:

  • data_env str - The environment variable.

Returns:

  • AIModelsBuilder - Returns self for chaining.

Example:

builder.with_env('MODEL_ID')

with_find_by_name

def with_find_by_name(find_name: str) -> "AIModelsBuilder"

Specify the name to find.

Arguments:

  • find_name str - The name of the model.

Returns:

  • AIModelsBuilder - Returns self for chaining.

Example:

builder.with_find_by_name('model_name.onnx')

with_config_file

def with_config_file(config_file: str, section: str,
                     config_key: str) -> "AIModelsBuilder"

Sets up the configuration file (.ini).

This method allows specifying a configuration file, a section within that file, and a key to retrieve a specific value from the section.

Arguments:

  • config_file str - The path to the.ini configuration file.
  • section str - The section name within the.ini file where the desired configuration is located.
  • config_key str - The key within the specified section whose value will be retrieved.

Raises:

  • TypeError - If the provided config_file is not a string.
  • TypeError - If the provided section is not a string.
  • TypeError - If the provided config_key is not a string.

Returns:

  • AIModelsBuilder - Returns itself to allow for method chaining.

Example:

[id]
model_id = afe07216-14ec-4134-97ae-c483b11d965a
builder.with_config_file('model_config.ini', 'id', 'model_id')

add_file

def add_file(file: str) -> "AIModelsBuilder"

Attaches the model file to upload, in PMML or ONNX format.

Arguments:

  • file str - The path to the file to be added.

Returns:

  • AIModelsBuilder - Returns itself to allow for method chaining.

Raises:

  • TypeError - If the provided file path is not a string.

Example:

builder.add_file('file.onnx')

with_prediction

def with_prediction(data_prediction: dict) -> "AIModelsBuilder"

Prediction with a model

Arguments:

  • data_prediction dict - Prediction

Raises:

  • TypeError - If the prediction is not a dict.

Returns:

  • AIModelsBuilder - Returns itself to allow for method chaining.

Example:

prediction = {
- `"X"` - [
{
- `"input_8"` - [
[
-0.5391107438074961,
-0.15950019784171535,
]
]
}
]
}
builder.with_prediction(prediction)

with_output_file_path

def with_output_file_path(output_file_path: str) -> "AIModelsBuilder"

Sets the output file path for the model.

This method allows you to specify the path where the output file will be saved. It is particularly useful for operations that involve downloading or saving files.

Arguments:

  • output_file_path str - The path where the output file will be saved.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class.

Example:

builder.with_output_file_path("rute/prueba.onnx")

create

def create() -> "AIModelsBuilder"

Initiates the creation process of a new model.

This method prepares the AIModelsBuilder instance to create a new model by setting up the necessary parameters such as the organization name and the file to be associated with the model.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").add_file("example.onnx").create()

find_one

def find_one() -> "AIModelsBuilder"

Retrieve a single model.

This method sets up the AIModelsBuilder instance to retrieve a specific model associated with the specified organization and identifier.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("organization_name").with_identifier("model_identifier").find_one()

find_all

def find_all() -> "AIModelsBuilder"

Retrieve all models.

This method sets up the AIModelsBuilder instance to retrieve all models associated with the specified organization.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").find_all()

update

def update() -> "AIModelsBuilder"

Update an existing model.

This method sets up the AIModelsBuilder instance to update a specific model associated with the specified organization and identifier.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").with_identifier("model_identifier").add_file("updated_model.onnx").update()
builder.with_organization_name("MyOrganization").with_find_by_name("model_name.onnx").add_file("updated_model.onnx").update()
builder.with_organization_name("MyOrganization").with_config_file('model_config.ini', 'id', 'model').add_file("updated_model.onnx").update()

delete

def delete() -> "AIModelsBuilder"

Delete an existing model within the organization.

This method sets up the AIModelsBuilder instance to delete a specific model associated with the specified organization and identifier.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").with_identifier("model_identifier").delete()
builder.with_organization_name("MyOrganization").with_find_by_name("model_name.onnx").delete()
builder.with_organization_name("MyOrganization").with_config_file('model_config.ini', 'id', 'model').delete()

validate

def validate() -> "AIModelsBuilder"

Validate the model configuration.

This method sets up the AIModelsBuilder instance to validate the configuration of a model associated with the specified organization.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").add_file("model.onnx").validate()

download

def download() -> "AIModelsBuilder"

Download the model file.

This method sets up the AIModelsBuilder instance to download the file of a specific model associated with the specified organization and identifier.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").with_identifier("model_identifier").with_output_file_path("model.onnx").download()
builder.with_organization_name("MyOrganization").with_find_by_name("model_name.onnx").with_output_file_path("model.onnx").download()
builder.with_organization_name("MyOrganization").with_config_file('model_config.ini', 'id', 'model').with_output_file_path("model.onnx").download()

prediction

def prediction() -> "AIModelsBuilder"

Make a prediction using the model.

This method sets up the AIModelsBuilder instance to make a prediction using a specific model associated with the specified organization and identifier.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

prediction_data = {
- `"X"` - [
{
- `"input_8"` - [
[
-0.5391107438074961,
-0.15950019784171535,
]
]
}
]
}
builder.with_organization_name("MyOrganization").with_identifier("model_identifier").with_prediction(prediction_data).prediction()
builder.with_organization_name("MyOrganization").with_find_by_name("model_name.onnx").with_prediction(prediction_data).prediction()
builder.with_organization_name("MyOrganization").with_config_file('model_config.ini', 'id', 'model').with_prediction(prediction_data).prediction()

save

def save() -> "AIModelsBuilder"

Save the model configuration.

This method sets up the AIModelsBuilder instance to save the configuration of a model associated with the specified organization.

Returns:

  • AIModelsBuilder - The instance of the AIModelsBuilder class itself, allowing for method chaining.

Example:

builder.with_organization_name("MyOrganization").with_env("env_var").save()
builder.with_organization_name("MyOrganization").with_config_file('model_config.ini', 'id', 'model').save()

set_config_file_identifier

def set_config_file_identifier() -> "AIModelsBuilder"

Selects the operation that writes the model identifier into a configuration file.

It stores the identifier given to with_identifier() under the section and key named with with_config_file(), so a later run can pick the model up from the file instead of carrying its identifier in the code. The key must already exist in the file.

Returns:

  • AIModelsBuilder - Returns itself to allow for method chaining.

Example:

builder.with_identifier("model_identifier").with_config_file(
'model_config.ini', 'id', 'model_id'
).set_config_file_identifier().build().execute()

set_env_identifier

def set_env_identifier() -> "AIModelsBuilder"

Selects the operation that writes the model identifier into the .env file.

It stores the identifier given to with_identifier() under the variable named with with_env(), so a later run can pick the model up from the environment. The variable must already exist in the .env file.

Returns:

  • AIModelsBuilder - Returns itself to allow for method chaining.

Raises:

  • ValueError - If the variable is not in the .env file.

Example:

builder.with_identifier("model_identifier").with_env(
"MODEL_ID"
).set_env_identifier().build().execute()

build

def build() -> "AIModelsBuilder"

Finalizes the construction of the IoT collection configuration.

This method prepares the builder to execute the collection by ensuring all necessary configurations are set and validates the overall integrity of the build. It should be called before executing the collection to ensure that the configuration is complete and valid.

The build process involves checking that mandatory fields such as the device identifier are set. It also ensures that method calls that are incompatible with each other (like build and build_execute) are not both used.

Returns:

  • AIModelsBuilder - Returns itself to allow for method chaining, enabling further actions like execute.

Raises:

  • ValueError - If required configurations are missing or if incompatible methods are used together.

Example:

builder.build()

build_execute

def build_execute()

Executes the data sets search immediately after building the configuration.

This method is a shortcut that combines building and executing in a single step.

Returns:

  • dict - A dictionary containing the execution response which includes the status code and potentially other metadata about the execution.

Raises:

  • ValueError - If build has already been called on this builder instance.

Example:

builder.build_execute()

execute

def execute() -> Response

Execute the configured operation and return the response.

This method executes the operation that has been configured using the builder pattern. It ensures that the build method has been called and that it is the last method invoked before execute. Depending on the configured method (e.g., create, find, update, delete), it calls the appropriate internal execution method.

Returns:

  • requests.Response - The response object from the executed request.

Raises:

  • Exception - If the build method has not been called or if it is not the last method invoked before execute.
  • ValueError - If the configured method is unsupported.

Example:

builder.execute()

opengate_data.ai_models