ModelConfig.md

ModelConfig

Properties

Name	Type	Description	Notes
access	AiConfigsAccess		[optional]
maintainer	AIConfigMaintainer		[optional]
name	str	Human-readable name of the model
key	str	Unique key for the model config in the project, in the format `Provider.model-id`. Use this value as `modelConfigKey` on a config variation.
id	str	Model ID that the provider's API uses. This is different from `key`. Use this value as `modelName` on a config variation.
icon	str	Icon for the model	[optional]
provider	str	Provider for the model. Matches the provider prefix of `key`.	[optional]
var_global	bool	Whether the model is in the LaunchDarkly global catalog. Global models are read-only. Model configs that you create are always custom, with `global` set to `false`.
params	object	Freeform JSON object. LaunchDarkly does not validate the keys. In a model config, `params` and `customParams` hold parameters that the provider's API accepts, for example `{"temperature": 0.7, "maxTokens": 4096}`.	[optional]
custom_params	object	Freeform JSON object. LaunchDarkly does not validate the keys. In a model config, `params` and `customParams` hold parameters that the provider's API accepts, for example `{"temperature": 0.7, "maxTokens": 4096}`.	[optional]
tags	List[str]	Tags for the model config. Tags can be used in custom role policies.
version	int	Version of the model config. Starts at 1, and each update that changes the model config adds a new version.
cost_per_input_token	float	Cost per input token in USD	[optional]
cost_per_output_token	float	Cost per output token in USD	[optional]
cost_per_cached_input_token	float	Cost per cached input token in USD	[optional]
is_restricted	bool	Whether the model is on the project's restricted list. Config variations cannot select a restricted model. To change this, use the add or remove restricted models endpoints.
used_in_count	int	Number of distinct AgentControl configs that reference this model	[optional]

Example

from launchdarkly_api.models.model_config import ModelConfig

# TODO update the JSON string below
json = "{}"
# create an instance of ModelConfig from a JSON string
model_config_instance = ModelConfig.from_json(json)
# print the JSON string representation of the object
print(ModelConfig.to_json())

# convert the object into a dict
model_config_dict = model_config_instance.to_dict()
# create an instance of ModelConfig from a dict
model_config_from_dict = ModelConfig.from_dict(model_config_dict)

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