### Watsonx.governance Factsheet (On Prem) Setup

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This section details how to initialize the AIGovFactsClient for on-premises installations using various authentication methods.

```APIDOC
## Watsonx.governance Factsheet (On Prem) Setup

### Description
Initializes the AIGovFactsClient for on-premises deployments.

### Method
Instantiation of `AIGovFactsClient`

### Parameters
#### Request Body
- **experiment_name** (string) - Required - The name of the experiment.
- **container_type** (string) - Required - Type of container ('space' or 'project').
- **container_id** (string) - Required - ID of the space or project.
- **cloud_pak_for_data_configs** (object) - Required - Configuration for Cloud Pak for Data.
  - **service_url** (string) - Required - The service URL.
  - **username** (string) - Required - The username.
  - **password** (string) - Optional - The password.
  - **api_key** (string) - Optional - The API key.
  - **bedrock_url** (string) - Optional - The cluster bedrock URL (for IAM enabled).
- **disable_tracing** (boolean) - Optional - If true, tracing is disabled.
- **external_model** (boolean) - Optional - If true, indicates an external model.

### Request Example (Username/Password)
```python
from ibm_aigov_facts_client import AIGovFactsClient, CloudPakforDataConfig

cpd_creds = CloudPakforDataConfig(service_url="<hosturl>", username="<username>", password="<password>")
client = AIGovFactsClient(experiment_name="<name of experiment>", container_type="<space or project>", container_id="<space_id or project_id>", cloud_pak_for_data_configs=cpd_creds)
```

### Request Example (API Key)
```python
from ibm_aigov_facts_client import AIGovFactsClient, CloudPakforDataConfig

cpd_creds = CloudPakforDataConfig(service_url="<hosturl>", username="<username>", api_key="<api_key>")
client = AIGovFactsClient(experiment_name="<name of experiment>", container_type="<space or project>", container_id="<space_id or project_id>", cloud_pak_for_data_configs=cpd_creds)
```

### Request Example (IAM Enabled)
```python
from ibm_aigov_facts_client import AIGovFactsClient, CloudPakforDataConfig

cpd_creds = CloudPakforDataConfig(service_url="<hosturl>", username="<username>", password="<password>", bedrock_url="<cluster bedrock url>")
client = AIGovFactsClient(experiment_name="<name of experiment>", container_type="<space or project>", container_id="<space_id or project_id>", cloud_pak_for_data_configs=cpd_creds)
```

### Request Example (Disable Tracing)
```python
client = AIGovFactsClient(container_type="<project or space>", container_id="<space or project id>", cloud_pak_for_data_configs=creds, disable_tracing=True)
client = AIGovFactsClient(external_model=True, cloud_pak_for_data_configs=creds, disable_tracing=True)
```

### Request Example (Standalone Localhost)
```python
from ibm_aigov_facts_client import AIGovFactsClient

client = AIGovFactsClient(experiment_name="test")
```
```

--------------------------------

### Retrieve Installed Version

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Retrieves the installed Cloud Pak for Data version.

```APIDOC
## Retrieve Installed Version

### Description
Gets the Cloud Pak for Data version. Returns 'SaaS' if it's a cloud environment.

### Method
GET

### Endpoint
`/cpd_version`

### Parameters
This method does not take any parameters.

### Response
#### Success Response (200)
- **cpd_version** (string) - The Cloud Pak for Data version or 'SaaS'.

### Usage
```python
client.assets.get_cpd_version()
```
```

--------------------------------

### Initialize AI Factsheet Client (Push Framework)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initializes the AI Factsheet client with the push framework enabled. Sets `enable_push_framework` to True.

```python
client = AIGovFactsClient(api_key=<API_KEY>, experiment_name="test",container_type="space or project",container_id=<space_id or project_id>,enable_push_framework=True)
```

--------------------------------

### Start tracing session with ManualLog

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/manual%20log

Starts a tracing session for manual logging. Optionally accepts an experiment ID to log under a specific experiment.

```python
client.manual_log.start_trace()
client.manual_log.start_trace(experiment_id="1")
```

--------------------------------

### Initialize watsonx.governance Factsheet Client with API Key

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This snippet demonstrates initializing the AIGovFactsClient using CloudPakforDataConfig with a service URL, username, and API key. This is an alternative authentication method for the client setup.

```python
from ibm_aigov_facts_client import AIGovFactsClient,CloudPakforDataConfig
cpd_creds=CloudPakforDataConfig(service_url="<hosturl>",username="<username>",api_key="<api_key>")
client = AIGovFactsClient(experiment_name="<name of experiment>",container_type="<space or project>",container_id="<space_id or project_id>",cloud_pak_for_data_configs=cpd_creds)
```

--------------------------------

### Initialize AI Factsheet Client (Existing Experiment)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initializes the AI Factsheet client to use an existing experiment. Sets `set_as_current_experiment` to True.

```python
client = AIGovFactsClient(api_key=<API_KEY>, experiment_name="test",container_type="space",container_id=<space_id>,set_as_current_experiment=True)
```

--------------------------------

### Get Access Token

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Retrieves the IAM access token.

```APIDOC
## Get Access Token

### Description
Method to get IAM access token.

### Method
GET

### Endpoint
`/get_access_token`

### Usage
```python
client.get_access_token()
```
```

--------------------------------

### Initialize AI Factsheet Client (Cloud)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initializes the AI Factsheet client for use with IBM Cloud. Requires an API key, experiment name, container type, and container ID.

```python
from ibm_aigov_facts_client import AIGovFactsClient
client = AIGovFactsClient(api_key=<API_KEY>, experiment_name="test",container_type="space or project",container_id=<space_id or project_id>)
```

--------------------------------

### Get model description with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Returns the descriptive information about the model. This description provides context about the model's purpose and functionality for documentation and user understanding.

```python
model.get_description()
```

--------------------------------

### Initialize AI Factsheet Client (External Model - Autolog)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initializes the AI Factsheet client for external models with autologging enabled. Sets `external_model` to True.

```python
client= AIGovFactsClient(api_key=API_KEY,experiment_name="external",external_model=True)
```

--------------------------------

### Create Detailed Detached Prompt in Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/detached_prompt

Creates a detached prompt with full configuration including prompt details. Depends on facts.client.assets.create_detached_prompt, DetachedPromptTemplate, and PromptTemplate. Inputs include all parameters from the minimal example plus prompt_details with variables and model parameters. Outputs an AIGovAssetUtilities object with enhanced prompt setup.

```python
detached_info = DetachedPromptTemplate(prompt_id="n/a",
                                model_id="arn:aws:bedrock:us-east1:123456789012:provisioned-model/anthropic.claude-v2",
                                model_provider="AWS Bedrock",
                                model_name="Anthropic Claude 2.0",
                                model_url="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-claude.html",
                                prompt_url="n/a",
                                prompt_additional_info={"AWS Region": "us-east1"}
                                )

prompt_template = PromptTemplate(
                                 prompt_variables= {"text": "value" },
                                 input="Input text to be given",
                                 model_parameters={"decoding_method":"greedy",
                                                  "max_new_tokens":2034,
                                                  "min_new_tokens":0,
                                                  "random_seed":0,
                                                  "top_k":0,
                                                  "top_p":0
                                                  }
                                 )

external_prompt = facts.client.assets.create_detached_prompt(name="External prompt sample (model AWS Bedrock Anthropic)", 
                                     model_id="anthropic.claude-v2", 
                                     task_id="summarization",
                                     detached_information=detached_info, 
                                     description="My First External Prompt", 
                                     prompt_details=prompt_template
                                    )
```

--------------------------------

### Initialize watsonx.governance Factsheet Client for Standalone Use

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This snippet shows how to initialize the AIGovFactsClient for standalone use in a localhost environment without factsheet functionality, only requiring an experiment name.

```python
from ibm_aigov_facts_client import AIGovFactsClient
client = AIGovFactsClient(experiment_name="test")
```

--------------------------------

### Get Cloud Pak for Data Version

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This method retrieves the Cloud Pak for Data version. If the environment is cloud-based, it will return 'SaaS'. This is useful for version checking and compatibility.

```python
client.assets.get_cpd_version()
```

--------------------------------

### Initialize AI Factsheet Client (External Model - Manual Log)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initializes the AI Factsheet client for external models with manual logging enabled. Sets `enable_autolog` to False and `external_model` to True.

```python
client= AIGovFactsClient(api_key=API_KEY,experiment_name="external",enable_autolog=False,external_model=True)
```

--------------------------------

### Create Prompt Template with Multiple Modes - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/regular_prompt

Demonstrates creating both structured and freeflow prompt templates using the create_prompt function. The structured mode includes additional configuration like input/output prefixes and examples, while freeflow mode provides a simpler approach. Both examples use the same API but with different input_mode parameters.

```python
# Example-1: Creating a Structured prompt template
prompt_template = PromptTemplate(model_version={"number": "2.0.0-rc.7", "tag": "tag", "description": "Description of the model version"},
                             input="Input text to be given",
                             prompt_variables= {"text": "value"},
                             prompt_instruction="Your prompt instruction",
                             input_prefix="Your input prefix,
                             output_prefix="Your output prefix",
                             examples={"What is the capidddtal of France{text}?": "The capital of France is Paris{text}.",
                                        "Who wrote '1984{text}'?": "George Orwell wrote '1984'{text}."},

                            model_parameters={"decoding_method":"greedy"
                                              "max_new_tokens":2034,
                                              "min_new_tokens":0,
                                              "random_seed":0,
                                              "top_k":0,
                                              "top_p":0
                                              }
                             )

structured_prompt = facts.client.asests.create_prompt(input_mode="structured",
                                                      name=" structured prompt sample",
                                                      task_id="summarization",
                                                      model_id="ibm/granite-13b-chat-v2", 
                                                      description="My First structured prompt",
                                                      prompt_details=prompt_template,
                                                      )

# Example-2: Creating a Freeflow prompt template
prompt_template = PromptTemplate(
                             input="Input text to be given",
                             prompt_variables= {"text": "value"},

                            model_parameters={"decoding_method":"greedy"
                                              "max_new_tokens":2034,
                                              "min_new_tokens":0,
                                              "random_seed":0,
                                              "top_k":0,
                                              "top_p":0
                                              }
                             )

freeflow_prompt = facts.client.asests.create_prompt(input_mode="freeflow",
                                                    name="Freeflow prompt sample",
                                                    task_id="summarization",
                                                    model_id="ibm/granite-13b-chat-v2", 
                                                    description="My First Freeflow prompt",
                                                    prompt_details=prompt_template,
                                                    )
```

--------------------------------

### AIGovFactsClient Initialization

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initialize the AI Governance Facts Client with various configuration options for different deployment scenarios including IBM Cloud, Cloud Pak for Data, regional deployments, external models, and push framework support.

```APIDOC
## INITIALIZE AIGovFactsClient

### Description
Initializes the AI Governance Facts Client for tracking and governing AI models. Supports IBM Cloud, Cloud Pak for Data deployments, with options for external models, regional endpoints, and various logging configurations.

### Method
CONSTRUCTOR

### Endpoint
Python Class: `ibm_aigov_facts_client.AIGovFactsClient`

### Parameters
#### Request Body
- **experiment_name** (string) - Required - Name of the Experiment.
- **container_type** (string) - Optional/Required - Name of the container where model would be saved. Supported: "SPACE" or "PROJECT". Required when using Watson Machine Learning.
- **container_id** (string) - Optional/Required - Container ID specific to container type. Required when using Watson Machine Learning.
- **api_key** (string) - Optional - IBM Cloud API Key. Either this or cloud_pak_for_data_configs should be provided.
- **bearer_token** (string) - Optional - Bearer token for authentication.
- **set_as_current_experiment** (boolean) - Optional - If True, existing experiment will be used instead of creating new. Default: False.
- **enable_autolog** (boolean) - Optional - If False, manual log option will be available. Default: True.
- **external_model** (boolean) - Optional - If True, external models tracing would be enabled. Default: False.
- **cloud_pak_for_data_configs** (CloudPakforDataConfig) - Optional - Cloud Pak for Data cluster details.
- **disable_tracing** (boolean) - Optional - If True, tracing and logging utilities will be disabled. Default: False.
- **enable_push_framework** (boolean) - Optional - If True, push framework support will be enabled. Default: False.
- **region** (string) - Optional - Region where watsonx.governance is hosted. Default: "us-south". Options: "frankfurt", "sydney", etc.

### Request Example
#### Basic Cloud Setup
```python
from ibm_aigov_facts_client import AIGovFactsClient
client = AIGovFactsClient(
    api_key="your_api_key",
    experiment_name="test",
    container_type="space",
    container_id="your_space_id"
)
```

#### Regional Setup (Sydney/Frankfurt)
```python
client = AIGovFactsClient(
    api_key="your_api_key",
    experiment_name="test",
    container_type="space",
    container_id="your_space_id",
    region="sydney"  # or "frankfurt"
)
```
```

--------------------------------

### Initialize AI Factsheet Client (Specific Regions)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

Initializes the AI Factsheet client for specific cloud regions like Sydney or Frankfurt. Includes the standard parameters plus a region specification.

```python
client = AIGovFactsClient(api_key=<API_KEY>, experiment_name="test",container_type="space or project",container_id=<space_id or project_id>,region="sydney" or "frankfurt")
```

--------------------------------

### Get All Runs and Experiment Management

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Experiment

Demonstrates getting all runs available in notebook experiment and how to get notebook experiment by name. Useful for experiment management and listing all experiment runs. Requires a saved model and CPD version >=4.6.4.

```python
>>> exp.get_all_runs()
>>> exp=model.get_experiment() # returns experiment
>>> exp=model.get_experiment(experiment_name="test") # In case notebook experiment not available, you can initiate a new one and run to add details.
```

--------------------------------

### PromptTemplate Class Definition

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/Main/helper

Defines the structure for a PromptTemplate, used for managing prompts with various configurations like model versions, variables, instructions, prefixes, examples, and model parameters. It allows for detailed customization of prompt elements.

```python
class _PromptTemplate(_model_version : dict = None_, _prompt_variables : dict = None_, _prompt_instruction : str = None_, _input_prefix : str = None_, _output_prefix : str = None_, _structured_examples : dict = None_, _input : str = None_, _model_parameters : dict = None_) :
    """Details of a PromptTemplate"""
    pass
```

--------------------------------

### Get Experiment Information and Details

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Experiment

Demonstrates how to retrieve experiment information and get experiment details in IBM AI Factsheet notebook experiments. Requires a saved model and CPD version >=4.6.4.

```python
>>> exp=model.get_experiment()
>>> exp.get_info()
```

--------------------------------

### Initialize watsonx.governance Factsheet Client with IAM Enabled

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This snippet shows how to initialize the AIGovFactsClient when the Watsonx Governance platform has IAM enabled, using username, password, and the cluster bedrock URL.

```python
from ibm_aigov_facts_client import AIGovFactsClient,CloudPakforDataConfig
cpd_creds=CloudPakforDataConfig(service_url="<hosturl>",username="<username>",password="<password>",bedrock_url="<cluster bedrock url>")
client = AIGovFactsClient(experiment_name="<name of experiment>",container_type="<space or project>",container_id="<space_id or project_id>",cloud_pak_for_data_configs=cpd_creds )
```

--------------------------------

### Get default inventory details with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Retrieves details for the default inventory along with the associated username. This method helps in understanding the default configuration and user context.

```python
client.assets.get_default_inventory_details()
```

--------------------------------

### Initialize watsonx.governance Factsheet Client with Username/Password

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This snippet shows how to initialize the AIGovFactsClient using CloudPakforDataConfig with service URL, username, and password. It's used for setting up the client for on-premises watsonx.governance.

```python
from ibm_aigov_facts_client import AIGovFactsClient,CloudPakforDataConfig
cpd_creds=CloudPakforDataConfig(service_url="<hosturl>",username="<username>",password="<password>")
client = AIGovFactsClient(experiment_name="<name of experiment>",container_type="<space or project>",container_id="<space_id or project_id>",cloud_pak_for_data_configs=cpd_creds)
```

--------------------------------

### Create Structured Prompt with BYOM (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/tuned_model

Shows how to create a structured prompt template using a 'BYOM' (Bring Your Own Model). This involves similar parameters to the 'tuned_model' example, but specifies `model_type` as 'byom'.

```python
prompt_template = PromptTemplate(
    model_version={"number": "2.0.0-rc.7", "tag": "tag", "description": "Description of the model version"},
    input="Input text to be given",
    prompt_variables={"text": "value"},
    prompt_instruction="Your prompt instruction",
    input_prefix="Your input prefix",
    output_prefix="Your output prefix",
    examples={
        "What is the capital of France{text}?": "The capital of France is Paris{text}.",
        "Who wrote '1984{text}'?": "George Orwell wrote '1984'{text}."
    },
    model_parameters={
        "decoding_method": "greedy",
        "max_new_tokens": 2034,
        "min_new_tokens": 0,
        "random_seed": 0,
        "top_k": 0,
        "top_p": 0
    }
)
structured_prompt = facts.client.assets.create_prompt(
    input_mode="structured",
    name="Structured prompt sample",
    task_id="summarization",
    model_type="byom",
    deployment_id="**********",
    description="My first structured prompt",
    prompt_details=prompt_template
)

```

--------------------------------

### Get All Facts - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Custom%20Facts

Retrieves all facts associated with a specific asset.

```python
model.get_all_facts()
```

--------------------------------

### Initialize watsonx.governance Factsheet Client with Tracing Disabled

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This snippet illustrates how to initialize the AIGovFactsClient with tracing disabled, allowing the use of other features like custom facts and facts definitions. It also shows initialization for external models.

```python
client = AIGovFactsClient(container_type=<project or space>,container_id=<space or project id>,cloud_pak_for_data_configs=creds,disable_tracing=True)
client = AIGovFactsClient(external_model=True,cloud_pak_for_data_configs=creds,disable_tracing=True)
```

--------------------------------

### Get model version details with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Retrieves version details of a model. This method is supported for CPD version >=4.7.0 and is useful for version control and compatibility checks.

```python
model.get_version()
```

--------------------------------

### Get model object details with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Retrieves detailed information about a model object. When verbose is set to True, additional model details are returned. This method is useful for debugging and monitoring model metadata.

```python
model.get_info()
model.get_info(verbose=True)
```

--------------------------------

### Save Azure ML Service External Model Asset

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/External

Provides an example of how to use the `save_external_model_asset` method to log an Azure ML Service model. It includes setting up schemas, training data references, and deployment details.

```python
from ibm_aigov_facts_client.supporting_classes.factsheet_utils import DeploymentDetails,TrainingDataReference,ExternalModelSchemas

external_schemas=ExternalModelSchemas(input=input_schema,output=output_schema)
trainingdataref=TrainingDataReference(schema=training_ref)
deployment=DeploymentDetails(identifier=<service_url in Azure>,name="deploymentname",deployment_type="online",scoring_endpoint="test/score")

client.external_model_facts.save_external_model_asset(model_identifier=<service_id in Azure>
                                                            ,name=<model_name>
                                                            ,model_details=<model_stub_details>
                                                            ,deployment_details=deployment
                                                            ,schemas=external_schemas
                                                            ,training_data_reference=tdataref)
```

```python
from ibm_aigov_facts_client.supporting_classes.factsheet_utils import DeploymentDetails,TrainingDataReference,ExternalModelSchemas

external_schemas=ExternalModelSchemas(input=input_schema,output=output_schema)
trainingdataref=TrainingDataReference(schema=training_ref)
deployment=DeploymentDetails(identifier=<service_url in Azure>,name="deploymentname",deployment_type="online",scoring_endpoint="test/score")

client.external_model_facts.save_external_model_asset(model_identifier=<service_id in Azure>
                                                            ,name=<model_name>
                                                            ,model_details=<model_stub_details>
                                                            ,deployment_details=deployment
                                                            ,schemas=external_schemas
                                                            ,training_data_reference=tdataref,
                                                            ,catalog_id=<catalog_id>) # Different catalog_id other than Platform Asset Catalog (PAC)
```

--------------------------------

### Get All Model Usecases in Catalog (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Retrieves all model use case assets within a specified catalog. If no catalog ID is provided, it returns all model use cases accessible to the user. This method is deprecated and `client.assets.get_ai_usecases()` should be used instead.

```python
client.assets.get_model_usecases(catalog_id=<catalog_id>)
client.assets.get_model_usecases()
```

--------------------------------

### Create Minimal Detached Prompt in Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/detached_prompt

Creates a detached prompt with essential parameters. Requires facts.client.assets.create_detached_prompt and DetachedPromptTemplate. Takes name, model_id, task_id, and detached_information as inputs, returning an AIGovAssetUtilities object. Limited to basic setup without optional prompt details.

```python
detached_info = DetachedPromptTemplate(prompt_id="n/a",
                                       model_id="arn:aws:bedrock:us-east1:123456789012:provisioned-model/anthropic.claude-v2",
                                       model_provider="AWS Bedrock",
                                       model_name="Anthropic Claude 2.0",
                                       model_url="https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-claude.html",
                                       prompt_url="n/a",
                                       prompt_additional_info={"AWS Region": "us-east1"}
                                       )

external_prompt = facts.client.assets.create_detached_prompt(name="External prompt sample (model AWS Bedrock Anthropic)",
                                                         task_id="summarization",
                                                         model_id="anthropic.claude-v2", 
                                                         description="My First External Prompt",
                                                         detached_information=detached_info)
```

--------------------------------

### Get Model Usecases

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Retrieves all model use case assets for a specified catalog, or all model use cases across all accessible catalogs if no catalog ID is provided. This method is deprecated and `client.assets.get_ai_usecases()` should be used instead.

```APIDOC
## GET /api/assets/get_model_usecases

### Description
Retrieves all model use case assets for a specified catalog, or all model use cases across all accessible catalogs if no catalog ID is provided. Deprecated.

### Method
GET

### Endpoint
/api/assets/get_model_usecases

### Parameters
#### Query Parameters
- **catalog_id** (string) - Optional - Catalog ID where model use cases are registered. If not provided, defaults to showing all model use cases in all catalogs to which the user has access.

### Request Example
```json
{
  "catalog_id": "<catalog_id>"
}
```

### Response
#### Success Response (200)
- **list** (array) - A list of ModelUsecaseUtilities objects, each representing a model use case asset.

#### Response Example
```json
[
  {
    "id": "usecase1",
    "name": "Usecase One",
    "description": "Description for usecase one"
  },
  {
    "id": "usecase2",
    "name": "Usecase Two",
    "description": "Description for usecase two"
  }
]
```
```

--------------------------------

### Get watsonx.governance Access Token

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/FactsClientAdapter

This method retrieves the IAM access token. It is a core function for authenticating with the watsonx.governance service.

```python
client.get_access_token()
```

--------------------------------

### Get Run Objects in Notebook Experiment

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Experiment

Shows how to get run objects available in notebook experiment, including getting the latest run or a specific run by ID. Requires a saved model and CPD version >=4.6.4.

```python
>>> run=exp.get_run() # returns latest run object available
>>> run=exp.get_run(run_id=run_id) # you can get specific run object
```

--------------------------------

### Create WKC Model Usecase Approach

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Approach

Creates a WKC Model usecase approach with optional name, description, icon, and color. Supported for CPD version 4.7.0 and above. Returns an ApproachUtilities object.

```python
ai_usecase.create_approach(name=<approach name>,description=<approach description>,icon=<approach icon>,color=<approach color>)
ai_usecase.create_approach(name=<approach name>)
```

--------------------------------

### Get model name with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Returns the name of the model. This is useful for identification and logging purposes in applications that manage multiple models.

```python
model.get_name()
```

--------------------------------

### Get model ID with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Returns the unique identifier of the model. This ID is crucial for model tracking, referencing, and management in automated systems.

```python
model.get_id()
```

--------------------------------

### Retrieve Approach Description

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Approach

Retrieves the description of an approach. Returns the approach description as a string.

```python
ai_usecase.get_approach.get_description()
```

--------------------------------

### Get model container type with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Returns the type of container used for the model deployment. This information is valuable for understanding deployment architecture and compatibility.

```python
model.get_container_type()
```

--------------------------------

### Create Structured Prompt with Tuned Model (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/tuned_model

Demonstrates creating a structured prompt template using a 'tuned_model'. It requires specifying `input_mode`, `name`, `task_id`, `model_type`, `deployment_id`, `description`, and `prompt_details`.

```python
prompt_template = PromptTemplate(
    model_version={"number": "2.0.0-rc.7", "tag": "tag", "description": "Description of the model version"},
    input="Input text to be given",
    prompt_variables={"text": "value"},
    prompt_instruction="Your prompt instruction",
    input_prefix="Your input prefix",
    output_prefix="Your output prefix",
    examples={
        "What is the capital of France{text}?": "The capital of France is Paris{text}.",
        "Who wrote '1984{text}'?": "George Orwell wrote '1984'{text}."
    },
    model_parameters={
        "decoding_method": "greedy",
        "max_new_tokens": 2034,
        "min_new_tokens": 0,
        "random_seed": 0,
        "top_k": 0,
        "top_p": 0
    }
)
structured_prompt = facts.client.assets.create_prompt(
    input_mode="structured",
    name="Structured prompt sample",
    task_id="summarization",
    model_type="tuned_model",
    deployment_id="**********",
    description="My first structured prompt",
    prompt_details=prompt_template
)

```

--------------------------------

### Retrieve Approach Versions

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Approach

Retrieves a list of versions for a WKC Model usecase approach. Supported for CPD version 4.7.0 and above. Returns a list.

```python
ai_usecase.get_approach.get_versions()
```

--------------------------------

### Get model container ID with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Returns the container ID associated with the model. This helps in identifying the deployment context and resource allocation for the model.

```python
model.get_container_id()
```

--------------------------------

### Get Custom Fact by ID - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Custom%20Facts

Retrieves the value(s) of a specific custom fact identified by its unique ID.

```python
model.get_custom_fact_by_id(fact_id="fact_id")
```

--------------------------------

### Get Custom Facts - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Custom%20Facts

Retrieves all custom facts defined for the 'modelfacts_user' fact type for a given asset.

```python
model.get_custom_facts()
```

--------------------------------

### Create AI Usecase Asset

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Creates a new AI usecase asset in the specified catalog. Requires catalog_id and name parameters. Optional parameters include description, status, risk, and tags. Returns an AIUsecaseUtilities object representing the created asset.

```python
client.assets.create_ai_usecase(catalog_id=<catalog_id>,name=<AI usecase name>,description=<AI usecase description>)
```

--------------------------------

### Get Supported Framework Names

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/Main/supportedframework

Retrieves only the names of the supported frameworks from the AI Factsheet client. This method returns a list or set of framework names.

```python
>>> client.FrameworkSupportNames.get()

```

--------------------------------

### get_info(_self_ , _verbose =False_)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/prompt_methods

Retrieves all available details of the fetched prompt as a dictionary. Optionally includes all details if verbose is True.

```APIDOC
## get_info

### Description
Retrieves all available details of the fetched prompt as a dictionary.

### Method
GET

### Endpoint
/prompts/get_info

### Parameters
#### Path Parameters
- **_self_** (object) - Required - The prompt object.

#### Query Parameters
- **_verbose** (boolean) - Optional - If True, include all details. Default is False.

#### Request Body
None

### Request Example
{
  "_self_": "prompt_object",
  "_verbose": false
}

### Response
#### Success Response (200)
- **dict** (dictionary) - A dictionary containing the prompt details.

#### Response Example
{
  "prompt_details": {
    "field1": "value1"
  }
}
```

--------------------------------

### Retrieve All WKC Model Usecase Approaches

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Approach

Retrieves a list of all WKC Model usecase approaches. Supported for CPD version 4.7.0 and above. Returns a list of ApproachUtilities objects.

```python
ai_useusecase.get_approaches()
```

--------------------------------

### Initialize ManualLog class

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/manual%20log

Initializes the ManualLog class with optional experiment name, setting as current experiment, and root directory.

```python
class ManualLog(experiment_name=None, set_as_current_exp=None, root_dir=None)
```

--------------------------------

### Update Approach Description

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Approach

Updates the description of a WKC Model usecase approach. Supported for CPD version 4.7.0 and above. Requires the new description as a string. Returns an updated ApproachUtilities object.

```python
ai_usecase.get_approach.set_description(description=<new approach description>)
```

--------------------------------

### Get Platform Asset Catalog ID with Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Miscellaneous%20Methods

Retrieves the Platform Asset Catalog (PAC) ID from the client assets. This ID is essential for asset tracking and management within the platform.

```python
client.assets.get_PAC_id()
```

--------------------------------

### Create Freeflow Prompt with Tuned Model (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/tuned_model

Illustrates the creation of a freeflow prompt template using a 'tuned_model'. Key parameters include `input_mode`, `name`, `task_id`, and `prompt_details` containing essential prompt information.

```python
prompt_template = PromptTemplate(
    input="Input text to be given",
    prompt_variables={"text": "value"},
    model_parameters={
        "decoding_method": "greedy",
        "max_new_tokens": 2034,
        "min_new_tokens": 0,
        "random_seed": 0,
        "top_k": 0,
        "top_p": 0
    }
)
freeflow_prompt = facts.client.assets.create_prompt(
    input_mode="freeflow",
    name="Freeflow prompt sample",
    task_id="summarization",

```

--------------------------------

### Get External Model Deployments - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Deployments

Retrieves all deployment details for an external model. This function is supported for CPD version 4.7.0 and above. It returns a list of Deployment objects.

```python
model.get_deployments()
```

--------------------------------

### Get Environment Type

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/Model%20Env

Retrieves the current environment details for a related model asset. This is useful for understanding the current state and configuration of your model.

```APIDOC
## GET /models/{model_id}/environment

### Description
Retrieves the current environment details for a related model asset.

### Method
GET

### Endpoint
`/models/{model_id}/environment`

### Parameters
#### Path Parameters
- **model_id** (string) - Required - The unique identifier of the model.

### Request Example
(No request body for GET request)

### Response
#### Success Response (200)
- **environment_type** (string) - The type of the current environment (e.g., "TEST", "DEVELOP", "DEPLOY").
- **container** (string) - The current container the model is in.

#### Response Example
```json
{
  "environment_type": "TEST",
  "container": "development"
}
```
```

--------------------------------

### Create a freeflow prompt template with BYOM in Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/tuned_model

This snippet shows how to define a PromptTemplate with input text, variables, and model parameters, then create a freeflow prompt using the facts client. It requires the PromptTemplate class and a configured facts client. The deployment ID should be replaced with a valid identifier.

```python
prompt_template = PromptTemplate(
    input=\"Input text to be given\",
    prompt_variables={\"text\": \"value\"},
    model_parameters={
        \"decoding_method\": \"greedy\",
        \"max_new_tokens\": 2034,
        \"min_new_tokens\": 0,
        \"random_seed\": 0,
        \"top_k\": 0,
        \"top_p\": 0
    }
)
freeflow_prompt = facts.client.assets.create_prompt(
    input_mode=\"freeflow\",
    name=\"Freeflow prompt sample\",
    task_id=\"summarization\",
    model_type=\"byom\",
    deployment_id=\"**********\",
    description=\"My first freeflow prompt\",
    prompt_details=prompt_template
)

```

--------------------------------

### Get Model Usecase

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Retrieves a specific model use case asset by its ID and catalog ID. This method is deprecated and `client.assets.get_ai_usecase()` should be used instead.

```APIDOC
## GET /api/assets/get_model_usecase

### Description
Retrieves a specific model use case asset by its ID and catalog ID. Deprecated.

### Method
GET

### Endpoint
/api/assets/get_model_usecase

### Parameters
#### Query Parameters
- **model_usecase_id** (string) - Required - Id of the model usecase.
- **catalog_id** (string) - Optional - Id of the catalog where model usecase is saved.

### Request Example
```json
{
  "model_usecase_id": "<model usecase id>",
  "catalog_id": "<catalog id>"
}
```

### Response
#### Success Response (200)
- **ModelUsecaseUtilities** (object) - Model usecase asset details

#### Response Example
```json
{
  "id": "<model usecase id>",
  "name": "Sample Usecase",
  "description": "A sample use case for testing."
}
```
```

--------------------------------

### Get Custom Facts by Type - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Custom%20Facts

Retrieves custom facts based on the asset type. Defaults to 'modelfacts_user' but can be set to 'modelfacts_user_op' for Openpages facts.

```python
model.get_facts_by_type(facts_type=<type name>)
```

--------------------------------

### Initialize AIGovFactsClient with Cloud Pak for Data

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/External

Shows how to initialize the AIGovFactsClient when integrating with Cloud Pak for Data. This requires providing configuration details like service URL, username, and password.

```python
from ibm_aigov_facts_client import AIGovFactsClient
from ibm_aigov_facts_client.config import CloudPakforDataConfig

creds=CloudPakforDataConfig(service_url="<HOST URL>",
                            username="<username>",
                            password="<password>")

client = AIGovFactsClient(experiment_name=<experiment_name>,external_model=True,cloud_pak_for_data_configs=creds)
```

--------------------------------

### Initialize AIGovFactsClient for External Models

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/External

Demonstrates how to initialize the AIGovFactsClient for external models with different tracing and autologging configurations. This is crucial for setting up the client to interact with external model services.

```python
from ibm_aigov_facts_client import AIGovFactsClient
client= AIGovFactsClient(api_key=API_KEY,experiment_name="external",enable_autolog=False,external_model=True)
```

```python
from ibm_aigov_facts_client import AIGovFactsClient
client= AIGovFactsClient(api_key=API_KEY,experiment_name="external",external_model=True)
```

```python
from ibm_aigov_facts_client import AIGovFactsClient
client= AIGovFactsClient(api_key=API_KEY,external_model=True,disable_tracing=True)
```

--------------------------------

### Show Supported Framework Names

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/Main/supportedframework

Displays all framework names supported by the AI Factsheet client. This is a simple call to the client's FrameworkSupportNames object.

```python
>>> client.FrameworkSupportNames.show()

```

--------------------------------

### List all associated workspaces for AI use case

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Associate%20Workspaces

Displays all associated workspaces for a given AI use case in tabular format. Shows associated workspaces for all phases. No parameters required. Requires client with proper authentication and access to the AI use case.

```python
client.assets.ai_usecase.list_all_associated_workspaces()
```

--------------------------------

### Search AI Usecase Assets by Name

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Searches for AI usecase assets by name with optional exact matching and inventory filtering. Returns a list of AIUsecaseUtilities objects that match the search criteria. Supports both exact and partial name matching.

```python
# Search for AI use cases with names that contain 'sample'
usecases = client.assets.get_ai_usecases_by_name(name='sample')
```

--------------------------------

### Save AWS Sagemaker External Model Asset

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/External

Illustrates how to log an AWS Sagemaker model using the `save_external_model_asset` method. This example focuses on specifying the necessary deployment details for Sagemaker.

```python
from ibm_aigov_facts_client.supporting_classes.factsheet_utils import DeploymentDetails,TrainingDataReference,ExternalModelSchemas

external_schemas=ExternalModelSchemas(input=input_schema,output=output_schema)
trainingdataref=TrainingDataReference(schema=training_ref)
deployment=DeploymentDetails(identifier=<endpoint_name in Sagemaker>,name="deploymentname",deployment_type="online",scoring_endpoint="test/score")

client.external_model_facts.save_external_model_asset(model_identifier=<model_name in Sagemaker>
```

--------------------------------

### Get All Facts in Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Custom%20Facts

Retrieves all facts related to the asset. Returns a dictionary of all facts. Requires model_usecase object, provides comprehensive fact data output.

```python
>>> model_usecase.get_all_facts()
```

--------------------------------

### Search AI Use Cases by Name (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Searches for AI use cases by their name with an exact match. This function is part of the assets client and requires the client object to be initialized.

```python
usecases = client.assets.get_ai_usecases_by_name(name='sample', exact_match=True)
```

--------------------------------

### Set Tags for a Run with AI Factsheet Client

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Basic%20Utilities

Logs multiple tags for a given run ID using the AI Factsheet client. Accepts a dictionary of tag names to values. Returns None.

```python
>>> client.runs.set_tags(run_id, {"engineering": "ML Platform",
"release.candidate": "RC1"})
```

--------------------------------

### Set multiple tags with ManualLog

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/manual%20log

Sets multiple tags for the active run. Accepts a dictionary of tag names and values.

```python
client.manual_log.set_tags({"engineering": "ML Platform", "release.candidate": "RC1"})
```

--------------------------------

### Log multiple parameters with ManualLog

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/manual%20log

Logs multiple parameters against the active run. Accepts a dictionary of parameter names and values.

```python
client.manual_log.log_params({"n_estimators": 3, "random_state": 42})
```

--------------------------------

### Get Model Usecase by ID (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Retrieves a specific model use case asset by its ID and optionally by catalog ID. This method is deprecated and `client.assets.get_ai_usecase()` should be used instead.

```python
client.assets.get_model_usecase(model_usecase_id=<model usecase id>, catalog_id=<catalog id>)
```

--------------------------------

### Get Facts Definitions - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Model%20Custom%20Facts

Retrieves all fact definitions for a specified asset fact type within a given container. It allows for optional filtering by container type and ID.

```python
client.assets.get_facts_definitions(type_name=<fact type>) # uses container type and id used initializing facts client
client.assets.get_facts_definitions(type_name=<fact type>,container_type=<container type>,container_id=<container id>)
```

--------------------------------

### List all inventories with optional filtering

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Inventory

Retrieves a list of all inventories associated with the account. Optionally filters results by name with support for exact or partial matching. Returns a list of AIGovInventoryUtilities instances.

```python
>>> inventories = client.assets.list_inventories()  # Retrieve all inventories
>>> inventories = client.assets.list_inventories(name="sample")  # Retrieve inventories matching "sample"
>>> inventories = client.assets.list_inventories(name="sample", exact_match=True)  # Retrieve inventories with an exact match for "sample"
```

--------------------------------

### Get Custom Fact by ID in Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Custom%20Facts

Retrieves custom fact value or values by ID. Returns the fact value. Requires model_usecase and fact_id string, outputs the corresponding value.

```python
>>> model_usecase.get_custom_fact_by_id(fact_id="fact_id")
```

--------------------------------

### Create Model Usecase (Python)

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Creates a new model use case within a specified catalog. This method is deprecated and `client.assets.create_ai_usecase()` should be used instead. It requires catalog_id and name, with an optional description.

```python
client.assets.create_model_usecase(catalog_id=<catalog_id>,name=<model usecase name>,description=<model usecase description>)
```

--------------------------------

### Get Current Run ID with AI Factsheet Client

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Basic%20Utilities

Retrieves the ID of the current active run using the AI Factsheet client. Returns a string representing the run ID.

```python
>>> client.runs.get_current_run_id()
```

--------------------------------

### Get Current Experiment ID with AI Factsheet Client

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Basic%20Utilities

Retrieves the ID of the current active experiment using the AI Factsheet client. Returns a string representing the experiment ID.

```python
>>> client.experiments.get_current_experiment_id()
```

--------------------------------

### Log Multiple Parameters with AI Factsheet Client

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/Basic%20Utilities

Logs multiple parameters for a given run ID using the AI Factsheet client. Accepts a dictionary of parameter names to values. Returns None.

```python
>>> client.runs.log_params(run_id, {"n_estimators": 3, "random_state": 42})
```

--------------------------------

### Log multiple metrics with ManualLog

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/manual%20log

Logs multiple metrics against the active run. Accepts a dictionary of metric names and values, and optional step.

```python
client.manual_log.log_metrics({"mse": 2000.00, "rmse": 50.00})
```

--------------------------------

### Get All Custom Facts in Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Model%20Usecase%20Custom%20Facts

Retrieves all defined custom facts for model_entry_user fact type. Returns a dictionary of facts. Requires model_usecase instance, outputs dict of fact IDs and values.

```python
>>> model_usecase.get_custom_facts()
```

--------------------------------

### Update Prompt Description - Python

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model/prompt_methods

Updates the description metadata for a prompt instance. Accepts a string parameter containing the new descriptive text. Modifies the prompt's documentation attribute.

```python
prompt.update_prompt_description(prompt_description="description")
```

--------------------------------

### Log single parameter with ManualLog

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/AIGov/manual%20log

Logs a single parameter against the active run. Accepts parameter name and value.

```python
client.manual_log.log_param("c", 1)
```

--------------------------------

### DetachedPromptTemplate Class Definition

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/Main/helper

Defines the structure for a DetachedPromptTemplate, used for referencing external prompts and models. It requires essential IDs and provider information, with optional fields for model name/URL and prompt URL/additional info, enforcing specific rules for providing external references.

```python
class _DetachedPromptTemplate(_prompt_id : str_, _model_id : str_, _model_provider : str_, _model_name : str = None_, _model_url : str = None_, _prompt_url : str = None_, _prompt_additional_info : list = None_) :
    """Details of a detached prompt template."""
    pass
```

--------------------------------

### Get AI Usecase Asset by ID

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Retrieves a specific AI usecase asset using its unique identifier. Requires ai_usecase_id and optionally accepts catalog_id. Returns an AIUsecaseUtilities object representing the requested asset.

```python
client.assets.get_ai_usecase(ai_usecase_id=<ai usecase id>, catalog_id=<catalog id>)
```

--------------------------------

### Create Model Usecase

Source: https://s3.us.cloud-object-storage.appdomain.cloud/aifactsheets-client/doc_files/asset_model/Model%20Usecase/Creation%20of%20Model%20Uscase

Creates a new model use case in a specified catalog. This method is deprecated and `client.assets.create_ai_usecase()` should be used instead.

```APIDOC
## POST /api/assets/create_model_usecase

### Description
Creates a new model use case in a specified catalog. Deprecated.

### Method
POST

### Endpoint
/api/assets/create_model_usecase

### Parameters
#### Query Parameters
- **catalog_id** (string) - Required - Catalog ID where this model usecase needs to create.
- **name** (string) - Required - Name of model usecase.
- **description** (string) - Optional - Model usecase description.

### Request Example
```json
{
  "catalog_id": "<catalog_id>",
  "name": "<model usecase name>",
  "description": "<model usecase description>"
}
```

### Response
#### Success Response (200)
- **ModelUsecaseUtilities** (object) - WKC Model usecase asset

#### Response Example
```json
{
  "message": "Model usecase created successfully"
}
```
```