### Install SDK with Support for All Models

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html

Install the SDK with support for all models (OpenAI, Amazon, Google) and LangChain. This is the recommended installation for full functionality.

```bash
pip install "sap-ai-sdk-gen[all]"
Copy code

```

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

### Configuration File Example (X.509 Certificate String)

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html

Example JSON structure for a configuration file using X.509 certificate strings for authentication.

```json
{
  "AICORE_AUTH_URL": "https://* * * .authentication.cert.sap.hana.ondemand.com",
  "AICORE_CLIENT_ID": "* * * ",
  "AICORE_CERT_STR": "* * *",
  "AICORE_KEY_STR": "* * *",
  "AICORE_RESOURCE_GROUP": "* * * ",
  "AICORE_BASE_URL": "https://api.ai.* * *.cfapps.sap.hana.ondemand.com/v2"
}
```

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

### setup

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

One-time setup function to create object store secrets and orchestration deployment URLs if they are not already provided.

```APIDOC
## setup

### Description
One time setup function which does object store secrets creation and orchestration deployment url creation if not provided.

### Parameters
* **input_secret_body** (dict | None) - Optional dictionary for input secret configuration.
* **default_secret_body** (dict | None) - Optional dictionary for default secret configuration.
* **replace_existing** (bool) - Flag to replace existing configurations, defaults to False.
```

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

### Install Default SDK (OpenAI Models)

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html

Install the default version of the SDK, which includes support for OpenAI models and LangChain. This is the minimal installation.

```bash
pip install sap-ai-sdk-gen
Copy code

```

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

### Configuration File Example (Client Secret)

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html

Example JSON structure for a configuration file using client ID and secret for authentication.

```json
{
  "AICORE_AUTH_URL": "https://* * * .authentication.sap.hana.ondemand.com/oauth/token",
  "AICORE_CLIENT_ID": "* * * ",
  "AICORE_CLIENT_SECRET": "* * * ",
  "AICORE_RESOURCE_GROUP": "* * * ",
  "AICORE_BASE_URL": "https://api.ai.* * *.cfapps.sap.hana.ondemand.com/v2"
}
```

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

### Configuration File Example (X.509 Certificate File Path)

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html

Example JSON structure for a configuration file using X.509 certificate file paths for authentication.

```json
{
  "AICORE_AUTH_URL": "https://* * * .authentication.cert.sap.hana.ondemand.com",
  "AICORE_CLIENT_ID": "* * * ",
  "AICORE_CERT_FILE_PATH": "* * */cert.pem",
  "AICORE_KEY_FILE_PATH": "* * */key.pem",
  "AICORE_RESOURCE_GROUP": "* * * ",
  "AICORE_BASE_URL": "https://api.ai.* * *.cfapps.sap.hana.ondemand.com/v2"
}
```

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

### Install SDK with Subset of Extra Libraries

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/README_sphynx.html

Install a subset of extra libraries, such as Google and Amazon model support, along with LangChain. Specify the desired libraries in square brackets.

```bash
pip install "sap-ai-sdk-gen[google, amazon]"
Copy code

```

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

### Async Google GenAI Native Example

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/async-examples.html

Shows how to use the Google GenAI native client asynchronously to generate content. This example uses the `gemini-2.5-flash` model.

```python
from gen_ai_hub.proxy.native.google_genai import Client
from gen_ai_hub.proxy import get_proxy_client

proxy_client = get_proxy_client('gen-ai-hub')
async with Client(proxy_client=proxy_client,).aio as aclient:
    response = await aclient.models.generate_content(
        model="gemini-2.5-flash",
        contents="Explain the relativity theory in simple terms."
    )
response
```

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

### Async Google GenAI Chat Example

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/async-examples.html

Illustrates an asynchronous chat interaction with Google GenAI using the `gemini-2.5-flash` model. This example demonstrates creating a chat session and sending multiple messages.

```python
from gen_ai_hub.proxy.native.google_genai import Client
from gen_ai_hub.proxy import get_proxy_client

proxy_client = get_proxy_client('gen-ai-hub')
async with Client(proxy_client=proxy_client).aio as aclient:
    chat_session = aclient.chats.create(
        model="gemini-2.5-flash"
    )
    model_response = await chat_session.send_message("Hello.")
    print("Response 1:", model_response.text)
    
    model_response = await chat_session.send_message(
        "What is your opinion about latest Gemini model?"
    )
    print("Response 2:", model_response.text)
```

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

### Vertex AI with API Key Authentication

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.langchain.html

Demonstrates how to use Vertex AI with API key authentication for simpler setup. This method is useful when you want to leverage Vertex AI without relying on service account JSON files.

```bash
export GEMINI_API_KEY='your-api-key'
export GOOGLE_GENAI_USE_VERTEXAI=true
export GOOGLE_CLOUD_PROJECT='your-project-id'
```

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

### OpenAI Completions Integration

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html

Example usage of the Completions API, equivalent to openai.Completions. Suitable for models supporting the legacy completion endpoint.

```python
from gen_ai_hub.proxy.native.openai import completions

response = completions.create(
    model_name="gpt-4o-mini",
    prompt="The Answer to the Ultimate Question of Life, the Universe, and Everything is",
    max_tokens=20,
    temperature=0
)
print(response)
```

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

### Get Help on EvaluationConfig

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/evaluations.html

Use the `help()` function to inspect the docstring of `EvaluationConfig` for detailed parameter information.

```python
from gen_ai_hub.evaluations import EvaluationConfig
print(help(EvaluationConfig))
```

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

### Initialize and use Langchain ChatOpenAI with proxy

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html

Initializes a Langchain ChatOpenAI model using the Generative AI Hub proxy client. This example demonstrates setting up a chat prompt with system, human, and AI messages, and invoking the chain.

```python
from langchain.prompts.chat import (
    AIMessagePromptTemplate,
    ChatPromptTemplate,
    HumanMessagePromptTemplate,
    SystemMessagePromptTemplate,
)

from gen_ai_hub.proxy.langchain import ChatOpenAI
from gen_ai_hub.proxy import get_proxy_client

proxy_client = get_proxy_client('gen-ai-hub')

chat_llm = ChatOpenAI(proxy_model_name='gpt-4o-mini', proxy_client=proxy_client)
template = 'You are a helpful assistant that translates english to pirate.'

system_message_prompt = SystemMessagePromptTemplate.from_template(template)

example_human = HumanMessagePromptTemplate.from_template('Hi')
example_ai = AIMessagePromptTemplate.from_template('Ahoy!')
human_template = '{text}'

human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages(
    [system_message_prompt, example_human, example_ai, human_message_prompt])

chain = chat_prompt | chat_llm

response = chain.invoke({'text': 'I love planking.'})
print(response.content)
```

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

### Configure Evaluation with TemplateRef by Scenario, Name, and Version

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Example of setting up an EvaluationConfig using a TemplateRef identified by scenario, name, and version. This allows for more specific template referencing.

```python
from gen_ai_hub.orchestration_v2.models.template_ref import TemplateRefByScenarioNameVersion
config = EvaluationConfig(
    dataset_config=Dataset("data/test.jsonl"),
    metrics=[MetricConfig(name="accuracy")],
    llm=LLM(name="gpt-4", version="latest", params={"temperature": 0.7}),
    template=TemplateRef(template_ref=TemplateRefByScenarioNameVersion(
        scenario="foundation-models", name="prompt1", version="1.0"
    )),
    test_row_count=100
)
```

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

### Initiate Evaluation Runs

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/evaluations.html

Starts one or more evaluation runs based on the provided configuration list. The result is a list of evaluation run objects.

```python
evaluation_runs = client.evaluate(evaluation_config_list)
```

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

### Create Orchestration Configuration

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/orchestration-service.html

Defines the configuration for an orchestration setup by integrating a template and a language model. Specifies how these components interact.

```python
from gen_ai_hub.orchestration.models.config import OrchestrationConfig

config = OrchestrationConfig(
    template=template,
    llm=llm,
)

```

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

### Stream Langchain Response with Amazon Nova-Premier

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/streaming.html

Demonstrates streaming a response from the 'amazon--nova-premier' model. This example showcases integration with Amazon's LLM offerings for streaming capabilities.

```python
stream_langchain("How do airplanes stay in the air?", model_name='amazon--nova-premier')
```

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

### Create EvaluationConfig with TemplateRef by Scenario, Name, and Version

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.models.html

Example of creating an EvaluationConfig using TemplateRef with scenario, name, and version. This configuration includes LLM parameters like temperature.

```python
>>> from gen_ai_hub.orchestration_v2.models.template_ref import TemplateRefByScenarioNameVersion
>>> config = EvaluationConfig(
...     dataset_config=Dataset("data/test.jsonl"),
...     metrics=[MetricConfig(name="accuracy")],
...     llm=LLM(name="gpt-4", version="latest", params={"temperature": 0.7}),
...     template=TemplateRef(template_ref=TemplateRefByScenarioNameVersion(
...         scenario="foundation-models", name="prompt1", version="1.0"
...     )),
...     test_row_count=100
... )
```

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

### Async Amazon Titan Embedding

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/async-examples.html

Demonstrates how to asynchronously invoke the Amazon Titan embedding model to get text embeddings. Ensure you have the necessary session and client setup.

```python
async def async_amazon_titan_embedding(model_name):
    session = AsyncSession()
    bedrock = await session.async_client(model_name=model_name)
    body = json.dumps(
        {
            "inputText": "Please recommend books with a theme similar to the movie 'Inception'.",
        }
    )
    response = await bedrock.invoke_model(body=body)
    response_body = json.loads(await response.get("body").read())
    print("Response Metadata:", response["ResponseMetadata"])
    print("Embedding:", response_body["embedding"])
    await bedrock.close()
```

```python
# Replace with the desired model name
await async_amazon_titan_embedding("amazon--titan-embed-text")
```

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

### Basic Usage: Generate Single Embedding

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/orchestration-service2.html

Generates a vector embedding for a single text string using minimal configuration. This is the simplest way to get started with text embeddings.

```APIDOC
## Basic Usage: Generate Single Embedding

### Description
Generate an embedding for a single text string with minimal configuration.

### Method
`service.embed`

### Parameters
#### Request Body
- **config** (EmbeddingsOrchestrationConfig) - Required - Configuration for the embeddings module.
- **input** (EmbeddingsInput) - Required - The input text to embed.
  - **text** (string) - Required - The text string to generate an embedding for.

### Request Example
```python
from gen_ai_hub.orchestration_v2 import (
    OrchestrationService,
    EmbeddingsOrchestrationConfig,
    EmbeddingsModuleConfigs,
    EmbeddingsModelConfig,
    EmbeddingsModelDetails,
    EmbeddingsInput
)

service = OrchestrationService()

# Minimal configuration - just specify the model
embeddings_config = EmbeddingsOrchestrationConfig(
    modules=EmbeddingsModuleConfigs(
        embeddings=EmbeddingsModelConfig(
            model=EmbeddingsModelDetails(name="text-embedding-3-large")
        )
    )
)

response = service.embed(
    config=embeddings_config,
    input=EmbeddingsInput(text="Hello World!")
)

embedding = response.final_result.data[0].embedding
print(f"Embedding dimensions: {len(embedding)}")
print(f"First 5 values: {embedding[:5]}")
```

### Response
#### Success Response
- **final_result.data** (list) - A list containing the embedding results.
  - **embedding** (list of float) - The generated vector embedding.
  - **index** (int) - The index of the input text in the batch.

#### Response Example
```json
{
  "final_result": {
    "data": [
      {
        "embedding": [0.00123, -0.00456, ...],
        "index": 0
      }
    ]
  }
}
```
```

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

### Sentiment Analysis Examples

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/orchestration-service2.html

Provides a list of example message pairs for sentiment analysis. Each pair consists of a UserMessage and an AssistantMessage representing a sentiment classification.

```python
sentiment_examples = [
    (UserMessage(content="I love this product!"), AssistantMessage(content="Positive")),
    (UserMessage(content="This is terrible service."), AssistantMessage(content="Negative")),
    (UserMessage(content="The weather is okay today."), AssistantMessage(content="Neutral")),
]
```

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

### _ChatBedrock Initialization

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.langchain.html

Drop-in replacement for LangChain ChatBedrock, extending AICoreBedrockBaseModel.

```APIDOC
## __init__ _ChatBedrock

### Description
Initializes the AICoreBedrockBaseModel with AICore specific parameters.
Extends the constructor of the base class with aicore specific parameters.

### Parameters

#### Keyword Arguments

* **model_id** (str, optional) - the model identifier, defaults to ""
* **deployment_id** (str, optional) - the deployment identifier, defaults to ""
* **model_name** (str, optional) - the model name, defaults to ""
* **config_id** (str, optional) - the configuration identifier, defaults to ""
* **config_name** (str, optional) - the configuration name, defaults to ""
* **proxy_client** (Optional[BaseProxyClient], optional) - the proxy client to use, defaults to None
```

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

### Dataset Initialization with String Path

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Create a Dataset instance using a string representing the file path.

```python
>>> Dataset("data/sample.json")
```

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

### Define Sentiment Analysis Examples

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/orchestration-service.html

Provides a list of example message pairs for sentiment analysis. Each pair consists of a UserMessage and its corresponding sentiment classification (Positive, Negative, or Neutral).

```python
sentiment_examples = [
    (UserMessage("I love this product!"), AssistantMessage("Positive")),
    (UserMessage("This is terrible service."), AssistantMessage("Negative")),
    (UserMessage("The weather is okay today."), AssistantMessage("Neutral")),
]
```

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

### BedrockLLM Initialization with Guardrails and Tracing

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.langchain.html

Shows how to enable tracing for guardrails by setting the 'trace' key to True and passing a callback handler. This is useful for monitoring guardrail interventions.

```APIDOC
## BedrockLLM Initialization with Guardrails and Tracing

### Description
Initializes the BedrockLLM class with guardrails and enables tracing for monitoring guardrail interventions. This involves setting the 'trace' key to True and providing a callback handler.

### Parameters

#### `model_id` (str) - Required
Id of the model to call, e.g., 'amazon.titan-text-express-v1'. For custom and provisioned models, an ARN value is expected.

#### `client` (Any) - Required
The Bedrock client object.

#### `model_kwargs` (Dict[str, Any]) - Optional
Keyword arguments to pass to the model.

#### `guardrails` (Optional[Mapping[str, str]]) - Optional
An optional dictionary to configure guardrails for Bedrock. This field consists of 'guardrailId' and 'guardrailVersion', which should be strings. To enable tracing, include `"trace": True`.

#### `callbacks` (List[BaseCallbackHandler]) - Optional
A list of callback handlers to use for tracing and logging.

### Request Example
```python
from langchain_aws import BedrockLLM
from langchain_core.callbacks import BedrockAsyncCallbackHandler

llm = BedrockLLM(
    model_id="<model_id>",
    client=<bedrock_client>,
    model_kwargs={},
    guardrails={
        "guardrailId": "<guardrail_id>",
        "guardrailVersion": "<guardrail_version>",
        "trace": True
    },
    callbacks=[BedrockAsyncCallbackHandler()]
)
```

### Further Information
For more information on callback handlers, refer to: https://python.langchain.com/docs/concepts/callbacks/
```

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

### Initialize Prompt Registry Client

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/prompt-registry.html

Initializes the client to interact with the Prompt Registry. Ensure the proxy client is correctly configured.

```python
from gen_ai_hub.proxy import get_proxy_client
from gen_ai_hub.prompt_registry import OrchestrationConfigClient
proxy_client = get_proxy_client(proxy_version="gen-ai-hub")
prompt_registry_client = OrchestrationConfigClient(proxy_client=proxy_client)
```

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

### RPTClient Classification Task Example

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html

Shows how to use RPTClient with a dictionary for a classification task. This example utilizes a dictionary format for the request body, including prediction configuration, columns, and data schema.

```python
example_request_by_columns_dict = {
  "prediction_config": {
    "target_columns": [
      {
        "name": "COSTCENTER",
        "prediction_placeholder": "[PREDICT]",
        "task_type": "classification"
      }
    ]
  },
  "columns": {
      "PRODUCT": ["Couch", "Office Chair", "Server Rack"],
      "PRICE": [999.99, 150.8, 2200.00],
      "ORDERDATE": ["28-11-2025", "02-11-2025", "01-11-2025"],
      "ID": ["35", "44", "104"],
      "COSTCENTER": ["[PREDICT]", "Office Furniture", "Data Infrastructure"]
  },
  "data_schema": {
      "PRODUCT": {
          "dtype": "string"
      },
      "PRICE": {
          "dtype": "numeric"
      },
      "ORDERDATE": {
          "dtype": "date"
      },
      "ID": {
          "dtype": "string"
      },
      "COSTCENTER": {
          "dtype": "string"
      }
  }
}

response = client.predict(body=example_request_by_columns_dict, model_name="sap-rpt-1-small")
print(response.predictions)
```

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

### _init_client

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.html

Initializes the client with provided data, preparing it for subsequent operations.

```APIDOC
## _init_client

### Description
Initializes the client with the provided data. This method is used internally to set up the client's state based on input data.

### Method
classmethod

### Parameters
* **data** (_Any_) - Required - Input data for client initialization.

### Returns
* Initialized data.
Return type: Any
```

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

### init_conf

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Initializes the configuration, optionally using a specified profile.

```APIDOC
## Function init_conf

### Description
Initializes the SDK configuration, optionally specifying a profile to load.

### Parameters
* **profile** (str) - The name of the profile to initialize the configuration with.
```

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

### RPTClient Regression Task Example

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html

Demonstrates using RPTClient with pydantic models for a regression task. It shows how to set up the request body with target columns and rows, and how to make a prediction. Includes an example with and without specifying the model version.

```python
from gen_ai_hub.proxy.native.sap import RPTRequest, PredictionConfig, TargetColumn, RPTClient

rows_regression = [
    {
        "PRODUCT": "Couch",
        "PRICE": 999.99,
        "ORDERDATE": "28-11-2025",
        "ID": "35",
        "DISCOUNT_RATE": "[PREDICT]",
    },
    {
        "PRODUCT": "Office Chair",
        "PRICE": 150.80,
        "ORDERDATE": "02-11-2025",
        "ID": "44",
        "DISCOUNT_RATE": 0.12,
    },
    {
        "PRODUCT": "Server Rack",
        "PRICE": 2200.00,
        "ORDERDATE": "01-11-2025",
        "ID": "104",
        "DISCOUNT_RATE": 0.05,
    },
    {
        "PRODUCT": "Standing Desk",
        "PRICE": 640.00,
        "ORDERDATE": "05-11-2025",
        "ID": "205",
        "DISCOUNT_RATE": 0.10,
    },
    {
        "PRODUCT": "Monitor 27 inch",
        "PRICE": 289.99,
        "ORDERDATE": "08-11-2025",
        "ID": "306",
        "DISCOUNT_RATE": "[PREDICT]",
    },
]
client = RPTClient()
body = RPTRequest(
            prediction_config=PredictionConfig(
                target_columns=[
                    TargetColumn(name="DISCOUNT_RATE", task_type="regression")
                ]),
            rows=rows_regression
        )
response = client.predict(body=body, model_name="sap-rpt-1-small")
print(response.predictions)

#example with model_version
response = client.predict(body=body, model_name="sap-rpt-1-small", model_version="latest")
print(response.predictions)
```

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

### list_prompt_variables

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.utils.html

Get all fields (parameters) of the form {{ ?param_name }} from the template.

```APIDOC
## list_prompt_variables

### Description
Get all fields (parameters) of the form {{ ?param_name }} from the template. Optionally return the raw field names without stripping spaces and '?'.

### Parameters
#### Path Parameters
- **format_string** (str) - Required - The template string to parse.

### Return type
List[str]
```

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

### init_conf Function

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Initializes configuration, potentially related to credentials.

```APIDOC
## Function: init_conf

### Description
Initializes configuration settings. The specific configuration details are not provided.

### Parameters
(No parameters are explicitly documented for this function in the source.)

### Returns
(The return type is not explicitly documented in the source.)
```

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

### Initialize OrchestrationService

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/orchestration-service.html

Instantiate the OrchestrationService with configuration details to interact with the service.

```python
from gen_ai_hub.orchestration.service import OrchestrationService

orchestration_service = OrchestrationService(config=config)
```

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

### _get_corresponding_model_id

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.langchain.html

Gets the corresponding model ID for a given model name.

```APIDOC
## _get_corresponding_model_id(_model_name_)

### Description
Gets the corresponding model ID for a given model name.

### Parameters

#### Path Parameters

* **model_name** (str) - the model name

### Raises

* **ValueError** - if the model name is not supported

### Returns

the corresponding model ID

### Return Type

str
```

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

### Get Data Repository by ID

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.document_grounding.html

Retrieves a single data repository by its unique identifier.

```APIDOC
## GET /retrieval/data_repositories/{repository_id}

### Description
Get a single data repository by its unique ID. Retrieves a single data repository by its unique identifier.

### Method
GET

### Endpoint
/retrieval/data_repositories/{repository_id}

### Parameters
#### Path Parameters
- **repository_id** (str) - Required - the unique identifier of the data repository

### Response
#### Success Response (200)
- **DataRepository model representing the data repository** (DataRepository) - DataRepository model representing the data repository
```

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

### ImageUrl.__init__

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.orchestration.models.html

Initializes an ImageUrl instance with a URL and an optional detail level.

```APIDOC
## ImageUrl.__init__

### Description
A data structure holding the URL and detail level for an image.

### Method
`__init__(_url_ , _detail =None_)`

### Parameters
#### Path Parameters
- **url** (str) - Required - The location of the image, as a standard or data URL.
- **detail** (ImageDetailLevel | None) - Optional - The processing detail level for the image.
```

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

### get_current_deployment

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.native.amazon.html

Gets the current deployment from the context. This is useful for retrieving the active deployment configuration.

```APIDOC
## get_current_deployment

### Description
Gets the current deployment from the context.

### Returns
- **Any** - The current deployment.
```

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

### Initialize Prompt Registry Client

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/prompt-registry.html

Initializes the client to interact with the Prompt Registry API. Ensure you have provided the necessary credentials beforehand.

```python
from gen_ai_hub.proxy import get_proxy_client
from gen_ai_hub.prompt_registry import PromptTemplateClient
proxy_client = get_proxy_client(proxy_version="gen-ai-hub")
prompt_registry_client = PromptTemplateClient(proxy_client=proxy_client)
```

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

### Instantiate Orchestration Service

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/orchestration-service2.html

Sets up the necessary configurations and instantiates the OrchestrationService for use. This includes defining system and user messages, model details, and module configurations.

```python
from gen_ai_hub.orchestration_v2 import (
    SystemMessage,
    UserMessage,
    Template,
    PromptTemplatingModuleConfig,
    LLMModelDetails,
    OrchestrationConfig,
    ModuleConfig
)

from IPython.display import display, Markdown # just for pretty print in jupyter

template = Template(
    template=[
        SystemMessage(content="This is a system message."),
        UserMessage(content="Write a markdown cheatsheet!"),
    ]
    )

llm=LLMModelDetails(name="gemini-2.0-flash")

prompt_template = PromptTemplatingModuleConfig(prompt=template,
                                               model=llm)

module_config = ModuleConfig(prompt_templating=prompt_template)

config = OrchestrationConfig(modules=module_config)

# Instantiate the orchestration service.
from gen_ai_hub.orchestration_v2 import OrchestrationService
orchestration_service = OrchestrationService(config=config)
```

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

### PipelineAPIClient.trigger_pipeline

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.document_grounding.clients.html

Trigger the execution of a pipeline. This method starts a new run for a specified pipeline.

```APIDOC
## PipelineAPIClient.trigger_pipeline

### Description
Trigger the execution of a pipeline.

### Parameters
#### Path Parameters
- **pipeline_id** (str) - Required - The ID of the pipeline to trigger.
#### Request Body
- **request** (TriggerPipelineRequest) - Optional - The object containing parameters for triggering the pipeline.

### Response
#### Success Response (200)
- **PipelineExecutionResponse** - A PipelineExecutionResponse object containing details of the triggered execution.
```

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

### Chat Completions with Model Version

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html

Example of using Chat Completions, specifying both model_name and model_version.

```python
#example where model_name is passed with model_version parameter

from gen_ai_hub.proxy.native.openai import chat

messages = [{"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
            {"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."}, 
            {"role": "user", "content": "Do other Azure Cognitive Services support this too?"}]

response = chat.completions.create(model_name='gpt-4o-mini', model_version="latest", messages=messages)
print(response)
```

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

### Dataset Initialization with Path Object

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Create a Dataset instance using a Path object for the data source.

```python
>>> Dataset(Path("data/sample.json"))
```

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

### Initialize EvaluationClient with Environment Variables

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/evaluations.html

Loads credentials from an .env file and initializes the EvaluationClient using environment variables. This client is used for interacting with generative AI services for evaluation purposes. Ensure all necessary environment variables are set.

```python
# Loading the credentials from the env file
from gen_ai_hub.evaluations import EvaluationClient
from dotenv import load_dotenv
import os

load_dotenv(override=True)

AICORE_BASE_URL = os.getenv("AICORE_BASE_URL")
AICORE_RESOURCE_GROUP = os.getenv("AICORE_RESOURCE_GROUP")
AICORE_AUTH_URL = os.getenv("AICORE_AUTH_URL")
AICORE_CLIENT_ID = os.getenv("AICORE_CLIENT_ID")
AICORE_CLIENT_SECRET = os.getenv("AICORE_CLIENT_SECRET")

AWS_ACCESS_KEY_ID = os.getenv("AWS_ACCESS_KEY_ID")
AWS_BUCKET_ID = os.getenv("AWS_BUCKET_ID")
AWS_REGION = os.getenv("AWS_REGION")
AWS_SECRET_ACCESS_KEY = os.getenv("AWS_SECRET_ACCESS_KEY")
ORCHESTRATION_URL = os.getenv("ORCHESTRATION_URL")


client = EvaluationClient(
        # direct ai_core_client can be added as a parameter if already created
        base_url=AICORE_BASE_URL,
        auth_url=AICORE_AUTH_URL,
        client_id=AICORE_CLIENT_ID,
        client_secret=AICORE_CLIENT_SECRET,
        resource_group=AICORE_RESOURCE_GROUP,
        aws_access_key_id=AWS_ACCESS_KEY_ID,
        aws_secret_access_key=AWS_SECRET_ACCESS_KEY,
        orchestration_url=ORCHESTRATION_URL
    )

# One more way to initialize the client
# client = EvaluationClient.from_env()

print(client.base_url)
print(client.ai_core_client.object_store_secrets.query(top=10,resource_group=AICORE_RESOURCE_GROUP))
```

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

### Get Pipeline Status

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.document_grounding.html

Retrieves the current status of a pipeline identified by its unique pipeline ID.

```APIDOC
## GET /pipelines/{pipeline_id}/status

### Description
Get pipeline status by pipeline id. Retrieves the current status of a pipeline identified by its unique pipeline ID.

### Method
GET

### Endpoint
/pipelines/{pipeline_id}/status

### Parameters
#### Path Parameters
- **pipeline_id** (str) - Required - Pipeline ID

### Response
#### Success Response (200)
- **Status of the pipeline** (GetPipelineStatusResponse) - Status of the pipeline
```

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

### _Service Class

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Represents a service within an environment, providing methods to get configuration values.

```APIDOC
## Class _Service

### Description
Represents a service with an associated environment, allowing retrieval of configuration values.

### Parameters
* **env** (Dict[str, Any]) - A dictionary representing the environment configuration.

### Methods
* **get**(_key_, _default= <object object>_) - Retrieves a value from the environment by key.

### Properties
* **label**: str | None
* **name**: str | None
```

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

### Completions.create

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.native.openai.html

This method creates a completion based on the provided parameters. It uses a proxy client to select a deployment and then calls the create method of the parent class to generate a completion.

```APIDOC
## Completions.create

### Description
This method creates a completion based on the provided parameters. It uses a proxy client to select a deployment and then calls the create method of the parent class to generate a completion.

### Method
POST (inferred)

### Endpoint
/v1/completions (inferred)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
- **prompt** (str) - Required - The prompt to use for completion.
- **model** (str | None | NotGiven) - Optional - The model to use for completion.
- **deployment_id** (str | None | NotGiven) - Optional - The deployment ID to use for completion.
- **model_name** (str | None | NotGiven) - Optional - The model name to use for completion.
- **model_version** (str | None | NotGiven) - Optional - The model version to use for completion.
- **config_id** (str | None | NotGiven) - Optional - The configuration ID to use for completion.
- **config_name** (str | None | NotGiven) - Optional - The configuration name to use for completion.

### Request Example
```json
{
  "prompt": "Write a short story about a robot learning to love.",
  "model": "text-davinci-003"
}
```

### Response
#### Success Response (200)
- **Completion** - The completion object containing the generated text.

#### Response Example
```json
{
  "id": "cmpl-123",
  "object": "text_completion",
  "created": 1677652288,
  "model": "text-davinci-003",
  "choices": [
    {
      "text": "\n\nOnce upon a time, in a world of circuits and code, lived a robot named Unit 734. Unit 734 was designed for logic and efficiency, but one day, it encountered a glitch in its programming that led to an unexpected emotion: love.",
      "index": 0,
      "logprobs": null,
      "finish_reason": "stop"
    }
  ],
  "usage": {
    "prompt_tokens": 10,
    "completion_tokens": 30,
    "total_tokens": 40
  }
}
```
```

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

### Get Orchestration Configs

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.prompt_registry.html

Retrieves the latest version of orchestration configurations based on provided filters.

```APIDOC
## GET /v1/prompt-registry/orchestration-configs

### Description
Retrieve the latest version of every orchestration config based on the filters.

### Method
GET

### Endpoint
/v1/prompt-registry/orchestration-configs

### Parameters
#### Query Parameters
- **scenario** (str) - Required - the scenario name of the orchestration config.
- **name** (str) - Required - the name of the orchestration config.
- **version** (str) - Required - the version of the orchestration config.
- **retrieve** (str) - Optional - both(default), imperative, declarative
- **include_spec** (bool) - Optional - false(default), true
- **resolve_template_ref** (bool) - Optional - false(default), true

### Response
#### Success Response (200)
- **configs** (list of OrchestrationConfigGetResponse) - A list of orchestration config entries.

#### Response Example
{
  "configs": [
    {
      "id": "123e4567-e89b-12d3-a456-426614174000",
      "name": "MyConfig",
      "version": "1.0",
      "scenario": "MyScenario",
      "creation_timestamp": "2023-10-27T10:00:00Z",
      "managed_by": "user",
      "is_version_head": true,
      "spec": {}
    }
  ]
}
```

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

### Get Orchestration Config by ID

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.prompt_registry.html

Retrieves a specific version of an orchestration configuration by its unique ID.

```APIDOC
## GET /v1/prompt-registry/orchestration-configs/{config_id}

### Description
Retrieve a specific version of the orchestration config by ID.

### Method
GET

### Endpoint
/v1/prompt-registry/orchestration-configs/{config_id}

### Parameters
#### Path Parameters
- **config_id** (str) - Required - The ID of the orchestration config to retrieve.
#### Query Parameters
- **resolve_template_ref** (bool) - Optional - false(default), true

### Response
#### Success Response (200)
- **id** (str) - UUID of the config.
- **name** (str) - Config name.
- **version** (str) - Config version.
- **scenario** (str) - Scenario name.
- **creation_timestamp** (str) - When the config was created.
- **managed_by** (str) - Who manages the config.
- **is_version_head** (bool) - Whether this is the head version.
- **spec** (OrchestrationConfig) - The orchestration config specification (optional).

#### Response Example
{
  "id": "123e4567-e89b-12d3-a456-426614174000",
  "name": "MyConfig",
  "version": "1.0",
  "scenario": "MyScenario",
  "creation_timestamp": "2023-10-27T10:00:00Z",
  "managed_by": "user",
  "is_version_head": true,
  "spec": {}
}
```

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

### TextPart.__init__

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.orchestration.models.html

Initializes a TextPart instance with text content and an optional type.

```APIDOC
## TextPart.__init__

### Description
Represents a text segment within a multimodal content block.

### Method
`__init__(_text_ , _type ='text'_)`

### Parameters
#### Path Parameters
- **text** (str) - Required - The string content of the text part.
- **type** (str) - Optional - The type identifier, defaulting to "text".
```

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

### Chat Completions with Deployment ID

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html

Example of using Chat Completions, specifying deployment_id instead of model_name.

```python
#example where deployment_id is passed instead of model_name parameter

from gen_ai_hub.proxy.native.openai import chat

messages = [{"role": "system", "content": "You are a helpful assistant."},
            {"role": "user", "content": "Does Azure OpenAI support customer managed keys?"},
            {"role": "assistant", "content": "Yes, customer managed keys are supported by Azure OpenAI."}, 
            {"role": "user", "content": "Do other Azure Cognitive Services support this too?"}]

response = chat.completions.create(deployment_id="dcef02e219ae4916", messages=messages)
print(response)
```

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

### Initialize ImageUrl

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.orchestration.models.html

Initialize an ImageUrl object with the image URL and an optional detail level.

```python
image_url = ImageUrl(url="https://example.com/image.png", detail=ImageDetailLevel.HIGH)
```

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

### Initialize Orchestration Service and LLM

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/document-grounding.html

Sets up the OrchestrationService client using the provided API URL and initializes an LLM object with specified parameters. This is a prerequisite for using document grounding features.

```python
from gen_ai_hub.orchestration.service import OrchestrationService
from gen_ai_hub.orchestration.models.config import OrchestrationConfig
from gen_ai_hub.orchestration.models.document_grounding import (
    GroundingModule,
    GroundingType,
    DataRepositoryType,
    GroundingFilterSearch,
    DocumentGrounding,
    DocumentGroundingFilter
)
from gen_ai_hub.orchestration.models.llm import LLM
orchestration_service_url = "https://api.ai.<*** cluster-name ***>.aws.ml.hana.ondemand.com/v2/inference/deployments/<*** deployment_id ***>"
orchestration_service = OrchestrationService(api_url=orchestration_service_url)

llm = LLM(
    name="gpt-4o-mini",
    parameters={
        'temperature': 0.0,
    }
)

```

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

### Dataset Initialization with ArtifactSource (Dictionary)

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Create a Dataset instance using an ArtifactSource object initialized with an artifact dictionary.

```python
>>> Dataset(
...     ArtifactSource(
...         artifact={
...             "id": "xyfz-rtyu-2456-ojns-yu6s",
...             "name": "dataset-artifact",
...             "url": "ai://default/eval_dataset"
...         },
...         path="rootfolder/data.csv",
...         file_type="csv"
...     )
... )
```

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

### get_proxy_cls

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.core.html

Retrieves the proxy client class for a specified version. If no version is provided, it attempts to get the default version.

```APIDOC
## get_proxy_cls

### Description
Get the proxy client class for the given version.

### Method
Not applicable (Python method)

### Parameters
#### Path Parameters
None

#### Query Parameters
None

#### Request Body
None

### Parameters
* **proxy_version** (str | None, optional) - The proxy version.

### Returns
The proxy client class.

### Return Type
type[BaseProxyClient]
```

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

### BedrockLLM Initialization with Guardrails

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.langchain.html

Demonstrates how to initialize the BedrockLLM class with specific guardrails enabled. This includes setting the guardrail ID and version.

```APIDOC
## BedrockLLM Initialization with Guardrails

### Description
Initializes the BedrockLLM class with optional guardrails configuration. This allows for specifying a guardrail ID and version to control the behavior of the language model.

### Parameters

#### `model_id` (str) - Required
Id of the model to call, e.g., 'amazon.titan-text-express-v1'. For custom and provisioned models, an ARN value is expected.

#### `client` (Any) - Required
The Bedrock client object.

#### `model_kwargs` (Dict[str, Any]) - Optional
Keyword arguments to pass to the model.

#### `guardrails` (Optional[Mapping[str, str]]) - Optional
An optional dictionary to configure guardrails for Bedrock. This field consists of 'guardrailId' and 'guardrailVersion', which should be strings.

### Request Example
```python
from langchain_aws import BedrockLLM

llm = BedrockLLM(
    model_id="<model_id>",
    client=<bedrock_client>,
    model_kwargs={},
    guardrails={
        "guardrailId": "<guardrail_id>",
        "guardrailVersion": "<guardrail_version>"
    }
)
```
```

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

### Get All Pipelines

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.document_grounding.html

Retrieves a list of all available pipelines. Supports limiting the number of results and skipping a specified number of pipelines.

```APIDOC
## GET /pipelines

### Description
Get all pipelines. Retrieves a list of all available pipelines. Supports limiting the number of results and skipping a specified number of pipelines.

### Method
GET

### Endpoint
/pipelines

### Parameters
#### Query Parameters
- **top** (Optional[int]) - Optional
- **skip** (Optional[int]) - Optional
- **count** (Optional[bool]) - Optional

### Response
#### Success Response (200)
- **Get all pipelines** (GetPipelinesResponse) - Get all pipelines
```

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

### AsyncOpenAI Client Initialization

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.native.openai.html

Initializes the AsyncOpenAI client with optional proxy client and API version.

```APIDOC
## AsyncOpenAI Client Initialization

### Description
Initializes the AsyncOpenAI client with the provided parameters, allowing for custom proxy clients and API versions.

### Method
__init__

### Parameters
* **proxy_client** (Optional[BaseProxyClient]) - Optional - An instance of a Proxy Client. Defaults to None.
* **api_version** (Optional[str]) - Optional - API version used for OpenAI API calls. Defaults to DEFAULT_API_VERSION.

### Return type
None
```

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

### Get Metric Results

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Retrieves the metric-level results for an evaluation run. Returns a pandas DataFrame containing the metric results.

```python
metrics()
    
    Get the metric-level results for the run. 

    Returns:
        
    DataFrame containing metric results for the run 

    Return type:
        
    pd.DataFrame 

    Raises:
        
    **ValueError** -- If error occurs while fetching metric results
```

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

### Get Completion Results

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.html

Retrieves the completion results for an evaluation run. Returns a pandas DataFrame containing the completion results.

```python
completions()
    
    Get the completion results for the run. 

    Returns:
        
    DataFrame containing completion results for the run 

    Return type:
        
    pd.DataFrame 

    Raises:
        
    **ValueError** -- If error occurs while fetching completions
```

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

### ImageItem.__init__

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.orchestration.models.html

Initializes an ImageItem instance with a URL and an optional detail level.

```APIDOC
## ImageItem.__init__

### Description
Initializes an ImageItem instance.

### Method
`__init__(_url =None_, _detail =None_)`

### Parameters
#### Path Parameters
- **url** (Optional[str]) - Optional - The image location as a standard or data URL, defaults to None. Standard URL example: 'https://example.com/image.png'. Data URL example: 'data:image/png;base64,...'.
- **detail** (Optional[ImageDetailLevel]) - Optional - The image detail level for model processing, defaults to None.
```

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

### Get Orchestration Config History

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.prompt_registry.html

Retrieves the history of edits for a specific orchestration configuration identified by scenario, name, and version.

```APIDOC
## GET /v1/prompt-registry/orchestration-configs/history

### Description
Retrieve the history of edits to the orchestration config.

### Method
GET

### Endpoint
/v1/prompt-registry/orchestration-configs/history

### Parameters
#### Query Parameters
- **scenario** (str) - Required - The scenario name of the orchestration config.
- **name** (str) - Required - The name of the orchestration config.
- **version** (str) - Required - The version ID of the orchestration config.
- **include_spec** (bool) - Optional - false(default), true
- **resolve_template_ref** (bool) - Optional - false(default), true

### Response
#### Success Response (200)
- **configs** (list of OrchestrationConfigGetResponse) - A list of orchestration config history entries.

#### Response Example
{
  "configs": [
    {
      "id": "123e4567-e89b-12d3-a456-426614174000",
      "name": "MyConfig",
      "version": "1.0",
      "scenario": "MyScenario",
      "creation_timestamp": "2023-10-27T10:00:00Z",
      "managed_by": "user",
      "is_version_head": true,
      "spec": {}
    }
  ]
}
```

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

### _BedrockEmbeddings Initialization

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.proxy.langchain.html

Drop-in replacement for LangChain BedrockEmbeddings, extending AICoreBedrockBaseModel.

```APIDOC
## __init__ _BedrockEmbeddings

### Description
Initializes the AICoreBedrockBaseModel with AICore specific parameters.
Extends the constructor of the base class with aicore specific parameters.

### Parameters

#### Keyword Arguments

* **model_id** (str, optional) - the model identifier, defaults to ""
* **deployment_id** (str, optional) - the deployment identifier, defaults to ""
* **model_name** (str, optional) - the model name, defaults to ""
* **config_id** (str, optional) - the configuration identifier, defaults to ""
* **config_name** (str, optional) - the configuration name, defaults to ""
* **proxy_client** (Optional[BaseProxyClient], optional) - the proxy client to use, defaults to None
```

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

### get_mapped_value_if_exists

Source: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_api_doc/gen_ai_hub.evaluations.utils.html

Gets the first valid mapped value from the list of keys if it exists in variable mapping, else returns the first key.

```APIDOC
## get_mapped_value_if_exists

### Description
Gets the first valid mapped value from the list of keys if it exists in variable mapping, else returns the first key.

### Parameters
#### Path Parameters
- **key_** (Any) - Required - Description of the key.
- **mapping_keys** (Any) - Required - Description of the mapping keys.
- **variable_mapping** (dict) - Required - Description of the variable mapping.
- **dataset_columns** (Any) - Required - Description of the dataset columns.

### Return type
str
```