Sap Cloud SDK AI Python

from gen_ai_hub.proxy.native.openai import OpenAI

client = OpenAI()
response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain CAP in one paragraph."}]
)
print(response.choices[0].message.content)

from gen_ai_hub.proxy.native.openai import OpenAI

client = OpenAI()
stream = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Explain SAP CAP."}],
    stream=True
)
for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="")

from gen_ai_hub.proxy.native.openai import embeddings

response = embeddings.create(
    input="Every decoding is another encoding.",
    model_name="text-embedding-3-small"
)
print(response.data[0].embedding)

from gen_ai_hub.proxy.langchain import init_llm, init_embedding_model

llm = init_llm("gpt-4o-mini", max_tokens=300)
result = llm.invoke("What is SAP BTP?")
print(result.content)

embeddings = init_embedding_model("text-embedding-3-small")
vector = embeddings.embed_query("SAP Business Technology Platform")

from gen_ai_hub.orchestration_v2 import (
    OrchestrationConfig, OrchestrationService,
    ModuleConfig, PromptTemplatingModuleConfig,
    Template, UserMessage, LLMModelDetails,
    FilteringModuleConfig, InputFiltering, OutputFiltering,
    AzureContentSafetyInput, AzureContentSafetyOutput, AzureThreshold
)

config = OrchestrationConfig(
    modules=ModuleConfig(
        prompt_templating=PromptTemplatingModuleConfig(
            prompt=Template(template=[UserMessage(role="user", content="{{?question}}")]),
            model=LLMModelDetails(name="gpt-4o-mini")
        ),
        filtering=FilteringModuleConfig(
            input=InputFiltering(filters=[
                AzureContentSafetyInput(hate=AzureThreshold.ALLOW_SAFE, violence=AzureThreshold.ALLOW_SAFE)
            ]),
            output=OutputFiltering(filters=[
                AzureContentSafetyOutput(hate=AzureThreshold.ALLOW_SAFE, violence=AzureThreshold.ALLOW_SAFE)
            ])
        )
    )
)

service = OrchestrationService(config=config)
response = service.run(placeholder_values={"question": "Explain SAP."})

from gen_ai_hub.orchestration_v2 import (
    OrchestrationConfig, OrchestrationService,
    ModuleConfig, PromptTemplatingModuleConfig,
    Template, UserMessage, LLMModelDetails,
    MaskingModuleConfig, MaskingProviderConfig,
    DPIStandardEntity, MaskingMethod, DataMaskingProviderName
)

config = OrchestrationConfig(
    modules=ModuleConfig(
        prompt_templating=PromptTemplatingModuleConfig(
            prompt=Template(template=[UserMessage(role="user", content="{{?text}}")]),
            model=LLMModelDetails(name="gpt-4o-mini")
        ),
        masking=MaskingModuleConfig(
            masking_providers=[
                MaskingProviderConfig(
                    type=DataMaskingProviderName.SAP_DATA_PRIVACY_INTEGRATION,
                    method=MaskingMethod.ANONYMIZATION,
                    entities=[
                        DPIStandardEntity(type="profile-email"),
                        DPIStandardEntity(type="profile-person")
                    ]
                )
            ]
        )
    )
)

service = OrchestrationService(config=config)
response = service.run(placeholder_values={"text": "Contact john@example.com for details."})

from gen_ai_hub.orchestration_v2 import (
    OrchestrationConfig, OrchestrationService,
    ModuleConfig, PromptTemplatingModuleConfig,
    Template, UserMessage, LLMModelDetails,
    GroundingModuleConfig, DocumentGroundingConfig,
    DocumentGroundingFilter, DocumentGroundingPlaceholders,
    GroundingSearchConfig, DataRepositoryType, GroundingType
)

config = OrchestrationConfig(
    modules=ModuleConfig(
        prompt_templating=PromptTemplatingModuleConfig(
            prompt=Template(template=[UserMessage(role="user", content="{{?question}}")]),
            model=LLMModelDetails(name="gpt-4o-mini")
        ),
        grounding=GroundingModuleConfig(
            type=GroundingType.DOCUMENT_GROUNDING_SERVICE,
            config=DocumentGroundingConfig(
                placeholders=DocumentGroundingPlaceholders(
                    input=["{{?question}}"],
                    output="{{?context}}"
                ),
                filters=[
                    DocumentGroundingFilter(
                        id="my-vector-repo-id",
                        data_repository_type=DataRepositoryType.VECTOR,
                        search_config=GroundingSearchConfig(max_chunk_count=5)
                    )
                ]
            )
        )
    )
)

service = OrchestrationService(config=config)
response = service.run(placeholder_values={"question": "What is the refund policy?"})