secondsky/sap-skills/sap-cloud-sdk-ai-python

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Integrates the SAP Cloud SDK for AI for Python (sap-ai-sdk-gen, formerly generative-ai-hub-sdk) into Python applications. Use when building Python apps with SAP AI Core, Generative AI Hub, or the Orchestration Service: chat completion, embeddings, streaming, LangChain integration, templating, content filtering, data masking, and document grounding. Supports OpenAI GPT models, Llama, Gemini, Amazon Nova, and other foundation models via SAP BTP.

Install

npx vskill@latest install secondsky/sap-skills/sap-cloud-sdk-ai-python

SKILL.md

SAP Cloud SDK for AI (Python)

Package rename: The PyPI package generative-ai-hub-sdk is deprecated (v4.12.4 is the last release).
Its successor is `sap-ai-sdk-gen` (currently v6.10.0 per public PyPI registry evidence from 2026-06-15). Code and tutorials referencing
generative-ai-hub-sdk should migrate to sap-ai-sdk-gen; the import name remains gen_ai_hub.

The official Python SDK for SAP Generative AI Hub and Orchestration Service. It wraps the native SDKs of model providers (OpenAI, Amazon Bedrock, Google GenAI) and offers a harmonised LangChain integration and a full Orchestration client — all routed through SAP AI Core with unified authentication. Package freshness is registry-verified; AI Core runtime behavior and exact model availability still require target-tenant validation.

Related Skills

  • sap-ai-core: Platform setup, deployments, resource groups, and model management in SAP AI Core
  • sap-cloud-sdk-ai: JavaScript/TypeScript and Java equivalents of this SDK
  • sap-hana-ml: HANA-side machine learning in Python
  • sap-dependency-security: Pip dependency hygiene and upgrade patterns
Related external skills

If your task involves working inside Databricks (notebooks, Unity Catalog, Spark, SAP Databricks in SAP Business Data Cloud), consider installing the Databricks agent skills plugin. Ask whether you would like help installing it — never install unprompted.

When to Use This Skill

Use this skill when:

  • Building Python applications that call LLMs through SAP AI Core / Generative AI Hub
  • Using the gen_ai_hub Python package (installed as sap-ai-sdk-gen)
  • Integrating OpenAI, Amazon Bedrock, or Google GenAI models via SAP's proxy
  • Implementing LangChain chains with SAP AI Core as the backend
  • Using the Orchestration Service from Python (templating, filtering, masking, grounding)
  • Migrating code from the deprecated generative-ai-hub-sdk to sap-ai-sdk-gen
  • Generating embeddings through SAP AI Core
  • Working with SAP RPT-1 (Relational Pretrained Transformer) for tabular predictions

Table of Contents

Quick Start

Native OpenAI Chat Completion
python
from gen_ai_hub.proxy.native.openai import chat
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "What is SAP BTP?"}
]
response = chat.completions.create(
    model_name="gpt-4o-mini",
    messages=messages
)
print(response.choices[0].message.content)
Orchestration Service
python
from gen_ai_hub.orchestration_v2 import (
    OrchestrationConfig, OrchestrationService,
    ModuleConfig, PromptTemplatingModuleConfig,
    Template, UserMessage, LLMModelDetails
)
config = OrchestrationConfig(
    modules=ModuleConfig(
        prompt_templating=PromptTemplatingModuleConfig(
            prompt=Template(
                template=[UserMessage(role="user", content="{{?question}}")]
            ),
            model=LLMModelDetails(name="gpt-4o-mini")
        )
    )
)
service = OrchestrationService(config=config)
response = service.run(placeholder_values={"question": "What is SAP?"})
print(response.final_result.choices[0].message.content)

Installation

bash
# All providers + LangChain support
pip install "sap-ai-sdk-gen[all]"
 
# Default (OpenAI only, no LangChain)
pip install sap-ai-sdk-gen
 
# Specific providers (without LangChain)
pip install "sap-ai-sdk-gen[google, amazon]"

Authentication

The SDK reads credentials via AICoreV2Client.from_env(), which resolves credentials in this order:

  1. Keyword arguments passed to GenAIHubProxyClient(...)
  2. Environment variables — AICORE_CLIENT_ID, AICORE_CLIENT_SECRET,

AICORE_AUTH_URL, AICORE_BASE_URL, AICORE_RESOURCE_GROUP

  1. Config file — $AICORE_HOME/config.json (or path set by AICORE_CONFIG);

use AICORE_PROFILE to select a named profile

  1. VCAP_SERVICES — automatic on Cloud Foundry/Kyma when the AI Core service is bound
Local Development (Environment Variables)
bash
export AICORE_CLIENT_ID="sb-..."
export AICORE_CLIENT_SECRET="..."
export AICORE_AUTH_URL="https://<tenant>.authentication.sap.hana.ondemand.com/oauth/token"
export AICORE_BASE_URL="https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com/v2"
export AICORE_RESOURCE_GROUP="default"
Config File Profile
bash
# ~/.aicore/config.json
{
  "AICORE_CLIENT_ID": "sb-...",
  "AICORE_CLIENT_SECRET": "...",
  "AICORE_AUTH_URL": "https://<tenant>.authentication.sap.hana.ondemand.com/oauth/token",
  "AICORE_BASE_URL": "https://api.ai.prod.eu-central-1.aws.ml.hana.ondemand.com/v2",
  "AICORE_RESOURCE_GROUP": "default"
}

For detailed auth setup and troubleshooting, see references/getting-started-auth.md.

Available Modules

ModuleImport PathPurpose
Proxy (native clients)gen_ai_hub.proxy.native.*Direct model access per provider
LangChain integrationgen_ai_hub.proxy.langchaininit_llm, init_embedding_model, ChatOpenAI, etc.
Orchestrationgen_ai_hub.orchestration_v2Templating, filtering, masking, grounding
Document Groundinggen_ai_hub.document_groundingPipeline, Vector, Retrieval APIs
Prompt Registrygen_ai_hub.prompt_registryTemplate management and config storage
Evaluationsgen_ai_hub.evaluationsModel evaluation runs and metrics
SAP RPT-1gen_ai_hub.proxy.native.sapTabular prediction (classification, regression)
Native Clients by Provider
ProviderImportKey Classes
OpenAIgen_ai_hub.proxy.native.openaiOpenAI, completions, chat, embeddings, responses
Amazon Bedrockgen_ai_hub.proxy.native.amazonSession, ClientWrapper
Google GenAIgen_ai_hub.proxy.native.google_genaiClient
SAP RPT-1gen_ai_hub.proxy.native.sapRPTClient, RPTRequest

Supported Models

The Generative AI Hub catalog includes models from multiple providers. Check SAP's model catalog and the target tenant catalog for the authoritative model IDs. Example families:

ProviderExample Families
OpenAIGPT-family chat, multimodal, reasoning, and embedding models
Anthropic (via Bedrock)Claude-family models
AmazonNova/Titan-family models
GoogleGemini-family models
MistralMistral-family models
SAPRPT-family tabular prediction models where enabled

Core Features

Chat Completion with OpenAI Client
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)
Streaming
python
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="")
Embeddings
python
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)
LangChain Integration
python
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")
Content Filtering (via Orchestration)
python
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."})
Data Masking (via Orchestration)
python
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."})
Document Grounding (via Orchestration)
python
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)
                    )
                ]
            )
        )
    )
)

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Repository stars
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Repository forks
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7-day trend score
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