sap-cloud-sdk-ai-python

name	sap-cloud-sdk-ai-python
description	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.
license	GPL-3.0
metadata	{"maintainer":"Eduard Jiglau","maintainer_email":"[e-mail]","website":"https://sap-ai-skills.com","version":"2.3.2","last_verified":"2026-06-15","sdk_package":"sap-ai-sdk-gen 6.10.0","package_evidence":"docs/project/package-evidence/2026-06-15.json","documentation_source":"https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html"}

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
• Installation
• Authentication
• Available Modules
• Supported Models
• Core Features
• Bundled Resources

Quick Start

Native OpenAI Chat Completion

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

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

# 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
3. Config file — $AICORE_HOME/config.json (or path set by AICORE_CONFIG); use AICORE_PROFILE to select a named profile
4. VCAP_SERVICES — automatic on Cloud Foundry/Kyma when the AI Core service is bound

Local Development (Environment Variables)

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

# ~/.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

Module	Import Path	Purpose
Proxy (native clients)	gen_ai_hub.proxy.native.*	Direct model access per provider
LangChain integration	gen_ai_hub.proxy.langchain	init_llm, init_embedding_model, ChatOpenAI, etc.
Orchestration	gen_ai_hub.orchestration_v2	Templating, filtering, masking, grounding
Document Grounding	gen_ai_hub.document_grounding	Pipeline, Vector, Retrieval APIs
Prompt Registry	gen_ai_hub.prompt_registry	Template management and config storage
Evaluations	gen_ai_hub.evaluations	Model evaluation runs and metrics
SAP RPT-1	gen_ai_hub.proxy.native.sap	Tabular prediction (classification, regression)

Native Clients by Provider

Provider	Import	Key Classes
OpenAI	gen_ai_hub.proxy.native.openai	OpenAI, completions, chat, embeddings, responses
Amazon Bedrock	gen_ai_hub.proxy.native.amazon	Session, ClientWrapper
Google GenAI	gen_ai_hub.proxy.native.google_genai	Client
SAP RPT-1	gen_ai_hub.proxy.native.sap	RPTClient, 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:

Provider	Example Families
OpenAI	GPT-family chat, multimodal, reasoning, and embedding models
Anthropic (via Bedrock)	Claude-family models
Amazon	Nova/Titan-family models
Google	Gemini-family models
Mistral	Mistral-family models
SAP	RPT-family tabular prediction models where enabled

Core Features

Chat Completion with OpenAI Client

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

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

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

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)

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)

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 [e-mail] for details."})

Document Grounding (via Orchestration)

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?"})

Common Errors

Error	Cause	Solution
No credentials found in any source	Missing AI Core service key/env vars	Set all AICORE_* environment variables or create a config file profile
No deployment found	Model not deployed in AI Core	Deploy the model in your resource group, or use deployment_id directly
AICORE_RESOURCE_GROUP not set	Missing resource group	Set AICORE_RESOURCE_GROUP env var or pass resource_group to the client
ModuleNotFoundError: No module named 'gen_ai_hub'	Wrong package installed	Install sap-ai-sdk-gen (not generative-ai-hub-sdk)
Import from generative_ai_hub_sdk fails	Using deprecated package name	The package was renamed; import from gen_ai_hub (installed via sap-ai-sdk-gen)
ValidationError on proxy client init	Incomplete credentials	Verify all four required env vars: AICORE_CLIENT_ID, AICORE_CLIENT_SECRET, AICORE_AUTH_URL, AICORE_BASE_URL

Bundled Resources

Reference Documentation

1. references/getting-started-auth.md - Installation, authentication, and config setup
2. references/native-clients-guide.md - Native client usage for OpenAI, Amazon, Google, and SAP RPT-1
3. references/orchestration-guide.md - Orchestration service: templating, filtering, masking, grounding, embeddings
4. references/langchain-guide.md - LangChain integration: LLM/embedding init, chains, structured outputs
5. references/troubleshooting.md - Common errors, version compatibility, migration from generative-ai-hub-sdk

Documentation Sources

Keep this skill updated using these sources:

• PyPI: https://pypi.org/pypi/sap-ai-sdk-gen/json — package metadata and README
• SDK Reference: https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html
• SAP Samples: https://github.com/SAP-samples/btp-gen-ai-hub-sdk-samples
• AI Core Help: https://help.sap.com/docs/sap-ai-core
• Deprecated Package: https://pypi.org/pypi/generative-ai-hub-sdk/json (for migration notes)