sap-cloud-sdk-ai-python

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SAP Cloud SDK for AI (Python)

SAP Cloud SDK for AI（Python版）

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.

包重命名说明：PyPI包
generative-ai-hub-sdk
已被弃用（v4.12.4为最后一个版本）。其替代包为**
sap-ai-sdk-gen
**（根据2026年6月15日公开PyPI注册表信息，当前版本为v6.10.0）。所有引用
generative-ai-hub-sdk
的代码和教程应迁移至
sap-ai-sdk-gen
；导入名称仍为
gen_ai_hub
。

这是SAP Generative AI Hub和编排服务的官方Python SDK。它封装了模型提供商（OpenAI、Amazon Bedrock、Google GenAI）的原生SDK，提供统一的LangChain集成和完整的编排客户端——所有请求均通过SAP AI Core路由并使用统一认证。包的更新状态已通过注册表验证；AI Core运行时行为和具体模型可用性仍需目标租户验证。

Related Skills

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

在以下场景中使用本技能：

构建通过SAP AI Core / Generative AI Hub调用大语言模型（LLM）的Python应用
使用
```
gen_ai_hub
```
Python包（通过安装
```
sap-ai-sdk-gen
```
获取）
通过SAP代理集成OpenAI、Amazon Bedrock或Google GenAI模型
以SAP AI Core为后端实现LangChain链
从Python中使用编排服务（模板化、过滤、掩码、检索增强生成）
将代码从已弃用的
```
generative-ai-hub-sdk
```
迁移至
```
sap-ai-sdk-gen
```
通过SAP AI Core生成嵌入向量
使用SAP RPT-1（关系预训练Transformer）进行表格预测

Quick Start

快速开始

Native OpenAI Chat Completion

原生OpenAI聊天补全

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)

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

编排服务

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)

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

安装

bash

undefined

bash

undefined

All providers + LangChain support

所有提供商 + LangChain支持

pip install "sap-ai-sdk-gen[all]"

Default (OpenAI only, no LangChain)

默认安装（仅OpenAI，无LangChain）

pip install sap-ai-sdk-gen

Specific providers (without LangChain)

指定提供商（无LangChain）

pip install "sap-ai-sdk-gen[google, amazon]"

undefined

pip install "sap-ai-sdk-gen[google, amazon]"

undefined

Authentication

认证

The SDK reads credentials via

AICoreV2Client.from_env()

, which resolves credentials in this order:

Keyword arguments passed to
```
GenAIHubProxyClient(...)
```

Environment variables —

AICORE_CLIENT_ID

AICORE_CLIENT_SECRET

AICORE_AUTH_URL

AICORE_BASE_URL

AICORE_RESOURCE_GROUP

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

SDK通过

AICoreV2Client.from_env()

读取凭证，凭证解析顺序如下：

关键字参数：传递给
```
GenAIHubProxyClient(...)
```
的参数

环境变量——

AICORE_CLIENT_ID

、

AICORE_CLIENT_SECRET

、

AICORE_AUTH_URL

、

AICORE_BASE_URL

、

AICORE_RESOURCE_GROUP

配置文件——
```
$AICORE_HOME/config.json
```
（或由
```
AICORE_CONFIG
```
设置的路径）；使用
```
AICORE_PROFILE
```
选择命名配置文件
VCAP_SERVICES——在Cloud Foundry/Kyma上绑定AI Core服务时自动读取

Local Development (Environment Variables)

本地开发（环境变量）

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"

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

配置文件配置

bash

undefined

bash

undefined

~/.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`.


如需详细的认证设置和故障排除，请参阅`references/getting-started-auth.md`。

Available Modules

可用模块

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)

模块	导入路径	用途
代理（原生客户端）	`gen_ai_hub.proxy.native.*`	按提供商直接访问模型
LangChain集成	`gen_ai_hub.proxy.langchain`	`init_llm` 、 `init_embedding_model` 、 `ChatOpenAI` 等
编排服务	`gen_ai_hub.orchestration_v2`	模板化、过滤、掩码、检索增强生成
文档检索增强生成	`gen_ai_hub.document_grounding`	流水线、向量、检索API
提示词注册表	`gen_ai_hub.prompt_registry`	模板管理和配置存储
评估模块	`gen_ai_hub.evaluations`	模型评估运行和指标
SAP RPT-1	`gen_ai_hub.proxy.native.sap`	表格预测（分类、回归）

Native Clients by Provider

各提供商原生客户端

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`

提供商	导入路径	核心类
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

支持的模型

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

Generative AI Hub目录包含来自多个提供商的模型。请查看SAP模型目录和目标租户目录获取权威模型ID。示例模型系列：

提供商	示例模型系列
OpenAI	GPT系列聊天、多模态、推理和嵌入模型
Anthropic（通过Bedrock）	Claude系列模型
Amazon	Nova/Titan系列模型
Google	Gemini系列模型
Mistral	Mistral系列模型
SAP	RPT系列表格预测模型（已启用时）

Core Features

核心功能

Chat Completion with OpenAI Client

使用OpenAI客户端实现聊天补全

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)

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

流式传输

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="")

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

嵌入向量生成

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)

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

LangChain集成

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")

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)

内容过滤（通过编排服务）

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."})

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)

数据掩码（通过编排服务）

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."})

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)

文档检索增强生成（通过编排服务）

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)
                    )
                ]
            )
        )
    )
)

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

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)
                    )
                ]
            )
        )
    )
)

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

Common Errors

常见错误

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`

错误信息	原因	解决方案
`No credentials found in any source`	缺少AI Core服务密钥/环境变量	设置所有 `AICORE_*` 环境变量或创建配置文件
`No deployment found`	模型未在AI Core中部署	在您的资源组中部署模型，或直接使用 `deployment_id`
`AICORE_RESOURCE_GROUP not set`	缺少资源组	设置 `AICORE_RESOURCE_GROUP` 环境变量或向客户端传递 `resource_group` 参数
`ModuleNotFoundError: No module named 'gen_ai_hub'`	安装了错误的包	安装 `sap-ai-sdk-gen` （而非 `generative-ai-hub-sdk` ）
从 `generative_ai_hub_sdk` 导入失败	使用了已弃用的包名	包已重命名；从 `gen_ai_hub` 导入（通过安装 `sap-ai-sdk-gen` 获取）
代理客户端初始化时出现 `ValidationError`	凭证不完整	验证四个必填环境变量： `AICORE_CLIENT_ID` 、 `AICORE_CLIENT_SECRET` 、 `AICORE_AUTH_URL` 、 `AICORE_BASE_URL`

Bundled Resources

捆绑资源

Reference Documentation

参考文档

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

```
references/getting-started-auth.md
```
- 安装、认证和配置设置
```
references/native-clients-guide.md
```
- OpenAI、Amazon、Google和SAP RPT-1的原生客户端使用指南
```
references/orchestration-guide.md
```
- 编排服务：模板化、过滤、掩码、检索增强生成、嵌入向量
```
references/langchain-guide.md
```
- LangChain集成：LLM/嵌入模型初始化、链、结构化输出

references/troubleshooting.md

- 常见错误、版本兼容性、从

generative-ai-hub-sdk

迁移指南

Documentation Sources

文档来源

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)

通过以下来源保持本技能更新：

PyPI：https://pypi.org/pypi/sap-ai-sdk-gen/json — 包元数据和README
SDK参考文档：https://help.sap.com/doc/generative-ai-hub-sdk/CLOUD/en-US/_reference/gen_ai_hub.html
SAP示例：https://github.com/SAP-samples/btp-gen-ai-hub-sdk-samples
AI Core帮助文档：https://help.sap.com/docs/sap-ai-core
已弃用包：https://pypi.org/pypi/generative-ai-hub-sdk/json（用于迁移说明）

sap-cloud-sdk-ai-python

Original

Translation

SAP Cloud SDK for AI (Python)

SAP Cloud SDK for AI（Python版）

Related Skills

相关技能

Related external skills

相关外部技能

When to Use This Skill

适用场景

Table of Contents

目录

Quick Start

快速开始

Native OpenAI Chat Completion

原生OpenAI聊天补全

Orchestration Service

编排服务

Installation

安装

All providers + LangChain support

所有提供商 + LangChain支持

Default (OpenAI only, no LangChain)

默认安装（仅OpenAI，无LangChain）

Specific providers (without LangChain)

指定提供商（无LangChain）

Authentication

认证

Local Development (Environment Variables)

本地开发（环境变量）

Config File Profile

配置文件配置

~/.aicore/config.json

~/.aicore/config.json

Available Modules

可用模块

Native Clients by Provider

各提供商原生客户端

Supported Models

支持的模型

Core Features

核心功能

Chat Completion with OpenAI Client

使用OpenAI客户端实现聊天补全

Streaming

流式传输

Embeddings

嵌入向量生成

LangChain Integration

LangChain集成

Content Filtering (via Orchestration)

内容过滤（通过编排服务）

Data Masking (via Orchestration)

数据掩码（通过编排服务）

Document Grounding (via Orchestration)

文档检索增强生成（通过编排服务）

Common Errors

常见错误

Bundled Resources

捆绑资源

Reference Documentation

参考文档

Documentation Sources

文档来源