Sample Code

September 8, 2025 · View on GitHub

Sample code to demonstrate the usage of the SAP Cloud SDK for AI. Parts of the sample code are also used in E2E tests.

Local Deployment
Usage
Tutorial

Local Deployment

Run pnpm install to install dependencies.
Create a .env file in the sample-code directory with the complete content of your AI core service key by adding the following line:
```
AICORE_SERVICE_KEY='{
  "clientid": "...",
  ...
}'
```
Run pnpm local to start the server.

Usage

AI API

Get all Deployments

GET /ai-api/deployments

Get all deployments in resource group default.

Get all Deployments with Custom Destination

GET /ai-api/deployments-with-destination

Get all deployments targeting a custom destination. Provide a destination when calling the execute() method.

To create a custom destination, the following template can be used in the .env file:

destinations='[{
  "name": "<destination name>",
  "url": "<destination url>",
  "authentication": "OAuth2ClientCredentials",
  "authTokens": [{
    "http_header": {
      "key": "Authorization",
      "value": "Bearer <your token>" }
  }]
}]'

Refer to our guide for more details on testing locally with custom destinations.

Create a Deployment

POST /ai-api/deployment/create

{
  "configurationId": "CONFIGURATION_ID"
}

Create a deployment with certain configuration ID in resource group default.

Batch Stop Deployments with Given Configuration ID

PATCH /ai-api/deployment/batch-stop

{
  "configurationId": "CONFIGURATION_ID"
}

Stop all deployments with a certain configuration ID in resource group default.

Batch Delete all Deployments with Status `STOPPED` or `UNKNOWN`

DELETE /ai-api/deployment/batch-delete

Delete all deployments with status STOPPED or UNKNOWN in resource group default.

Get all Scenarios

GET /ai-api/scenarios

Get all scenarios in resource group default.

Get all Foundation Models

GET /ai-api/models

Get all foundation models in resource group default.

Foundation Models (Azure OpenAI)

Chat Completion

GET /azure-openai/chat-completion

Get chat completion response.

Chat Completion with Custom Destination

GET /azure-openai/chat-completion-with-destination

Get chat completion response targeting a custom destination. Provide a destination when initializing the AzureOpenAiChatClient.

To reference a custom destination check out: Get all Deployments with Custom Destination

Chat Completion Streaming

GET /azure-openai/chat-completion-stream

Get chat completion response with streaming.

The response header is set with Content-Type: text/event-stream to stream the text.

AbortController is used to cancel the request in case user closes or refreshes the page, or there is an error.

The toContentStream() method is called to extract the content of the chunk for convenience.

Once the streaming is done, finish reason and token usage are printed out.

Embedding

GET /azure-openai/embedding

Get embedding vector for the input.

Orchestration

Simple Chat Completion

GET /orchestration/simple

Get chat completion response for a given static input.

Templating

GET /orchestration/template

Get chat completion response with template and input parameters. Define variable by wrapping it with {{?...}}.

Templating with Reference

GET /orchestration/templateRef

Get chat completion response with template reference and input parameters.

Message History

GET /orchestration/messageHistory

Get chat completion response and use it as input for subsequent chat completion requests through message history.

Input Filtering

GET /orchestration/inputFiltering

Get chat completion response with Azure content filter for the input. For example, use buildAzureContentSafetyFilter() function to build Azure content filter.

Output Filtering

GET /orchestration/outputFiltering

Get chat completion response with Azure content filter for the output. For example, use buildAzureContentSafetyFilter() function to build Azure content filter.

Custom Request Config

GET /orchestration/requestConfig

Send chat completion request with a custom header as the custom request configuration.

Orchestration Client from JSON config

GET /orchestration/fromJson

Get chat completion response with a JSON config initialized orchestration client.

Image Recognition

GET /orchestration/image

Get chat completion response with image input.

Structured Response

GET /orchestration/responseFormat

Get chat completion response with structured response format.

Mask Grounding Input

GET /orchestration/maskGroundingInput

Get chat completion response with masked grounding input.

Chat Completion Streaming

POST /orchestration-stream/chat-completion-stream

Get a chat completion response with streaming.

You can set the streaming options in the body of the request.

An example for setting the chunk size would look like this:

curl -X POST http://localhost:8080/orchestration-stream/chat-completion-stream \
-H "Content-Type: application/json" \
-d '{
  "global": {
    "chunk_size": 10
  }
}'

The response header is set with Content-Type: text/event-stream to stream the text.

AbortController is used to cancel the request in case user closes or refreshes the page, or there is an error.

The toContentStream() method is called to extract the content of the chunk for convenience.

Once the streaming is done, finish reason and token usage are printed out.

Chat Completion Streaming With JSON Module Config

GET /orchestration-stream/chat-completion-stream-json

Get a chat completion response with streaming with a JSON Module Config initalized client.

The response header is set with Content-Type: text/event-stream to stream the text.

AbortController is used to cancel the request in case user closes or refreshes the page, or there is an error.

The toContentStream() method is called to extract the content of the chunk for convenience.

Once the streaming is done, finish reason and token usage are printed out.

Translation

GET /orchestration/translation

Get chat completion response with input and output translation. For example, use buildTranslationConfig() function to build a SAP Document Translation config.

LangChain

Invoke with a Simple Input

GET /langchain/invoke

Invoke LangChain Azure OpenAI client with a simple input to get chat completion response.

Invoke a Chain with Templating

GET /langchain/invoke-chain

Invoke chain to get chat completion response from Azure OpenAI. The chain contains a template and a string parser.

Invoke a Chain with Templating and Orchestration Client

GET /langchain/invoke-chain-orchestration

Invoke a chain to get a orchestration response from the orchestration service. The chain has a built-in template and is chained with a string parser.

Invoke a Chain with Orchestration Input Filtering

GET /langchain/invoke-chain-orchestration-input-filter

Invoke a chain to trigger input filtering of orchestration service.

Invoke a Chain with Orchestration Output Filtering

GET /langchain/invoke-chain-orchestration-output-filter

Invoke a chain to trigger output filtering of orchestration service.

Invoke a Chain with Orchestration Masking

GET /langchain/invoke-chain-orchestration-masking

Invoke a chain to trigger data masking of orchestration service.

Invoke a Chain with Retrieval-Augmented Generation (RAG)

GET /langchain/invoke-rag-chain

Invoke a chain to embed documents and get chat completion response with context from Azure OpenAI. The chain performs RAG with the chat and embedding client.

Stream with Azure OpenAI

GET /langchain/stream-azure-openai

Stream chat completion response with Azure OpenAI using LangChain.

Stream with Orchestration

GET /langchain/stream-orchestration

Stream chat completion response with orchestration service using LangChain.

Document Grounding

E2E flow for Orchestration Grounding using `vector` as the Data Repository

GET /document-grounding/orchestration-grounding-vector

This scenario demonstrates the end-to-end flow for creating collections and documents using the document grounding service, and then using the orchestration grounding module to get a chat completion response with a retrieved context based on vector data repository.

The flow will first create an empty collection and then add a document to it. The document contains a statement with the current timestamp. It will be retrieved with a user question asking for the latest timestamp by orchestration grounding module. Then, orchestration service will send a chat completion request with the context to LLM. The response should contain the same timestamp.

The created collection will be deleted at the end of the flow.

E2E flow for Retrieving Documents

GET /document-grounding/retrieve-documents

This scenario demonstrates the end-to-end flow for creating collections and documents using the document grounding service, and then retrieving the documents with a query.

The response should contain chunks of documents similar to the query.

The created collection will be deleted at the end of the flow.

E2E flow for Orchestration Grounding using `help.sap.com` as the Data Repository

GET /document-grounding/orchestration-grounding-help-sap-com

This scenario uses the orchestration grounding module to get a chat completion response with a retrieved context based on help.sap.com data repository.

Orchestration service will send a chat completion request with the context to LLM. The response should contain the same timestamp.

The created collection will be deleted at the end of the flow.

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