docs: Add sample code

pyproject.toml

@@ -13,7 +13,10 @@ sap-ai-sdk-core = { workspace = true }
 sap-ai-sdk-gen = { workspace = true }
 
 [tool.uv.workspace]
-members = ["packages/*"]
+members = [
+    "packages/*",
+    "sample-code",
+]
 
 [tool.pip-licenses]
 # Blue Oak Council Bronze+ permissive licenses (https://blueoakcouncil.org/list)

sample-code/Makefile

@@ -0,0 +1,2 @@
+server:
+	uv run uvicorn sample_code.server:app --app-dir src --env-file .env --reload

sample-code/README.md

@@ -0,0 +1,41 @@
+# Sample Code - Work in Progress
+
+Sample code to demonstrate the usage of the SAP Cloud SDK for AI.
+
+## Prerequisites
+
+Before running the application, ensure the following prerequisites are met:
+
+- Python installation (3.10 or higher)
+- uv installation (0.12)
+- Credentials for [SAP AI Core](https://help.sap.com/docs/sap-ai-core/sap-ai-core-service-guide/what-is-sap-ai-core) service configured.
+- Deployments of the orchestration service as well as the following models in the resource group specified in the `.env` file below:
+  - `gpt-5.4-nano`
+  - `text-embedding-3-small`
+  - `anthropic--claude-4.6-sonnet`
+  - `gemini-3.5-flash`
+
+## Local Deployment
+
+Create a `.env` file in the sample-code directory with the complete content of your AI core service key by adding the following lines:
+
+```bash
+AICORE_CLIENT_ID="..."
+AICORE_CLIENT_SECRET="..."
+AICORE_AUTH_URL="..."
+AICORE_BASE_URL="..."
+```
+
+Optionally, you can add the `AICORE_RESOURCE_GROUP` environment variable to specify a resource group different from the `default` one.
+
+The server can be started with
+
+```bash
+uv run uvicorn sample_code.server:app --app-dir src --env-file .env --reload
+```
+
+or by running ```make```.
+
+## Usage
+
+When the server is running, head to `http://localhost:8000/docs` to see all available endpoints.

sample-code/pyproject.toml

@@ -0,0 +1,18 @@
+[project]
+name = "sample-code"
+version = "0.1.0"
+description = "Sample code for using the AI Core Python SDK"
+readme = "README.md"
+requires-python = ">=3.10"
+dependencies = [
+    "fastapi>=0.141.1",
+    "sap-ai-sdk-gen",
+    "uvicorn>=0.52.1",
+]
+
+[build-system]
+requires = ["uv_build>=0.12.1,<0.13.0"]
+build-backend = "uv_build"
+
+[tool.uv.sources]
+sap-ai-sdk-gen = { workspace = true, editable = true }

sample-code/pyrightconfig.json

@@ -0,0 +1,9 @@
+{
+  "venvPath": "..",
+  "venv": ".venv",
+  "extraPaths": [
+    "../packages/base",
+    "../packages/core",
+    "../packages/gen"
+  ]
+}

sample-code/src/sample_code/amazon.py

@@ -0,0 +1,25 @@
+from gen_ai_hub.proxy.native.amazon import Session
+
+
+def converse():
+    """
+    Run chat example for Claude 4.6 Sonnet.
+
+    Returns:
+        JSON object containing the model response as result.
+    """
+    bedrock = Session().client(model_name="anthropic--claude-4.6-sonnet")
+    conversation = [
+        {
+            "role": "user",
+            "content": [
+                {
+                    "text": "Describe the purpose of a 'Hello World' program in one sentence."
+                }
+            ],
+        }
+    ]
+    response = bedrock.converse(
+        messages=conversation,
+    )
+    return {"result": response["output"]["message"]["content"][0]["text"]}

sample-code/src/sample_code/core.py

@@ -0,0 +1,80 @@
+from typing import Annotated
+
+from ai_api_client_sdk.models.parameter_binding import ParameterBinding
+from ai_core_sdk.ai_core_v2_client import AICoreV2Client
+from fastapi import Body
+
+
+def get_configurations():
+    """
+    Get all configurations for the resource group specified in the .env file.
+
+    Returns:
+        A dict containing the configurations in a ConfigurationQueryResponse object.
+    """
+    client = AICoreV2Client.from_env()
+    return client.configuration.query()
+
+
+def create_configuration():
+    """
+    Create configuration for GPT-5.4-nano.
+
+    The configuration is created for the resource group specified in the .env file.
+    """
+    client = AICoreV2Client.from_env()
+    # for illustrative purposes the example config is hardcoded
+    parameter_bindings = [
+        ParameterBinding.from_dict({"key": "modelName", "value": "gpt-5.4-nano"}),
+        ParameterBinding.from_dict({"key": "modelVersion", "value": "latest"}),
+    ]
+    return client.configuration.create(
+        name="my-gpt-5.4-nano-config",
+        scenario_id="foundation-models",
+        executable_id="azure-openai",
+        parameter_bindings=parameter_bindings,
+        input_artifact_bindings=[],
+    )
+
+
+def get_deployments():
+    """
+    Get all deployments for the resource group specified in the .env file.
+
+    Returns:
+        A dict containing the deployments in a DeploymentQueryResponse object.
+    """
+    client = AICoreV2Client.from_env()
+    return client.deployment.query()
+
+
+def create_deployment(configuration_id: Annotated[str, Body(embed=True)]):
+    """
+    Create deployment for the configuration_id in the request body.
+
+    The deployment is created for the resource group specified in the .env file.
+    """
+    client = AICoreV2Client.from_env()
+    return client.deployment.create(configuration_id=configuration_id)
+
+
+def get_scenarios():
+    """
+    Get all scenarios.
+
+    Returns:
+        A dict containing the scenarios in a ScenarioQueryResponse object.
+    """
+    client = AICoreV2Client.from_env()
+    return client.scenario.query()
+
+
+def get_models():
+    """
+    Get all available models.
+
+    Returns:
+        A dict containing the models in a ModelQueryResponse object.
+    """
+    client = AICoreV2Client.from_env()
+    return client.model.query()

sample-code/src/sample_code/google.py

@@ -0,0 +1,69 @@
+from fastapi.responses import StreamingResponse
+from gen_ai_hub.proxy import get_proxy_client
+from gen_ai_hub.proxy.native.google_genai import Client
+from google.genai import types
+
+
+def generate():
+    """
+    Run chat example for Gemini 3.5 Flash.
+
+    Returns:
+        JSON object containing the model response as result.
+    """
+    proxy_client = get_proxy_client("gen-ai-hub")
+    client = Client(proxy_client=proxy_client)
+    response = client.models.generate_content(
+        model="gemini-3.5-flash", contents="How many paws are there for a dog?"
+    )
+    return {"result": response.candidates[0].content.parts[0].text}
+
+
+def generate_stream():
+    """
+    Run chat example with streaming response for Gemini 3.5 Flash.
+
+    Returns:
+        Streaming response emitting the produced text.
+    """
+    proxy_client = get_proxy_client("gen-ai-hub")
+
+    client = Client(
+        proxy_client=proxy_client,
+    )
+
+    def stream():
+        stream = client.models.generate_content_stream(
+            model="gemini-3.5-flash", contents="Explain singularity in short terms."
+        )
+        for chunk in stream:
+            if chunk.text:
+                yield chunk.text
+
+    return StreamingResponse(stream(), media_type="text/plain")
+
+
+def tool_call():
+    """
+    Run chat example including a tool call for Gemini 3.5 Flash.
+
+    Returns:
+        JSON object containing the model response as result.
+    """
+
+    # addition tool to call
+    def add(a: int, b: int) -> int:
+        """Add two numbers."""
+        return a + b
+
+    proxy_client = get_proxy_client("gen-ai-hub")
+
+    client = Client(
+        proxy_client=proxy_client,
+    )
+    response = client.models.generate_content(
+        model="gemini-3.5-flash",
+        contents="What is 769 + 348?",
+        config=types.GenerateContentConfig(tools=[add]),
+    )
+    return {"result": response.candidates[0].content.parts[0].text}