Requirements for creating AI service assets

When you use an Integrated Development Environment (IDE) such as VSCode, eclipse, PyCharm, or more to build your generative AI application, you must create a Python file to store your AI service. Once you define the function, you must compress the AI service to create a gzip archive (.gz file format).

When you use the watsonx.ai Python client library to create your AI service asset, the library automatically stores the function in gzip archive for you. However, when you create an AI service asset by using the REST API, you must follow the process of manually compressing your Python file in a gzip archive.

You must use the runtime-24.1-py3.11 software specification to create and deploy an AI service asset that is coded in Python.

Creating AI service assets with Python client library

You can use the store_ai_service function of the watsonx.ai Python client library to create an AI service asset.

The following code sample shows how to create an AI service asset by using the Python client library:

documentation_request = {
    "application/json": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "type": "object",
        "properties": {
            "query": {"type": "string"},
            "parameters": {
                "properties": {
                    "max_new_tokens": {"type": "integer"},
                    "top_p": {"type": "number"},
                },
                "required": ["max_new_tokens", "top_p"],
            },
        },
        "required": ["query"],
    }
}

documentation_response = {
    "application/json": {
        "$schema": "http://json-schema.org/draft-07/schema#",
        "type": "object",
        "properties": {"query": {"type": "string"}, "result": {"type": "string"}},
        "required": ["query", "result"],
    }
}


meta_props = {
    client.repository.AIServiceMetaNames.NAME: "AI service example",
    client.repository.AIServiceMetaNames.DESCRIPTION: "This is AI service function",
    client.repository.AIServiceMetaNames.SOFTWARE_SPEC_ID: client.software_specifications.get_id_by_name(
        "runtime-24.1-py3.11"
    ),
    client.repository.AIServiceMetaNames.REQUEST_DOCUMENTATION: documentation_request,
    client.repository.AIServiceMetaNames.RESPONSE_DOCUMENTATION: documentation_response,
}

stored_ai_service_details = client.repository.store_ai_service(
    basic_generate_demo, meta_props
)

ai_service_id = client.repository.get_ai_service_id(stored_ai_service_details)
print("The AI service asset id:", ai_service_id)

For more information, see watsonx.ai Python client library documentation for creating an AI service asset.

Learn more

• Deploying AI service assets

Parent topic: Deploying AI services with direct coding