Testing AI services with Python client library

To test the logic of your AI service locally by using the RuntimeContext class of the watsonx.ai Python client library, follow these steps:

1. Use the RuntimeContext class of the Python client library to test your AI service locally:

   from ibm_watsonx_ai.deployments import RuntimeContext
   
   context = RuntimeContext(
       api_client=client, request_payload_json={}
   )
   
   # custom is optional argument which is specified during the time of creation of deployment
   custom_object = {"space_id": space_id}
   
   generate, generate_stream, generate_batch = basic_generate_demo(context, **custom_object)
   
   

   For more information, see watsonx.ai Python client library documentation for using RuntimeContext for AI services.

2. Depending on your use case, you can test the generate(), generate_stream(), or generate_batch() functions as follows:

   • To test the generate() function:

     context.request_payload_json = { "test": "ai_service inference payload"}
     print(generate(context))
     

   • To test the generate_stream() function:

     context.request_payload_json = {"sse": ["ai_service_stream", "inference", "test"]}
     for data in generate_stream(context):
         print(data)
     

   • To test the generate_batch() function:

     input_data_references = [
         {
             "type": "connection_asset",
             "connection": {"id": "2d07a6b4-8fa9-43ab-91c8-befcd9dab8d2"},
             "location": {
                 "bucket": "wml-v4-fvt-batch-pytorch-connection-input",
                 "file_name": "testing-123",
             },
         }
     ]
     output_data_reference = {
         "type": "data_asset",
         "location": {"name": "nb-pytorch_output.zip"},
     }
     
     generate_batch(input_data_references, output_data_reference)
     

Learn more

• Creating AI service assets

Parent topic: Deploying AI services with direct coding