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# SDK for SAP ABAP를 사용한 Amazon Bedrock 런타임 예
<a name="sap-abap_bedrock-runtime_code_examples"></a>

다음 코드 예제에서는 Amazon Bedrock 런타임과 함께 AWS SDK for SAP ABAP를 사용하여 작업을 수행하고 일반적인 시나리오를 구현하는 방법을 보여줍니다.

각 예시에는 전체 소스 코드에 대한 링크가 포함되어 있으며, 여기에서 컨텍스트에 맞춰 코드를 설정하고 실행하는 방법에 대한 지침을 찾을 수 있습니다.

**Topics**
+ [Anthropic Claude](#anthropic_claude)
+ [Stable Diffusion](#stable_diffusion)

## Anthropic Claude
<a name="anthropic_claude"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModel_AnthropicClaude_sap-abap_topic"></a>

다음 코드 예제에서는 Invoke Model API를 사용하여 Anthropic Claude에 텍스트 메시지를 보내는 방법을 보여줍니다.

**SDK for SAP ABAP API**  
 GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/bdr#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요.
Anthropic Claude 2 파운데이션 모델을 간접 호출하여 텍스트를 생성합니다. 이 예에서는 일부 NetWeaver 버전에서는 사용할 수 없는 /US2/CL\$1JSON 기능을 사용합니다.  

```
    "Claude V2 Input Parameters should be in a format like this:
*   {
*     "prompt":"\n\nHuman:\\nTell me a joke\n\nAssistant:\n",
*     "max_tokens_to_sample":2048,
*     "temperature":0.5,
*     "top_k":250,
*     "top_p":1.0,
*     "stop_sequences":[]
*   }

    DATA: BEGIN OF ls_input,
            prompt               TYPE string,
            max_tokens_to_sample TYPE /aws1/rt_shape_integer,
            temperature          TYPE /aws1/rt_shape_float,
            top_k                TYPE /aws1/rt_shape_integer,
            top_p                TYPE /aws1/rt_shape_float,
            stop_sequences       TYPE /aws1/rt_stringtab,
          END OF ls_input.

    "Leave ls_input-stop_sequences empty.
    ls_input-prompt = |\n\nHuman:\\n{ iv_prompt }\n\nAssistant:\n|.
    ls_input-max_tokens_to_sample = 2048.
    ls_input-temperature = '0.5'.
    ls_input-top_k = 250.
    ls_input-top_p = 1.

    "Serialize into JSON with /ui2/cl_json -- this assumes SAP_UI is installed.
    DATA(lv_json) = /ui2/cl_json=>serialize(
      data = ls_input
                pretty_name   = /ui2/cl_json=>pretty_mode-low_case ).

    TRY.
        DATA(lo_response) = lo_bdr->invokemodel(
          iv_body = /aws1/cl_rt_util=>string_to_xstring( lv_json )
          iv_modelid = 'anthropic.claude-v2'
          iv_accept = 'application/json'
          iv_contenttype = 'application/json' ).

        "Claude V2 Response format will be:
*       {
*         "completion": "Knock Knock...",
*         "stop_reason": "stop_sequence"
*       }
        DATA: BEGIN OF ls_response,
                completion  TYPE string,
                stop_reason TYPE string,
              END OF ls_response.

        /ui2/cl_json=>deserialize(
          EXPORTING jsonx = lo_response->get_body( )
                    pretty_name = /ui2/cl_json=>pretty_mode-camel_case
          CHANGING  data  = ls_response ).

        DATA(lv_answer) = ls_response-completion.
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.
```
L2 상위 수준 클라이언트를 사용해 텍스트를 생성하도록 Anthropic Claude 2 파운데이션 모델을 간접 호출합니다.  

```
    TRY.
        DATA(lo_bdr_l2_claude) = /aws1/cl_bdr_l2_factory=>create_claude_2( lo_bdr ).
        " iv_prompt can contain a prompt like 'tell me a joke about Java programmers'.
        DATA(lv_answer) = lo_bdr_l2_claude->prompt_for_text( iv_prompt ).
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.
```
L2 상위 수준 클라이언트를 사용해 텍스트를 생성하도록 Anthropic Claude 3 파운데이션 모델을 간접 호출합니다.  

```
    TRY.
        " Choose a model ID from Anthropic that supports the Messages API - currently this is
        " Claude v2, Claude v3 and v3.5.  For the list of model ID, see:
        " https://docs.aws.amazon.com/bedrock/latest/userguide/model-ids.html

        " for the list of models that support the Messages API see:
        " https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
        DATA(lo_bdr_l2_claude) = /aws1/cl_bdr_l2_factory=>create_anthropic_msg_api(
          io_bdr = lo_bdr
          iv_model_id = 'anthropic.claude-3-sonnet-20240229-v1:0' ).  " choosing Claude v3 Sonnet
        " iv_prompt can contain a prompt like 'tell me a joke about Java programmers'.
        DATA(lv_answer) = lo_bdr_l2_claude->prompt_for_text( iv_prompt = iv_prompt
                                                             iv_max_tokens = 100 ).
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.
```
+  API에 대한 세부 정보는 *AWS SDK for SAP ABAP API 참조*의 [InvokeModel](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)을 참조하세요.

## Stable Diffusion
<a name="stable_diffusion"></a>

### InvokeModel
<a name="bedrock-runtime_InvokeModel_StableDiffusion_sap-abap_topic"></a>

다음 코드 예제에서는 이미지 생성을 위해 Amazon Bedrock에서 Stability.ai Stable Diffusion XL 모델을 간접적으로 호출하는 방법을 보여줍니다.

**SDK for SAP ABAP API**  
 GitHub에 더 많은 내용이 있습니다. [AWS 코드 예 리포지토리](https://github.com/awsdocs/aws-doc-sdk-examples/tree/main/sap-abap/services/bdr#code-examples)에서 전체 예를 찾고 설정 및 실행하는 방법을 배워보세요.
Stable Diffusion을 사용하여 이미지를 생성합니다.  

```
    "Stable Diffusion Input Parameters should be in a format like this:
*   {
*     "text_prompts": [
*       {"text":"Draw a dolphin with a mustache"},
*       {"text":"Make it photorealistic"}
*     ],
*     "cfg_scale":10,
*     "seed":0,
*     "steps":50
*   }
    TYPES: BEGIN OF prompt_ts,
             text TYPE /aws1/rt_shape_string,
           END OF prompt_ts.

    DATA: BEGIN OF ls_input,
            text_prompts TYPE STANDARD TABLE OF prompt_ts,
            cfg_scale    TYPE /aws1/rt_shape_integer,
            seed         TYPE /aws1/rt_shape_integer,
            steps        TYPE /aws1/rt_shape_integer,
          END OF ls_input.

    APPEND VALUE prompt_ts( text = iv_prompt ) TO ls_input-text_prompts.
    ls_input-cfg_scale = 10.
    ls_input-seed = 0. "or better, choose a random integer.
    ls_input-steps = 50.

    DATA(lv_json) = /ui2/cl_json=>serialize(
      data = ls_input
                pretty_name   = /ui2/cl_json=>pretty_mode-low_case ).

    TRY.
        DATA(lo_response) = lo_bdr->invokemodel(
          iv_body = /aws1/cl_rt_util=>string_to_xstring( lv_json )
          iv_modelid = 'stability.stable-diffusion-xl-v1'
          iv_accept = 'application/json'
          iv_contenttype = 'application/json' ).

        "Stable Diffusion Result Format:
*       {
*         "result": "success",
*         "artifacts": [
*           {
*             "seed": 0,
*             "base64": "iVBORw0KGgoAAAANSUhEUgAAAgAAA....
*             "finishReason": "SUCCESS"
*           }
*         ]
*       }
        TYPES: BEGIN OF artifact_ts,
                 seed         TYPE /aws1/rt_shape_integer,
                 base64       TYPE /aws1/rt_shape_string,
                 finishreason TYPE /aws1/rt_shape_string,
               END OF artifact_ts.

        DATA: BEGIN OF ls_response,
                result    TYPE /aws1/rt_shape_string,
                artifacts TYPE STANDARD TABLE OF artifact_ts,
              END OF ls_response.

        /ui2/cl_json=>deserialize(
          EXPORTING jsonx = lo_response->get_body( )
                    pretty_name = /ui2/cl_json=>pretty_mode-camel_case
          CHANGING  data  = ls_response ).
        IF ls_response-artifacts IS NOT INITIAL.
          DATA(lv_image) = cl_http_utility=>if_http_utility~decode_x_base64( ls_response-artifacts[ 1 ]-base64 ).
        ENDIF.
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.
```
L2 상위 수준 클라이언트를 사용하여 Stability.ai Stable Diffusion XL 파운데이션 모델을 간접적으로 호출해 이미지를 생성합니다.  

```
    TRY.
        DATA(lo_bdr_l2_sd) = /aws1/cl_bdr_l2_factory=>create_stable_diffusion_xl_1( lo_bdr ).
        " iv_prompt contains a prompt like 'Show me a picture of a unicorn reading an enterprise financial report'.
        DATA(lv_image) = lo_bdr_l2_sd->text_to_image( iv_prompt ).
      CATCH /aws1/cx_bdraccessdeniedex INTO DATA(lo_ex).
        WRITE / lo_ex->get_text( ).
        WRITE / |Don't forget to enable model access at https://console.aws.amazon.com/bedrock/home?#/modelaccess|.

    ENDTRY.
```
+  API에 대한 세부 정보는 *AWS SDK for SAP ABAP API 참조*의 [InvokeModel](https://docs.aws.amazon.com/sdk-for-sap-abap/v1/api/latest/index.html)을 참조하세요.