OpenAI #FtJob: Create

// GraalJS standard mode Script Script (engine type: 3)
// cf. 'engine type: 2': "GraalJS Nashorn compatible mode" (renamed from "GraalJS" at 20230526)


//////// START "main()" /////////////////////////////////////////////////////////////////

main();
function main(){ 

////// == Config Retrieving / 工程コンフィグの参照 ==
const strAuthzSetting     = configs.get( "AuthzConfU1" );             /// REQUIRED
  engine.log( " AutomatedTask Config: Authz Setting: " + strAuthzSetting );
const strOrgId            = configs.get( "StrConfU2" );               // NotRequired
  engine.log( " AutomatedTask Config: OpenAI-Organization: " + strOrgId );

const strFileIdTraining   = configs.get( "StrConfA1" );               /// REQUIRED
const strFileIdValidation = configs.get( "StrConfA2" );               // NotRequired
const strBaseModel        = configs.get( "StrConfA3" );               // NotRequired
const strSuffix           = configs.get( "StrConfA4" );               // NotRequired

const strPocketJobId      = configs.getObject( "SelectConfB1" );      /// REQUIRED



////// == Data Retrieving / ワークフローデータの参照 ==
// (Nothing. Retrieved via Expression Language in Config Retrieving)



////// == Calculating / 演算 ==

//// OpenAI API > Documentation > API REFERENCE > Fine-tunes > Create fine-tune
//// https://platform.openai.com/docs/api-reference/fine-tunes/create

/// prepare json
let strJson = {};
    strJson.training_file = strFileIdTraining;
    if ( strFileIdValidation !== "" ){
      strJson.validation_file = strFileIdValidation;
    }
    if ( strBaseModel !== "" ){
      strJson.model = strBaseModel;
    }
    if ( strSuffix !== "" ){
      strJson.suffix = strSuffix;
    }

/// prepare request1
let request1Uri = "https://api.openai.com/v1/fine-tunes";
let request1 = httpClient.begin(); // HttpRequestWrapper
    request1 = request1.authSetting( strAuthzSetting ); // with "Authorization: Bearer XX"
    if ( strOrgId !== "" ){
      request1 = request1.header( "OpenAI-Organization", strOrgId );
    }
    request1 = request1.body( JSON.stringify( strJson ), "application/json" );

/// try request1
const response1     = request1.post( request1Uri ); // HttpResponseWrapper
engine.log( " AutomatedTask ApiRequest1 Start: " + request1Uri );
const response1Code = response1.getStatusCode() + ""; // JavaNum to string
const response1Body = response1.getResponseAsString();
engine.log( " AutomatedTask ApiResponse1 Status: " + response1Code );
if( response1Code !== "200"){
  throw new Error( "\n AutomatedTask UnexpectedResponseError: " +
                    response1Code + "\n" + response1Body + "\n" );
}
/* engine.log( response1Body ); // debug
{
  "object": "fine-tune",
  "id": "ft-lN1WPLOp8rtoalbMG5Nctc0T",
  "hyperparams": {
    "n_epochs": 4,
    "batch_size": null,
    "prompt_loss_weight": 0.01,
    "learning_rate_multiplier": null
  },
  "organization_id": "org-xxxxxyyyyyzzzzzXXXXXYYYY",
  "model": "curie",
  "training_files": [
    {
      "object": "file",
      "id": "file-rM8tP15b9pZ2tJSLZEfUl7fG",
      "purpose": "fine-tune",
      "filename": "training-data-questetra-bpm-suite-assistant.jsonl",
      "bytes": 6823,
      "created_at": 1690249576,
      "status": "processed",
      "status_details": null
    }
  ],
  "validation_files": [],
  "result_files": [],
  "created_at": 1690354635,
  "updated_at": 1690354635,
  "status": "pending",
  "fine_tuned_model": null,
  "events": [
    {
      "object": "fine-tune-event",
      "level": "info",
      "message": "Created fine-tune: ft-lN1WPLOp8rtoalbMG5Nctc0T",
      "created_at": 1690354635
    }
  ]
}
*/

/// parse response1
const response1Obj = JSON.parse( response1Body );



////// == Data Updating / ワークフローデータへの代入 ==
if( strPocketJobId !== null ){
  engine.setData( strPocketJobId, response1Obj.id );
}


} //////// END "main()" /////////////////////////////////////////////////////////////////


/*
Notes:
- This "Automated Step" will start to generate a Fine-Tuned MODEL on the OpenAI server.
    - It is possible to specify the base MODEL.
    - The confirmation step is placed downstream. (OpenAI #FtJob: Retrieve)
- If you place this "Automated Step" in the Workflow diagram, the request will be automatically sent every time the process token arrives.
    - A request is automatically sent to the OpenAI API server. (REST API)
    - The response from the OpenAI API server is automatically parsed.

APPENDIX
- To activate a Workflow App that includes this Automated Step, "HTTP Authz Setting" is required
    - Obtain a "Secret API Key" in advance.
    - Set the key as the communication token in "Token Fixed Value"
- This automated step does not support the following parameters.
     - `n_epochs`
     - `batch_size`
     - `learning_rate_multiplier`
     - `prompt_loss_weight`
     - `compute_classification_metrics`
     - `classification_n_classes`
     - `classification_positive_class`
     -`classification_betas`
- Check the OpenAI document for the MODEL that can be specified as the Base MODEL for fine tuning.
    - https://platform.openai.com/docs/guides/fine-tuning/what-models-can-be-fine-tuned
    - Asof 202307: Fine-tuning is currently only available for the following base models: `davinci`, `curie`, `babbage`, and `ada`. We are working on safely enabling fine-tuning for GPT-4 and GPT-3.5 Turbo and expect this feature to be available later this year.
- When setting SUFFIX, set it within 40 characters.
     - eg: "custom-model-name"
     - MODEL name: "ada:ft-your-org:custom-model-name-2022-02-15-04-21-04"


Notes-ja:
- この［自動工程］は、OpenAI サーバ上の Fine-Tuned MODEL 生成を開始させます。
    - ベースとなる MODEL を指定できます。
    - ファインチューニング（微調整）の成果は、下流に進捗確認工程を配置し確認します。（OpenAI #FtJob: 進捗確認）
- この［自動工程］をワークフロー図に配置すれば、案件が到達する度にリクエストが自動送信されます。
    - OpenAI API サーバに対してリクエストが自動送出されます。（REST API通信）
    - OpenAI API サーバからのレスポンスが自動保存解析されます。

APPENDIX-ja
- この［アドオン自動工程］を含むワークフローアプリを運用するには［HTTP 認証設定］が必要です。
    - あらかじめ "Secret API Key" を取得しておいてください。
    - "Secret API Key" を通信トークンとしてセットします。［トークン直接指定］
- この自動工程は、以下のパラメータに対応していません。
    - `n_epochs`
    - `batch_size`
    - `learning_rate_multiplier`
    - `prompt_loss_weight`
    - `compute_classification_metrics`
    - `classification_n_classes`
    - `classification_positive_class`
    - `classification_betas`
- ファインチューニングの Base MODEL として指定可能な MODEL については OpenAI ドキュメントを参照ください。
    - https://platform.openai.com/docs/guides/fine-tuning/what-models-can-be-fine-tuned
    - Asof 202307: Fine-tuning is currently only available for the following base models: `davinci`, `curie`, `babbage`, and `ada`. We are working on safely enabling fine-tuning for GPT-4 and GPT-3.5 Turbo and expect this feature to be available later this year.
- SUFFIX を設定する場合は40文字以内で設定します。
    - eg: "custom-model-name"
    - MODEL name: "ada:ft-your-org:custom-model-name-2022-02-15-04-21-04"
*/