Revert "chore: use node specify llm to auto generate prompt" (#6555)

web/app/components/app/configuration/config-prompt/simple-prompt-input.tsx

 import cn from '@/utils/classnames'
 import { type PromptVariable } from '@/models/debug'
 import Tooltip from '@/app/components/base/tooltip'
-import type { CompletionParams } from '@/types/app'
 import { AppType } from '@/types/app'
 import { getNewVar, getVars } from '@/utils/var'
 import AutomaticBtn from '@/app/components/app/configuration/config/automatic/automatic-btn'
   const { eventEmitter } = useEventEmitterContextContext()
   const {
     modelConfig,
-    completionParams,
     dataSets,
     setModelConfig,
     setPrevPromptConfig,
       {showAutomatic && (
         <GetAutomaticResModal
           mode={mode as AppType}
-          model={
-            {
-              provider: modelConfig.provider,
-              name: modelConfig.model_id,
-              mode: modelConfig.mode,
-              completion_params: completionParams as CompletionParams,
-            }
-          }
           isShow={showAutomatic}
           onClose={showAutomaticFalse}
           onFinished={handleAutomaticRes}

web/app/components/app/configuration/config/automatic/get-automatic-res.tsx

 import Toast from '@/app/components/base/toast'
 import { generateRule } from '@/service/debug'
 import ConfigPrompt from '@/app/components/app/configuration/config-prompt'
-import type { Model } from '@/types/app'
 import { AppType } from '@/types/app'
 import ConfigVar from '@/app/components/app/configuration/config-var'
 import OpeningStatement from '@/app/components/app/configuration/features/chat-group/opening-statement'
 
 export type IGetAutomaticResProps = {
   mode: AppType
-  model: Model
   isShow: boolean
   onClose: () => void
   onFinished: (res: AutomaticRes) => void
 
 const GetAutomaticRes: FC<IGetAutomaticResProps> = ({
   mode,
-  model,
   isShow,
   onClose,
   isInLLMNode,
       return
     setLoadingTrue()
     try {
-      const { error, ...res } = await generateRule({
+      const res = await generateRule({
         instruction,
-        model_config: model,
       })
       setRes(res)
-      if (error) {
-        Toast.notify({
-          type: 'error',
-          message: error,
-        })
-      }
     }
     finally {
       setLoadingFalse()

web/app/components/workflow/nodes/_base/components/prompt/editor.tsx

 import { useBoolean } from 'ahooks'
 import { BlockEnum, EditionType } from '../../../../types'
 import type {
-  ModelConfig,
   Node,
   NodeOutPutVar,
   Variable,
   availableNodes?: Node[]
   isSupportPromptGenerator?: boolean
   onGenerated?: (prompt: string) => void
-  modelConfig?: ModelConfig
   // for jinja
   isSupportJinja?: boolean
   editionType?: EditionType
   varList = [],
   handleAddVariable,
   onGenerated,
-  modelConfig,
 }) => {
   const { t } = useTranslation()
   const { eventEmitter } = useEventEmitterContextContext()
             <div className='flex items-center'>
               <div className='leading-[18px] text-xs font-medium text-gray-500'>{value?.length || 0}</div>
               {isSupportPromptGenerator && (
-                <PromptGeneratorBtn className='ml-[5px]' onGenerated={onGenerated} modelConfig={modelConfig} />
+                <PromptGeneratorBtn className='ml-[5px]' onGenerated={onGenerated} />
               )}
 
               <div className='w-px h-3 ml-2 mr-2 bg-gray-200'></div>

web/app/components/workflow/nodes/llm/components/config-prompt-item.tsx

 import { uniqueId } from 'lodash-es'
 import { useTranslation } from 'react-i18next'
 import { RiQuestionLine } from '@remixicon/react'
-import type { ModelConfig, PromptItem, Variable } from '../../../types'
+import type { PromptItem, Variable } from '../../../types'
 import { EditionType } from '../../../types'
 import { useWorkflowStore } from '../../../store'
 import Editor from '@/app/components/workflow/nodes/_base/components/prompt/editor'
   availableNodes: any
   varList: Variable[]
   handleAddVariable: (payload: any) => void
-  modelConfig?: ModelConfig
 }
 
 const roleOptions = [
   availableNodes,
   varList,
   handleAddVariable,
-  modelConfig,
 }) => {
   const { t } = useTranslation()
   const workflowStore = useWorkflowStore()
       availableNodes={availableNodes}
       isSupportPromptGenerator={payload.role === PromptRole.system}
       onGenerated={handleGenerated}
-      modelConfig={modelConfig}
       isSupportJinja
       editionType={payload.edition_type}
       onEditionTypeChange={onEditionTypeChange}

web/app/components/workflow/nodes/llm/components/config-prompt.tsx

 import produce from 'immer'
 import { ReactSortable } from 'react-sortablejs'
 import { v4 as uuid4 } from 'uuid'
-import type { ModelConfig, PromptItem, ValueSelector, Var, Variable } from '../../../types'
+import type { PromptItem, ValueSelector, Var, Variable } from '../../../types'
 import { EditionType, PromptRole } from '../../../types'
 import useAvailableVarList from '../../_base/hooks/use-available-var-list'
 import { useWorkflowStore } from '../../../store'
   }
   varList?: Variable[]
   handleAddVariable: (payload: any) => void
-  modelConfig: ModelConfig
 }
 
 const ConfigPrompt: FC<Props> = ({
   hasSetBlockStatus,
   varList = [],
   handleAddVariable,
-  modelConfig,
 }) => {
   const { t } = useTranslation()
   const workflowStore = useWorkflowStore()
                           availableNodes={availableNodesWithParent}
                           varList={varList}
                           handleAddVariable={handleAddVariable}
-                          modelConfig={modelConfig}
                         />
                       </div>
                     )
               onEditionTypeChange={handleCompletionEditionTypeChange}
               handleAddVariable={handleAddVariable}
               onGenerated={handleGenerated}
-              modelConfig={modelConfig}
             />
           </div>
         )}

web/app/components/workflow/nodes/llm/components/prompt-generator-btn.tsx

 import GetAutomaticResModal from '@/app/components/app/configuration/config/automatic/get-automatic-res'
 import { AppType } from '@/types/app'
 import type { AutomaticRes } from '@/service/debug'
-import type { ModelConfig } from '@/app/components/workflow/types'
-import type { Model } from '@/types/app'
-
 type Props = {
   className?: string
   onGenerated?: (prompt: string) => void
-  modelConfig?: ModelConfig
 }
 
 const PromptGeneratorBtn: FC<Props> = ({
   className,
   onGenerated,
-  modelConfig,
 }) => {
   const [showAutomatic, { setTrue: showAutomaticTrue, setFalse: showAutomaticFalse }] = useBoolean(false)
   const handleAutomaticRes = useCallback((res: AutomaticRes) => {
           isShow={showAutomatic}
           onClose={showAutomaticFalse}
           onFinished={handleAutomaticRes}
-          model={modelConfig as Model}
           isInLLMNode
         />
       )}

web/app/components/workflow/nodes/llm/panel.tsx

             hasSetBlockStatus={hasSetBlockStatus}
             varList={inputs.prompt_config?.jinja2_variables || []}
             handleAddVariable={handleAddVariable}
-            modelConfig={model}
           />
         )}
 

web/service/debug.ts

   prompt: string
   variables: string[]
   opening_statement: string
-  error?: string
 }
 
 export const sendChatMessage = async (appId: string, body: Record<string, any>, { onData, onCompleted, onThought, onFile, onError, getAbortController, onMessageEnd, onMessageReplace }: {

web/types/app.ts

   react = 'react',
 }
 
-export type CompletionParams = {
-  /** Maximum number of tokens in the answer message returned by Completion */
-  max_tokens: number
-  /**
-   * A number between 0 and 2.
-   * The larger the number, the more random the result;
-   * otherwise, the more deterministic.
-   * When in use, choose either `temperature` or `top_p`.
-   * Default is 1.
-   */
-  temperature: number
-  /**
-   * Represents the proportion of probability mass samples to take,
-   * e.g., 0.1 means taking the top 10% probability mass samples.
-   * The determinism between the samples is basically consistent.
-   * Among these results, the `top_p` probability mass results are taken.
-   * When in use, choose either `temperature` or `top_p`.
-   * Default is 1.
-   */
-  top_p: number
-  /** When enabled, the Completion Text will concatenate the Prompt content together and return it. */
-  echo: boolean
-  /**
-   * Specify up to 4 to automatically stop generating before the text specified in `stop`.
-   * Suitable for use in chat mode.
-   * For example, specify "Q" and "A",
-   * and provide some Q&A examples as context,
-   * and the model will give out in Q&A format and stop generating before Q&A.
-   */
-  stop: string[]
-  /**
-   * A number between -2.0 and 2.0.
-   * The larger the value, the less the model will repeat topics and the more it will provide new topics.
-   */
-  presence_penalty: number
-  /**
-   * A number between -2.0 and 2.0.
-   * A lower setting will make the model appear less cultured,
-   * always repeating expressions.
-   * The difference between `frequency_penalty` and `presence_penalty`
-   * is that `frequency_penalty` penalizes a word based on its frequency in the training data,
-   * while `presence_penalty` penalizes a word based on its occurrence in the input text.
-   */
-  frequency_penalty: number
-}
 /**
  * Model configuration. The backend type.
  */
-export type Model = {
-  /** LLM provider, e.g., OPENAI */
-  provider: string
-  /** Model name, e.g, gpt-3.5.turbo */
-  name: string
-  mode: ModelModeType
-  /** Default Completion call parameters */
-  completion_params: CompletionParams
-}
-
 export type ModelConfig = {
   opening_statement: string
   suggested_questions?: string[]
     strategy?: AgentStrategy
     tools: ToolItem[]
   }
-  model: Model
+  model: {
+    /** LLM provider, e.g., OPENAI */
+    provider: string
+    /** Model name, e.g, gpt-3.5.turbo */
+    name: string
+    mode: ModelModeType
+    /** Default Completion call parameters */
+    completion_params: {
+      /** Maximum number of tokens in the answer message returned by Completion */
+      max_tokens: number
+      /**
+       * A number between 0 and 2.
+       * The larger the number, the more random the result;
+       * otherwise, the more deterministic.
+       * When in use, choose either `temperature` or `top_p`.
+       * Default is 1.
+       */
+      temperature: number
+      /**
+       * Represents the proportion of probability mass samples to take,
+       * e.g., 0.1 means taking the top 10% probability mass samples.
+       * The determinism between the samples is basically consistent.
+       * Among these results, the `top_p` probability mass results are taken.
+       * When in use, choose either `temperature` or `top_p`.
+       * Default is 1.
+       */
+      top_p: number
+      /** When enabled, the Completion Text will concatenate the Prompt content together and return it. */
+      echo: boolean
+      /**
+       * Specify up to 4 to automatically stop generating before the text specified in `stop`.
+       * Suitable for use in chat mode.
+       * For example, specify "Q" and "A",
+       * and provide some Q&A examples as context,
+       * and the model will give out in Q&A format and stop generating before Q&A.
+       */
+      stop: string[]
+      /**
+       * A number between -2.0 and 2.0.
+       * The larger the value, the less the model will repeat topics and the more it will provide new topics.
+       */
+      presence_penalty: number
+      /**
+       * A number between -2.0 and 2.0.
+       * A lower setting will make the model appear less cultured,
+       * always repeating expressions.
+       * The difference between `frequency_penalty` and `presence_penalty`
+       * is that `frequency_penalty` penalizes a word based on its frequency in the training data,
+       * while `presence_penalty` penalizes a word based on its occurrence in the input text.
+       */
+      frequency_penalty: number
+    }
+  }
   dataset_configs: DatasetConfigs
   file_upload?: {
     image: VisionSettings