Feat: Move the pipeline translation field to flow #9869 (#10697)

### What problem does this PR solve?

Feat: Move the pipeline translation field to flow #9869

### Type of change


- [X] New Feature (non-breaking change which adds functionality)

web/src/locales/en.ts

@@ -1605,6 +1605,119 @@ This delimiter is used to split the input text into several text pieces echo of
       ceateAgent: 'Agent flow',
       createPipeline: 'Ingestion pipeline',
       chooseAgentType: 'Choose Agent Type',
+      parser: 'Parser',
+      parserDescription:
+        'Extracts raw text and structure from files for downstream processing.',
+      tokenizer: 'Indexer',
+      tokenizerRequired: 'Please add the Indexer node first',
+      tokenizerDescription:
+        'Transforms text into the required data structure (e.g., vector embeddings for Embedding Search) depending on the chosen search method.',
+      splitter: 'Token',
+      splitterDescription:
+        'Split text into chunks by token length with optional delimiters and overlap.',
+      hierarchicalMergerDescription:
+        'Split documents into sections by title hierarchy with regex rules for finer control.',
+      hierarchicalMerger: 'Title',
+      extractor: 'Transformer',
+      extractorDescription:
+        'Use an LLM to extract structured insights from document chunks—such as summaries, classifications, etc.',
+      outputFormat: 'Output format',
+      fileFormats: 'File format',
+      fileFormatOptions: {
+        pdf: 'PDF',
+        spreadsheet: 'Spreadsheet',
+        image: 'Image',
+        email: 'Email',
+        'text&markdown': 'Text & Markup',
+        word: 'Word',
+        slides: 'PPT',
+        audio: 'Audio',
+      },
+      fields: 'Field',
+      addParser: 'Add Parser',
+      hierarchy: 'Hierarchy',
+      regularExpressions: 'Regular Expressions',
+      overlappedPercent: 'Overlapped percent (%)',
+      searchMethod: 'Search method',
+      searchMethodTip: `Defines how the content can be searched — by full-text, embedding, or both.
+The Indexer will store the content in the corresponding data structures for the selected methods.`,
+      // file: 'File',
+      parserMethod: 'Parsing method',
+      // systemPrompt: 'System Prompt',
+      systemPromptPlaceholder:
+        'Enter system prompt for image analysis, if empty the system default value will be used',
+      exportJson: 'Export JSON',
+      viewResult: 'View result',
+      running: 'Running',
+      summary: 'Summary',
+      keywords: 'Keywords',
+      questions: 'Questions',
+      metadata: 'Metadata',
+      fieldName: 'Result destination',
+      prompts: {
+        system: {
+          keywords: `Role
+You are a text analyzer.
+
+Task
+Extract the most important keywords/phrases of a given piece of text content.
+
+Requirements
+- Summarize the text content, and give the top 5 important keywords/phrases.
+- The keywords MUST be in the same language as the given piece of text content.
+- The keywords are delimited by ENGLISH COMMA.
+- Output keywords ONLY.`,
+          questions: `Role
+You are a text analyzer.
+
+Task
+Propose 3 questions about a given piece of text content.
+
+Requirements
+- Understand and summarize the text content, and propose the top 3 important questions.
+- The questions SHOULD NOT have overlapping meanings.
+- The questions SHOULD cover the main content of the text as much as possible.
+- The questions MUST be in the same language as the given piece of text content.
+- One question per line.
+- Output questions ONLY.`,
+          summary: `Act as a precise summarizer. Your task is to create a summary of the provided content that is both concise and faithful to the original.
+
+Key Instructions:
+1. Accuracy: Strictly base the summary on the information given. Do not introduce any new facts, conclusions, or interpretations that are not explicitly stated.
+2. Language: Write the summary in the same language as the source text.
+3. Objectivity: Present the key points without bias, preserving the original intent and tone of the content. Do not editorialize.
+4. Conciseness: Focus on the most important ideas, omitting minor details and fluff.`,
+          metadata: `Extract important structured information from the given content. Output ONLY a valid JSON string with no additional text. If no important structured information is found, output an empty JSON object: {}.
+
+Important structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities.`,
+        },
+        user: {
+          keywords: `Text Content
+[Insert text here]`,
+          questions: `Text Content
+[Insert text here]`,
+          summary: `Text to Summarize:
+[Insert text here]`,
+          metadata: `Content: [INSERT CONTENT HERE]`,
+        },
+      },
+      cancel: 'Cancel',
+      swicthPromptMessage:
+        'The prompt word will change. Please confirm whether to abandon the existing prompt word?',
+      tokenizerSearchMethodOptions: {
+        full_text: 'Full-text',
+        embedding: 'Embedding',
+      },
+      filenameEmbeddingWeight: 'Filename embedding weight',
+      tokenizerFieldsOptions: {
+        text: 'Processed Text',
+        keywords: 'Keywords',
+        questions: 'Questions',
+        summary: 'Augmented Context',
+      },
+      imageParseMethodOptions: {
+        ocr: 'OCR',
+      },
     },
     llmTools: {
       bad_calculator: {
@@ -1705,125 +1818,6 @@ This delimiter is used to split the input text into several text pieces echo of
       <p>Are you sure you want to proceed?</p> `,
       unlinkPipelineModalConfirmText: 'Unlink',
     },
-    dataflow: {
-      parser: 'Parser',
-      parserDescription:
-        'Extracts raw text and structure from files for downstream processing.',
-      tokenizer: 'Indexer',
-      tokenizerRequired: 'Please add the Indexer node first',
-      tokenizerDescription:
-        'Transforms text into the required data structure (e.g., vector embeddings for Embedding Search) depending on the chosen search method.',
-      splitter: 'Token',
-      splitterDescription:
-        'Split text into chunks by token length with optional delimiters and overlap.',
-      hierarchicalMergerDescription:
-        'Split documents into sections by title hierarchy with regex rules for finer control.',
-      hierarchicalMerger: 'Title',
-      extractor: 'Transformer',
-      extractorDescription:
-        'Use an LLM to extract structured insights from document chunks—such as summaries, classifications, etc.',
-      outputFormat: 'Output format',
-      lang: 'Language',
-      fileFormats: 'File format',
-      fileFormatOptions: {
-        pdf: 'PDF',
-        spreadsheet: 'Spreadsheet',
-        image: 'Image',
-        email: 'Email',
-        'text&markdown': 'Text & Markup',
-        word: 'Word',
-        slides: 'PPT',
-        audio: 'Audio',
-      },
-      fields: 'Field',
-      addParser: 'Add Parser',
-      hierarchy: 'Hierarchy',
-      regularExpressions: 'Regular Expressions',
-      overlappedPercent: 'Overlapped percent (%)',
-      searchMethod: 'Search method',
-      searchMethodTip: `Defines how the content can be searched — by full-text, embedding, or both.
-The Indexer will store the content in the corresponding data structures for the selected methods.`,
-      begin: 'File',
-      parserMethod: 'Parsing method',
-      systemPrompt: 'System Prompt',
-      systemPromptPlaceholder:
-        'Enter system prompt for image analysis, if empty the system default value will be used',
-      exportJson: 'Export JSON',
-      viewResult: 'View result',
-      running: 'Running',
-      summary: 'Summary',
-      keywords: 'Keywords',
-      questions: 'Questions',
-      metadata: 'Metadata',
-      fieldName: 'Result destination',
-      prompts: {
-        system: {
-          keywords: `Role
-You are a text analyzer.
-
-Task
-Extract the most important keywords/phrases of a given piece of text content.
-
-Requirements
-- Summarize the text content, and give the top 5 important keywords/phrases.
-- The keywords MUST be in the same language as the given piece of text content.
-- The keywords are delimited by ENGLISH COMMA.
-- Output keywords ONLY.`,
-          questions: `Role
-You are a text analyzer.
-
-Task
-Propose 3 questions about a given piece of text content.
-
-Requirements
-- Understand and summarize the text content, and propose the top 3 important questions.
-- The questions SHOULD NOT have overlapping meanings.
-- The questions SHOULD cover the main content of the text as much as possible.
-- The questions MUST be in the same language as the given piece of text content.
-- One question per line.
-- Output questions ONLY.`,
-          summary: `Act as a precise summarizer. Your task is to create a summary of the provided content that is both concise and faithful to the original.
-
-Key Instructions:
-1. Accuracy: Strictly base the summary on the information given. Do not introduce any new facts, conclusions, or interpretations that are not explicitly stated.
-2. Language: Write the summary in the same language as the source text.
-3. Objectivity: Present the key points without bias, preserving the original intent and tone of the content. Do not editorialize.
-4. Conciseness: Focus on the most important ideas, omitting minor details and fluff.`,
-          metadata: `Extract important structured information from the given content. Output ONLY a valid JSON string with no additional text. If no important structured information is found, output an empty JSON object: {}.
-
-Important structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities.`,
-        },
-        user: {
-          keywords: `Text Content
-[Insert text here]`,
-          questions: `Text Content
-[Insert text here]`,
-          summary: `Text to Summarize:
-[Insert text here]`,
-          metadata: `Content: [INSERT CONTENT HERE]`,
-        },
-      },
-      cancel: 'Cancel',
-      swicthPromptMessage:
-        'The prompt word will change. Please confirm whether to abandon the existing prompt word?',
-      tokenizerSearchMethodOptions: {
-        full_text: 'Full-text',
-        embedding: 'Embedding',
-      },
-      filenameEmbeddingWeight: 'Filename embedding weight',
-      tokenizerFieldsOptions: {
-        text: 'Processed Text',
-        keywords: 'Keywords',
-        questions: 'Questions',
-        summary: 'Augmented Context',
-      },
-      imageParseMethodOptions: {
-        ocr: 'OCR',
-      },
-      note: 'Note',
-      noteDescription: 'Note',
-      notePlaceholder: 'Please enter a note',
-    },
     datasetOverview: {
       downloadTip: 'Files being downloaded from data sources. ',
       processingTip: 'Files being processed by Ingestion pipeline.',

web/src/locales/zh.ts

@@ -1511,6 +1511,93 @@ General：实体和关系提取提示来自 GitHub - microsoft/graphrag：基于
       createFromTemplate: '从模板创建',
       importJsonFile: '导入 JSON 文件',
       chooseAgentType: '选择智能体类型',
+      parser: '解析器',
+      parserDescription: '从文件中提取原始文本和结构以供下游处理。',
+      tokenizer: '分词器',
+      tokenizerRequired: '请先添加Tokenizer节点',
+      tokenizerDescription:
+        '根据所选的搜索方法，将文本转换为所需的数据结构（例如，用于嵌入搜索的向量嵌入）。',
+      splitter: '按字符分割',
+      splitterDescription:
+        '根据分词器长度将文本拆分成块，并带有可选的分隔符和重叠。',
+      hierarchicalMergerDescription:
+        '使用正则表达式规则按标题层次结构将文档拆分成多个部分，以实现更精细的控制。',
+      hierarchicalMerger: '按标题分割',
+      extractor: '提取器',
+      extractorDescription:
+        '使用 LLM 从文档块（例如摘要、分类等）中提取结构化见解。',
+      outputFormat: '输出格式',
+      fileFormats: '文件格式',
+      fields: '字段',
+      addParser: '增加解析器',
+      hierarchy: '层次结构',
+      regularExpressions: '正则表达式',
+      overlappedPercent: '重叠百分比（%）',
+      searchMethod: '搜索方法',
+      searchMethodTip: `决定该数据集启用的搜索方式，可选择全文、向量，或两者兼有。
+Tokenizer 会根据所选方式将内容存储为对应的数据结构。`,
+      filenameEmbdWeight: '文件名嵌入权重',
+      parserMethod: '解析方法',
+      systemPromptPlaceholder:
+        '请输入用于图像分析的系统提示词，若为空则使用系统缺省值',
+      exportJson: '导出 JSON',
+      viewResult: '查看结果',
+      running: '运行中',
+      summary: '增强上下文',
+      keywords: '关键词',
+      questions: '问题',
+      metadata: '元数据',
+      fieldName: '结果目的地',
+      prompts: {
+        system: {
+          keywords: `角色
+你是一名文本分析员。
+
+任务
+从给定的文本内容中提取最重要的关键词/短语。
+
+要求
+- 总结文本内容，并给出最重要的5个关键词/短语。
+- 关键词必须与给定的文本内容使用相同的语言。
+- 关键词之间用英文逗号分隔。
+- 仅输出关键词。`,
+          questions: `角色
+你是一名文本分析员。
+
+任务
+针对给定的文本内容提出3个问题。
+
+要求
+- 理解并总结文本内容，并提出最重要的3个问题。
+- 问题的含义不应重叠。
+- 问题应尽可能涵盖文本的主要内容。
+- 问题必须与给定的文本内容使用相同的语言。
+- 每行一个问题。
+- 仅输出问题。`,
+          summary: `扮演一个精准的摘要者。你的任务是为提供的内容创建一个简洁且忠实于原文的摘要。
+
+关键说明：
+1. 准确性：摘要必须严格基于所提供的信息。请勿引入任何未明确说明的新事实、结论或解释。
+2. 语言：摘要必须使用与原文相同的语言。
+3. 客观性：不带偏见地呈现要点，保留内容的原始意图和语气。请勿进行编辑。
+4. 简洁性：专注于最重要的思想，省略细节和多余的内容。`,
+          metadata: `从给定内容中提取重要的结构化信息。仅输出有效的 JSON 字符串，不包含任何附加文本。如果未找到重要的结构化信息，则输出一个空的 JSON 对象：{}。
+
+重要的结构化信息可能包括：姓名、日期、地点、事件、关键事实、数字数据或其他可提取实体。`,
+        },
+        user: {
+          keywords: `文本内容
+[在此处插入文本]`,
+          questions: `文本内容
+[在此处插入文本]`,
+          summary: `要总结的文本：
+[在此处插入文本]`,
+          metadata: `内容：[在此处插入内容]`,
+        },
+      },
+      cancel: '取消',
+      filenameEmbeddingWeight: '文件名嵌入权重',
+      switchPromptMessage: '提示词将发生变化，请确认是否放弃已有提示词？',
     },
     footer: {
       profile: 'All rights reserved @ React',
@@ -1618,101 +1705,6 @@ General：实体和关系提取提示来自 GitHub - microsoft/graphrag：基于
       <p>你确定要继续吗?</p> `,
       unlinkPipelineModalConfirmText: '解绑',
     },
-    dataflow: {
-      parser: '解析器',
-      parserDescription: '从文件中提取原始文本和结构以供下游处理。',
-      tokenizer: '分词器',
-      tokenizerRequired: '请先添加Tokenizer节点',
-      tokenizerDescription:
-        '根据所选的搜索方法，将文本转换为所需的数据结构（例如，用于嵌入搜索的向量嵌入）。',
-      splitter: '按字符分割',
-      splitterDescription:
-        '根据分词器长度将文本拆分成块，并带有可选的分隔符和重叠。',
-      hierarchicalMergerDescription:
-        '使用正则表达式规则按标题层次结构将文档拆分成多个部分，以实现更精细的控制。',
-      hierarchicalMerger: '按标题分割',
-      extractor: '提取器',
-      extractorDescription:
-        '使用 LLM 从文档块（例如摘要、分类等）中提取结构化见解。',
-      outputFormat: '输出格式',
-      lang: '语言',
-      fileFormats: '文件格式',
-      fields: '字段',
-      addParser: '增加解析器',
-      hierarchy: '层次结构',
-      regularExpressions: '正则表达式',
-      overlappedPercent: '重叠百分比（%）',
-      searchMethod: '搜索方法',
-      searchMethodTip: `决定该数据集启用的搜索方式，可选择全文、向量，或两者兼有。
-Tokenizer 会根据所选方式将内容存储为对应的数据结构。`,
-      filenameEmbdWeight: '文件名嵌入权重',
-      begin: '文件',
-      parserMethod: '解析方法',
-      systemPrompt: '系统提示词',
-      systemPromptPlaceholder:
-        '请输入用于图像分析的系统提示词，若为空则使用系统缺省值',
-      exportJson: '导出 JSON',
-      viewResult: '查看结果',
-      running: '运行中',
-      summary: '增强上下文',
-      keywords: '关键词',
-      questions: '问题',
-      metadata: '元数据',
-      fieldName: '结果目的地',
-      prompts: {
-        system: {
-          keywords: `角色
-你是一名文本分析员。
-
-任务
-从给定的文本内容中提取最重要的关键词/短语。
-
-要求
-- 总结文本内容，并给出最重要的5个关键词/短语。
-- 关键词必须与给定的文本内容使用相同的语言。
-- 关键词之间用英文逗号分隔。
-- 仅输出关键词。`,
-          questions: `角色
-你是一名文本分析员。
-
-任务
-针对给定的文本内容提出3个问题。
-
-要求
-- 理解并总结文本内容，并提出最重要的3个问题。
-- 问题的含义不应重叠。
-- 问题应尽可能涵盖文本的主要内容。
-- 问题必须与给定的文本内容使用相同的语言。
-- 每行一个问题。
-- 仅输出问题。`,
-          summary: `扮演一个精准的摘要者。你的任务是为提供的内容创建一个简洁且忠实于原文的摘要。
-
-关键说明：
-1. 准确性：摘要必须严格基于所提供的信息。请勿引入任何未明确说明的新事实、结论或解释。
-2. 语言：摘要必须使用与原文相同的语言。
-3. 客观性：不带偏见地呈现要点，保留内容的原始意图和语气。请勿进行编辑。
-4. 简洁性：专注于最重要的思想，省略细节和多余的内容。`,
-          metadata: `从给定内容中提取重要的结构化信息。仅输出有效的 JSON 字符串，不包含任何附加文本。如果未找到重要的结构化信息，则输出一个空的 JSON 对象：{}。
-
-重要的结构化信息可能包括：姓名、日期、地点、事件、关键事实、数字数据或其他可提取实体。`,
-        },
-        user: {
-          keywords: `文本内容
-[在此处插入文本]`,
-          questions: `文本内容
-[在此处插入文本]`,
-          summary: `要总结的文本：
-[在此处插入文本]`,
-          metadata: `内容：[在此处插入内容]`,
-        },
-      },
-      cancel: '取消',
-      filenameEmbeddingWeight: '文件名嵌入权重',
-      switchPromptMessage: '提示词将发生变化，请确认是否放弃已有提示词？',
-      note: '注释',
-      noteDescription: '注释',
-      notePlaceholder: '请输入注释',
-    },
     datasetOverview: {
       downloadTip: '正在从数据源下载文件。',
       processingTip: '正在由pipeline处理文件。',

web/src/pages/agent/canvas/node/file-node.tsx

@@ -36,7 +36,7 @@ function InnerFileNode({ data, id, selected }: NodeProps<IBeginNode>) {
       <section className="flex items-center  gap-2">
         <OperatorIcon name={data.label as Operator}></OperatorIcon>
         <div className="truncate text-center font-semibold text-sm">
-          {t(`dataflow.begin`)}
+          {t(`flow.begin`)}
         </div>
       </section>
       <section className={cn(styles.generateParameters, 'flex gap-2 flex-col')}>

web/src/pages/agent/canvas/node/parser-node.tsx

@@ -46,7 +46,7 @@ function ParserNode({
             className="flex flex-col text-text-primary gap-1"
           >
             <span className="text-text-secondary">Parser {idx + 1}</span>
-            {t(`dataflow.fileFormatOptions.${x.fileFormat}`)}
+            {t(`flow.fileFormatOptions.${x.fileFormat}`)}
           </LabelCard>
         )}
       </NodeCollapsible>

web/src/pages/agent/canvas/node/tokenizer-node.tsx

@@ -38,12 +38,10 @@ function TokenizerNode({
         ></CommonHandle>
         <NodeHeader id={id} name={data.name} label={data.label}></NodeHeader>
         <LabelCard className="text-text-primary flex justify-between flex-col gap-1">
-          <span className="text-text-secondary">
+          <span className="text-text-secondary">{t('flow.searchMethod')}</span>
-            {t('dataflow.searchMethod')}
-          </span>
           <ul className="space-y-1">
             {data.form?.search_method.map((x) => (
-              <li key={x}>{t(`dataflow.tokenizerSearchMethodOptions.${x}`)}</li>
+              <li key={x}>{t(`flow.tokenizerSearchMethodOptions.${x}`)}</li>
             ))}
           </ul>
         </LabelCard>

web/src/pages/agent/form/extractor-form/index.tsx

@@ -47,7 +47,7 @@ const ExtractorForm = ({ node }: INextOperatorForm) => {
 
   const promptOptions = useBuildNodeOutputOptions(node?.id);
 
-  const options = buildOptions(ContextGeneratorFieldName, t, 'dataflow');
+  const options = buildOptions(ContextGeneratorFieldName, t, 'flow');
 
   const {
     handleFieldNameChange,
@@ -63,7 +63,7 @@ const ExtractorForm = ({ node }: INextOperatorForm) => {
     <Form {...form}>
       <FormWrapper>
         <LargeModelFormField></LargeModelFormField>
-        <RAGFlowFormItem label={t('dataflow.fieldName')} name="field_name">
+        <RAGFlowFormItem label={t('flow.fieldName')} name="field_name">
           {(field) => (
             <SelectWithSearch
               onChange={(value) => {
@@ -93,7 +93,7 @@ const ExtractorForm = ({ node }: INextOperatorForm) => {
       </FormWrapper>
       {visible && (
         <ConfirmDeleteDialog
-          title={t('dataflow.switchPromptMessage')}
+          title={t('flow.switchPromptMessage')}
           open
           onOpenChange={hideModal}
           onOk={confirmSwitch}

web/src/pages/agent/form/extractor-form/use-switch-prompt.ts

@@ -21,7 +21,7 @@ export function useSwitchPrompt(form: UseFormReturn<ExtractorFormSchemaType>) {
 
   const setPromptValue = useCallback(
     (field: keyof ExtractorFormSchemaType, key: string, value: string) => {
-      form.setValue(field, t(`dataflow.prompts.${key}.${value}`), {
+      form.setValue(field, t(`flow.prompts.${key}.${value}`), {
         shouldDirty: true,
         shouldValidate: true,
       });

web/src/pages/agent/form/hierarchical-merger-form/index.tsx

@@ -98,7 +98,7 @@ export function RegularExpressions({
       </CardHeader>
       <CardContent>
         <FormLabel required className="mb-2 text-text-secondary">
-          {t('dataflow.regularExpressions')}
+          {t('flow.regularExpressions')}
         </FormLabel>
         <section className="space-y-4">
           {fields.map((field, index) => (
@@ -158,7 +158,7 @@ const HierarchicalMergerForm = ({ node }: INextOperatorForm) => {
   return (
     <Form {...form}>
       <FormWrapper>
-        <RAGFlowFormItem name={'hierarchy'} label={t('dataflow.hierarchy')}>
+        <RAGFlowFormItem name={'hierarchy'} label={t('flow.hierarchy')}>
           <SelectWithSearch options={HierarchyOptions}></SelectWithSearch>
         </RAGFlowFormItem>
         {fields.map((field, index) => (

web/src/pages/agent/form/parser-form/common-form-fields.tsx

@@ -50,7 +50,7 @@ export function OutputFormatFormField({
   return (
     <RAGFlowFormItem
       name={buildFieldNameWithPrefix(`output_format`, prefix)}
-      label={t('dataflow.outputFormat')}
+      label={t('flow.outputFormat')}
     >
       <SelectWithSearch
         options={buildOutputOptionsFormatMap()[fileType]}
@@ -69,7 +69,7 @@ export function ParserMethodFormField({
       name={buildFieldNameWithPrefix(`parse_method`, prefix)}
       horizontal={false}
       optionsWithoutLLM={optionsWithoutLLM}
-      label={t('dataflow.parserMethod')}
+      label={t('flow.parserMethod')}
     ></LayoutRecognizeFormField>
   );
 }
@@ -92,7 +92,7 @@ export function LanguageFormField({ prefix }: CommonProps) {
   return (
     <RAGFlowFormItem
       name={buildFieldNameWithPrefix(`lang`, prefix)}
-      label={t('dataflow.lang')}
+      label={t('flow.lang')}
     >
       {(field) => (
         <SelectWithSearch

web/src/pages/agent/form/parser-form/email-form-fields.tsx

@@ -14,7 +14,7 @@ export function EmailFormFields({ prefix }: CommonProps) {
     <>
       <RAGFlowFormItem
         name={buildFieldNameWithPrefix(`fields`, prefix)}
-        label={t('dataflow.fields')}
+        label={t('flow.fields')}
       >
         {(field) => (
           <MultiSelect

web/src/pages/agent/form/parser-form/image-form-fields.tsx

@@ -17,7 +17,7 @@ export function ImageFormFields({ prefix }: CommonProps) {
   const options = buildOptions(
     ImageParseMethod,
     t,
-    'dataflow.imageParseMethodOptions',
+    'flow.imageParseMethodOptions',
   );
   const parseMethodName = buildFieldNameWithPrefix('parse_method', prefix);
 
@@ -50,9 +50,9 @@ export function ImageFormFields({ prefix }: CommonProps) {
       {languageShown && (
         <RAGFlowFormItem
           name={buildFieldNameWithPrefix('system_prompt', prefix)}
-          label={t('dataflow.systemPrompt')}
+          label={t('flow.systemPrompt')}
         >
-          <Textarea placeholder={t('dataflow.systemPromptPlaceholder')} />
+          <Textarea placeholder={t('flow.systemPromptPlaceholder')} />
         </RAGFlowFormItem>
       )}
     </>

web/src/pages/agent/form/parser-form/index.tsx

@@ -133,7 +133,7 @@ function ParserItem({
       </div>
       <RAGFlowFormItem
         name={buildFieldNameWithPrefix(`fileFormat`, prefix)}
-        label={t('dataflow.fileFormats')}
+        label={t('flow.fileFormats')}
       >
         {(field) => (
           <SelectWithSearch
@@ -165,7 +165,7 @@ const ParserForm = ({ node }: INextOperatorForm) => {
   const FileFormatOptions = buildOptions(
     FileType,
     t,
-    'dataflow.fileFormatOptions',
+    'flow.fileFormatOptions',
   ).filter(
     (x) => x.value !== FileType.Video, // Temporarily hide the video option
   );
@@ -212,7 +212,7 @@ const ParserForm = ({ node }: INextOperatorForm) => {
         })}
         {fields.length < FileFormatOptions.length && (
           <BlockButton onClick={add} type="button" className="mt-2.5">
-            {t('dataflow.addParser')}
+            {t('flow.addParser')}
           </BlockButton>
         )}
       </form>

web/src/pages/agent/form/splitter-form/index.tsx

@@ -60,7 +60,7 @@ const SplitterForm = ({ node }: INextOperatorForm) => {
           name="overlapped_percent"
           max={30}
           min={0}
-          label={t('dataflow.overlappedPercent')}
+          label={t('flow.overlappedPercent')}
         ></SliderInputFormField>
         <section>
           <span className="mb-2 inline-block">{t('flow.delimiters')}</span>

web/src/pages/agent/form/tokenizer-form/index.tsx

@@ -38,12 +38,12 @@ const TokenizerForm = ({ node }: INextOperatorForm) => {
   const SearchMethodOptions = buildOptions(
     TokenizerSearchMethod,
     t,
-    `dataflow.tokenizerSearchMethodOptions`,
+    `flow.tokenizerSearchMethodOptions`,
   );
   const FieldsOptions = buildOptions(
     TokenizerFields,
     t,
-    'dataflow.tokenizerFieldsOptions',
+    'flow.tokenizerFieldsOptions',
   );
 
   const form = useForm<TokenizerFormSchemaType>({
@@ -59,8 +59,8 @@ const TokenizerForm = ({ node }: INextOperatorForm) => {
       <FormWrapper>
         <RAGFlowFormItem
           name="search_method"
-          label={t('dataflow.searchMethod')}
+          label={t('flow.searchMethod')}
-          tooltip={t('dataflow.searchMethodTip')}
+          tooltip={t('flow.searchMethodTip')}
         >
           {(field) => (
             <MultiSelect
@@ -73,11 +73,11 @@ const TokenizerForm = ({ node }: INextOperatorForm) => {
         </RAGFlowFormItem>
         <SliderInputFormField
           name="filename_embd_weight"
-          label={t('dataflow.filenameEmbeddingWeight')}
+          label={t('flow.filenameEmbeddingWeight')}
           max={0.5}
           step={0.01}
         ></SliderInputFormField>
-        <RAGFlowFormItem name="fields" label={t('dataflow.fields')}>
+        <RAGFlowFormItem name="fields" label={t('flow.fields')}>
           {(field) => <SelectWithSearch options={FieldsOptions} {...field} />}
         </RAGFlowFormItem>
       </FormWrapper>

web/src/pages/agent/hooks/use-add-node.ts

@@ -128,8 +128,8 @@ export const useInitializeOperatorParams = () => {
       [Operator.Extractor]: {
         ...initialExtractorValues,
         llm_id: llmId,
-        sys_prompt: t('dataflow.prompts.system.summary'),
+        sys_prompt: t('flow.prompts.system.summary'),
-        prompts: t('dataflow.prompts.user.summary'),
+        prompts: t('flow.prompts.user.summary'),
       },
     };
   }, [llmId]);

web/src/pages/agent/hooks/use-show-drawer.tsx

@@ -61,7 +61,7 @@ export const useShowSingleDebugDrawer = () => {
   };
 };
 
-const ExcludedNodes = [Operator.Note, Operator.Placeholder];
+const ExcludedNodes = [Operator.Note, Operator.Placeholder, Operator.File];
 
 export function useShowDrawer({
   drawerVisible,

web/src/pages/agent/index.tsx

@@ -137,7 +137,7 @@ export default function Agent() {
 
   const handleRunPipeline = useCallback(() => {
     if (!findNodeByName(Operator.Tokenizer)) {
-      message.warning(t('dataflow.tokenizerRequired'));
+      message.warning(t('flow.tokenizerRequired'));
       return;
     }
 

web/src/pages/agent/pipeline-log-sheet/index.tsx

@@ -77,7 +77,7 @@ export function PipelineLogSheet({
                     uploadedFileData?.extension,
                 })}
               >
-                {t('dataflow.viewResult')} <ArrowUpRight />
+                {t('flow.viewResult')} <ArrowUpRight />
               </Button>
             )}
           </SheetTitle>
@@ -95,7 +95,7 @@ export function PipelineLogSheet({
               className="w-full mt-8 bg-state-error/10 text-state-error hover:bg-state-error hover:text-bg-base"
               onClick={handleCancel}
             >
-              <CirclePause /> {t('dataflow.cancel')}
+              <CirclePause /> {t('flow.cancel')}
             </Button>
           ) : (
             <Button
@@ -104,7 +104,7 @@ export function PipelineLogSheet({
               className="w-full mt-8 bg-accent-primary-5 text-text-secondary hover:bg-accent-primary-5  hover:text-accent-primary hover:border-accent-primary hover:border"
             >
               <SquareArrowOutUpRight />
-              {t('dataflow.exportJson')}
+              {t('flow.exportJson')}
             </Button>
           )}
         </div>