mirror of
https://github.com/infiniflow/ragflow.git
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### 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)
This commit is contained in:
balibabu
@ -1605,6 +1605,119 @@ This delimiter is used to split the input text into several text pieces echo of
|
||||
ceateAgent: 'Agent flow',
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||||
createPipeline: 'Ingestion pipeline',
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||||
chooseAgentType: 'Choose Agent Type',
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||||
parser: 'Parser',
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||||
parserDescription:
|
||||
'Extracts raw text and structure from files for downstream processing.',
|
||||
tokenizer: 'Indexer',
|
||||
tokenizerRequired: 'Please add the Indexer node first',
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||||
tokenizerDescription:
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||||
'Transforms text into the required data structure (e.g., vector embeddings for Embedding Search) depending on the chosen search method.',
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||||
splitter: 'Token',
|
||||
splitterDescription:
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||||
'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.',
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||||
hierarchicalMerger: 'Title',
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||||
extractor: 'Transformer',
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||||
extractorDescription:
|
||||
'Use an LLM to extract structured insights from document chunks—such as summaries, classifications, etc.',
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||||
outputFormat: 'Output format',
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||||
fileFormats: 'File format',
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||||
fileFormatOptions: {
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||||
pdf: 'PDF',
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||||
spreadsheet: 'Spreadsheet',
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||||
image: 'Image',
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||||
email: 'Email',
|
||||
'text&markdown': 'Text & Markup',
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||||
word: 'Word',
|
||||
slides: 'PPT',
|
||||
audio: 'Audio',
|
||||
},
|
||||
fields: 'Field',
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||||
addParser: 'Add Parser',
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||||
hierarchy: 'Hierarchy',
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||||
regularExpressions: 'Regular Expressions',
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||||
overlappedPercent: 'Overlapped percent (%)',
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||||
searchMethod: 'Search method',
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||||
searchMethodTip: `Defines how the content can be searched — by full-text, embedding, or both.
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||||
The Indexer will store the content in the corresponding data structures for the selected methods.`,
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// file: 'File',
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||||
parserMethod: 'Parsing method',
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// systemPrompt: 'System Prompt',
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systemPromptPlaceholder:
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||||
'Enter system prompt for image analysis, if empty the system default value will be used',
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exportJson: 'Export JSON',
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||||
viewResult: 'View result',
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||||
running: 'Running',
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||||
summary: 'Summary',
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||||
keywords: 'Keywords',
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||||
questions: 'Questions',
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||||
metadata: 'Metadata',
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||||
fieldName: 'Result destination',
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||||
prompts: {
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||||
system: {
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||||
keywords: `Role
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||||
You are a text analyzer.
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||||
|
||||
Task
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||||
Extract the most important keywords/phrases of a given piece of text content.
|
||||
|
||||
Requirements
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||||
- Summarize the text content, and give the top 5 important keywords/phrases.
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- The keywords MUST be in the same language as the given piece of text content.
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||||
- The keywords are delimited by ENGLISH COMMA.
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- Output keywords ONLY.`,
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||||
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.`,
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||||
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.
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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.`,
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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.`,
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||||
},
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||||
user: {
|
||||
keywords: `Text Content
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||||
[Insert text here]`,
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||||
questions: `Text Content
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||||
[Insert text here]`,
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||||
summary: `Text to Summarize:
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||||
[Insert text here]`,
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||||
metadata: `Content: [INSERT CONTENT HERE]`,
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||||
},
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||||
},
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||||
cancel: 'Cancel',
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||||
swicthPromptMessage:
|
||||
'The prompt word will change. Please confirm whether to abandon the existing prompt word?',
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||||
tokenizerSearchMethodOptions: {
|
||||
full_text: 'Full-text',
|
||||
embedding: 'Embedding',
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||||
},
|
||||
filenameEmbeddingWeight: 'Filename embedding weight',
|
||||
tokenizerFieldsOptions: {
|
||||
text: 'Processed Text',
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||||
keywords: 'Keywords',
|
||||
questions: 'Questions',
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||||
summary: 'Augmented Context',
|
||||
},
|
||||
imageParseMethodOptions: {
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||||
ocr: 'OCR',
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||||
},
|
||||
},
|
||||
llmTools: {
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||||
bad_calculator: {
|
||||
@ -1705,125 +1818,6 @@ This delimiter is used to split the input text into several text pieces echo of
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||||
<p>Are you sure you want to proceed?</p> `,
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||||
unlinkPipelineModalConfirmText: 'Unlink',
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||||
},
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||||
dataflow: {
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||||
parser: 'Parser',
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||||
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.',
|
||||
|
||||
@ -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',
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||||
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。',
|
||||
|
||||
@ -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')}>
|
||||
|
||||
@ -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>
|
||||
|
||||
@ -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">
|
||||
{t('dataflow.searchMethod')}
|
||||
</span>
|
||||
<span className="text-text-secondary">{t('flow.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>
|
||||
|
||||
@ -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}
|
||||
|
||||
@ -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,
|
||||
});
|
||||
|
||||
@ -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) => (
|
||||
|
||||
@ -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
|
||||
|
||||
@ -14,7 +14,7 @@ export function EmailFormFields({ prefix }: CommonProps) {
|
||||
<>
|
||||
<RAGFlowFormItem
|
||||
name={buildFieldNameWithPrefix(`fields`, prefix)}
|
||||
label={t('dataflow.fields')}
|
||||
label={t('flow.fields')}
|
||||
>
|
||||
{(field) => (
|
||||
<MultiSelect
|
||||
|
||||
@ -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>
|
||||
)}
|
||||
</>
|
||||
|
||||
@ -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>
|
||||
|
||||
@ -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>
|
||||
|
||||
@ -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')}
|
||||
tooltip={t('dataflow.searchMethodTip')}
|
||||
label={t('flow.searchMethod')}
|
||||
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>
|
||||
|
||||
@ -128,8 +128,8 @@ export const useInitializeOperatorParams = () => {
|
||||
[Operator.Extractor]: {
|
||||
...initialExtractorValues,
|
||||
llm_id: llmId,
|
||||
sys_prompt: t('dataflow.prompts.system.summary'),
|
||||
prompts: t('dataflow.prompts.user.summary'),
|
||||
sys_prompt: t('flow.prompts.system.summary'),
|
||||
prompts: t('flow.prompts.user.summary'),
|
||||
},
|
||||
};
|
||||
}, [llmId]);
|
||||
|
||||
@ -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,
|
||||
|
||||
@ -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;
|
||||
}
|
||||
|
||||
|
||||
@ -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>
|
||||
|
||||
Reference in New Issue
Block a user