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.',
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||||
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',
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||||
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',
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||||
slides: 'PPT',
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||||
audio: 'Audio',
|
||||
},
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||||
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.
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||||
|
||||
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: {}.
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||||
|
||||
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',
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||||
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。',
|
||||
|
||||
Reference in New Issue
Block a user