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{
"id" : 23 ,
"title" : {
"en" : "Advanced Ingestion Pipeline" ,
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"de" : "Erweiterte Ingestion Pipeline" ,
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"zh" : "编排复杂的 Ingestion Pipeline"
} ,
"description" : {
"en" : "This template demonstrates how to use an LLM to generate summaries, keywords, Q&A, and metadata for each chunk to support diverse retrieval needs." ,
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"de" : "Diese Vorlage demonstriert, wie ein LLM verwendet wird, um Zusammenfassungen, Schlüsselwörter, Fragen & Antworten und Metadaten für jedes Segment zu generieren, um vielfältige Abrufanforderungen zu unterstützen." ,
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"zh" : "此模板演示如何利用大模型为切片生成摘要、关键词、问答及元数据，以满足多样化的召回需求。"
} ,
"canvas_type" : "Ingestion Pipeline" ,
"canvas_category" : "dataflow_canvas" ,
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"dsl" : {
"components" : {
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"Extractor:CurlyEmusJam" : {
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"downstream" : [
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"Tokenizer:WittySunsListen"
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] ,
"obj" : {
"component_name" : "Extractor" ,
"params" : {
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"field_name" : "metadata" ,
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"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
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"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
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"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
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} ,
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"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
"prompts" : [
{
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"content" : "Content:\n{Extractor:SmartWindowsHammer@chunks}" ,
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"role" : "user"
}
] ,
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"sys_prompt" : "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: {}.\n\nImportant structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities." ,
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"temperature" : 0.1 ,
"temperatureEnabled" : true ,
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"tenant_llm_id" : 63 ,
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"topPEnabled" : true ,
"top_p" : 0.3
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}
} ,
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"upstream" : [
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"Extractor:SmartWindowsHammer"
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]
} ,
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"Extractor:LazyCarpetsKiss" : {
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"downstream" : [
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"Extractor:LovelyPearsRest"
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] ,
"obj" : {
"component_name" : "Extractor" ,
"params" : {
"field_name" : "summary" ,
"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
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"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
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"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
"prompts" : [
{
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"content" : "Text to Summarize:\n{TokenChunker:BumpyStarsPress@chunks}" ,
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"role" : "user"
}
] ,
"sys_prompt" : "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.\n\nKey Instructions:\n1. Accuracy: Strictly base the summary on the information given. Do not introduce any new facts, conclusions, or interpretations that are not explicitly stated.\n2. Language: Write the summary in the same language as the source text.\n3. Objectivity: Present the key points without bias, preserving the original intent and tone of the content. Do not editorialize.\n4. Conciseness: Focus on the most important ideas, omitting minor details and fluff." ,
"temperature" : 0.1 ,
"temperatureEnabled" : true ,
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"tenant_llm_id" : 63 ,
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"topPEnabled" : true ,
"top_p" : 0.3
}
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} ,
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"upstream" : [
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"TokenChunker:BumpyStarsPress"
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]
} ,
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"Extractor:LovelyPearsRest" : {
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"downstream" : [
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"Extractor:SmartWindowsHammer"
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] ,
"obj" : {
"component_name" : "Extractor" ,
"params" : {
"field_name" : "keywords" ,
"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
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"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
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"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
"prompts" : [
{
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"content" : "Text Content\n{Extractor:LazyCarpetsKiss@chunks}" ,
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"role" : "user"
}
] ,
"sys_prompt" : "Role\nYou are a text analyzer.\n\nTask\nExtract the most important keywords/phrases of a given piece of text content.\n\nRequirements\n- Summarize the text content, and give the top 5 important keywords/phrases.\n- The keywords MUST be in the same language as the given piece of text content.\n- The keywords are delimited by ENGLISH COMMA.\n- Output keywords ONLY." ,
"temperature" : 0.1 ,
"temperatureEnabled" : true ,
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"tenant_llm_id" : 63 ,
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"topPEnabled" : true ,
"top_p" : 0.3
}
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} ,
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"upstream" : [
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"Extractor:LazyCarpetsKiss"
]
} ,
"Extractor:SmartWindowsHammer" : {
"downstream" : [
"Extractor:CurlyEmusJam"
] ,
"obj" : {
"component_name" : "Extractor" ,
"params" : {
"field_name" : "questions" ,
"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
"prompts" : [
{
"content" : "Text Content\n{Extractor:LovelyPearsRest@chunks}" ,
"role" : "user"
}
] ,
"sys_prompt" : "Role\nYou are a text analyzer.\n\nTask\nPropose 3 questions about a given piece of text content.\n\nRequirements\n- Understand and summarize the text content, and propose the top 3 important questions.\n- The questions SHOULD NOT have overlapping meanings.\n- The questions SHOULD cover the main content of the text as much as possible.\n- The questions MUST be in the same language as the given piece of text content.\n- One question per line.\n- Output questions ONLY." ,
"temperature" : 0.1 ,
"temperatureEnabled" : true ,
"tenant_llm_id" : 63 ,
"topPEnabled" : true ,
"top_p" : 0.3
}
} ,
"upstream" : [
"Extractor:LovelyPearsRest"
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]
} ,
"File" : {
"downstream" : [
"Parser:HipSignsRhyme"
] ,
"obj" : {
"component_name" : "File" ,
"params" : { }
} ,
"upstream" : [ ]
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} ,
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"Parser:HipSignsRhyme" : {
"downstream" : [
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"TokenChunker:BumpyStarsPress"
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] ,
"obj" : {
"component_name" : "Parser" ,
"params" : {
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"outputs" : {
"html" : {
"type" : "string" ,
"value" : ""
} ,
"json" : {
"type" : "Array<object>" ,
"value" : [ ]
} ,
"markdown" : {
"type" : "string" ,
"value" : ""
} ,
"text" : {
"type" : "string" ,
"value" : ""
}
} ,
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"setups" : {
"doc" : {
"output_format" : "json" ,
"preprocess" : "main_content" ,
"suffix" : [
"doc"
]
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} ,
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"docx" : {
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"flatten_media_to_text" : false ,
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"output_format" : "json" ,
"preprocess" : "main_content" ,
"suffix" : [
"docx"
] ,
"vlm" : { }
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} ,
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"email" : {
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"fields" : [
"from" ,
"to" ,
"cc" ,
"bcc" ,
"date" ,
"subject" ,
"body" ,
"attachments"
] ,
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"output_format" : "text" ,
"preprocess" : "main_content" ,
"suffix" : [
"eml" ,
"msg"
]
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} ,
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"html" : {
"output_format" : "json" ,
"preprocess" : "main_content" ,
"suffix" : [
"htm" ,
"html"
]
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} ,
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"image" : {
"output_format" : "text" ,
"parse_method" : "ocr" ,
"preprocess" : "main_content" ,
"suffix" : [
"jpg" ,
"jpeg" ,
"png" ,
"gif"
] ,
"system_prompt" : ""
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} ,
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"markdown" : {
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"flatten_media_to_text" : false ,
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"output_format" : "json" ,
"preprocess" : "main_content" ,
"suffix" : [
"md" ,
"markdown" ,
"mdx"
] ,
"vlm" : { }
} ,
"pdf" : {
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"flatten_media_to_text" : false ,
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"output_format" : "json" ,
"parse_method" : "DeepDOC" ,
"preprocess" : "main_content" ,
"suffix" : [
"pdf"
] ,
"vlm" : { }
} ,
"slides" : {
"output_format" : "json" ,
"parse_method" : "DeepDOC" ,
"preprocess" : "main_content" ,
"suffix" : [
"pptx" ,
"ppt"
]
} ,
"spreadsheet" : {
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"flatten_media_to_text" : false ,
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"output_format" : "html" ,
"parse_method" : "DeepDOC" ,
"preprocess" : "main_content" ,
"suffix" : [
"xls" ,
"xlsx" ,
"csv"
] ,
"vlm" : { }
} ,
"text&code" : {
"output_format" : "json" ,
"preprocess" : "main_content" ,
"suffix" : [
"txt" ,
"py" ,
"js" ,
"java" ,
"c" ,
"cpp" ,
"h" ,
"php" ,
"go" ,
"ts" ,
"sh" ,
"cs" ,
"kt" ,
"sql"
]
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}
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}
}
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} ,
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"upstream" : [
"File"
]
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} ,
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"TokenChunker:BumpyStarsPress" : {
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"downstream" : [
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"Extractor:LazyCarpetsKiss"
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] ,
"obj" : {
"component_name" : "TokenChunker" ,
"params" : {
"children_delimiters" : [ ] ,
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"chunk_token_size" : 512 ,
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"delimiter_mode" : "token_size" ,
"delimiters" : [ ] ,
"image_context_size" : 0 ,
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"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
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"overlapped_percent" : 0 ,
"table_context_size" : 0
}
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} ,
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"upstream" : [
"Parser:HipSignsRhyme"
]
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} ,
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"Tokenizer:WittySunsListen" : {
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"downstream" : [ ] ,
"obj" : {
"component_name" : "Tokenizer" ,
"params" : {
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"fields" : "text" ,
"filename_embd_weight" : 0.1 ,
"outputs" : { } ,
"search_method" : [
"embedding" ,
"full_text"
]
}
} ,
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"upstream" : [
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"Extractor:CurlyEmusJam"
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]
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}
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} ,
"globals" : {
"sys.history" : [ ]
} ,
"graph" : {
"edges" : [
{
"id" : "xy-edge__Filestart-Parser:HipSignsRhymeend" ,
"source" : "File" ,
"sourceHandle" : "start" ,
"target" : "Parser:HipSignsRhyme" ,
"targetHandle" : "end"
} ,
{
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"id" : "xy-edge__Parser:HipSignsRhymestart-TokenChunker:BumpyStarsPressend" ,
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"source" : "Parser:HipSignsRhyme" ,
"sourceHandle" : "start" ,
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"target" : "TokenChunker:BumpyStarsPress" ,
"targetHandle" : "end"
} ,
{
"id" : "xy-edge__TokenChunker:BumpyStarsPressstart-Extractor:LazyCarpetsKissend" ,
"source" : "TokenChunker:BumpyStarsPress" ,
"sourceHandle" : "start" ,
"target" : "Extractor:LazyCarpetsKiss" ,
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"targetHandle" : "end"
} ,
{
"data" : {
"isHovered" : false
} ,
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"id" : "xy-edge__Extractor:LazyCarpetsKissstart-Extractor:LovelyPearsRestend" ,
"source" : "Extractor:LazyCarpetsKiss" ,
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"sourceHandle" : "start" ,
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"target" : "Extractor:LovelyPearsRest" ,
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"targetHandle" : "end"
} ,
{
"data" : {
"isHovered" : false
} ,
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"id" : "xy-edge__Extractor:LovelyPearsReststart-Extractor:SmartWindowsHammerend" ,
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"selected" : false ,
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"source" : "Extractor:LovelyPearsRest" ,
2026-04-10 19:04:52 +08:00
"sourceHandle" : "start" ,
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"target" : "Extractor:SmartWindowsHammer" ,
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"targetHandle" : "end"
} ,
{
"data" : {
"isHovered" : false
} ,
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"id" : "xy-edge__Extractor:SmartWindowsHammerstart-Extractor:CurlyEmusJamend" ,
"selected" : false ,
"source" : "Extractor:SmartWindowsHammer" ,
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"sourceHandle" : "start" ,
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"target" : "Extractor:CurlyEmusJam" ,
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"targetHandle" : "end"
} ,
{
"data" : {
"isHovered" : false
} ,
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"id" : "xy-edge__Extractor:CurlyEmusJamstart-Tokenizer:WittySunsListenend" ,
"source" : "Extractor:CurlyEmusJam" ,
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"sourceHandle" : "start" ,
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"target" : "Tokenizer:WittySunsListen" ,
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"targetHandle" : "end"
}
] ,
"nodes" : [
{
"data" : {
"label" : "File" ,
"name" : "File"
} ,
"id" : "File" ,
"measured" : {
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"height" : 50 ,
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"width" : 200
} ,
"position" : {
"x" : 50 ,
"y" : 200
} ,
"sourcePosition" : "left" ,
"targetPosition" : "right" ,
"type" : "beginNode"
} ,
{
"data" : {
"form" : {
"outputs" : {
"html" : {
"type" : "string" ,
"value" : ""
} ,
"json" : {
"type" : "Array<object>" ,
"value" : [ ]
} ,
"markdown" : {
"type" : "string" ,
"value" : ""
} ,
"text" : {
"type" : "string" ,
"value" : ""
}
} ,
"setups" : [
{
"fileFormat" : "pdf" ,
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"flatten_media_to_text" : false ,
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"output_format" : "json" ,
"parse_method" : "DeepDOC" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "spreadsheet" ,
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"flatten_media_to_text" : false ,
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"output_format" : "html" ,
"parse_method" : "DeepDOC" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "image" ,
"output_format" : "text" ,
"parse_method" : "ocr" ,
"preprocess" : "main_content" ,
"system_prompt" : ""
} ,
{
"fields" : [
"from" ,
"to" ,
"cc" ,
"bcc" ,
"date" ,
"subject" ,
"body" ,
"attachments"
] ,
"fileFormat" : "email" ,
"output_format" : "text" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "markdown" ,
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"flatten_media_to_text" : false ,
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"output_format" : "json" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "text&code" ,
"output_format" : "json" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "html" ,
"output_format" : "json" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "doc" ,
"output_format" : "json" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "docx" ,
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"flatten_media_to_text" : false ,
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"output_format" : "json" ,
"preprocess" : "main_content"
} ,
{
"fileFormat" : "slides" ,
"output_format" : "json" ,
"parse_method" : "DeepDOC" ,
"preprocess" : "main_content"
}
]
} ,
"label" : "Parser" ,
"name" : "Parser_0"
} ,
"dragging" : false ,
"id" : "Parser:HipSignsRhyme" ,
"measured" : {
"height" : 57 ,
"width" : 200
} ,
"position" : {
"x" : 316.99524094206413 ,
"y" : 195.39629819663406
} ,
"selected" : false ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "parserNode"
} ,
{
"data" : {
"form" : {
"children_delimiters" : [ ] ,
"chunk_token_size" : 512 ,
"delimiter_mode" : "token_size" ,
"delimiters" : [
{
"value" : "\n"
}
] ,
"image_table_context_window" : 0 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"overlapped_percent" : 0
} ,
"label" : "TokenChunker" ,
"name" : "Token Chunker_0"
} ,
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"id" : "TokenChunker:BumpyStarsPress" ,
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"measured" : {
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"height" : 74 ,
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"width" : 200
} ,
"position" : {
"x" : 616.9952409420641 ,
"y" : 195.39629819663406
} ,
"selected" : false ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "chunkerNode"
} ,
{
"data" : {
"form" : {
"field_name" : "summary" ,
"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
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"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
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"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
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"prompts" : "Text to Summarize:\n{TokenChunker:BumpyStarsPress@chunks}" ,
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"sys_prompt" : "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.\n\nKey Instructions:\n1. Accuracy: Strictly base the summary on the information given. Do not introduce any new facts, conclusions, or interpretations that are not explicitly stated.\n2. Language: Write the summary in the same language as the source text.\n3. Objectivity: Present the key points without bias, preserving the original intent and tone of the content. Do not editorialize.\n4. Conciseness: Focus on the most important ideas, omitting minor details and fluff." ,
"temperature" : 0.1 ,
"temperatureEnabled" : true ,
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"tenant_llm_id" : 63 ,
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"topPEnabled" : true ,
"top_p" : 0.3
} ,
"label" : "Extractor" ,
"name" : "Summarization"
} ,
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"id" : "Extractor:LazyCarpetsKiss" ,
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"measured" : {
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"height" : 90 ,
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"width" : 200
} ,
"position" : {
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"x" : 916.9952409420641 ,
"y" : 195.39629819663406
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} ,
"selected" : false ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "contextNode"
} ,
{
"data" : {
"form" : {
"field_name" : "keywords" ,
"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
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"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
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"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
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"prompts" : "Text Content\n{Extractor:LazyCarpetsKiss@chunks}" ,
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"sys_prompt" : "Role\nYou are a text analyzer.\n\nTask\nExtract the most important keywords/phrases of a given piece of text content.\n\nRequirements\n- Summarize the text content, and give the top 5 important keywords/phrases.\n- The keywords MUST be in the same language as the given piece of text content.\n- The keywords are delimited by ENGLISH COMMA.\n- Output keywords ONLY." ,
"temperature" : 0.1 ,
"temperatureEnabled" : true ,
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"tenant_llm_id" : 63 ,
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"topPEnabled" : true ,
"top_p" : 0.3
} ,
"label" : "Extractor" ,
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"name" : "Auto Keyword"
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} ,
"dragging" : false ,
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"id" : "Extractor:LovelyPearsRest" ,
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"measured" : {
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"height" : 90 ,
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"width" : 200
} ,
"position" : {
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"x" : 983.5410692821999 ,
"y" : 301.1557383781162
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} ,
"selected" : false ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "contextNode"
} ,
{
"data" : {
"form" : {
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"field_name" : "questions" ,
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"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
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"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
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"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
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"prompts" : "Text Content\n{Extractor:LovelyPearsRest@chunks}" ,
"sys_prompt" : "Role\nYou are a text analyzer.\n\nTask\nPropose 3 questions about a given piece of text content.\n\nRequirements\n- Understand and summarize the text content, and propose the top 3 important questions.\n- The questions SHOULD NOT have overlapping meanings.\n- The questions SHOULD cover the main content of the text as much as possible.\n- The questions MUST be in the same language as the given piece of text content.\n- One question per line.\n- Output questions ONLY." ,
"temperature" : 0.1 ,
"temperatureEnabled" : true ,
"tenant_llm_id" : 63 ,
"topPEnabled" : true ,
"top_p" : 0.3
} ,
"label" : "Extractor" ,
"name" : "Auto Question"
} ,
"dragging" : false ,
"id" : "Extractor:SmartWindowsHammer" ,
"measured" : {
"height" : 90 ,
"width" : 200
} ,
"position" : {
"x" : 1021.1009769800036 ,
"y" : 421.67760363913044
} ,
"selected" : false ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "contextNode"
} ,
{
"data" : {
"form" : {
"field_name" : "metadata" ,
"frequencyPenaltyEnabled" : true ,
"frequency_penalty" : 0.7 ,
"llm_id" : "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW" ,
"maxTokensEnabled" : false ,
"max_tokens" : 256 ,
"outputs" : {
"chunks" : {
"type" : "Array<Object>" ,
"value" : [ ]
}
} ,
"presencePenaltyEnabled" : true ,
"presence_penalty" : 0.4 ,
"prompts" : "Content:\n{Extractor:SmartWindowsHammer@chunks}" ,
"sys_prompt" : "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: {}.\n\nImportant structured information may include: names, dates, locations, events, key facts, numerical data, or other extractable entities." ,
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"temperature" : 0.1 ,
"temperatureEnabled" : true ,
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"tenant_llm_id" : 63 ,
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"topPEnabled" : true ,
"top_p" : 0.3
} ,
"label" : "Extractor" ,
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"name" : "Auto Metadata"
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} ,
"dragging" : false ,
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"id" : "Extractor:CurlyEmusJam" ,
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"measured" : {
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"height" : 90 ,
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"width" : 200
} ,
"position" : {
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"x" : 1065.7115140232393 ,
"y" : 527.4370438206126
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} ,
"selected" : true ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "contextNode"
} ,
{
"data" : {
"form" : {
"fields" : "text" ,
"filename_embd_weight" : 0.1 ,
"outputs" : { } ,
"search_method" : [
"embedding" ,
"full_text"
]
} ,
"label" : "Tokenizer" ,
"name" : "Indexer_0"
} ,
"dragging" : false ,
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"id" : "Tokenizer:WittySunsListen" ,
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"measured" : {
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"height" : 114 ,
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"width" : 200
} ,
"position" : {
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"x" : 1327.3247542536642 ,
"y" : 164.72133416115918
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} ,
"selected" : false ,
"sourcePosition" : "right" ,
"targetPosition" : "left" ,
"type" : "tokenizerNode"
}
]
} ,
"history" : [ ] ,
"messages" : [ ] ,
"path" : [ ] ,
"retrieval" : [ ] ,
"variables" : [ ]
2025-10-14 21:29:42 +08:00
} ,
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2026-04-03 19:26:45 +08:00
}
