{ "id": 23, "title": { "en": "Advanced Ingestion Pipeline", "de": "Erweiterte Ingestion Pipeline", "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.", "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.", "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", "dsl": { "components": { "Extractor:CurlyEmusJam": { "downstream": [ "Tokenizer:WittySunsListen" ], "obj": { "component_name": "Extractor", "params": { "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", "value": [] } }, "presencePenaltyEnabled": true, "presence_penalty": 0.4, "prompts": [ { "content": "Content:\n{Extractor:SmartWindowsHammer@chunks}", "role": "user" } ], "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.", "temperature": 0.1, "temperatureEnabled": true, "tenant_llm_id": 63, "topPEnabled": true, "top_p": 0.3 } }, "upstream": [ "Extractor:SmartWindowsHammer" ] }, "Extractor:LazyCarpetsKiss": { "downstream": [ "Extractor:LovelyPearsRest" ], "obj": { "component_name": "Extractor", "params": { "field_name": "summary", "frequencyPenaltyEnabled": true, "frequency_penalty": 0.7, "llm_id": "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW", "maxTokensEnabled": false, "max_tokens": 256, "outputs": { "chunks": { "type": "Array", "value": [] } }, "presencePenaltyEnabled": true, "presence_penalty": 0.4, "prompts": [ { "content": "Text to Summarize:\n{TokenChunker:BumpyStarsPress@chunks}", "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, "tenant_llm_id": 63, "topPEnabled": true, "top_p": 0.3 } }, "upstream": [ "TokenChunker:BumpyStarsPress" ] }, "Extractor:LovelyPearsRest": { "downstream": [ "Extractor:SmartWindowsHammer" ], "obj": { "component_name": "Extractor", "params": { "field_name": "keywords", "frequencyPenaltyEnabled": true, "frequency_penalty": 0.7, "llm_id": "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW", "maxTokensEnabled": false, "max_tokens": 256, "outputs": { "chunks": { "type": "Array", "value": [] } }, "presencePenaltyEnabled": true, "presence_penalty": 0.4, "prompts": [ { "content": "Text Content\n{Extractor:LazyCarpetsKiss@chunks}", "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, "tenant_llm_id": 63, "topPEnabled": true, "top_p": 0.3 } }, "upstream": [ "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", "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" ] }, "File": { "downstream": [ "Parser:HipSignsRhyme" ], "obj": { "component_name": "File", "params": {} }, "upstream": [] }, "Parser:HipSignsRhyme": { "downstream": [ "TokenChunker:BumpyStarsPress" ], "obj": { "component_name": "Parser", "params": { "outputs": { "html": { "type": "string", "value": "" }, "json": { "type": "Array", "value": [] }, "markdown": { "type": "string", "value": "" }, "text": { "type": "string", "value": "" } }, "setups": { "doc": { "output_format": "json", "preprocess": "main_content", "suffix": [ "doc" ] }, "docx": { "flatten_media_to_text": false, "output_format": "json", "preprocess": "main_content", "suffix": [ "docx" ], "vlm": {} }, "email": { "fields": [ "from", "to", "cc", "bcc", "date", "subject", "body", "attachments" ], "output_format": "text", "preprocess": "main_content", "suffix": [ "eml", "msg" ] }, "html": { "output_format": "json", "preprocess": "main_content", "suffix": [ "htm", "html" ] }, "image": { "output_format": "text", "parse_method": "ocr", "preprocess": "main_content", "suffix": [ "jpg", "jpeg", "png", "gif" ], "system_prompt": "" }, "markdown": { "flatten_media_to_text": false, "output_format": "json", "preprocess": "main_content", "suffix": [ "md", "markdown", "mdx" ], "vlm": {} }, "pdf": { "flatten_media_to_text": false, "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": { "flatten_media_to_text": false, "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" ] } } } }, "upstream": [ "File" ] }, "TokenChunker:BumpyStarsPress": { "downstream": [ "Extractor:LazyCarpetsKiss" ], "obj": { "component_name": "TokenChunker", "params": { "children_delimiters": [], "chunk_token_size": 512, "delimiter_mode": "token_size", "delimiters": [], "image_context_size": 0, "outputs": { "chunks": { "type": "Array", "value": [] } }, "overlapped_percent": 0, "table_context_size": 0 } }, "upstream": [ "Parser:HipSignsRhyme" ] }, "Tokenizer:WittySunsListen": { "downstream": [], "obj": { "component_name": "Tokenizer", "params": { "fields": "text", "filename_embd_weight": 0.1, "outputs": {}, "search_method": [ "embedding", "full_text" ] } }, "upstream": [ "Extractor:CurlyEmusJam" ] } }, "globals": { "sys.history": [] }, "graph": { "edges": [ { "id": "xy-edge__Filestart-Parser:HipSignsRhymeend", "source": "File", "sourceHandle": "start", "target": "Parser:HipSignsRhyme", "targetHandle": "end" }, { "id": "xy-edge__Parser:HipSignsRhymestart-TokenChunker:BumpyStarsPressend", "source": "Parser:HipSignsRhyme", "sourceHandle": "start", "target": "TokenChunker:BumpyStarsPress", "targetHandle": "end" }, { "id": "xy-edge__TokenChunker:BumpyStarsPressstart-Extractor:LazyCarpetsKissend", "source": "TokenChunker:BumpyStarsPress", "sourceHandle": "start", "target": "Extractor:LazyCarpetsKiss", "targetHandle": "end" }, { "data": { "isHovered": false }, "id": "xy-edge__Extractor:LazyCarpetsKissstart-Extractor:LovelyPearsRestend", "source": "Extractor:LazyCarpetsKiss", "sourceHandle": "start", "target": "Extractor:LovelyPearsRest", "targetHandle": "end" }, { "data": { "isHovered": false }, "id": "xy-edge__Extractor:LovelyPearsReststart-Extractor:SmartWindowsHammerend", "selected": false, "source": "Extractor:LovelyPearsRest", "sourceHandle": "start", "target": "Extractor:SmartWindowsHammer", "targetHandle": "end" }, { "data": { "isHovered": false }, "id": "xy-edge__Extractor:SmartWindowsHammerstart-Extractor:CurlyEmusJamend", "selected": false, "source": "Extractor:SmartWindowsHammer", "sourceHandle": "start", "target": "Extractor:CurlyEmusJam", "targetHandle": "end" }, { "data": { "isHovered": false }, "id": "xy-edge__Extractor:CurlyEmusJamstart-Tokenizer:WittySunsListenend", "source": "Extractor:CurlyEmusJam", "sourceHandle": "start", "target": "Tokenizer:WittySunsListen", "targetHandle": "end" } ], "nodes": [ { "data": { "label": "File", "name": "File" }, "id": "File", "measured": { "height": 50, "width": 200 }, "position": { "x": 50, "y": 200 }, "sourcePosition": "left", "targetPosition": "right", "type": "beginNode" }, { "data": { "form": { "outputs": { "html": { "type": "string", "value": "" }, "json": { "type": "Array", "value": [] }, "markdown": { "type": "string", "value": "" }, "text": { "type": "string", "value": "" } }, "setups": [ { "fileFormat": "pdf", "flatten_media_to_text": false, "output_format": "json", "parse_method": "DeepDOC", "preprocess": "main_content" }, { "fileFormat": "spreadsheet", "flatten_media_to_text": false, "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", "flatten_media_to_text": false, "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", "flatten_media_to_text": false, "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", "value": [] } }, "overlapped_percent": 0 }, "label": "TokenChunker", "name": "Token Chunker_0" }, "id": "TokenChunker:BumpyStarsPress", "measured": { "height": 74, "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, "llm_id": "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW", "maxTokensEnabled": false, "max_tokens": 256, "outputs": { "chunks": { "type": "Array", "value": [] } }, "presencePenaltyEnabled": true, "presence_penalty": 0.4, "prompts": "Text to Summarize:\n{TokenChunker:BumpyStarsPress@chunks}", "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, "tenant_llm_id": 63, "topPEnabled": true, "top_p": 0.3 }, "label": "Extractor", "name": "Summarization" }, "id": "Extractor:LazyCarpetsKiss", "measured": { "height": 90, "width": 200 }, "position": { "x": 916.9952409420641, "y": 195.39629819663406 }, "selected": false, "sourcePosition": "right", "targetPosition": "left", "type": "contextNode" }, { "data": { "form": { "field_name": "keywords", "frequencyPenaltyEnabled": true, "frequency_penalty": 0.7, "llm_id": "THUDM/GLM-4.1V-9B-Thinking@SILICONFLOW", "maxTokensEnabled": false, "max_tokens": 256, "outputs": { "chunks": { "type": "Array", "value": [] } }, "presencePenaltyEnabled": true, "presence_penalty": 0.4, "prompts": "Text Content\n{Extractor:LazyCarpetsKiss@chunks}", "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, "tenant_llm_id": 63, "topPEnabled": true, "top_p": 0.3 }, "label": "Extractor", "name": "Auto Keyword" }, "dragging": false, "id": "Extractor:LovelyPearsRest", "measured": { "height": 90, "width": 200 }, "position": { "x": 983.5410692821999, "y": 301.1557383781162 }, "selected": false, "sourcePosition": "right", "targetPosition": "left", "type": "contextNode" }, { "data": { "form": { "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", "value": [] } }, "presencePenaltyEnabled": true, "presence_penalty": 0.4, "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", "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.", "temperature": 0.1, "temperatureEnabled": true, "tenant_llm_id": 63, "topPEnabled": true, "top_p": 0.3 }, "label": "Extractor", "name": "Auto Metadata" }, "dragging": false, "id": "Extractor:CurlyEmusJam", "measured": { "height": 90, "width": 200 }, "position": { "x": 1065.7115140232393, "y": 527.4370438206126 }, "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, "id": "Tokenizer:WittySunsListen", "measured": { "height": 114, "width": 200 }, "position": { "x": 1327.3247542536642, "y": 164.72133416115918 }, "selected": false, "sourcePosition": "right", "targetPosition": "left", "type": "tokenizerNode" } ] }, "history": [], "messages": [], "path": [], "retrieval": [], "variables": [] }, "avatar": 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" }