mirror of
https://github.com/infiniflow/ragflow.git
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726 lines
41 KiB
JSON
726 lines
41 KiB
JSON
{
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"id": 23,
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"title": {
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"en": "Advanced Ingestion Pipeline",
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"zh": "编排复杂的 Ingestion Pipeline"
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},
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"description": {
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"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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"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。"
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},
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"canvas_type": "Ingestion Pipeline",
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"canvas_category": "dataflow_canvas",
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"dsl": {
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"components": {
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"File": {
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"obj": {
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"component_name": "File",
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"params": {}
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},
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"downstream": [
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"Parser:HipSignsRhyme"
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],
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"upstream": []
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},
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"Parser:HipSignsRhyme": {
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"obj": {
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"component_name": "Parser",
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"params": {
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"outputs": {
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"html": {
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"type": "string",
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"value": ""
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},
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"json": {
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"type": "Array<object>",
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"value": []
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},
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"markdown": {
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"type": "string",
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"value": ""
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},
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"text": {
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"type": "string",
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"value": ""
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}
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},
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"setups": {
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"pdf": {
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"output_format": "markdown",
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"suffix": [
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"pdf"
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],
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"parse_method": "DeepDOC"
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},
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"spreadsheet": {
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"output_format": "html",
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"suffix": [
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"xls",
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"xlsx",
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"csv"
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]
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},
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"image": {
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"output_format": "text",
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"suffix": [
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"jpg",
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"jpeg",
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"png",
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"gif"
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],
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"parse_method": "ocr"
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},
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"email": {
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"output_format": "text",
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"suffix": [
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"eml",
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"msg"
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],
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"fields": [
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"from",
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"to",
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"cc",
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"bcc",
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"date",
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"subject",
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"body",
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"attachments"
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]
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},
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"text&markdown": {
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"output_format": "text",
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"suffix": [
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"md",
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"markdown",
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"mdx",
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"txt"
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]
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},
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"word": {
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"output_format": "json",
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"suffix": [
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"doc",
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"docx"
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]
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},
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"slides": {
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"output_format": "json",
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"suffix": [
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"pptx"
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]
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}
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}
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}
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},
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"downstream": [
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"Splitter:KindDingosJam"
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],
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"upstream": [
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"File"
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]
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},
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"Splitter:KindDingosJam": {
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"obj": {
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"component_name": "Splitter",
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"params": {
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"chunk_token_size": 512,
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"delimiters": [
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"\n"
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],
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"outputs": {
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"chunks": {
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"type": "Array<Object>",
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"value": []
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}
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},
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"overlapped_percent": 0.002
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}
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},
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"downstream": [
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"Extractor:NineTiesSin"
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],
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"upstream": [
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"Parser:HipSignsRhyme"
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]
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},
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"Extractor:NineTiesSin": {
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"obj": {
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"component_name": "Extractor",
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"params": {
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"field_name": "summary",
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"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
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"maxTokensEnabled": false,
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": [
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{
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"content": "Text to Summarize:\n{Splitter:KindDingosJam@chunks}",
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"role": "user"
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}
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],
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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.",
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"temperature": 0.1,
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"temperatureEnabled": false,
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"topPEnabled": false,
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"top_p": 0.3
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}
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},
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"downstream": [
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"Extractor:TastyPointsLay"
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],
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"upstream": [
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"Splitter:KindDingosJam"
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]
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},
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"Extractor:TastyPointsLay": {
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"obj": {
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"component_name": "Extractor",
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"params": {
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"field_name": "keywords",
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"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
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"maxTokensEnabled": false,
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": [
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{
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"content": "Text Content:\n{Splitter:KindDingosJam@chunks}\n",
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"role": "user"
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}
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],
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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.",
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"temperature": 0.1,
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"temperatureEnabled": false,
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"topPEnabled": false,
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"top_p": 0.3
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}
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},
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"downstream": [
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"Extractor:BlueResultsWink"
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],
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"upstream": [
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"Extractor:NineTiesSin"
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]
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},
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"Extractor:BlueResultsWink": {
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"obj": {
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"component_name": "Extractor",
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"params": {
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"field_name": "questions",
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"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
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"maxTokensEnabled": false,
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": [
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{
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"content": "Text Content:\n\n{Splitter:KindDingosJam@chunks}\n",
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"role": "user"
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}
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],
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"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.",
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"temperature": 0.1,
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"temperatureEnabled": false,
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"topPEnabled": false,
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"top_p": 0.3
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}
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},
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"downstream": [
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"Extractor:CuteBusesBet"
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],
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"upstream": [
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"Extractor:TastyPointsLay"
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]
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},
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"Extractor:CuteBusesBet": {
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"obj": {
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"component_name": "Extractor",
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"params": {
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"field_name": "metadata",
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"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
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"maxTokensEnabled": false,
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": [
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{
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"content": "Content: \n\n{Splitter:KindDingosJam@chunks}",
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"role": "user"
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}
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],
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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,
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"temperatureEnabled": false,
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"topPEnabled": false,
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"top_p": 0.3
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}
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},
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"downstream": [
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"Tokenizer:LegalHorsesCheer"
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],
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"upstream": [
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"Extractor:BlueResultsWink"
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]
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},
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"Tokenizer:LegalHorsesCheer": {
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"obj": {
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"component_name": "Tokenizer",
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"params": {
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"fields": "text",
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"filename_embd_weight": 0.1,
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"outputs": {},
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"search_method": [
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"embedding",
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"full_text"
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]
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}
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},
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"downstream": [],
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"upstream": [
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"Extractor:CuteBusesBet"
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]
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}
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},
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"globals": {},
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"graph": {
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"nodes": [
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{
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"data": {
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"label": "File",
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"name": "File"
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},
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"dragging": false,
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"id": "File",
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"measured": {
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"height": 48,
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"width": 200
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},
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"position": {
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"x": -301.4128436198721,
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"y": 375.86728431988394
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},
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"selected": false,
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"sourcePosition": "left",
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"targetPosition": "right",
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"type": "beginNode"
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},
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{
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"data": {
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"form": {
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"outputs": {
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"html": {
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"type": "string",
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"value": ""
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},
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"json": {
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"type": "Array<object>",
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"value": []
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},
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"markdown": {
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"type": "string",
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"value": ""
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},
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"text": {
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"type": "string",
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"value": ""
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}
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},
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"setups": [
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{
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"fileFormat": "pdf",
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"output_format": "markdown",
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"parse_method": "DeepDOC"
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},
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{
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"fileFormat": "spreadsheet",
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"output_format": "html"
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},
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{
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"fileFormat": "image",
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"output_format": "text",
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"parse_method": "ocr"
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},
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{
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"fields": [
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"from",
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"to",
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"cc",
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"bcc",
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"date",
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"subject",
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"body",
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"attachments"
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],
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"fileFormat": "email",
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"output_format": "text"
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},
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{
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"fileFormat": "text&markdown",
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"output_format": "text"
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},
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{
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"fileFormat": "word",
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"output_format": "json"
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},
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{
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"fileFormat": "slides",
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"output_format": "json"
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}
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]
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},
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"label": "Parser",
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"name": "Parser"
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},
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"dragging": false,
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"id": "Parser:HipSignsRhyme",
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"measured": {
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"height": 56,
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"width": 200
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},
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"position": {
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"x": -297.12089864837964,
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"y": 532.2084591689336
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},
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"selected": false,
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"sourcePosition": "right",
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"targetPosition": "left",
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"type": "parserNode"
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},
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{
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"data": {
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"form": {
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"chunk_token_size": 512,
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"delimiters": [
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{
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"value": "\n"
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}
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],
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"outputs": {
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"chunks": {
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"type": "Array<Object>",
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"value": []
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}
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},
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"overlapped_percent": 0.2
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},
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"label": "Splitter",
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"name": "Token Chunker"
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},
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"dragging": false,
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"id": "Splitter:KindDingosJam",
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"measured": {
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"height": 80,
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"width": 200
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},
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"position": {
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"x": 7.288275851418206,
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"y": 371.19722568785704
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},
|
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"selected": false,
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"sourcePosition": "right",
|
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"targetPosition": "left",
|
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"type": "splitterNode"
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},
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{
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"data": {
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"form": {
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"field_name": "summary",
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"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
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"maxTokensEnabled": false,
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": "Text to Summarize:\n{Splitter:KindDingosJam@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.",
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"temperature": 0.1,
|
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"temperatureEnabled": false,
|
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"topPEnabled": false,
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"top_p": 0.3
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},
|
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"label": "Extractor",
|
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"name": "Summarization"
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},
|
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"dragging": false,
|
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"id": "Extractor:NineTiesSin",
|
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"measured": {
|
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"height": 84,
|
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"width": 200
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},
|
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"position": {
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"x": 9.537168313582939,
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"y": 461.26662127765564
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},
|
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"selected": false,
|
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"sourcePosition": "right",
|
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"targetPosition": "left",
|
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"type": "contextNode"
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},
|
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{
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"data": {
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"form": {
|
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"field_name": "keywords",
|
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"frequencyPenaltyEnabled": false,
|
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"frequency_penalty": 0.7,
|
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"llm_id": "deepseek-chat@DeepSeek",
|
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"maxTokensEnabled": false,
|
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"max_tokens": 256,
|
|
"outputs": {},
|
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"presencePenaltyEnabled": false,
|
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"presence_penalty": 0.4,
|
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"prompts": "Text Content:\n{Splitter:KindDingosJam@chunks}\n",
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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.",
|
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"temperature": 0.1,
|
|
"temperatureEnabled": false,
|
|
"topPEnabled": false,
|
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"top_p": 0.3
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},
|
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"label": "Extractor",
|
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"name": "Auto Keywords"
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},
|
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"dragging": false,
|
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"id": "Extractor:TastyPointsLay",
|
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"measured": {
|
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"height": 84,
|
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"width": 200
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},
|
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"position": {
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"x": 7.473032067783009,
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"y": 533.0519245332371
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},
|
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"selected": false,
|
|
"sourcePosition": "right",
|
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"targetPosition": "left",
|
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"type": "contextNode"
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},
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{
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"data": {
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"form": {
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"field_name": "questions",
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"frequencyPenaltyEnabled": false,
|
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
|
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"maxTokensEnabled": false,
|
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
|
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"presence_penalty": 0.4,
|
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"prompts": "Text Content:\n\n{Splitter:KindDingosJam@chunks}\n",
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"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.",
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|
"temperature": 0.1,
|
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"temperatureEnabled": false,
|
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"topPEnabled": false,
|
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"top_p": 0.3
|
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},
|
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"label": "Extractor",
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"name": "Auto Questions"
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},
|
|
"dragging": false,
|
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"id": "Extractor:BlueResultsWink",
|
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"measured": {
|
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"height": 84,
|
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"width": 200
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},
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"position": {
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"x": 2.905601749296892,
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"y": 617.0420857433816
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},
|
|
"selected": false,
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"sourcePosition": "right",
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"targetPosition": "left",
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"type": "contextNode"
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},
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{
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"data": {
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"form": {
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"field_name": "metadata",
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"frequencyPenaltyEnabled": false,
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"frequency_penalty": 0.7,
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"llm_id": "deepseek-chat@DeepSeek",
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"maxTokensEnabled": false,
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"max_tokens": 256,
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"outputs": {},
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"presencePenaltyEnabled": false,
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"presence_penalty": 0.4,
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"prompts": "Content: \n\n{Splitter:KindDingosJam@chunks}",
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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,
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"temperatureEnabled": false,
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"topPEnabled": false,
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"top_p": 0.3
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},
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"label": "Extractor",
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"name": "Generate Metadata"
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},
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"dragging": false,
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"id": "Extractor:CuteBusesBet",
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"measured": {
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"height": 84,
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"width": 200
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},
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"position": {
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"x": 327.16477358029204,
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"y": 374.11630810111944
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"type": "contextNode"
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{
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"data": {
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"form": {
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"fields": "text",
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"filename_embd_weight": 0.1,
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"outputs": {},
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"search_method": [
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"embedding",
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"full_text"
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]
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},
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"label": "Tokenizer",
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"name": "Indexer"
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},
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"dragging": false,
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"id": "Tokenizer:LegalHorsesCheer",
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"measured": {
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"height": 120,
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"width": 200
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},
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"position": {
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"x": 345.50155210663667,
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"y": 533.0511852267863
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},
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"selected": false,
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"sourcePosition": "right",
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"targetPosition": "left",
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"type": "tokenizerNode"
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},
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{
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"id": "Note:CruelSidesStick",
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"type": "noteNode",
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"position": {
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"x": -29,
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"y": 765
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},
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"data": {
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"label": "Note",
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"name": "Add more attributes",
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"form": {
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"text": "Using LLM to generate summaries, keywords, Q&A, and metadata."
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}
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},
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"sourcePosition": "right",
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"targetPosition": "left",
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"dragHandle": ".note-drag-handle",
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"measured": {
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"width": 281,
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"height": 130
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},
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"width": 281,
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"height": 130,
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"resizing": false
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}
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],
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"edges": [
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{
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"data": {
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"isHovered": false
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},
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"id": "xy-edge__Filestart-Parser:HipSignsRhymeend",
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"source": "File",
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"sourceHandle": "start",
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"target": "Parser:HipSignsRhyme",
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"targetHandle": "end"
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},
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{
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"data": {
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"isHovered": false
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},
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"id": "xy-edge__Splitter:KindDingosJamstart-Extractor:NineTiesSinend",
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"source": "Splitter:KindDingosJam",
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"sourceHandle": "start",
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"target": "Extractor:NineTiesSin",
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"targetHandle": "end"
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},
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{
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"data": {
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"isHovered": false
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},
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"id": "xy-edge__Extractor:NineTiesSinstart-Extractor:TastyPointsLayend",
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"source": "Extractor:NineTiesSin",
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"sourceHandle": "start",
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"target": "Extractor:TastyPointsLay",
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"targetHandle": "end"
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},
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{
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"data": {
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"isHovered": false
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},
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"id": "xy-edge__Extractor:TastyPointsLaystart-Extractor:BlueResultsWinkend",
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"source": "Extractor:TastyPointsLay",
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"sourceHandle": "start",
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"target": "Extractor:BlueResultsWink",
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"targetHandle": "end"
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},
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{
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"data": {
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"isHovered": false
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},
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"id": "xy-edge__Extractor:BlueResultsWinkstart-Extractor:CuteBusesBetend",
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"source": "Extractor:BlueResultsWink",
|
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"sourceHandle": "start",
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"target": "Extractor:CuteBusesBet",
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"targetHandle": "end"
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},
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{
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"data": {
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"isHovered": false
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},
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"id": "xy-edge__Extractor:CuteBusesBetstart-Tokenizer:LegalHorsesCheerend",
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"source": "Extractor:CuteBusesBet",
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"sourceHandle": "start",
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"target": "Tokenizer:LegalHorsesCheer",
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"targetHandle": "end"
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},
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{
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"data": {
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"isHovered": false
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},
|
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"id": "xy-edge__Parser:HipSignsRhymestart-Splitter:KindDingosJamend",
|
|
"markerEnd": "logo",
|
|
"source": "Parser:HipSignsRhyme",
|
|
"sourceHandle": "start",
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|
"style": {
|
|
"stroke": "rgba(91, 93, 106, 1)",
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"strokeWidth": 1
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},
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"target": "Splitter:KindDingosJam",
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"targetHandle": "end",
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"type": "buttonEdge",
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"zIndex": 1001
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}
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]
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},
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"history": [],
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"messages": [],
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|
"path": [],
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"retrieval": []
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},
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"
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} |