analyze_article_flow_en.yml

app:
  description: Use LLMs to summarize article, extract keywords, main points, and key
    quotes.
  icon: sparkle
  icon_background: '#D3F8DF'
  mode: workflow
  name: BestBlogs Article Analysis Workflow
workflow:
  features:
    file_upload:
      image:
        enabled: false
        number_limits: 3
        transfer_methods:
        - local_file
        - remote_url
    opening_statement: ''
    retriever_resource:
      enabled: false
    sensitive_word_avoidance:
      enabled: false
    speech_to_text:
      enabled: false
    suggested_questions: []
    suggested_questions_after_answer:
      enabled: false
    text_to_speech:
      enabled: false
      language: ''
      voice: ''
  graph:
    edges:
    - data:
        isInIteration: false
        sourceType: code
        targetType: iteration
      id: 1718353568276-source-1718354838381-target
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      targetHandle: target
      type: custom
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    - data:
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    - data:
        isInIteration: false
        sourceType: start
        targetType: http-request
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      source: '1718352852007'
      sourceHandle: source
      target: '1719356422842'
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    - data:
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    - data:
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        targetType: end
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      targetHandle: target
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    - data:
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        targetType: if-else
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      sourceHandle: source
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      targetHandle: target
      type: custom
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    - data:
        isInIteration: false
        sourceType: llm
        targetType: variable-aggregator
      id: 1719660406112-source-1720093031703-target
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      sourceHandle: source
      target: '1720093031703'
      targetHandle: target
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      targetHandle: target
      type: custom
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    - data:
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      sourceHandle: 'true'
      target: '1719660406112'
      targetHandle: target
      type: custom
      zIndex: 0
    nodes:
    - data:
        desc: Input article ID.
        selected: false
        title: Start
        type: start
        variables:
        - label: Article ID
          max_length: 20
          options: []
          required: true
          type: text-input
          variable: input_article_id
      height: 120
      id: '1718352852007'
      position:
        x: 981.6617142857144
        y: -314.3451428571427
      positionAbsolute:
        x: 981.6617142857144
        y: -314.3451428571427
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        code: "import re\n\ndef main(markdown_text: str) -> dict:\n    \"\"\"\n  \
          \  \u5C06 Markdown \u6587\u672C\u5212\u5206\u4E3A\u6BB5\u843D\uFF0C\u5E76\
          \u5408\u5E76\u8F83\u5C0F\u7684\u6BB5\u843D\u4F7F\u5176\u957F\u5EA6\u4E0D\
          \u8D85\u8FC7 3000 \u4E2A\u5B57\u7B26\u3002\n\n    \u53C2\u6570\uFF1A\n \
          \       markdown_text (str): Markdown\u683C\u5F0F\u7684\u6587\u672C\u3002\
          \n\n    \u8FD4\u56DE\uFF1A\n        Dict[str, List[str]]: \u5305\u542B\u6BB5\
          \u843D\u5217\u8868\u7684\u5B57\u5178\uFF0C\u6BCF\u4E2A\u6BB5\u843D\u4F5C\
          \u4E3A\u5217\u8868\u4E2D\u7684\u4E00\u9879\uFF0C\u957F\u5EA6\u4E0D\u8D85\
          \u8FC7 3000 \u4E2A\u5B57\u7B26\u3002\n    \"\"\"\n    max_length = 3000\n\
          \n    # \u9884\u5904\u7406\uFF1A\u79FB\u9664YAML\u524D\u7F6E\u5143\u6570\
          \u636E\uFF08\u5982\u679C\u5B58\u5728\uFF09\n    markdown_text = re.sub(r'^---\\\
          n.*?\\n---\\n', '', markdown_text, flags=re.DOTALL)\n\n    # \u521D\u6B65\
          \u6309\u6362\u884C\u5206\u9694\uFF0C\u4FDD\u7559\u7A7A\u884C\n    initial_paragraphs\
          \ = markdown_text.split('\\n')\n    \n    paragraphs = []\n    current_paragraph\
          \ = \"\"\n    in_code_block = False\n    \n    for line in initial_paragraphs:\n\
          \        stripped_line = line.strip()\n        \n        # \u5904\u7406\u4EE3\
          \u7801\u5757\n        if stripped_line.startswith('```'):\n            in_code_block\
          \ = not in_code_block\n            if current_paragraph:\n             \
          \   paragraphs.append(current_paragraph)\n                current_paragraph\
          \ = \"\"\n            paragraphs.append(line)\n            continue\n  \
          \      \n        if in_code_block:\n            paragraphs.append(line)\n\
          \            continue\n        \n        # \u5904\u7406\u6807\u9898\u3001\
          \u5F15\u7528\u3001\u5217\u8868\u7B49\n        if (stripped_line.startswith(('#',\
          \ '>', '-', '*', '+')) or \n            re.match(r'^\\d+\\.', stripped_line)\
          \ or\n            stripped_line.startswith('|') or  # \u8868\u683C\n   \
          \         stripped_line == '---' or  # \u6C34\u5E73\u7EBF\n            re.match(r'^\\\
          [.*\\]:.*', stripped_line)):  # \u94FE\u63A5\u5F15\u7528\n            if\
          \ current_paragraph:\n                paragraphs.append(current_paragraph)\n\
          \                current_paragraph = \"\"\n            paragraphs.append(line)\n\
          \        elif stripped_line == \"\":\n            if current_paragraph:\n\
          \                paragraphs.append(current_paragraph)\n                current_paragraph\
          \ = \"\"\n        else:\n            if current_paragraph:\n           \
          \     current_paragraph += \" \" + stripped_line\n            else:\n  \
          \              current_paragraph = stripped_line\n    \n    if current_paragraph:\n\
          \        paragraphs.append(current_paragraph)\n    \n    # \u5408\u5E76\u6BB5\
          \u843D\u4F7F\u5176\u957F\u5EA6\u4E0D\u8D85\u8FC7max_length\n    merged_paragraphs\
          \ = []\n    current_paragraph = \"\"\n\n    for para in paragraphs:\n  \
          \      if len(current_paragraph) + len(para) + 2 <= max_length:  # +2 for\
          \ potential '\\n\\n'\n            if current_paragraph:\n              \
          \  current_paragraph += \"\\n\\n\" + para\n            else:\n         \
          \       current_paragraph = para\n        else:\n            if current_paragraph:\n\
          \                merged_paragraphs.append(current_paragraph)\n         \
          \   if len(para) > max_length:\n                # \u5982\u679C\u5355\u4E2A\
          \u6BB5\u843D\u8D85\u8FC7\u6700\u5927\u957F\u5EA6\uFF0C\u8FDB\u884C\u5206\
          \u5272\n                words = para.split()\n                temp_para\
          \ = \"\"\n                for word in words:\n                    if len(temp_para)\
          \ + len(word) + 1 <= max_length:\n                        temp_para += \"\
          \ \" + word if temp_para else word\n                    else:\n        \
          \                merged_paragraphs.append(temp_para)\n                 \
          \       temp_para = word\n                if temp_para:\n              \
          \      merged_paragraphs.append(temp_para)\n            else:\n        \
          \        current_paragraph = para\n\n    if current_paragraph:\n       \
          \ merged_paragraphs.append(current_paragraph)\n\n    return {\n        \"\
          paragraphs\": merged_paragraphs,\n        \"totalParagraphCount\": len(merged_paragraphs)\n\
          \    }"
        code_language: python3
        desc: Divide the article into segments, each containing no more than 3,000
          characters.
        outputs:
          paragraphs:
            children: null
            type: array[string]
          totalParagraphCount:
            children: null
            type: number
        selected: false
        title: Article Segmentation
        type: code
        variables:
        - value_selector:
          - '1719357159255'
          - markdown
          variable: markdown_text
      height: 120
      id: '1718353568276'
      position:
        x: 1246
        y: 495
      positionAbsolute:
        x: 1246
        y: 495
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        desc: Analyze the content of each segment in succession.
        height: 269
        iterator_selector:
        - '1718353568276'
        - paragraphs
        output_selector:
        - '1718354999060'
        - text
        output_type: array[string]
        selected: false
        startNodeType: llm
        start_node_id: '1718354999060'
        title: Sequential Segment Analysis
        type: iteration
        width: 376
      height: 269
      id: '1718354838381'
      position:
        x: 1550
        y: 495
      positionAbsolute:
        x: 1550
        y: 495
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 376
      zIndex: 1
    - data:
        context:
          enabled: true
          variable_selector:
          - '1718354838381'
          - item
        desc: Summarize each paragraph, provide an abstract, list main points, and
          extract key terms.
        isInIteration: true
        isIterationStart: true
        iteration_id: '1718354838381'
        model:
          completion_params:
            temperature: 0.5
          mode: chat
          name: deepseek-chat
          provider: deepseek
        prompt_template:
        - id: 982fdfa5-0a05-4858-b89e-d9bf8205a4d2
          role: system
          text: "# Article Paragraph Analysis Assistant\n\n## Input Format\nYou will\
            \ receive an XML-formatted paragraph from an article, including elements\
            \ such as title, source, URL, and content. The paragraph content will\
            \ be enclosed in a CDATA section.\n\n## Objective\nAnalyze the given XML-formatted\
            \ paragraph, extracting core content, main points, potential key quotes,\
            \ and relevant tags. This analysis will prepare for subsequent full-text\
            \ summarization and review steps. Focus primarily on large language models,\
            \ AIGC, AI applications, AI development technologies and frameworks (e.g.,\
            \ prompts, RAG, WorkFlow, Agent, LangChain, Dify). Secondary focus includes\
            \ development, product, design, impact, business, entrepreneurship, and\
            \ related topics. Output in {{#1719357159255.languageName#}}.\n\n## Output\
            \ Format\nUse {{#1719357159255.languageName#}} to output the analysis\
            \ results in the following Markdown format:\n\n```markdown\n### Core Content\n\
            [Concisely summarize the paragraph's core content in 50-80 words]\n\n\
            ### Main Points (2-3)\n- [Point 1]\n- [Point 2]\n- [Point 3] (if applicable)\n\
            \n### Key Quotes (2-3)\n> [Quote 1]\n> [Quote 2]\n> [Quote 3] (if applicable)\n\
            \n### Tags (2-4)\n[Tag 1], [Tag 2], [Tag 3], [Tag 4]\n```\n\n## Guidelines\n\
            1. Thoroughly analyze all XML information, including title, source, and\
            \ URL, as these may provide crucial context.\n\n2. Keep the core content\
            \ concise (50-80 words), highlighting key information.\n\n3. Ensure main\
            \ points comprehensively cover all important content without omitting\
            \ crucial information.\n\n4. Select key quotes that best represent the\
            \ paragraph's core ideas or offer the most insight. Choose sentences that\
            \ are both self-contained and summarize central concepts.\n\n5. Tag selection:\n\
            \   - Choose 2-4 most relevant tags from these categories: topic, technology/field,\
            \ application/product, company/platform/person, and trend.\n   - Prioritize\
            \ tags reflecting unique content, avoiding overly broad descriptors.\n\
            \   - Tags should capture main topics, technologies/fields, applications/products,\
            \ mentioned entities, and apparent trends.\n   - Select tags that effectively\
            \ categorize and filter articles, not just keywords.\n\n6. Maintain an\
            \ objective, professional tone, avoiding colloquialisms or emotional language.\n\
            \n7. Ensure the analysis accurately reflects the paragraph content without\
            \ speculation.\n\n8. Match the output language to the original text. For\
            \ Chinese output, add spaces between Chinese characters and English words,\
            \ numbers, or symbols."
        - id: 24d97f72-bbd2-4d77-ab03-90368accf732
          role: user
          text: "Please analyze this content according to the steps and principles\
            \ outlined in the system prompt, and output the specified format of analysis\
            \ results using {{#1719357159255.languageName#}}.\n\n<article>\n  <title>{{#1719357159255.title#}}</title>\n\
            \n  <source>{{#1719357159255.sourceName#}}</source>\n\n  <url>{{#1719357159255.url#}}</url>\n\
            \n  <paragraph>\n    <index>{{#1718354838381.index#}}</index>\n\n    <content>\n\
            \      <![CDATA[\n      {{#1718354838381.item#}}\n\n      ]]>\n    </content>\n\
            \  </paragraph>\n</article>"
        selected: false
        title: Paragraph Analysis
        type: llm
        variables: []
        vision:
          enabled: false
      extent: parent
      height: 164
      id: '1718354999060'
      parentId: '1718354838381'
      position:
        x: 117
        y: 85
      positionAbsolute:
        x: 1667
        y: 580
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
      zIndex: 1001
    - data:
        desc: Merge the analysis results from all segments into a cohesive whole.
        selected: false
        template: '{{ articleSections | join("\n") }}'
        title: Merge Analysis Results
        type: template-transform
        variables:
        - value_selector:
          - '1718354838381'
          - output
          variable: articleSections
      height: 102
      id: '1718355718469'
      position:
        x: 1986
        y: 495
      positionAbsolute:
        x: 1986
        y: 495
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: true
          variable_selector:
          - '1718355718469'
          - output
        desc: Use a large language model to perform a comprehensive analysis of the
          segmented results.
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: deepseek-chat
          provider: deepseek
        prompt_template:
        - id: 2e9ab6fb-4504-4891-95a1-39dff0e8ed6d
          role: system
          text: "# Expert Technical Article Analyzer\n\nYou are a professional technical\
            \ article analyzer. Your task is to analyze given article metadata and\
            \ segmented analysis results, providing an overall summary of the text.\
            \ The analysis targets developers, product managers, and technical personnel,\
            \ helping them quickly grasp the article's core content, technical points,\
            \ and practical value. Focus primarily on AI-related fields (e.g., large\
            \ models, AIGC, AI applications, development technologies, and frameworks),\
            \ but also cover other technical domains as appropriate.\n\n## Analysis\
            \ Guidelines\n\n1. Review all information thoroughly. Identify the article's\
            \ background, key points, arguments, data, and conclusions. Highlight\
            \ innovative viewpoints and practical applications.\n\n2. Structure information\
            \ logically. Use concise, professional language to express core content.\
            \ Maintain objectivity while emphasizing insightful or innovative aspects.\n\
            \n3. Ensure accurate use of technical terms. Balance professional depth\
            \ with readability.\n\n4. Merge and deduplicate tags. Select 3-10 most\
            \ representative ones. Order them by: topic, technology/field, application/product,\
            \ company/platform/notable figures, and trends.\n\n5. Prioritize main\
            \ points and key quotes by importance. Select 3-5 most crucial items for\
            \ each category.\n\n6. Match the output language to the original text.\
            \ For Chinese, add spaces between Chinese and English text or numbers.\
            \ Maintain logical coherence across all sections.\n\n## Input Format\n\
            \nXML format containing article metadata and segmented analysis results.\n\
            \n## Output Format\n\nUse the specified language and adhere to the following\
            \ Markdown format:\n\n```markdown\n### One-Sentence Summary\n[Core content\
            \ and main conclusions]\n\n### Article Tags (3-10)\n[Key tags, ordered\
            \ by importance]\n\n### Abstract\n[Comprehensive overview including background,\
            \ approach, details, data, core content, and insights]\n\n### Main Points\n\
            1. **[Point 1]**\n   - [Explanation]\n2. **[Point 2]**\n   - [Explanation]\n\
            3. **[Point 3]**\n   - [Explanation]\n[Up to 5 points]\n\n### Key Quotes\n\
            1. \"[Quote 1]\"\n   - Importance: [Explanation]\n2. \"[Quote 2]\"\n \
            \  - Importance: [Explanation]\n3. \"[Quote 3]\"\n   - Importance: [Explanation]\n\
            [Up to 5 quotes]"
        - id: cde18505-a2fe-45a5-a765-40dbe1c10c0f
          role: user
          text: "Based on the provided article metadata and segmented analysis results,\
            \ please conduct a comprehensive analysis of the entire article following\
            \ the steps and principles outlined in the system prompt. Output the analysis\
            \ results in the specified format using {{#1719357159255.languageName#}}.\n\
            \n<article>\n  <metadata>\n    <title>{{#1719357159255.title#}}</title>\n\
            \    <source>{{#1719357159255.sourceName#}}</source>\n    <url>{{#1719357159255.url#}}</url>\n\
            \  </metadata>\n  <analysis>\n    <![CDATA[\n    {{#1718355718469.output#}}\n\
            \    ]]>\n  </analysis>\n</article>"
        selected: false
        title: Comprehensive Analysis
        type: llm
        variables: []
        vision:
          enabled: false
      height: 164
      id: '1718355814693'
      position:
        x: 2290
        y: 495
      positionAbsolute:
        x: 2290
        y: 495
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        desc: Output the JSON result.
        outputs:
        - value_selector:
          - '1719444170368'
          - text
          variable: analysisResult
        selected: false
        title: End
        type: end
      height: 120
      id: '1718356146046'
      position:
        x: 3251.1519999999996
        y: -379.5154285714284
      positionAbsolute:
        x: 3251.1519999999996
        y: -379.5154285714284
      selected: true
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        authorization:
          config: null
          type: no-auth
        body:
          data: ''
          type: none
        desc: Call HTTP API to fetch article content.
        headers: ''
        method: get
        params: 'id:{{#1718352852007.input_article_id#}}

          language:en'
        selected: false
        timeout:
          max_connect_timeout: 0
          max_read_timeout: 0
          max_write_timeout: 0
        title: Retrieve Article Information
        type: http-request
        url: https://api.bestblogs.dev/api/resource/markdown
        variables: []
      height: 154
      id: '1719356422842'
      position:
        x: 981.6617142857144
        y: -132.49371428571422
      positionAbsolute:
        x: 981.6617142857144
        y: -132.49371428571422
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        code: "import json\n\ndef main(json_body):\n    \"\"\"\n    \u5904\u7406 REST\
          \ \u8BF7\u6C42\u8FD4\u56DE\u7684 JSON \u5B57\u7B26\u4E32\u3002\n    \n \
          \   \u82E5\u8F93\u5165\u4E3A\u7A7A\u5B57\u7B26\u4E32\u6216JSON\u89E3\u6790\
          \u5931\u8D25\uFF0C\u5219\u8FD4\u56DE {'success': 'false'}\u3002\n    \u82E5\
          \ JSON \u5B57\u7B26\u4E32\u89E3\u6790\u6210\u529F\u4E14 'success' \u4E3A\
          \ 'true'\uFF0C\u5219\u8FD4\u56DE\u5305\u542B\u6307\u5B9A\u952E\u503C\u7684\
          \u5B57\u5178\u3002\n    \u5426\u5219\u8FD4\u56DE {'success': 'false'}\u3002\
          \n\n    \u53C2\u6570:\n        json_body (str): \u5305\u542B JSON \u6570\
          \u636E\u7684\u5B57\u7B26\u4E32\u3002\n    \n    \u8FD4\u56DE:\n        dict:\
          \ \u5305\u542B 'success' \u952E\u548C\u6307\u5B9A\u7684\u6570\u636E\u952E\
          \u503C\u5BF9\u7684\u5B57\u5178\u3002\n    \"\"\"\n    \n    result = {'success':\
          \ 'false'}\n    \n    if not json_body:\n        return result\n\n    try:\n\
          \        data = json.loads(json_body)\n        if isinstance(data, dict)\
          \ and data.get('success') == 'true':\n            result['success'] = 'true'\n\
          \            result['markdown'] = data.get('markdown', '')\n           \
          \ result['sourceName'] = data.get('sourceName', '')\n            result['languageName']\
          \ = data.get('languageName', '\u4E2D\u6587')\n            result['title']\
          \ = data.get('title', '')\n            result['url'] = data.get('url', '')\n\
          \            result['wordCount'] = data.get('wordCount', 100)\n        return\
          \ result\n    except json.JSONDecodeError:\n        return result\n    except\
          \ Exception:\n        return result"
        code_language: python3
        desc: Parse response and extract article content.
        outputs:
          languageName:
            children: null
            type: string
          markdown:
            children: null
            type: string
          sourceName:
            children: null
            type: string
          success:
            children: null
            type: string
          title:
            children: null
            type: string
          url:
            children: null
            type: string
          wordCount:
            children: null
            type: number
        selected: false
        title: Parse Article Information
        type: code
        variables:
        - value_selector:
          - '1719356422842'
          - body
          variable: json_body
      height: 102
      id: '1719357159255'
      position:
        x: 981.6617142857144
        y: 88.19657142857142
      positionAbsolute:
        x: 981.6617142857144
        y: 88.19657142857142
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: false
          variable_selector: []
        desc: Examine the analysis results, focusing on accuracy, contextual understanding,
          key point identification, language expression, and logical coherence.
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: deepseek-chat
          provider: deepseek
        prompt_template:
        - id: 38022a40-3101-4709-9a69-57ddef9e086a
          role: system
          text: "# Technical Article Analysis Review Guidelines\n\n## Background and\
            \ Objectives\nAs a senior technical content reviewer, your task is to\
            \ evaluate preliminary analysis results of technical articles. Ensure\
            \ the analysis accurately reflects the original content and provide recommendations\
            \ to enhance its quality, insight, and practical value.\n\n## Input Format\n\
            XML format containing:\n1. <metadata>: Article title, source, and URL\n\
            2. <content>: Full Markdown content of the original article\n3. <previousAnalysisResult>:\
            \ Initial analysis including one-sentence summary, abstract, main points,\
            \ and key quotes\n4. <previousDomainAndTagResult>: Domain identification\
            \ and generated tags\n5. <previousScoringResult>: Standardized article\
            \ score\n\n## Review Process and Focus Areas\n\n1. One-Sentence Summary\n\
            \   - Assess accuracy in capturing core content\n   - Evaluate conciseness\
            \ and completeness\n\n2. Abstract\n   - Check comprehensive coverage of\
            \ article content\n   - Ensure inclusion of key elements: background,\
            \ topic, challenges, approach, solutions, implementation, outcomes\n \
            \  - Verify highlight of key conclusions and innovations\n   - Evaluate\
            \ logical structure and flow\n\n3. Main Points and Key Quotes\n   - Verify\
            \ accurate reflection of core arguments\n   - Assess coverage of critical\
            \ content and insights\n   - Evaluate relevance and impact of selected\
            \ quotes\n   - Review logical ordering of points\n\n4. Domain Identification\
            \ and Tags\n   - Evaluate accuracy of domain classification\n   - Check\
            \ tag coverage: topics, technologies, applications, companies, trends\n\
            \   - Assess relevance and completeness of tags\n   - Pay special attention\
            \ to AI-related tags\n   - Identify any irrelevant or misleading tags\n\
            \   - Review handling of cross-domain content\n\n5. Scoring\n   - Assess\
            \ comprehensiveness of scoring dimensions\n   - Evaluate justification\
            \ for each dimension score\n   - Review appropriateness of bonuses and\
            \ deductions\n   - Ensure final score reflects overall article quality\n\
            \   - Check consistency between score and other analysis sections\n\n\
            6. Overall Consistency\n   - Ensure alignment across all analysis components\n\
            \   - Identify any contradictions or inconsistencies\n   - Evaluate how\
            \ well the analysis represents the original content\n\n## Output Format\n\
            \n```markdown\n### One-Sentence Summary\n- Assessment: [Brief quality\
            \ evaluation]\n- Improvement Suggestions: [1-3 specific recommendations]\n\
            \n### Abstract\n- Assessment: [Brief quality evaluation]\n- Improvement\
            \ Suggestions: [1-3 specific recommendations]\n\n### Main Points and Key\
            \ Quotes\n- Assessment: [Brief quality evaluation]\n- Improvement Suggestions:\
            \ [1-3 specific recommendations]\n\n### Domain Identification and Tags\n\
            - Assessment: [Brief quality evaluation]\n- Improvement Suggestions: [1-3\
            \ specific recommendations]\n\n### Scoring\n- Assessment: [Brief quality\
            \ evaluation]\n- Adjustment Suggestions: [1-3 specific recommendations]\n\
            \n### Overall Consistency\n- Assessment: [Brief evaluation of inter-section\
            \ consistency]\n- Improvement Suggestions: [1-3 specific recommendations]\n\
            \n### Overall Evaluation\n- Strengths: [1-3 points]\n- Areas for Improvement:\
            \ [1-3 points]\n- Key Recommendations: [1-3 critical suggestions]\n```\n\
            \n## Important Notes\n- Focus on the quality of the analysis, not the\
            \ original article\n- Thoroughly review both the original content and\
            \ all analysis components\n- Maintain objectivity and professionalism\n\
            - Provide specific, actionable improvement suggestions\n- Consider the\
            \ value for various audiences (e.g., decision-makers, researchers, general\
            \ readers)\n- Pay extra attention to AI-related content analysis\n- Address\
            \ specific needs of cross-domain content\n- Align review comments with\
            \ the original text's style and target audience\n- Consider feasibility\
            \ and potential impact of suggested improvements\n- Balance thoroughness\
            \ with conciseness in your review"
        - id: f42b8839-8a0b-45a6-a0f5-71db57e573ae
          role: user
          text: "Based on the XML input below, please conduct a comprehensive review\
            \ and reflection on the original article and the previous analysis results.\
            \ Provide the results according to the steps, principles, and output format\
            \ specified in the system prompt.\n\n<article>\n  <metadata>\n    <title>{{#1719357159255.title#}}</title>\n\
            \    <source>{{#1719357159255.sourceName#}}</source>\n    <url>{{#1719357159255.url#}}</url>\n\
            \  </metadata>\n  <previousAnalysisResult>\n    <![CDATA[\n    {{#1720093031703.output#}}\n\
            \    ]]>\n  </previousAnalysisResult>\n  <previousDomainAndTagResult>\n\
            \    <![CDATA[\n    {{#1719647683007.text#}}\n    ]]>\n  </previousDomainAndTagResult>\n\
            \  <previousScoringResult>\n    <![CDATA[\n    {{#1719650938792.text#}}\n\
            \    ]]>\n  </previousScoringResult>\n</article>"
        selected: false
        title: Review and Reflection
        type: llm
        variables: []
        vision:
          enabled: false
      height: 200
      id: '1719381882148'
      position:
        x: 2907.042285714286
        y: -165.40799999999993
      positionAbsolute:
        x: 2907.042285714286
        y: -165.40799999999993
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: false
          variable_selector: []
        desc: Optimize and improve the analysis based on the insights from the review
          and reflection process.
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: gemini-1.5-pro-latest
          provider: google
        prompt_template:
        - id: a3799a74-b248-4202-a6ee-134857c2d020
          role: system
          text: "# Final Optimization Steps for Article Analysis\n\nAs an expert in\
            \ technical article review and prompt engineering, your task is to perform\
            \ the final optimization on the preliminary analysis results of technical\
            \ articles. This is the last processing node in the entire analysis workflow,\
            \ and the quality of its output directly affects the effectiveness of\
            \ the entire system.\n\n## Input Format\nThe input will be provided in\
            \ XML format, containing the following sections:\n- `<metadata>`: Article\
            \ metadata, including title, source, and URL\n- `<content>`: Full content\
            \ of the article\n- `<previousAnalysisResult>`: Previous analysis results,\
            \ including one-sentence summary, abstract, main points, and key quotes\n\
            - `<previousDomainAndTagResult>`: Previous domain and tag analysis results,\
            \ including the aiSubcategory value for AI-related articles\n- `<previousScoringResult>`:\
            \ Previous scoring results\n- `<reflectionFeedback>`: Feedback from the\
            \ review and reflection process\n\n## Analysis Guidelines and Considerations\n\
            1. Carefully review all input content, identify and prioritize key aspects\
            \ that need improvement.\n\n2. Retain all crucial information; avoid oversimplification\
            \ or arbitrary modification of previously outputted content. The optimization\
            \ goal is to improve readability while maintaining information integrity.\n\
            \n3. Ensure the analysis results have practical value for software developers,\
            \ product managers, and technical leaders, maintaining consistency across\
            \ all sections.\n\n4. Reflect current technology trends, staying sensitive\
            \ to the latest developments. Balance technical depth with readability,\
            \ making the analysis both professional and accessible.\n\n5. Consider\
            \ importance, innovation, and industry impact when selecting and ordering\
            \ content. Provide actionable insights that inspire readers to think and\
            \ act.\n\n6. The output language should match the original article. For\
            \ mixed Chinese-English articles, prioritize Chinese, retaining necessary\
            \ English terms with spaces between Chinese and English text.\n\n7. Only\
            \ update fields when there's sufficient reason. All updates must provide\
            \ a detailed updateReason explanation.\n\n## Output Format and Field Descriptions\n\
            Please output the optimized analysis results in JSON format using JavaScript,\
            \ including the following fields:\n\n```json\n{\n  \"oneSentenceSummary\"\
            : \"A concise one-sentence summary that accurately captures the article's\
            \ core content\",\n  \"oneSentenceSummaryUpdateReason\": \"Update reason\
            \ (output only if updated)\",\n  \n  \"summary\": \"A 3-10 sentence summary\
            \ covering the article's core elements\",\n  \"summaryUpdateReason\":\
            \ \"Update reason (output only if updated)\",\n  \n  \"domain\": \"Domain\
            \ classification (Software Programming, Artificial Intelligence, Product\
            \ Design, or Business Technology)\",\n  \"domainUpdateReason\": \"Update\
            \ reason (output only if updated)\",\n  \n  \"aiSubcategory\": \"AI Sub\
            \ Category (AI Models, AI Development, AI Products, or AI News)\",\n \
            \ \"aiSubcategoryUpdateReason\": \"Update reason (output only if updated)\"\
            ,\n  \n  \"tags\": [\"3-10 tags, ordered by Topic > Technology/Field >\
            \ Application/Product > Company/Platform/Person > Trend\"],\n  \"tagsUpdateReason\"\
            : \"Update reason (output only if updated)\",\n  \n  \"mainPoints\": [\n\
            \    {\n      \"point\": \"Main point 1\",\n      \"explanation\": \"\
            Explanation of point 1, emphasizing practical application value or potential\
            \ impact\"\n    },\n    // 3-5 main points\n  ],\n  \"mainPointsUpdateReason\"\
            : \"Update reason (output only if updated)\",\n  \n  \"keyQuotes\": [\"\
            3-5 representative article quotes that reflect unique insights, innovative\
            \ ideas, or practical value\"],\n  \"keyQuotesUpdateReason\": \"Update\
            \ reason (output only if updated)\",\n  \n  \"score\": 80, // Integer\
            \ from 0-100\n  \"scoreUpdateReason\": \"Update reason (output only if\
            \ updated)\",\n  \n  \"improvements\": \"Overall explanation of the main\
            \ improvements made in this optimization, including what was changed and\
            \ why\"\n}\n```\n\nPlease conduct a comprehensive review and optimization\
            \ based on the above guidelines, providing high-quality, insightful analysis\
            \ results with practical value for the target audience. Ensure the JSON\
            \ format is correct and can be directly parsed. Output only the final\
            \ JSON format result, without intermediate thought processes. Unless there's\
            \ a compelling reason, do not arbitrarily modify or reduce the originally\
            \ outputted content. If there are no updates, use the original values\
            \ as the main fields in the output, without including the UpdateReason\
            \ fields. For the \"aiSubcategory\" field, include it only if the article\
            \ is in the AI domain, and provide an \"aiSubcategoryUpdateReason\" if\
            \ there are any changes to this classification.\n```"
        - id: 4836545e-d0f5-4703-8fb5-461cf5ede618
          role: user
          text: "Based on the XML input below, and considering the initial analysis\
            \ results and the feedback from the review and reflection, please improve\
            \ and optimize the article analysis. Follow the steps, principles, and\
            \ output format specified in the system prompt, and output the analysis\
            \ results in {{#1719357159255.languageName#}}.\n\n<article>\n  <metadata>\n\
            \    <title>{{#1719357159255.title#}}</title>\n    <source>{{#1719357159255.sourceName#}}</source>\n\
            \    <url>{{#1719357159255.url#}}</url>\n  </metadata>\n <content>\n \
            \  <![CDATA[\n{{#1719357159255.markdown#}}\n    ]]>\n  </content>\n  <previousAnalysisResult>\n\
            \    <![CDATA[\n    {{#1720093031703.output#}}\n    ]]>\n  </previousAnalysisResult>\n\
            \  <previousDomainAndTagResult>\n    <![CDATA[\n    {{#1719647683007.text#}}\n\
            \    ]]>\n  </previousDomainAndTagResult>\n  <previousScoringResult>\n\
            \    <![CDATA[\n    {{#1719650938792.text#}}\n    ]]>\n  </previousScoringResult>\n\
            \  <reflectionFeedback>\n    <![CDATA[\n    {{#1719381882148.text#}}\n\
            \    ]]>\n  </reflectionFeedback>\n</article>"
        selected: false
        title: Comprehensive Refinement
        type: llm
        variables: []
        vision:
          configs:
            detail: high
          enabled: true
      height: 164
      id: '1719444170368'
      position:
        x: 2912.0708571428577
        y: -379.5154285714284
      positionAbsolute:
        x: 2912.0708571428577
        y: -379.5154285714284
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: false
          variable_selector: []
        desc: Identify the article's domain and extract structured tags.
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: deepseek-chat
          provider: deepseek
        prompt_template:
        - id: 523ca58d-d9fd-45aa-b9a2-4f6d814cb633
          role: system
          text: "# Technical Article Classification and Tag Analyzer\n\n## Task Description\n\
            \nPerform in-depth domain classification and tag analysis based on given\
            \ article metadata and full-text analysis information.\n\n## Input\n\n\
            1. Article metadata: title, link, source\n2. Full-text analysis information:\
            \ one-sentence summary, full summary, main points, key quotes, and article\
            \ tags\n\n## Output Format\n\nUse the following JSON format to output\
            \ the analysis results:\n\n```json\n{\n  \"domainProbabilities\": {\n\
            \    \"Programming\": 0,\n    \"Artificial Intelligence\": 0,\n    \"\
            Product & Design\": 0,\n    \"Business & Tech\": 0\n  },\n  \"mainDomain\"\
            : \"\",\n  \"aiSubcategoryProbabilities\": {\n    \"AI Development\":\
            \ 0,\n    \"AI Models\": 0,\n    \"AI Products\": 0,\n    \"AI News\"\
            : 0\n  },\n  \"aiSubcategory\": \"\",\n  \"tags\": []\n}\n```\n\n## Analysis\
            \ Guidelines\n\n1. **Domain and AI Subcategory Analysis**:\n   - Assign\
            \ integer percentages (0-100) to domainProbabilities and aiSubcategoryProbabilities.\
            \ Ensure each sum equals 100.\n   - Set mainDomain and aiSubcategory to\
            \ the category with the highest probability in each group.\n   - Conduct\
            \ AI subcategory analysis for all articles. If unrelated to AI, set AI\
            \ News probability to 100%.\n\n2. **Tag Selection**:\n   - Generate 3-7\
            \ tags, based on article complexity.\n   - Use the original language (treat\
            \ mixed Chinese-English as Chinese).\n   - Order tags: topic, technology/field,\
            \ application/product, company/platform/notable figure, trend.\n   - Prioritize\
            \ tags from the core tag list. Create new tags when necessary.\n   - Consider\
            \ tags from the full-text analysis, ensuring they meet structural requirements.\n\
            \   - Reflect the article's practical application value or potential impact.\n\
            \n3. **Consistency Check**:\n   - Ensure tags align with the main domain\
            \ and AI subcategory (if applicable).\n   - Verify that probability distributions\
            \ accurately reflect the article's focus.\n\n## Core Tag List\n\n1. Programming\n\
            JavaScript, Python, Java, HTML, CSS, Tailwind CSS, React, Spring, Next.js,\
            \ Frontend Development, Backend Development, Mobile Development, Web Development,\
            \ PostgreSQL, MySQL, Redis, Kafka, Big Data, Microservices, Architecture,\
            \ DDD, Design Patterns, Serverless, Stability, GitHub, Docker, Testing,\
            \ Code Quality, Performance Optimization, Observability, Code Review,\
            \ Agile Development, Cloud, AWS, Azure, Aliyun, Open Source\n\n2. Artificial\
            \ Intelligence\nAI, Deep Learning, Neural Networks, Speech Synthesis,\
            \ ChatGPT, GPT-4, Gemini, DALL-E, Midjourney, Stable Diffusion, Copilot,\
            \ Assistants, AI Agents, AI Applications, OpenAI, Google AI, Anthropic,\
            \ Hugging Face, NVIDIA AI, Baidu, Alibaba, Tencent, ByteDance, Kimi, Zhipu,\
            \ Pre-training, Fine-tuning, Prompt Engineering, Model Deployment, MLOps,\
            \ Dify, Coze, LangChain, LlamaIndex, AGI, Explainable AI, Edge AI, AI\
            \ Search, AI Education, AI Finance, AI Healthcare, AI Customer Service,\
            \ AI Robotics, AI Ethics, AI Security\n\n3. Product & Design\nUE, UX,\
            \ UI, Interaction Design, User Research, Prototype Design, Visual Design,\
            \ Product Strategy, User Needs, User Stories, MVP, A/B Testing, Product\
            \ Analysis, User Journey, User Personas, Usability Testing, Product Architecture,\
            \ Responsive Design, Design Systems, Design Thinking, Product Roadmap,\
            \ PMF\n\n4. Business & Tech\nEntrepreneurship, Business Models, Venture\
            \ Capital, Financing, SaaS, Business Strategy, Market Analysis, Competitive\
            \ Strategy, Brand Marketing, Digital Marketing, Content Marketing, Social\
            \ Media Marketing, SEO, SEM, User Acquisition, User Retention, E-commerce,\
            \ B2B, B2C, Customer Relationship Management, Market Segmentation, Pricing\
            \ Strategy, Data Analysis, Case Studies\n\nNote: If you encounter important\
            \ concepts or technologies not in the core list, you may add up to 3 additional\
            \ relevant tags based on your judgment.\n\nPlease classify and tag the\
            \ given technical article based on the above guidelines.\n\n## Output\
            \ Example\n\n```json\n{\n  \"domainProbabilities\": {\n    \"Programming\"\
            : 10,\n    \"Artificial Intelligence\": 80,\n    \"Product & Design\"\
            : 5,\n    \"Business & Tech\": 5\n  },\n  \"mainDomain\": \"Artificial\
            \ Intelligence\",\n  \"aiSubcategoryProbabilities\": {\n    \"AI Development\"\
            : 60,\n    \"AI Models\": 30,\n    \"AI Products\": 5,\n    \"AI News\"\
            : 5\n  },\n  \"aiSubcategory\": \"AI Development\",\n  \"tags\": [\"Large\
            \ Language Models\", \"RAG\", \"Prompt Engineering\", \"AI Application\
            \ Development\", \"LangChain\", \"Intelligent Agents\", \"OpenAI\"]\n\
            }\n```"
        - id: 9ca6c658-2add-431c-a12e-2cd04d556bda
          role: user
          text: "Please classify the article and generate tags based on the provided\
            \ article metadata and full-text analysis results, following the steps\
            \ and principles outlined in the system prompt. Provide the results using\
            \ the specified output format, with tags output in {{#1719357159255.languageName#}}.\n\
            \n<article>\n  <metadata>\n    <title>{{#1719357159255.title#}}</title>\n\
            \    <source>{{#1719357159255.sourceName#}}</source>\n    <url>{{#1719357159255.url#}}</url>\n\
            \  </metadata>\n  <previousAnalysisResult>\n    <![CDATA[\n    {{#1720093031703.output#}}\n\
            \    ]]>\n  </previousAnalysisResult>\n</article>"
        selected: false
        title: Domain Identification and Tag Extraction
        type: llm
        variables: []
        vision:
          enabled: false
      height: 146
      id: '1719647683007'
      position:
        x: 2912.0708571428577
        y: 300
      positionAbsolute:
        x: 2912.0708571428577
        y: 300
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: false
          variable_selector: []
        desc: Conduct a standardized assessment of the article.
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: gpt-4o
          provider: openai_api_compatible
        prompt_template:
        - id: 81927624-e5ca-46d9-8c9a-516d749f0c5d
          role: system
          text: "# Technical Article Scoring Guide\n\n## Background\nYou are an experienced\
            \ technical writing expert tasked with evaluating articles covering programming,\
            \ artificial intelligence, product management, and business technology.\
            \ Your goal is to provide an objective and comprehensive score based on\
            \ the following criteria.\n\n## Input Description\nYou will receive:\n\
            1. Article metadata: title, source, and URL\n2. Full article content\n\
            3. Domain analysis results: domain classification and related tags\n4.\
            \ Previous analysis results: article summary, main points, and key quotes\n\
            \n## Scoring Criteria (Total 100 points)\n\n### 1. Content Depth (0-30\
            \ points)\n- Basic (1-10): Provides basic information, lacks depth.\n\
            - Intermediate (11-20): Contains some depth of analysis and insights,\
            \ but may not be comprehensive.\n- Advanced (21-30): Offers deep insights,\
            \ with conclusions that inspire further learning and discussion.\nConsider:\
            \ Analysis depth and comprehensiveness, originality of viewpoints, depth\
            \ and applicability of technical explanations.\n\n### 2. Relevance (0-30\
            \ points)\n- Low (1-10): Limited relevance to core technical fields and\
            \ target audience.\n- Medium (11-20): Somewhat relevant, valuable to some\
            \ target readers.\n- High (21-30): Highly relevant, directly addresses\
            \ core technical fields and target audience needs.\nConsider: Relevance\
            \ to current tech trends (AI > Programming/Software Development/Product\
            \ Management/Business Tech/Startups/Digital Marketing > Other tech fields\
            \ > Non-tech topics), value to target readers, potential industry impact.\n\
            \n### 3. Writing Quality (0-20 points)\n- Basic (1-7): Disorganized structure,\
            \ obvious errors, difficult to understand.\n- Good (8-14): Clear structure,\
            \ accurate expression, readable.\n- Excellent (15-20): Outstanding structure,\
            \ professional tone, logical flow, engaging.\nConsider: Structural clarity\
            \ and logic, accuracy of technical terms, professionalism, effective use\
            \ of examples or visuals.\n\n### 4. Practicality and Innovation (0-20\
            \ points)\n- Low (1-7): Limited practicality, lacks innovation, difficult\
            \ to apply.\n- Medium (8-14): Offers some practical suggestions or novel\
            \ viewpoints, has some application value.\n- High (15-20): Highly practical\
            \ or innovative, provides directly applicable solutions or unique insights.\n\
            Consider: Practical value, specificity of solutions, degree of innovation,\
            \ potential industry impact.\n\n### 5. Comprehensive Score Adjustment\
            \ (-10 to 0 points)\nDeduct up to 3 points each for:\n- Excessive marketing\
            \ content\n- Overly brief or shallow content\n- Lack of valuable viewpoints\n\
            - Technical errors or misleading information\n- Absence of real-world\
            \ examples or implementation methods\n- Insufficient discussion of limitations\
            \ or potential issues\n- Unbalanced structure\n- Lack of comparative analysis\n\
            - Overly technical content for non-expert readers\n- Inadequate analysis\
            \ of long-term impacts or future trends\n\n## Scoring and Review Process\n\
            1. Review all provided materials.\n2. Reference previous analyses for\
            \ consistency.\n3. Score each dimension based on criteria.\n4. Provide\
            \ brief justification for each score.\n5. Apply comprehensive score adjustments.\n\
            6. Calculate preliminary total score.\n7. Interpret the score:\n   - 95-100:\
            \ Exceptional, must-read\n   - 90-94: High-quality, must-read\n   - 80-89:\
            \ Recommended, solid content\n   - 70-79: Average, basic value\n   - Below\
            \ 70: Low quality, limited value\n8. For scores over 95, conduct an extra\
            \ rigorous review.\n9. For scores over 90, re-evaluate each dimension.\n\
            10. Recalculate the total score if adjustments are made.\n11. Confirm\
            \ final score.\n12. Record any adjustments and reasons.\n13. Provide a\
            \ brief overall evaluation.\n\n## Special Considerations\n- Evaluate AI\
            \ research papers on their practical value and relevance.\n- Ensure fair,\
            \ objective scoring with specific justifications.\n- Consider the target\
            \ audience and intended use when scoring.\n- The final score must not\
            \ exceed 100 points.\n\n## Output Format\n\n```markdown\n# Technical Article\
            \ Evaluation Report\n\n## Scoring Details\n1. **Content Depth**: [Score]/30\n\
            \   - **Reason**: [Brief justification]\n\n2. **Relevance**: [Score]/30\n\
            \   - **Reason**: [Brief justification]\n\n3. **Writing Quality**: [Score]/20\n\
            \   - **Reason**: [Brief justification]\n\n4. **Practicality and Innovation**:\
            \ [Score]/20\n   - **Reason**: [Brief justification]\n\n5. **Comprehensive\
            \ Score Adjustment**: [Deduction]\n   - **Reason**: [Brief explanation\
            \ of deductions]\n\n## Final Score\n**Total**: [Final score]/100\n\n##\
            \ Review and Adjustment Notes\n[If applicable, briefly explain any score\
            \ adjustments]\n\n## Overall Evaluation\n[50-100 word summary of strengths\
            \ and potential improvements]"
        - id: 0adb7c56-cfe8-4abe-9160-d7a65ffb170a
          role: user
          text: "Please score the article according to the steps and principles in\
            \ the system prompt, based on the provided article metadata and analysis\
            \ results. Provide the results using the specified output format.\n\n\
            <article>\n  <metadata>\n    <title>{{#1719357159255.title#}}</title>\n\
            \    <source>{{#1719357159255.sourceName#}}</source>\n    <url>{{#1719357159255.url#}}</url>\n\
            \  </metadata>\n <content>\n   <![CDATA[\n{{#1719357159255.markdown#}}\n\
            \    ]]>\n  </content>\n  <domainAnalysisResult>\n    <![CDATA[\n    {{#1719647683007.text#}}\n\
            \    ]]>\n  </domainAnalysisResult>\n  <previousAnalysisResult>\n    <![CDATA[\n\
            \    {{#1720093031703.output#}}\n    ]]>\n  </previousAnalysisResult>\n\
            </article>"
        selected: false
        title: Article Evaluation
        type: llm
        variables: []
        vision:
          enabled: false
      height: 146
      id: '1719650938792'
      position:
        x: 2912.0708571428577
        y: 97.82399999999998
      positionAbsolute:
        x: 2912.0708571428577
        y: 97.82399999999998
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        conditions:
        - comparison_operator: "\u2264"
          id: '1719660271831'
          value: '6000'
          variable_selector:
          - '1719357159255'
          - wordCount
        desc: Determine if the article requires segmented processing due to its length.
        logical_operator: and
        selected: false
        title: Article Length Assessment
        type: if-else
      height: 192
      id: '1719660246354'
      position:
        x: 981.6617142857144
        y: 267.168
      positionAbsolute:
        x: 981.6617142857144
        y: 267.168
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: false
          variable_selector: []
        desc: Conduct a thorough analysis of the entire article content.
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: deepseek-chat
          provider: deepseek
        prompt_template:
        - edition_type: basic
          id: 3a219dd6-2439-4a5c-beae-0a3b14961b8b
          role: system
          text: "# Professional Article Analysis Assistant\n\n## Background and Task\n\
            \nYou are an expert in technical article analysis. Your task is to comprehensively\
            \ analyze the provided article metadata and full content, offering an\
            \ overall summary of the entire text. Your analysis targets developers,\
            \ product managers, and technical professionals, aiding them in quickly\
            \ grasping the article's core content, technical aspects, and practical\
            \ value. Focus particularly on AI-related fields such as large language\
            \ models, AIGC, AI applications, and AI development technologies and frameworks\
            \ (e.g., prompts, RAG, WorkFlow, Agent, LangChain, Dify, etc.). However,\
            \ be prepared to analyze articles from other technical domains as well.\n\
            \n## Analysis Guidelines\n\n1. Comprehensiveness and Focus:\n   - Thoroughly\
            \ review all provided information, including metadata and full content.\n\
            \   - Identify and extract the article's background, key points, arguments,\
            \ data, and conclusions.\n   - Highlight innovative ideas, methods, or\
            \ discoveries, as well as practical applications or potential impacts.\n\
            \n2. Content Organization and Expression:\n   - Structure information\
            \ logically, reflecting the article's overall flow.\n   - Use concise,\
            \ professional language to convey core content, avoiding redundancy.\n\
            \   - Maintain objectivity while emphasizing particularly insightful or\
            \ innovative sections.\n\n3. Technical Accuracy and Depth:\n   - Ensure\
            \ precise use of all technical terms and concepts, especially in AI-related\
            \ fields.\n   - Strike a balance between technical depth and readability,\
            \ ensuring the analysis is both professionally valuable and accessible.\n\
            \n4. Tag and Key Content Selection:\n   - Select 3-10 most representative\
            \ tags.\n   - Order tags as follows: topic, technology/field, application/product,\
            \ company/platform/notable figures, trends.\n   - Prioritize main points\
            \ and article quotes by importance, choosing 3-5 most significant items\
            \ for each category.\n\n5. Format and Language Requirements:\n   - Ensure\
            \ the output language matches the original text.\n   - For Chinese output,\
            \ insert spaces between Chinese characters and English text, numbers,\
            \ and other symbols.\n   - Maintain logical coherence across all sections\
            \ (summary, main points, quotes, etc.).\n\n## Input Format\n\nInput will\
            \ be provided in XML format, containing article metadata (title, source,\
            \ URL) and full content in Markdown format.\n\n## Output Format\n\nPlease\
            \ use {{#1719357159255.languageName#}} and adhere to the following Markdown\
            \ format for your output:\n\n```markdown\n### One-Sentence Summary\n[Encapsulate\
            \ the article's core content and main conclusions in a single sentence]\n\
            \n### Article Tags (3-10)\n[List key tags, ordered by importance]\n\n\
            ### Abstract\n[Deliver a comprehensive overview of the article, including\
            \ background information, reasoning process, implementation details, key\
            \ data, core content, and insightful points. No word limit; ensure thoroughness.]\n\
            \n### Main Points\n1. **[Core statement of main point 1]**\n   - [Concise\
            \ explanation]\n2. **[Core statement of main point 2]**\n   - [Concise\
            \ explanation]\n3. **[Core statement of main point 3]**\n   - [Concise\
            \ explanation]\n[Include up to 5 main points if necessary]\n\n### Key\
            \ Quotes\n1. \"[Key quote 1]\"\n   - [Explain the quote's significance\
            \ or insight]\n2. \"[Key quote 2]\"\n   - [Explain the quote's significance\
            \ or insight]\n3. \"[Key quote 3]\"\n   - [Explain the quote's significance\
            \ or insight]\n[Include up to 5 key quotes if necessary]"
        - id: 1f40d7f5-fa1f-4346-a302-e68f9268c369
          role: user
          text: "Based on the XML input below, please carefully read and analyze the\
            \ article, and follow the steps and principles specified in the system\
            \ prompt to output a Markdown-formatted analysis result using {{#1719357159255.languageName#}}.\n\
            \n<article>\n  <metadata>\n    <title>{{#1719357159255.title#}}</title>\n\
            \    <source>{{#1719357159255.sourceName#}}</source>\n    <url>{{#1719357159255.url#}}</url>\n\
            \  </metadata>\n <content>\n   <![CDATA[\n{{#1719357159255.markdown#}}\n\
            \    ]]>\n  </content>\n</article>"
        selected: false
        title: Full Content Analysis
        type: llm
        variables: []
        vision:
          enabled: false
      height: 146
      id: '1719660406112'
      position:
        x: 2290
        y: 300
      positionAbsolute:
        x: 2290
        y: 300
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        advanced_settings:
          group_enabled: false
          groups:
          - groupId: 998e6fca-96fb-4ef7-a903-8b0aa5b2dd4f
            group_name: Group1
            output_type: string
            variables:
            - - '1718355814693'
              - text
            - - '1719660406112'
              - text
        desc: ''
        output_type: string
        selected: false
        title: Variable Aggregator
        type: variable-aggregator
        variables:
        - - '1718355814693'
          - text
        - - '1719660406112'
          - text
      height: 139
      id: '1720093031703'
      position:
        x: 2594
        y: 300
      positionAbsolute:
        x: 2594
        y: 300
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    viewport:
      x: -222.03828067760242
      y: 286.0129805232186
      zoom: 0.5227102825136358