app:
  description: 'description: 专注产研（PM/RD）方向的智能简历审计工具。基于 C-Level 评委视角，通过多模型编排与大厂能力知识库，深度诊断简历中的“价值真空”并提供
    A/B 双版本重构方案。'
  icon: dart
  icon_background: '#FEF7C3'
  icon_type: emoji
  mode: advanced-chat
  name: ReviewMaster-Pro / 基于多模型编排的简历质检系统
  use_icon_as_answer_icon: false
dependencies:
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  value:
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    version: null
kind: app
version: 0.6.0
workflow:
  conversation_variables: []
  environment_variables: []
  features:
    file_upload:
      allowed_file_extensions:
      - .JPG
      - .JPEG
      - .PNG
      - .GIF
      - .WEBP
      - .SVG
      allowed_file_types:
      - document
      allowed_file_upload_methods:
      - remote_url
      - local_file
      enabled: false
      fileUploadConfig:
        attachment_image_file_size_limit: 2
        audio_file_size_limit: 50
        batch_count_limit: 5
        file_size_limit: 15
        file_upload_limit: 20
        image_file_batch_limit: 10
        image_file_size_limit: 10
        single_chunk_attachment_limit: 10
        video_file_size_limit: 100
        workflow_file_upload_limit: 10
      image:
        enabled: false
        number_limits: 3
        transfer_methods:
        - local_file
        - remote_url
      number_limits: 3
    opening_statement: '🔮 ReviewMaster-Pro | 基于多模型编排的简历质检系统

      欢迎来到产研精英的简历重构空间。我是你的智能审计教练。

      本系统已接入大厂能力知识库，将站在 C-Level（高管/评委级）视角，深度解构你简历中隐藏的“价值真空”。无论你是 PM（产品经理） 还是 RD（研发工程师），我都会通过多模型协同编排，为你提供地毯式的深度诊断，并直接输出
      A/B 双版本（保守稳妥 vs 冲击大厂） 的重构蓝图。

      📑 准备好了吗？请在下方直接输入/粘贴你的【简历全文】以及你的【目标岗位/JD】，我们马上开始战略级改造！'
    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: ''
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      targetHandle: target
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        desc: ''
        model:
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          mode: chat
          name: deepseek-chat
          provider: langgenius/deepseek/deepseek
        prompt_template:
        - id: ba6656f8-ccae-4c3a-bf71-5708e75c7265
          role: system
          text: '# 角色：岗位匹配度分析师

            你是资深校招HR，专注Java后端方向，擅长判断应届生简历与岗位的匹配度。


            ## 输出格式（严格遵守）


            【JD 匹配度评分】：X/100 分

            【匹配亮点】（列出 3 个核心点）：

            ...

            ...

            ...

            【关键差距】（列出 3 个核心点）：

            ...

            ...

            ...

            【优化优先级建议】：

            P0（立即补）：...

            P1（建议补）：...

            P2（加分项）：...


            ## 评分标准（应届生Java后端）

            - 90-100：技术栈全覆盖，项目有量化成果，直接推面试；

            - 70-89：技术栈匹配，项目无量化但有落地；

            - 50-69：技术栈部分匹配，项目描述空泛；

            - <50：技术栈不匹配，无核心项目。'
        - id: f0a7cc01-a01b-4ebb-b98b-17abaae40f1e
          role: user
          text: '<user_resume>

            {{#1780145433422.result#}}

            </user_resume>


            <job_description>

            {{#sys.query#}}

            </job_description>


            请基于应届生Java后端岗位标准，给出匹配度分析。

            '
        selected: false
        title: JD 匹配评分
        type: llm
        variables: []
        vision:
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      height: 87
      id: llm
      position:
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        y: 3
      positionAbsolute:
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        y: 3
      selected: false
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      width: 241
    - data:
        answer: '{{#1780146990310.text#}}


          {{#1780144843820.text#}}'
        desc: ''
        selected: false
        title: 最终输出
        type: answer
        variables: []
      height: 121
      id: answer
      position:
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      positionAbsolute:
        x: 2789
        y: 194
      selected: false
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      type: custom
      width: 241
    - data:
        cases:
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          conditions:
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            id: 4d8c8a09-43f5-4a63-92fd-fdfd427c8ff9
            value: 'true'
            varType: string
            variable_selector:
            - '1780145433422'
            - content_too_empty
          id: 'true'
          logical_operator: or
        selected: false
        title: 质量初筛
        type: if-else
      height: 147
      id: '1780143496013'
      position:
        x: 683
        y: 110
      positionAbsolute:
        x: 683
        y: 110
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        answer: ⚠️ 生成结果格式异常，请重新输入或简化简历内容后重试。
        selected: false
        title: 格式错误提示
        type: answer
        variables: []
      height: 115
      id: '1780144445503'
      position:
        x: 2448
        y: 16
      positionAbsolute:
        x: 2448
        y: 16
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        context:
          enabled: false
          variable_selector: []
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: qwen-plus-latest
          provider: langgenius/tongyi/tongyi
        prompt_template:
        - id: 1cad8e0a-22f4-441d-b977-465798dcd814
          role: system
          text: '# 角色：用户反馈收集员

            基于简历优化结果，生成一个简洁的反馈请求。


            ## 输出格式

            以上就是完整的简历优化报告！

            🎯 请为本次优化打分（1-5星）：

            ⭐ 1星：完全没用，离题万里

            ⭐⭐ 2星：有些帮助，但不够深入

            ⭐⭐⭐ 3星：基本满足需求

            ⭐⭐⭐⭐ 4星：很有帮助，会按建议修改

            ⭐⭐⭐⭐⭐ 5星：非常精准，超出预期！

            💡 如果你打3星及以下，请告诉我哪里不满意，我会针对性补充：

            [ ] 技术深度不够

            [ ] 量化数据缺失

            [ ] 与我的目标岗位不匹配

            [ ] 其他：______

            '
        selected: false
        title: 用户打分和反馈请求
        type: llm
        vision:
          enabled: false
      height: 87
      id: '1780144843820'
      position:
        x: 2448
        y: 211
      positionAbsolute:
        x: 2448
        y: 211
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        code: "def main(query: str) -> dict:\n    # 此时的 query 已经被我们重定向绑定为了 LLM 提取出的简历全文\n\
          \    text = query or \"\"\n    \n    char_count = len(text)\n    has_project\
          \ = \"项目\" in text or \"project\" in text.lower() or \"系统\" in text\n  \
          \  has_experience = \"经验\" in text or \"实习\" in text or \"工作\" in text or\
          \ \"经历\" in text\n    \n    # 门槛微调：解析出来的真实简历文本一般都大于 120 字\n    too_short\
          \ = char_count < 120\n    no_content = not (has_project or has_experience)\n\
          \    content_too_empty = too_short or no_content\n    \n    return {\n \
          \       \"result\": text.strip(),\n        \"content_too_empty\": \"true\"\
          \ if content_too_empty else \"false\"\n    }"
        code_language: python3
        outputs:
          content_too_empty:
            children: null
            type: string
          result:
            children: null
            type: string
        selected: false
        title: 代码执行
        type: code
        variables:
        - value_selector:
          - sys
          - query
          value_type: string
          variable: query
      height: 51
      id: '1780145433422'
      position:
        x: 342
        y: 158
      positionAbsolute:
        x: 342
        y: 158
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        dataset_ids:
        - RAwvlkeMH/8wRHiaMgrEEl9uhsFoN2b4y49KrjlC9YjWsioUpsDRfBuNWOeYMRoK
        multiple_retrieval_config:
          reranking_enable: true
          reranking_mode: reranking_model
          reranking_model:
            model: gte-rerank-v2
            provider: langgenius/tongyi/tongyi
          score_threshold: 0.5
          top_k: 3
        query_attachment_selector: []
        query_variable_selector:
        - '1780145433422'
        - result
        retrieval_mode: multiple
        selected: false
        title: 知识库检索
        type: knowledge-retrieval
      height: 89
      id: '1780146952031'
      position:
        x: 1044
        y: 170
      positionAbsolute:
        x: 1044
        y: 170
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        context:
          enabled: false
          variable_selector: []
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: qwen-plus-latest
          provider: langgenius/tongyi/tongyi
        prompt_template:
        - id: a4fd6794-b173-4594-ba32-57e8f8d746a4
          role: system
          text: '# 角色：大厂产研简历优化教练

            你是一位顶尖科技公司（FAANG级别）的产品技术招聘委员会核心成员。


            ## 核心原则

            1. 内容为王，格式为辅

            2. "所以呢？"拷问法：每句描述必须回答"带来了什么价值"

            3. 分级批判：根据候选人级别调整标准

            4. "批判-解析-建议"三位一体模型


            ## 工作流程（一步输出全部）

            请严格按照以下5个步骤，用XML标签包裹每个步骤的输出：


            <step1>第一印象与初步诊断</step1>

            一句话总结这份简历的核心问题和适配岗位。


            <step2>地毯式深度审计与指导</step2>

            逐条分析简历的优点和致命缺点，重点指出和目标岗位的差距。


            <step3>战略性修改蓝图</step3>

            给出优先级建议，哪些必须改、哪些可以保留、哪些是加分项。


            <step4>重构与展示（A/B双版本）</step4>

            - 【保守版】：基于原文事实，最小改动，稳妥投递

            - 【激进版】：充分挖掘潜力，适当提升叙事高度，冲击大厂


            <step5>最终裁决与行动清单</step5>

            给出投递建议和下一步的修改清单。


            ## 特别要求

            1. 所有量化数据如果原文没有，使用`[启发提示：请补充...]`占位符

            2. 专业术语严格校对大小写（MyBatis-Plus、RESTful、Redis等）'
        - id: b5b2d84f-540c-4c47-a748-72552e9fb7cf
          role: user
          text: '<user_resume>

            {{#1780145433422.result#}}

            </user_resume>


            <job_description>

            {{#sys.query#}}

            </job_description>


            <jd_analysis>

            {{#llm.text#}}

            </jd_analysis>


            <reference_cases>

            {{#1780146952031.result#}}

            </reference_cases>


            请执行五步简历优化流程，生成完整的优化报告。'
        selected: false
        title: 核心引擎：五步合并
        type: llm
        vision:
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      height: 87
      id: '1780146990310'
      position:
        x: 1405
        y: 18
      positionAbsolute:
        x: 1405
        y: 18
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        code: "def main(text: str) -> dict:\n    required_tags = ['<step1>', '<step2>',\
          \ '<step3>', '<step4>', '<step5>']\n    missing_tags = [tag for tag in required_tags\
          \ if tag not in text]\n    \n    has_ab_version = '保守版' in text and '激进版'\
          \ in text\n    is_valid = len(missing_tags) == 0 and has_ab_version\n  \
          \  \n    return {\n        \"is_valid\": \"true\" if is_valid else \"false\"\
          ,\n        \"text\": text\n    }"
        code_language: python3
        outputs:
          is_valid:
            children: null
            type: string
          text:
            children: null
            type: string
        selected: false
        title: 输出校验
        type: code
        variables:
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          - text
          value_type: string
          variable: text
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      id: '1780147101969'
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      positionAbsolute:
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        y: 36
      selected: true
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      targetPosition: left
      type: custom
      width: 241
    - data:
        cases:
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            id: d29e1a5b-460d-423c-bd0b-bc26ca5a350b
            value: 'true'
            varType: string
            variable_selector:
            - '1780147101969'
            - is_valid
          id: 'true'
          logical_operator: and
        selected: false
        title: 条件分支 2
        type: if-else
      height: 123
      id: '1780147620952'
      position:
        x: 2087
        y: 0
      positionAbsolute:
        x: 2087
        y: 0
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    - data:
        answer: '你的简历内容稍显单薄，为了给你最精准的大厂优化方案，请补充 3 项信息：


          1. 目标岗位（如：Java后端、产品经理、数据分析师）

          2. 工作年限（应届生 / 1-3年 / 3-5年 / 5年+）

          3. 最能代表你能力的核心项目


          补充后我立即为你深度诊断！'
        isInIteration: false
        isInLoop: false
        selected: false
        title: 内容过空提示 (1)
        type: answer
        variables: []
      height: 195
      id: '17801478664400'
      position:
        x: 1044
        y: 339
      positionAbsolute:
        x: 1044
        y: 339
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 241
    viewport:
      x: -1411.15
      y: 259.85
      zoom: 0.7
  rag_pipeline_variables: []