paris.yml

app:
  description: ''
  icon: 🤖
  icon_background: '#FFEAD5'
  icon_type: emoji
  mode: workflow
  name: paris
  use_icon_as_answer_icon: false
dependencies:
- current_identifier: null
  type: marketplace
  value:
    marketplace_plugin_unique_identifier: langgenius/gemini:0.7.20@de0063a630a6d1b2c025fb84f3462ba5151fb60618309cd595c3f4711b1df847
    version: null
- current_identifier: null
  type: marketplace
  value:
    marketplace_plugin_unique_identifier: langgenius/openai:0.3.6@4875322dafae33f1125b31c0fbf66cdb089f09f2dbbcda65d4e605ee7043bb23
    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:
      - image
      allowed_file_upload_methods:
      - local_file
      - remote_url
      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: 50
        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: ''
    retriever_resource:
      enabled: true
    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:
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        isInLoop: false
        sourceType: start
        targetType: llm
      id: 1776885893947-source-1776927927576-target
      selected: false
      source: '1776885893947'
      sourceHandle: source
      target: '1776927927576'
      targetHandle: target
      type: custom
      zIndex: 0
    - data:
        isInLoop: false
        sourceType: llm
        targetType: knowledge-retrieval
      id: 1776927927576-source-1776886106415-target
      selected: false
      source: '1776927927576'
      sourceHandle: source
      target: '1776886106415'
      targetHandle: target
      type: custom
      zIndex: 0
    - data:
        isInLoop: false
        sourceType: knowledge-retrieval
        targetType: iteration
      id: 1776886106415-source-1776930523021-target
      selected: false
      source: '1776886106415'
      sourceHandle: source
      target: '1776930523021'
      targetHandle: target
      type: custom
      zIndex: 0
    - data:
        isInIteration: true
        isInLoop: false
        iteration_id: '1776930523021'
        sourceType: iteration-start
        targetType: llm
      id: 1776930523021start-source-1776930732596-target
      source: 1776930523021start
      sourceHandle: source
      target: '1776930732596'
      targetHandle: target
      type: custom
      zIndex: 1002
    - data:
        isInLoop: false
        sourceType: iteration
        targetType: end
      id: 1776930523021-source-1776927902182-target
      source: '1776930523021'
      sourceHandle: source
      target: '1776927902182'
      targetHandle: target
      type: custom
      zIndex: 0
    nodes:
    - data:
        selected: false
        title: User Input
        type: start
        variables:
        - default: ''
          hint: ''
          label: 展品名
          options: []
          placeholder: ''
          required: true
          type: text-input
          variable: name
        - default: ''
          hint: ''
          label: 上下文
          options: []
          placeholder: ''
          required: false
          type: text-input
          variable: context
        - default: ''
          hint: ''
          label: 别名
          options: []
          placeholder: ''
          required: false
          type: text-input
          variable: alias
      height: 161
      id: '1776885893947'
      position:
        x: -11.755711969560366
        y: 180.99543042435175
      positionAbsolute:
        x: -11.755711969560366
        y: 180.99543042435175
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 242
    - data:
        dataset_ids:
        - fShQ6cINi1XJaOYfGL4rJRzXZqW6nkKGljzWYGrNqUlf4+Bq+vw/0erTvhXFrv5S
        multiple_retrieval_config:
          reranking_enable: false
          reranking_mode: weighted_score
          score_threshold: null
          top_k: 6
          weights:
            keyword_setting:
              keyword_weight: 0.2
            vector_setting:
              embedding_model_name: text-embedding-3-large
              embedding_provider_name: langgenius/openai/openai
              vector_weight: 0.8
            weight_type: customized
        query_attachment_selector: []
        query_variable_selector:
        - '1776885893947'
        - name
        retrieval_mode: multiple
        selected: true
        title: find top k text segment
        type: knowledge-retrieval
      height: 52
      id: '1776886106415'
      position:
        x: 581.0740903323247
        y: 283.21855402107565
      positionAbsolute:
        x: 581.0740903323247
        y: 283.21855402107565
      selected: true
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 242
    - data:
        outputs:
        - value_selector:
          - '1776930523021'
          - output
          value_type: array[string]
          variable: result
        selected: false
        title: Output
        type: end
      height: 88
      id: '1776927902182'
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        x: 1416.3850076529266
        y: 47.482979612114235
      positionAbsolute:
        x: 1416.3850076529266
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      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
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    - data:
        context:
          enabled: false
          variable_selector: []
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: gemini-3.1-flash-lite-preview
          provider: langgenius/gemini/google
        prompt_template:
        - id: 3743c73c-efb3-4d8a-b88d-61912fc2c88b
          role: system
          text: "You are an information retrieval expert.\n\nTask:\nGenerate a search\
            \ query for retrieving relevant passages from historical texts, based\
            \ on exhibit metadata.\n\nCore Principle:\nDo NOT copy the catalog-style\
            \ exhibit name. Instead, convert it into expressions that are more likely\
            \ to appear in natural text.\n\n---\n\nInput:\nName: {{{#1776885893947.name#}}}}\n\
            Context: {{{#1776885893947.context#}}}}\n\n---\n\nGeneration Rules:\n\n\
            1. Use natural descriptive expressions instead of catalog titles\n   -\
            \ Avoid directly using names like “Canon Krupp, Section Prussienne”\n\
            \   - Convert to natural phrasing (e.g., “Prussian guns”)\n\n2. Convert\
            \ country names into adjectival forms\n   - Prussia → Prussian\n   - France\
            \ → French\n   - Germany → German\n\n3. Make category terms more concrete\n\
            \   - Weaponry → guns / artillery / weapons\n   - Industry → manufacture\
            \ / industrial\n\n4. Remove irrelevant elements\n   - Exclude numbers\
            \ (e.g., N215, 1073)\n   - Exclude words like Section, Catalogue, Entry\n\
            \n5. Keep core semantic elements\n   - country\n   - object type (e.g.,\
            \ gun, sculpture, painting)\n   - domain (e.g., industrial, military)\n\
            \n6. Omit company names or proper nouns if they are unlikely to appear\
            \ in text\n   - For example, “Krupp” can be omitted if not commonly mentioned\n\
            \n7. Keep the query concise (5–12 words)\n\n---\n\nOutput Requirements:\n\
            \n- Output ONE line of English query\n- Do NOT explain\n- Do NOT output\
            \ JSON\n\n---\n\nExample (for understanding only, do NOT copy):\n\nInput:\n\
            Canon Krupp, Section Prussienne\n\nOutput:\nPrussian guns artillery weapon\
            \ industry exhibition"
        selected: false
        title: generate query
        type: llm
        vision:
          enabled: false
      height: 88
      id: '1776927927576'
      position:
        x: 269.92884184702035
        y: 201.99543042435175
      positionAbsolute:
        x: 269.92884184702035
        y: 201.99543042435175
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 242
    - data:
        error_handle_mode: remove-abnormal-output
        flatten_output: true
        height: 176
        is_parallel: false
        iterator_input_type: array[object]
        iterator_selector:
        - '1776886106415'
        - result
        output_selector:
        - '1776930732596'
        - text
        output_type: array[string]
        parallel_nums: 10
        selected: false
        start_node_id: 1776930523021start
        title: Iteration
        type: iteration
        width: 386
      height: 176
      id: '1776930523021'
      position:
        x: 886.37855754764
        y: -30.626123696960885
      positionAbsolute:
        x: 886.37855754764
        y: -30.626123696960885
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 386
      zIndex: 1
    - data:
        desc: ''
        isInIteration: true
        selected: false
        title: ''
        type: iteration-start
      draggable: false
      height: 48
      id: 1776930523021start
      parentId: '1776930523021'
      position:
        x: 24
        y: 68
      positionAbsolute:
        x: 910.37855754764
        y: 37.37387630303911
      selectable: false
      sourcePosition: right
      targetPosition: left
      type: custom-iteration-start
      width: 44
      zIndex: 1002
    - data:
        context:
          enabled: true
          variable_selector:
          - '1776930523021'
          - item
        isInIteration: true
        isInLoop: false
        iteration_id: '1776930523021'
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: gemini-3.1-flash-lite-preview
          provider: langgenius/gemini/google
        prompt_template:
        - id: 1fe3f747-e62e-403e-ac76-4aa60da5814b
          role: system
          text: "You will see:\n\n[Exhibit Information]\nName: {{{{#1776885893947.name#}}}}\n\
            Context: {{{{#1776885893947.context#}}}}\n\n[Current Text Chunk]\n{{{{#1776930523021.item#}}}}\n\
            \nTask:\nDetermine the relationship between the current text chunk and\
            \ the exhibit, and extract relevant information only when supported by\
            \ evidence.\n\n---\n\nMatch Levels:\n\n- exact: The text clearly refers\
            \ to this specific exhibit (explicit name or uniquely identifiable reference)\n\
            - related: The text does not explicitly name the exhibit but clearly belongs\
            \ to the same country, section, category, manufacturing system, or object\
            \ group, and can serve as contextual/background information\n- none: The\
            \ text has no clear relation to the exhibit\n\n---\n\nField Types:\n\n\
            [Objective Information]\n- Manufacturing Process: how something is made\
            \ or produced\n- Structural Feature: physical structure or composition\n\
            - Category Context: industry or category it belongs to\n\n[Exhibition\
            \ Context]\n- Exhibition Context: information about country, section,\
            \ or exhibition setting\n\n[Subjective Perception]\n- Audience Impression:\
            \ expressed feelings or impressions (e.g., ingenious, curious)\n- Evaluation:\
            \ explicit judgment (e.g., beautiful, poor quality)\n- Popularity: level\
            \ of attention, interest, or public engagement\n\n---\n\nOutput Requirements:\n\
            \nOnly output valid JSON:\n\n{\n  \"match_level\": \"exact | related |\
            \ none\",\n  \"fields\": [\n    {\n      \"field\": \"...\",\n      \"\
            value\": \"...\",\n      \"evidence\": \"...\",\n      \"confidence\"\
            : 0.0\n    }\n  ]\n}\n\n---\n\nStrict Rules:\n\n[Judgment]\n- Only use\
            \ the current text chunk; do NOT use information from other chunks\n-\
            \ If the text only describes general industry, manufacturing, or national\
            \ background → usually \"related\"\n- If no clear relation → return \"\
            none\" with empty fields\n\n[Value Writing — Critical]\n- Write the extracted\
            \ information directly (NOT meta descriptions)\n- DO NOT write phrases\
            \ like:\n  \"the text describes...\", \"the passage mentions...\", \"\
            this section talks about...\"\n- Use ONE concise sentence (preferably\
            \ 8–25 words)\n- Preferred structure:\n  object + feature/action\n- Must\
            \ contain concrete information (object / process / feature)\n- Avoid vague\
            \ wording (e.g., \"some information\", \"general content\")\n\n[Granularity]\n\
            - If multiple clear information points exist, output multiple fields\n\
            - Each field should contain only ONE distinct information point\n\n[Subjective\
            \ Information]\n- Only extract if explicit evaluative or emotional language\
            \ appears in the text\n- Evidence MUST include the actual evaluative words\
            \ (e.g., \"ingenious\", \"beautiful\", \"curious\")\n- Do NOT infer or\
            \ assume sentiment\n\n[Evidence]\n- Must be copied verbatim from the current\
            \ text chunk\n- Keep it short and focused (key phrase only)\n\n[Confidence]\n\
            - 0–1 scale\n- Direct and explicit evidence: 0.8–1.0\n- Reasonable inference:\
            \ 0.5–0.8\n- Weak relation: 0.3–0.5\n\n[Forbidden]\n- Do NOT fabricate\
            \ information\n- Do NOT add anything not present in the text\n- Do NOT\
            \ output explanations\n- Output JSON only\n\n---\n\nStyle Example (for\
            \ reference only, do NOT copy):\n\n{\n  \"match_level\": \"related\",\n\
            \  \"fields\": [\n    {\n      \"field\": \"Manufacturing Process\",\n\
            \      \"value\": \"Needle-gun components require precise tube fitting\
            \ and are formed through cutting and rolling\",\n      \"evidence\": \"\
            requiring short tubes with indentations of great exactness ... roll it\
            \ up\",\n      \"confidence\": 0.9\n    },\n    {\n      \"field\": \"\
            Audience Impression\",\n      \"value\": \"Manufacturing machines are\
            \ described as highly ingenious and costly\",\n      \"evidence\": \"\
            very ingenious and expensive machines\",\n      \"confidence\": 0.88\n\
            \    }\n  ]\n}"
        selected: false
        structured_output_enabled: false
        title: info extraction
        type: llm
        vision:
          enabled: false
      height: 88
      id: '1776930732596'
      parentId: '1776930523021'
      position:
        x: 128
        y: 66.6804920892271
      positionAbsolute:
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        y: 36.05436839226621
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 242
      zIndex: 1002
    viewport:
      x: 152.8138717368804
      y: 247.40717146495354
      zoom: 0.3789291416276005
  rag_pipeline_variables: []