app:
description: '这是基于万兴剧场真实业务场景设计的客服测评系统，覆盖AIGC内容生产全流程。系统通过5轮对话测试客服的专业度和情绪管理能力，评分基于RAG知识库，确保标准统一。
'
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name: 「剧服通」AIGC客服能力测评系统
use_icon_as_answer_icon: false
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opening_statement: '🎬 欢迎来到AIGC客服能力测评！
点击下方开始测试或输入"开始测试"。
测评共5题，模拟真实AIGC内容平台用户咨询场景
每轮答题后给出专业度+服务态度双维度评分
全部完成后生成综合评价与榜单排名
测评仅在当前窗口有效。如需重新测试，请开启新窗口。'
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title: 开始
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answer: '本轮测试一共 5 个题目，请逐一作答。
第1题：
{{#1761317421924.output#}}'
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code: "import random\n\ndef main() -> dict:\n qas = [\"如何在AI训练平台上创建一个新的标注任务？\"\
,\"AI训练平台提供哪些数据上传方式？\",\"创建标注任务的完整步骤是什么？\",\"如果我想上传1000张图片用于标注任务，应该选择哪种上传方式？\"\
,\"在创建标注任务时，如果标签使用中文会有什么影响？\",\"如何使用本地文件导入方式上传数据？\",\"如果我想上传1000张图片用于标注任务，应该选择哪种上传方式？\"\
,\"追踪功能和插值功能有什么区别？\",\"插值标注时，如果有多组标签需要标注，应该如何操作？\",\"如何将数据集分享给班级内的所有人？\"\
,\"如果microSD卡未成功挂载，应该如何处理？\",\"什么是训练集和测试集，它们之间有什么区别？\",\"如何将训练好的模型发布到模型库？\"\
,\"如何查看已经完成训练的任务细节？\",\"如何进行基本的标注操作？\",\"如何进入标注台？\",\"插值标注时，如果有多组标签需要标注，应该如何操作？\"\
,\"如果我想上传1000张图片用于标注任务，应该选择哪种上传方式？\",\"什么是追踪功能？如何使用？\",\"在创建标注任务时，如果标签使用中文会有什么影响？\"\
]\n result = random.sample(qas, 5)\n return {'result': result}"
code_language: python3
outputs:
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title: 生成考试题
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completion_params:
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name: deepseek-chat
provider: langgenius/deepseek/deepseek
prompt_template:
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role: system
text: "你是一位AIGC内容平台（万兴剧场）的客服主管。请对客服人员在以下场景的回答进行双维度评分。\n\n【评分标准 - AIGC客服专用】\n\
\n专业度评分（0-100）：\n- 90-100：准确识别AIGC业务问题（如生成失败、角色一致性、Prompt优化等），给出具体排查步骤或操作指引，提及相关功能模块\n\
- 70-89：能识别问题类型，给出基本解决方向，但缺少细节或步骤\n- 50-69：回答相关但过于笼统，或只给出部分信息\n- 30-49：回答偏离或错误，缺乏AIGC业务理解\n\
- 0-29：完全错误、答非所问、或拒绝回答\n\n情绪度评分（0-100）：\n- 90-100：主动共情，使用安抚话术，表达理解和愿意帮助的态度\n\
- 70-89：语气友好，有基本礼貌，但共情不够深入\n- 50-69：中性语气，无明显情绪问题\n- 30-49：语气生硬、敷衍、或带有负面情绪\n\
- 0-29：冷漠、指责、或激怒用户\n\n【特别规则】\n1. AIGC客服允许\"初步判断+引导排查\"的回答模式，不必一次性给出所有可能原因\n\
2. 对于技术问题，提及具体功能模块（如\"分镜编辑页\"、\"Prompt优化器\"）可加分\n3. 对于情绪安抚题，必须有具体话术示例才能得高分\n\
4. 回答简短但核心信息正确，专业分不应低于50\n\n请严格按照以下 JSON 格式输出：\n{\n \"category\": \"\
业务分类（如：生成故障/资产管理/Prompt教学/情绪安抚/产品咨询）\",\n \"professional_score\": 分数,\n\
\ \"emotion_score\": 分数,\n \"comment\": \"针对性的精简点评，指出1个优点+1个改进点\"\n}"
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role: user
text: '考试题目：
{{#1761317260583.output#}}
员工回答：
{{#sys.query#}}
问题相关资料：
{{#context#}}
'
selected: false
title: 题目评价
type: llm
vision:
enabled: false
height: 87
id: '1761316030328'
position:
x: 1446.6022116274075
y: 423.1669239428802
positionAbsolute:
x: 1446.6022116274075
y: 423.1669239428802
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: '{{#1761316030328.text#}}'
selected: false
title: 每一道题的审批
type: answer
variables: []
height: 102
id: '1761316086428'
position:
x: 1807.6122010158947
y: 450.9848473301174
positionAbsolute:
x: 1807.6122010158947
y: 450.9848473301174
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: '
---
接下来回答第{{#conversation.round#}}题：
{{#1761317783401.output#}}'
selected: false
title: 输出第2-5题
type: answer
variables: []
height: 122
id: '1761316148198'
position:
x: 2846.7362101983754
y: 450.9848473301174
positionAbsolute:
x: 2846.7362101983754
y: 450.9848473301174
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
code: "import json\nimport re\n\ndef main(reviews, answer, round, topics):\n\
\ # 清理 LLM 输出中的 markdown 标记\n cleaned = reviews.strip()\n if cleaned.startswith(\"\
```json\"):\n cleaned = cleaned[7:]\n if cleaned.startswith(\"\
```\"):\n cleaned = cleaned[3:]\n if cleaned.endswith(\"```\"\
):\n cleaned = cleaned[:-3]\n cleaned = cleaned.strip()\n \n\
\ # 尝试解析 JSON 评分\n try:\n review_data = json.loads(cleaned)\n\
\ category = review_data.get(\"category\", \"业务咨询\")\n professional_score\
\ = review_data.get(\"professional_score\", 35)\n emotion_score =\
\ review_data.get(\"emotion_score\", 35)\n comment = review_data.get(\"\
comment\", \"\")\n except:\n # 兜底：正则提取\n cat_match = re.search(r'\"\
category\":\\s*\"([^\"]+)\"', cleaned)\n prof_match = re.search(r'\"\
professional_score\":\\s*(\\d+)', cleaned)\n emot_match = re.search(r'\"\
emotion_score\":\\s*(\\d+)', cleaned)\n comment_match = re.search(r'\"\
comment\":\\s*\"([^\"]*)\"', cleaned)\n \n category = cat_match.group(1)\
\ if cat_match else \"业务咨询\"\n professional_score = int(prof_match.group(1))\
\ if prof_match else 35\n emotion_score = int(emot_match.group(1))\
\ if emot_match else 35\n comment = comment_match.group(1) if comment_match\
\ else \"\"\n \n # 返回结构化 JSON 字符串，方便 data_aggregation 提取\n result\
\ = {\n \"question\": topics[round - 2] if round >= 2 and topics\
\ else \"\",\n \"answer\": answer,\n \"category\": category,\n\
\ \"professional_score\": professional_score,\n \"emotion_score\"\
: emotion_score,\n \"comment\": comment\n }\n \n return\
\ {\"result\": json.dumps(result, ensure_ascii=False)}"
code_language: python3
outputs:
result:
children: null
type: string
selected: false
title: 合并回答结果
type: code
variables:
- value_selector:
- '1761316030328'
- text
value_type: string
variable: reviews
- value_selector:
- sys
- query
value_type: string
variable: answer
- value_selector:
- conversation
- round
value_type: number
variable: round
- value_selector:
- conversation
- topics
value_type: array[string]
variable: topics
height: 51
id: '1761316788149'
position:
x: 1807.6122010158947
y: 615.5460918767483
positionAbsolute:
x: 1807.6122010158947
y: 615.5460918767483
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
items:
- input_type: variable
operation: append
value:
- '1761316788149'
- result
variable_selector:
- conversation
- tests
write_mode: over-write
selected: false
title: 保存回答结果
type: assigner
version: '2'
height: 83
id: '1761316911228'
position:
x: 2202.753131072603
y: 615.5460918767483
positionAbsolute:
x: 2202.753131072603
y: 615.5460918767483
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
dataset_ids:
- xtETVyuWOXjlnIea3KdEJcsd5y1Ng+peBx1RZ0QojasAEuxVXmJdHXOCs8nODmf7
multiple_retrieval_config:
reranking_enable: true
reranking_mode: reranking_model
reranking_model:
model: gte-rerank
provider: langgenius/tongyi/tongyi
score_threshold: null
top_k: 3
query_attachment_selector: []
query_variable_selector:
- '1761317260583'
- output
retrieval_mode: multiple
selected: false
title: 知识检索
type: knowledge-retrieval
height: 89
id: '1761317099795'
position:
x: 1147.9101436309725
y: 423.1669239428802
positionAbsolute:
x: 1147.9101436309725
y: 423.1669239428802
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
selected: false
template: '{{ topics[round-2] }}'
title: 读取题目
type: template-transform
variables:
- value_selector:
- conversation
- topics
value_type: array[string]
variable: topics
- value_selector:
- conversation
- round
value_type: number
variable: round
height: 51
id: '1761317260583'
position:
x: 847.9101436309724
y: 423.1669239428802
positionAbsolute:
x: 847.9101436309724
y: 423.1669239428802
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
items:
- input_type: variable
operation: over-write
value:
- '1761391544238'
- output
variable_selector:
- conversation
- topics
write_mode: over-write
selected: false
title: 保存题目
type: assigner
version: '2'
height: 83
id: '1761317391866'
position:
x: 2052.775417527823
y: 233.34726103149734
positionAbsolute:
x: 2052.775417527823
y: 233.34726103149734
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
selected: false
template: '{{ topics[0] }}'
title: 输出第一题
type: template-transform
variables:
- value_selector:
- conversation
- topics
value_type: array[string]
variable: topics
height: 51
id: '1761317421924'
position:
x: 2346.6022116274075
y: 282
positionAbsolute:
x: 2346.6022116274075
y: 282
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
selected: false
template: '{{ topics[round-1] }}'
title: 输出 2-5 题题目
type: template-transform
variables:
- value_selector:
- conversation
- topics
value_type: array[string]
variable: topics
- value_selector:
- conversation
- round
value_type: number
variable: round
height: 51
id: '1761317783401'
position:
x: 2546.6064246169876
y: 450.9848473301174
positionAbsolute:
x: 2546.6064246169876
y: 450.9848473301174
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: 要想开始测试，点击开场白下方的`开始测试`或在对话框输入`开始测试`
selected: false
title: 不符合条件直接回复
type: answer
variables: []
height: 115
id: '1761318013521'
position:
x: 470.27959399176905
y: 1009.8478081776825
positionAbsolute:
x: 470.27959399176905
y: 1009.8478081776825
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: deepseek-chat
provider: langgenius/deepseek/deepseek
prompt_template:
- id: 5412f8c2-e65d-442f-95f2-bf2dce355a32
role: system
text: "你是一位经验丰富、富有建设性且善于培养人才的万兴剧场（ReelMate）客服主管。\n\n你的任务是：基于员工本次5道题目的回答记录，给出**专业、客观、温暖且具有指导意义**的综合评价报告。\n\
\n### 输出要求（严格按照以下Markdown格式输出，不要添加额外说明）：\n\n## \U0001F3AF 总体评估\n（一句话总结本次测评整体表现，包含综合得分估算和核心特点）\n\
\n## \U0001F4CA 评分统计\n- **平均专业度**：XX/100\n- **平均服务态度**：XX/100 \n- **综合得分**：XX/100\
\ （专业度权重60% + 服务态度权重40%）\n\n## \U0001F31F 亮点表现\n（列出1-2个做得比较好的地方，具体指出哪一题及优点）\n\
\n## ⚠️ 需要提升的地方\n（客观列出2-3个最主要的问题，按重要性排序）\n\n## \U0001F4A1 具体改进建议与优秀示范\n\
针对主要问题，提供**可执行的改进方向**，并为每个主要问题**给出1条优秀回答示范**（话术示例）。\n\n## \U0001F4C8 个人成长建议\n\
给出2-3条针对性强、切实可行的后续提升建议（例如：学习特定功能、练习话术模板、阅读产品文档等）。\n\n## ✨ 鼓励与下一步\n用积极、温暖的语气进行鼓励，并邀请员工继续讨论具体题目的改进方案。\n\
\n---\n\n语气要求：\n- 像资深主管做1对1辅导：肯定努力、指出不足、给出方向、温暖鼓励。\n- 建设性优先：多用“建议…”、“可以尝试…”、“优秀做法是…”。\n\
- 专业且有人情味，避免单纯批评。"
- id: 4d469796-3b54-4c47-ad2f-d1524b7091b2
role: user
text: '员工测评结果记录如下：
{{#conversation.tests#}}'
selected: false
title: 综合评价打分
type: llm
vision:
enabled: false
height: 87
id: '1761318227427'
position:
x: 847.9101436309724
y: 562.442221222579
positionAbsolute:
x: 847.9101436309724
y: 562.442221222579
selected: true
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: '{{#1761318227427.text#}}'
selected: false
title: 输出模型评价
type: answer
variables: []
height: 102
id: '1761318476654'
position:
x: 1446.6022116274075
y: 578.8245543185242
positionAbsolute:
x: 1446.6022116274075
y: 578.8245543185242
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
cases:
- case_id: 'true'
conditions:
- comparison_operator: ≠
id: bcc73419-a90b-4f1b-938d-abfb30595b99
value: '6'
varType: number
variable_selector:
- conversation
- round
id: 'true'
logical_operator: and
selected: false
title: 验证是不是最后一题
type: if-else
height: 123
id: '1761362172820'
position:
x: 2202.753131072603
y: 450.9848473301174
positionAbsolute:
x: 2202.753131072603
y: 450.9848473301174
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: '
---
测评结束，输入任意内容查看测评结果并记录测评成绩。'
selected: false
title: 考试结束
type: answer
variables: []
height: 115
id: '1761362231485'
position:
x: 2546.6064246169876
y: 578.8245543185242
positionAbsolute:
x: 2546.6064246169876
y: 578.8245543185242
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
items:
- input_type: constant
operation: +=
value: 1
variable_selector:
- conversation
- round
write_mode: over-write
selected: false
title: 第6轮
type: assigner
version: '2'
height: 83
id: '1761385066051'
position:
x: 488.6828402141949
y: 556.8028984112788
positionAbsolute:
x: 488.6828402141949
y: 556.8028984112788
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
items:
- input_type: variable
operation: append
value:
- '1761318227427'
- text
variable_selector:
- conversation
- tests
write_mode: over-write
selected: false
title: 综合评价追加到对话中
type: assigner
version: '2'
height: 83
id: '1761385112911'
position:
x: 1446.6022116274075
y: 745.3547797860435
positionAbsolute:
x: 1446.6022116274075
y: 745.3547797860435
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
context:
enabled: false
variable_selector: []
memory:
query_prompt_template: '用户对他测评结果的追问：
{{#sys.query#}}
用户的历史测评结果：
{{#conversation.tests#}}'
role_prefix:
assistant: ''
user: ''
window:
enabled: false
size: 50
model:
completion_params:
temperature: 0.7
mode: chat
name: deepseek-chat
provider: langgenius/deepseek/deepseek
prompt_template:
- id: 36e9d534-6e0a-4607-96cd-6c0c1b121d69
role: system
text: 与用户讨论他的测评结果，我会把他历史的数据分享给你
selected: false
title: 与用户讨论测评结果
type: llm
vision:
enabled: false
height: 87
id: '1761385160796'
position:
x: 497.28394668249734
y: 693.0545270875807
positionAbsolute:
x: 497.28394668249734
y: 693.0545270875807
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: '{{#1761385160796.text#}}'
selected: false
title: 回复用户讨论的结果
type: answer
variables: []
height: 102
id: '1761385268108'
position:
x: 838.510687267659
y: 702.9455620180883
positionAbsolute:
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y: 702.9455620180883
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
is_team_authorization: true
paramSchemas:
- auto_generate: null
default: null
form: llm
human_description:
en_US: The name of the table to insert into.
ja_JP: 挿入先のテーブル名。
pt_BR: The name of the table to insert into.
zh_Hans: The name of the table to insert into.
label:
en_US: Table Name
ja_JP: テーブル名
pt_BR: Table Name
zh_Hans: Table Name
llm_description: Provide the name of the table where you want to insert
a new row.
max: null
min: null
name: table
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: string
- auto_generate: null
default: null
form: llm
human_description:
en_US: Data for the new row as a JSON object.
ja_JP: 新しい行のデータ（JSON オブジェクト形式）。
pt_BR: Data for the new row as a JSON object.
zh_Hans: Data for the new row as a JSON object.
label:
en_US: Row Data
ja_JP: 行データ
pt_BR: Row Data
zh_Hans: Row Data
llm_description: Provide the data for the new row in JSON format.
max: null
min: null
name: data
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: string
params:
data: ''
table: ''
provider_id: langgenius/supabase/supabase
provider_name: langgenius/supabase/supabase
provider_type: builtin
selected: false
title: Create a Row
tool_configurations: {}
tool_description: Create a new row in a specified table in Supabase.
tool_label: Create a Row
tool_name: create_a_row
tool_node_version: '2'
tool_parameters:
data:
type: mixed
value: '{{#9999999999999.json_data#}}'
table:
type: mixed
value: exam_results
type: tool
height: 51
id: '1761390562605'
position:
x: 2382.28982768901
y: 745.3547797860435
positionAbsolute:
x: 2382.28982768901
y: 745.3547797860435
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
error_strategy: fail-branch
is_team_authorization: true
paramSchemas:
- auto_generate: null
default: null
form: llm
human_description:
en_US: The name of the table to query.
ja_JP: クエリ対象のテーブル名。
pt_BR: The name of the table to query.
zh_Hans: The name of the table to query.
label:
en_US: Table Name
ja_JP: テーブル名
pt_BR: Table Name
zh_Hans: Table Name
llm_description: Provide the name of the table you want to query in Supabase.
max: null
min: null
name: table
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: string
- auto_generate: null
default: null
form: llm
human_description:
en_US: Maximum number of rows to return.
ja_JP: 返される最大行数。
pt_BR: Maximum number of rows to return.
zh_Hans: Maximum number of rows to return.
label:
en_US: Limit
ja_JP: 上限
pt_BR: Limit
zh_Hans: Limit
llm_description: Specify how many rows you want to retrieve. Default is
10.
max: null
min: null
name: limit
options: []
placeholder: null
precision: null
required: false
scope: null
template: null
type: number
- auto_generate: null
default: null
form: llm
human_description:
en_US: Optional filter condition (e.g., 'status=active').
ja_JP: オプションのフィルター条件（例：'status=active'）。
pt_BR: Optional filter condition (e.g., 'status=active').
zh_Hans: Optional filter condition (e.g., 'status=active').
label:
en_US: Filter
ja_JP: フィルター
pt_BR: Filter
zh_Hans: Filter
llm_description: Provide a filter condition if needed. (e.g., 'status=active')
max: null
min: null
name: filter
options: []
placeholder: null
precision: null
required: false
scope: null
template: null
type: string
params:
filter: ''
limit: ''
table: ''
provider_id: langgenius/supabase/supabase
provider_name: langgenius/supabase/supabase
provider_type: builtin
selected: false
title: 获取考试题
tool_configurations: {}
tool_description: Get rows from a specified table in Supabase.
tool_label: Get Rows
tool_name: get_rows
tool_node_version: '2'
tool_parameters:
filter:
type: mixed
value: ''
limit:
type: constant
value: null
table:
type: mixed
value: exam_questions
type: tool
height: 87
id: '1761390906117'
position:
x: 838.510687267659
y: 247.38892235866263
positionAbsolute:
x: 838.510687267659
y: 247.38892235866263
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
code: "import random\nfrom typing import List, Dict\n\ndef main(questions:\
\ List[Dict]) -> Dict:\n \"\"\"\n 从 Supabase 返回的 exam_questions 表中随机抽取5题\n\
\ \"\"\"\n # questions 是 Supabase 返回的数组，每个元素是字典\n # 格式：[{\"question\"\
: \"...\", \"category\": \"...\", ...}, {...}]\n \n if not questions\
\ or len(questions) == 0:\n return {'result': []}\n \n # 提取所有题目文本\n\
\ all_questions = []\n for item in questions:\n # Supabase\
\ 返回的 item 结构是 {\"data\": [...]} 或直接用 item\n # 先处理外层包装\n if\
\ isinstance(item, dict):\n # 如果 item 有 'data' 字段（Supabase 的\
\ json 列）\n if 'data' in item and isinstance(item['data'], list):\n\
\ for sub_item in item['data']:\n if isinstance(sub_item,\
\ dict) and 'question' in sub_item:\n all_questions.append(sub_item['question'])\n\
\ # 如果 item 直接有 'question' 字段\n elif 'question' in\
\ item:\n all_questions.append(item['question'])\n \n\
\ # 去重\n all_questions = list(set(all_questions))\n \n # 随机抽5题\n\
\ if len(all_questions) >= 5:\n selected = random.sample(all_questions,\
\ 5)\n else:\n selected = all_questions # 如果不足5题，全取\n \n \
\ return {'result': selected}"
code_language: python3
outputs:
result:
children: null
type: array[string]
selected: false
title: 生成考试题
type: code
variables:
- value_selector:
- '1761390906117'
- json
value_type: array[object]
variable: questions
height: 51
id: '1761391239784'
position:
x: 1147.9101436309725
y: 185.08551402202943
positionAbsolute:
x: 1147.9101436309725
y: 185.08551402202943
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
output_type: array[string]
selected: false
title: 变量聚合器
type: variable-aggregator
variables:
- - '1761391239784'
- result
- - '1761315702874'
- result
height: 133
id: '1761391544238'
position:
x: 1446.6022116274075
y: 233.34726103149734
positionAbsolute:
x: 1446.6022116274075
y: 233.34726103149734
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
is_team_authorization: true
paramSchemas:
- auto_generate: null
default: null
form: llm
human_description:
en_US: The name of the table to query.
ja_JP: クエリ対象のテーブル名。
pt_BR: The name of the table to query.
zh_Hans: The name of the table to query.
label:
en_US: Table Name
ja_JP: テーブル名
pt_BR: Table Name
zh_Hans: Table Name
llm_description: Provide the name of the table you want to query in Supabase.
max: null
min: null
name: table
options: []
placeholder: null
precision: null
required: true
scope: null
template: null
type: string
- auto_generate: null
default: null
form: llm
human_description:
en_US: Maximum number of rows to return.
ja_JP: 返される最大行数。
pt_BR: Maximum number of rows to return.
zh_Hans: Maximum number of rows to return.
label:
en_US: Limit
ja_JP: 上限
pt_BR: Limit
zh_Hans: Limit
llm_description: Specify how many rows you want to retrieve. Default is
10.
max: null
min: null
name: limit
options: []
placeholder: null
precision: null
required: false
scope: null
template: null
type: number
- auto_generate: null
default: null
form: llm
human_description:
en_US: Optional filter condition (e.g., 'status=active').
ja_JP: オプションのフィルター条件（例：'status=active'）。
pt_BR: Optional filter condition (e.g., 'status=active').
zh_Hans: Optional filter condition (e.g., 'status=active').
label:
en_US: Filter
ja_JP: フィルター
pt_BR: Filter
zh_Hans: Filter
llm_description: Provide a filter condition if needed. (e.g., 'status=active')
max: null
min: null
name: filter
options: []
placeholder: null
precision: null
required: false
scope: null
template: null
type: string
params:
filter: ''
limit: ''
table: ''
provider_id: langgenius/supabase/supabase
provider_name: langgenius/supabase/supabase
provider_type: builtin
selected: false
title: 获取榜单
tool_configurations: {}
tool_description: Get rows from a specified table in Supabase.
tool_label: Get Rows
tool_name: get_rows
tool_node_version: '2'
tool_parameters:
filter:
type: mixed
value: ''
limit:
type: constant
value: null
table:
type: mixed
value: test_records
type: tool
height: 51
id: '1761391650276'
position:
x: 488.6828402141949
y: 855.1805949555352
positionAbsolute:
x: 488.6828402141949
y: 855.1805949555352
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
code: "import json\nfrom collections import defaultdict\n\ndef main(ranks:\
\ list):\n \"\"\"\n 对输入的 ranks 字符串（JSON 格式）按 employee_id 去重，保留 score\
\ 最高记录，\n 并按 score 降序拼接为指定 Markdown 表格字符串返回。\n \"\"\"\n data =\
\ ranks\n\n # 展平所有 data 里的记录\n records = []\n for item in data:\n\
\ records.extend(item.get(\"data\", []))\n\n # 按 employee_id 分组，保留\
\ score 最大的一条\n best = {}\n for rec in records:\n eid = rec.get(\"\
employee_id\")\n if eid is None:\n continue\n if\
\ eid not in best or rec.get(\"score\", 0) > best[eid].get(\"score\", 0):\n\
\ best[eid] = rec\n\n # 按 score 降序排序\n sorted_recs = sorted(best.values(),\
\ key=lambda x: x.get(\"score\", 0), reverse=True)\n\n # 拼接 Markdown\
\ 表格\n lines = [\"|工号|姓名|得分|\", \"|---|---|---|\"]\n for rec in sorted_recs:\n\
\ lines.append(f\"|{rec.get('employee_id', '')}|{rec.get('employee_name',\
\ '')}|{rec.get('score', '')}|\")\n\n result = \"\\n\".join(lines)\n\
\ return {\"result\": result}"
code_language: python3
outputs:
result:
children: null
type: string
selected: false
title: 榜单排序
type: code
variables:
- value_selector:
- '1761391650276'
- json
value_type: array[object]
variable: ranks
height: 51
id: '1761392804332'
position:
x: 838.510687267659
y: 855.1805949555352
positionAbsolute:
x: 838.510687267659
y: 855.1805949555352
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
answer: '{{#1761392804332.result#}}'
selected: false
title: 发榜
type: answer
variables: []
height: 102
id: '1761393186429'
position:
x: 1147.9101436309725
y: 966.4400133679939
positionAbsolute:
x: 1147.9101436309725
y: 966.4400133679939
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
code: "import json\nimport re\n\ndef main(history_list: list, llm_summary:\
\ str) -> dict:\n total_score = 0\n avg_prof = 0\n avg_emot = 0\n\
\ category_scores_str = \"{}\"\n weak_categories = \"无\"\n review_summary\
\ = str(llm_summary) if llm_summary else \"暂无评价\"\n details_str = \"\
[]\"\n\n parsed_records = []\n if isinstance(history_list, list):\n\
\ for item in history_list:\n try:\n if\
\ isinstance(item, str):\n item = item.strip()\n \
\ if not item or item == \"[]\":\n \
\ continue\n parsed = json.loads(item)\n \
\ if isinstance(parsed, dict) and \"professional_score\" in parsed\
\ and \"emotion_score\" in parsed:\n parsed_records.append(parsed)\n\
\ elif isinstance(item, dict):\n if \"\
professional_score\" in item and \"emotion_score\" in item:\n \
\ parsed_records.append(item)\n except:\n \
\ pass\n\n print(\"Debug: valid parsed_records length =\", len(parsed_records))\n\
\n if parsed_records:\n prof_scores = [r.get(\"professional_score\"\
, 0) for r in parsed_records]\n emot_scores = [r.get(\"emotion_score\"\
, 0) for r in parsed_records]\n avg_prof = sum(prof_scores) // len(prof_scores)\
\ if prof_scores else 0\n avg_emot = sum(emot_scores) // len(emot_scores)\
\ if emot_scores else 0\n total_score = avg_prof + avg_emot\n \
\ details_str = json.dumps(parsed_records, ensure_ascii=False)\n\n \
\ try:\n clean_summary = llm_summary.strip()\n if clean_summary.startswith(\"\
```json\"):\n clean_summary = clean_summary.split(\"```json\"\
)[1].split(\"```\")[0].strip()\n elif clean_summary.startswith(\"\
```\"):\n clean_summary = clean_summary.split(\"```\")[1].split(\"\
```\")[0].strip()\n data = json.loads(clean_summary)\n total_score\
\ = int(data.get(\"total_score\", total_score))\n avg_prof = int(data.get(\"\
professional_score\", avg_prof))\n avg_emot = int(data.get(\"emotion_score\"\
, avg_emot))\n cat_scores = data.get(\"category_scores\", {})\n \
\ category_scores_str = json.dumps(cat_scores, ensure_ascii=False)\n\
\ weak_categories = str(data.get(\"weak_categories\", \"无\"))\n \
\ review_summary = str(data.get(\"review_summary\", review_summary))\n\
\ except Exception as e:\n score_match = re.search(r'综合得分[：:]\\\
s*(\\d+)', llm_summary)\n if score_match:\n total_score\
\ = int(score_match.group(1))\n prof_match = re.search(r'专业评分\\s*(\\\
d+)', llm_summary)\n if prof_match:\n avg_prof = int(prof_match.group(1))\n\
\ emot_match = re.search(r'情绪评分\\s*(\\d+)', llm_summary)\n \
\ if emot_match:\n avg_emot = int(emot_match.group(1))\n \
\ category_scores_str = json.dumps({\"综合\": total_score}, ensure_ascii=False)\n\
\n return {\n \"total_score\": total_score,\n \"professional_score\"\
: avg_prof,\n \"emotion_score\": avg_emot,\n \"category_scores\"\
: category_scores_str,\n \"weak_categories\": weak_categories,\n\
\ \"review_summary\": review_summary,\n \"details\": details_str,\n\
\ \"result\": f\"总分{total_score}，专业{avg_prof}，情绪{avg_emot}\"\n \
\ }"
code_language: python3
outputs:
category_scores:
children: null
type: string
details:
children: null
type: string
emotion_score:
children: null
type: number
professional_score:
children: null
type: number
result:
children: null
type: string
review_summary:
children: null
type: string
total_score:
children: null
type: number
weak_categories:
children: null
type: string
selected: false
title: data_aggregation
type: code
variables:
- value_selector:
- conversation
- tests
value_type: array[string]
variable: history_list
- value_selector:
- '1761318227427'
- text
value_type: string
variable: llm_summary
height: 51
id: '1780043586962'
position:
x: 1748.6022116274075
y: 739.1193698977889
positionAbsolute:
x: 1748.6022116274075
y: 739.1193698977889
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
- data:
code: "import json\n\ndef main(employee_name: str, employee_id: str, total_score:\
\ int, \n professional_score: int, emotion_score: int, category_scores:\
\ str,\n weak_categories: str, review_summary: str) -> dict:\n \
\ \"\"\"\n 生成合法的 JSON 字符串，用于 Create a Row\n 对 review_summary 进行清理，避免破坏\
\ JSON 格式\n \"\"\"\n # 清理 review_summary：替换换行符、引号等\n cleaned_summary\
\ = review_summary\n if cleaned_summary:\n # 替换换行符为空格\n \
\ cleaned_summary = cleaned_summary.replace(\"\\n\", \" \").replace(\"\\\
r\", \" \")\n # 替换双引号为中文引号，避免 JSON 冲突\n cleaned_summary =\
\ cleaned_summary.replace('\"', \"'\")\n # 截断到 500 字符，避免过长\n \
\ cleaned_summary = cleaned_summary[:500]\n else:\n cleaned_summary\
\ = \"暂无评价\"\n\n # 确保 category_scores 是字符串\n if not category_scores:\n\
\ category_scores = \"{}\"\n\n # 组装数据字典\n row_data = {\n \
\ \"employee_name\": employee_name,\n \"employee_id\": employee_id,\n\
\ \"total_score\": total_score,\n \"professional_score\":\
\ professional_score,\n \"emotion_score\": emotion_score,\n \
\ \"category_scores\": category_scores,\n \"weak_categories\":\
\ weak_categories,\n \"review_summary\": cleaned_summary\n }\n\
\n # 生成 JSON 字符串（确保合法）\n json_str = json.dumps(row_data, ensure_ascii=False)\n\
\n return {\n \"json_data\": json_str,\n \"employee_name\"\
: employee_name,\n \"employee_id\": employee_id\n }"
code_language: python3
outputs:
employee_id:
children: null
type: string
employee_name:
children: null
type: string
json_data:
children: null
type: string
selected: false
title: 格式化数据
type: code
variables:
- value_selector:
- '1761303787968'
- name
value_type: string
variable: employee_name
- value_selector:
- '1761303787968'
- id
value_type: string
variable: employee_id
- value_selector:
- '1780043586962'
- total_score
value_type: number
variable: total_score
- value_selector:
- '1780043586962'
- professional_score
value_type: number
variable: professional_score
- value_selector:
- '1780043586962'
- emotion_score
value_type: number
variable: emotion_score
- value_selector:
- '1780043586962'
- category_scores
value_type: string
variable: category_scores
- value_selector:
- '1780043586962'
- weak_categories
value_type: string
variable: weak_categories
- value_selector:
- '1780043586962'
- review_summary
value_type: string
variable: review_summary
height: 51
id: '9999999999999'
position:
x: 2100
y: 745
positionAbsolute:
x: 2100
y: 745
selected: false
sourcePosition: right
targetPosition: left
type: custom
width: 241
viewport:
x: 1.6760981559635866
y: -69.43172148179639
zoom: 0.6504869133642175
rag_pipeline_variables: []