一、同时生成线图、饼图、柱图

1、调整数据流，在原有柱状图基础上添加线性图表和饼图，如下图：

2、参照柱状图设置线图及饼图。

3、修改直接回复，增加线性图表及饼图输出

4、导入数据进行验证

二、生成多维柱状图或线性图

目前提供插件，暂时未找到生成多维图的方法，参考网络其他大侠代码，将Echarts插件改为代码执行，使用python3代码实现需求功能：

1、删除Echarts插件、增加代码执行组件，修改后流程入下：

2、修改参数提取器，增加csv_data参数，提示词指令增加输出数值为CSV格式并赋值。

你是一位数据抽取专家；请根据{{#1743643949037.text#}}的数据,进行抽取,将标题赋值给title，y轴的数据赋值给ydata，x轴的数据赋值给xdate，其中每个ydata与xdata每个文本用单引号引起之间用 "," 分隔。并按CSV格式赋值给csv_data。

3、修改代码执行，增加三个变量（第三个想作为图例，暂未实现，有回弄的说一声），在python代码中输入解析代码（参考网络大侠，加了点东西）

import csv
import json

def main(csv_data,title,xdata):
    # 解析CSV
    reader = csv.DictReader(csv_data.strip().splitlines())
    rows = list(reader)
    
    # 自动识别列结构
    headers = reader.fieldnames
    if not headers or len(headers) < 2:
        return {'result': 'Error: 需要至少两列数据（1个分类列+1个数值列）'}
    
    # 默认第一列为分类轴，其余为数值列
    category_col = headers[0]
    value_columns = headers[1:]
    
    # 提取数据
    categories = [row[category_col] for row in rows]
    series_data = [
        {
            "name": col,
            "type": "bar",#line为线图
            "data": [float(row[col]) for row in rows]  # 处理浮点数
        } for col in value_columns
    ]
    
    # 构建ECharts配置
    echarts_config = {
        "title": {"left": "center",
        "text": title},
        "legend": {"data": [xdata]},
        "xAxis": {"type": "category", "data": categories},
        "yAxis": {"type": "value"},
        "series": series_data
    }
    
    # 返回结果
    return {'result': f'```echarts\n{json.dumps(echarts_config, ensure_ascii=False)}\n```'}

4、修改直接恢复，增加代码执行返回结果

5、导入多维表进行验证：

北京商场销售统计表（样例）
商场名	1月	2月	3月	4月	5月
朝阳	12318	12408	8418	6299	12318
高井	11326	11802	6570	6299	11326
通州	8035	8418	6360	6001	8035
宣武	7150	6570	11326	1364	7150
京桥	6113	6360	8035	6360	6113
潞城	8502	6299	7150	6299	8502
京安	6073	6299	6113	6299	6073
京阳	6298	6001	6298	6001	6298
华潞	1294	6360	8035	11326	11802

三、导出DSL文件供大家分享，欢迎批评指正，谢谢

app:
  description: 利用内置Echart插件，借助deepseek参数提取器，自动提取文本、csv、excel文件相关信息，生成统计图表
  icon: 🤖
  icon_background: '#FFEAD5'
  mode: advanced-chat
  name: 多维统计图表测试ByEchart
  use_icon_as_answer_icon: false
dependencies:
- current_identifier: null
  type: package
  value:
    plugin_unique_identifier: langgenius/openai_api_compatible:0.0.5@188b29121e708c1d0b26841bc2e66874a06c6cae73a2049c9215f57cb6cbca44
kind: app
version: 0.1.5
workflow:
  conversation_variables: []
  environment_variables: []
  features:
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      allowed_file_extensions:
      - .JPG
      - .JPEG
      - .PNG
      - .GIF
      - .WEBP
      - .SVG
      allowed_file_types:
      - image
      allowed_file_upload_methods:
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      - remote_url
      enabled: false
      fileUploadConfig:
        audio_file_size_limit: 50
        batch_count_limit: 5
        file_size_limit: 15
        image_file_size_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: document-extractor
      id: 1743643939754-source-1743643949037-target
      source: '1743643939754'
      sourceHandle: source
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      targetHandle: target
      type: custom
      zIndex: 0
    - data:
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        sourceType: document-extractor
        targetType: parameter-extractor
      id: 1743643949037-source-1743643957985-target
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      sourceHandle: source
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      targetHandle: target
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    - data:
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        sourceType: parameter-extractor
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      targetHandle: target
      type: custom
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    - data:
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        sourceType: code
        targetType: answer
      id: 1743651304269-source-answer-target
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      source: '17436714603540'
      sourceHandle: source
      target: answer
      targetHandle: target
      type: custom
      zIndex: 0
    nodes:
    - data:
        desc: ''
        selected: false
        title: 开始
        type: start
        variables:
        - allowed_file_extensions: []
          allowed_file_types:
          - document
          allowed_file_upload_methods:
          - local_file
          - remote_url
          label: 请上传包含统计数据的文件
          max_length: 48
          options: []
          required: true
          type: file
          variable: efiles
      height: 90
      id: '1743643939754'
      position:
        x: 221.00000000000028
        y: 210
      positionAbsolute:
        x: 221.00000000000028
        y: 210
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        answer: '文档提取：{{#1743643949037.text#}}

          标题：{{#1743643957985.title#}}

          Y轴数值：{{#1743643957985.ydata#}}

          X轴名称：{{#1743643957985.xdata#}}

          是否成功：{{#1743643957985.__is_success#}}

          错误原因：{{#1743643957985.__reason#}}

          CSV文件：

          {{#1743643957985.csv_data#}}

          柱状图：

          {{#1743651304269.result#}}

          线型图：

          {{#17436714603540.result#}}'
        desc: ''
        selected: false
        title: 直接回复
        type: answer
        variables: []
      height: 280
      id: answer
      position:
        x: 937.6858015640123
        y: 243.84674531236243
      positionAbsolute:
        x: 937.6858015640123
        y: 243.84674531236243
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        desc: ''
        is_array_file: false
        selected: false
        title: 文档提取器
        type: document-extractor
        variable_selector:
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        - efiles
      height: 92
      id: '1743643949037'
      position:
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      positionAbsolute:
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        y: 350
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        desc: ''
        instruction: 你是一位数据抽取专家；请根据{{#1743643949037.text#}}的数据,进行抽取,将标题赋值给title，y轴的数据赋值给ydata，x轴的数据赋值给xdate，其中每个ydata与xdata每个文本用单引号引起之间用
          "," 分隔。并按CSV格式赋值给csv_data。
        model:
          completion_params:
            temperature: 0.7
          mode: chat
          name: deepseek-r1
          provider: langgenius/openai_api_compatible/openai_api_compatible
        parameters:
        - description: 标题
          name: title
          required: false
          type: string
        - description: Y轴数值
          name: ydata
          required: false
          type: string
        - description: X轴名称
          name: xdata
          required: false
          type: string
        - description: csv_data
          name: csv_data
          required: false
          type: string
        query:
        - '1743643949037'
        - text
        reasoning_mode: prompt
        selected: false
        title: 参数提取器
        type: parameter-extractor
        variables: []
        vision:
          enabled: false
      height: 90
      id: '1743643957985'
      position:
        x: 525
        y: 185.99999999999983
      positionAbsolute:
        x: 525
        y: 185.99999999999983
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        code: "import csv\nimport json\n\ndef main(csv_data,title,xdata):\n    # 解析CSV\n\
          \    reader = csv.DictReader(csv_data.strip().splitlines())\n    rows =\
          \ list(reader)\n    \n    # 自动识别列结构\n    headers = reader.fieldnames\n \
          \   if not headers or len(headers) < 2:\n        return {'result': 'Error:\
          \ 需要至少两列数据（1个分类列+1个数值列）'}\n    \n    # 默认第一列为分类轴，其余为数值列\n    category_col\
          \ = headers[0]\n    value_columns = headers[1:]\n    \n    # 提取数据\n    categories\
          \ = [row[category_col] for row in rows]\n    series_data = [\n        {\n\
          \            \"name\": col,\n            \"type\": \"bar\",#line为线图\n  \
          \          \"data\": [float(row[col]) for row in rows]  # 处理浮点数\n      \
          \  } for col in value_columns\n    ]\n    \n    # 构建ECharts配置\n    echarts_config\
          \ = {\n        \"title\": {\"left\": \"center\",\n        \"text\": title},\n\
          \        \"legend\": {\"data\": [xdata]},\n        \"xAxis\": {\"type\"\
          : \"category\", \"data\": categories},\n        \"yAxis\": {\"type\": \"\
          value\"},\n        \"series\": series_data\n    }\n    \n    # 返回结果\n  \
          \  return {'result': f'```echarts\\n{json.dumps(echarts_config, ensure_ascii=False)}\\\
          n```'}\n\n"
        code_language: python3
        desc: ''
        outputs:
          result:
            children: null
            type: string
        selected: false
        title: 代码执行
        type: code
        variables:
        - value_selector:
          - '1743643957985'
          - csv_data
          variable: csv_data
        - value_selector:
          - '1743643957985'
          - title
          variable: title
        - value_selector:
          - '1743643957985'
          - xdata
          variable: xdata
      height: 54
      id: '1743651304269'
      position:
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      positionAbsolute:
        x: 634.0000000000002
        y: 426.99999999999983
      selected: true
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        code: "import csv\nimport json\n\ndef main(csv_data,title,xdata):\n    # 解析CSV\n\
          \    reader = csv.DictReader(csv_data.strip().splitlines())\n    rows =\
          \ list(reader)\n    \n    # 自动识别列结构\n    headers = reader.fieldnames\n \
          \   if not headers or len(headers) < 2:\n        return {'result': 'Error:\
          \ 需要至少两列数据（1个分类列+1个数值列）'}\n    \n    # 默认第一列为分类轴，其余为数值列\n    category_col\
          \ = headers[0]\n    value_columns = headers[1:]\n    \n    # 提取数据\n    categories\
          \ = [row[category_col] for row in rows]\n    series_data = [\n        {\n\
          \            \"name\": col,\n            \"type\": \"line\",#bar为柱图\n  \
          \          \"data\": [float(row[col]) for row in rows]  # 处理浮点数\n      \
          \  } for col in value_columns\n    ]\n    \n    # 构建ECharts配置\n    echarts_config\
          \ = {\n        \"title\": {\"left\": \"center\",\n        \"text\": title},\n\
          \        \"legend\": {\"data\": [xdata]},\n        \"xAxis\": {\"type\"\
          : \"category\", \"data\": categories},\n        \"yAxis\": {\"type\": \"\
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          \  return {'result': f'```echarts\\n{json.dumps(echarts_config, ensure_ascii=False)}\\\
          n```'}\n\n"
        code_language: python3
        desc: ''
        outputs:
          result:
            children: null
            type: string
        selected: false
        title: 代码执行 (线图)
        type: code
        variables:
        - value_selector:
          - '1743643957985'
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      zoom: 0.9999999999999972