mirror of
https://github.com/svcvit/Awesome-Dify-Workflow.git
synced 2026-06-04 10:13:51 +08:00
增加LLM代码执行
This commit is contained in:
svcvit
@@ -0,0 +1,435 @@
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app:
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description: ''
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icon: 🤖
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icon_background: '#FFEAD5'
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mode: workflow
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name: runLLMCode
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use_icon_as_answer_icon: false
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kind: app
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version: 0.1.4
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workflow:
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conversation_variables: []
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environment_variables: []
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features:
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file_upload:
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allowed_file_extensions:
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- .JPG
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- .JPEG
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- .PNG
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- .GIF
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- .WEBP
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- .SVG
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allowed_file_types:
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- image
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allowed_file_upload_methods:
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- local_file
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- remote_url
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enabled: false
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fileUploadConfig:
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audio_file_size_limit: 50
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batch_count_limit: 5
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file_size_limit: 15
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image_file_size_limit: 10
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video_file_size_limit: 100
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workflow_file_upload_limit: 10
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image:
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enabled: false
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number_limits: 3
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transfer_methods:
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- local_file
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- remote_url
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number_limits: 3
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opening_statement: ''
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retriever_resource:
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enabled: true
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sensitive_word_avoidance:
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enabled: false
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speech_to_text:
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enabled: false
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suggested_questions: []
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suggested_questions_after_answer:
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enabled: false
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text_to_speech:
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enabled: false
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language: ''
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voice: ''
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graph:
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edges:
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- data:
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isInIteration: false
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sourceType: start
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targetType: code
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id: 1733304372042-source-1733304400929-target
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source: '1733304372042'
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sourceHandle: source
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target: '1733304400929'
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targetHandle: target
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type: custom
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zIndex: 0
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- data:
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isInIteration: false
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sourceType: llm
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targetType: code
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id: 1733308734162-source-1733309556954-target
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source: '1733308734162'
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sourceHandle: source
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target: '1733309556954'
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targetHandle: target
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type: custom
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zIndex: 0
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- data:
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isInIteration: false
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sourceType: code
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targetType: http-request
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id: 1733309556954-source-1733305706063-target
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source: '1733309556954'
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sourceHandle: source
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target: '1733305706063'
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targetHandle: target
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type: custom
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zIndex: 0
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- data:
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isInIteration: false
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sourceType: http-request
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targetType: code
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id: 1733305706063-source-1733310096303-target
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source: '1733305706063'
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sourceHandle: source
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target: '1733310096303'
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targetHandle: target
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type: custom
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zIndex: 0
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- data:
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isInIteration: false
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sourceType: code
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targetType: end
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id: 1733310096303-source-1733304425429-target
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source: '1733310096303'
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sourceHandle: source
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target: '1733304425429'
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targetHandle: target
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type: custom
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zIndex: 0
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- data:
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isInIteration: false
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sourceType: code
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targetType: code
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id: 1733304400929-source-1733364687479-target
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source: '1733304400929'
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sourceHandle: source
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target: '1733364687479'
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targetHandle: target
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type: custom
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zIndex: 0
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- data:
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isInIteration: false
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sourceType: code
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targetType: llm
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id: 1733364687479-source-1733308734162-target
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source: '1733364687479'
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sourceHandle: source
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target: '1733308734162'
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targetHandle: target
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type: custom
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zIndex: 0
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nodes:
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- data:
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desc: ''
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selected: false
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title: 开始
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type: start
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variables:
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- allowed_file_extensions: []
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allowed_file_types:
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- document
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- image
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- audio
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- video
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allowed_file_upload_methods:
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- local_file
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- remote_url
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label: File
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max_length: 48
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options: []
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required: true
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type: file
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variable: File
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- label: query
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max_length: 1000
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options: []
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required: true
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type: paragraph
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variable: query
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height: 116
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id: '1733304372042'
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position:
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positionAbsolute:
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x: 30
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y: 258
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selected: false
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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code: "import os\nimport time\nfrom datetime import datetime, timedelta\n\n\
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def main(filesize):\n # 存储符合条件的文件列表\n matched_files = []\n \n \
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\ # 获取当前时间\n current_time = time.time()\n \n # 检查 upload_files\
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\ 文件夹\n upload_dir = '/upload_files'\n \n # 确保文件夹存在\n if not\
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\ os.path.exists(upload_dir):\n return {\"file_path\":\"None\"}\n\
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\ \n # 递归遍历文件夹中的所有文件\n for root, dirs, files in os.walk(upload_dir):\n\
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\ for filename in files: # 只处理文件，忽略文件夹\n file_path =\
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\ os.path.join(root, filename)\n \n # 获取文件状态信息\n \
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\ file_stat = os.stat(file_path)\n \n #\
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\ 获取文件修改时间\n file_mtime = file_stat.st_mtime\n \n\
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\ # 计算文件时间差（分钟）\n time_diff = (current_time - file_mtime)\
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\ / 60\n \n # 检查文件大小和修改时间是否符合条件\n if file_stat.st_size\
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\ == filesize:\n matched_files.append((file_path, file_mtime))\n\
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\n if matched_files:\n # 按修改时间排序，取最新的文件\n newest_file =\
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\ max(matched_files, key=lambda x: x[1])\n return {\"file_path\"\
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:str(newest_file[0])}\n else:\n return {\"file_path\":\"None\"\
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}\n"
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code_language: python3
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desc: ''
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outputs:
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file_path:
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children: null
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type: string
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selected: false
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title: 获取文件路径
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type: code
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variables:
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- value_selector:
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- '1733304372042'
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- File
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- size
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variable: filesize
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height: 54
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id: '1733304400929'
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position:
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positionAbsolute:
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x: 334
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y: 258
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selected: false
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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desc: ''
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outputs:
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- value_selector:
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- '1733310096303'
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- result
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variable: output
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selected: false
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title: 结束
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type: end
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height: 90
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id: '1733304425429'
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position:
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x: 942
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y: 779.4285714285714
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positionAbsolute:
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x: 942
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y: 779.4285714285714
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selected: true
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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authorization:
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config: null
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type: no-auth
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body:
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data:
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- id: key-value-707
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key: ''
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type: text
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value: '{"code":{{#1733309556954.code#}},"language":"python3"}'
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type: json
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desc: ''
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headers: X-Api-Key:dify-sandbox
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method: post
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params: ''
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selected: false
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timeout:
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max_connect_timeout: 0
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max_read_timeout: 0
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max_write_timeout: 0
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title: sandbox 执行代码
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type: http-request
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url: http://sandbox:8194/v1/sandbox/run
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variables: []
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height: 110
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id: '1733305706063'
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position:
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x: 942
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y: 543.7142857142858
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positionAbsolute:
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x: 942
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y: 543.7142857142858
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selected: false
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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context:
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enabled: false
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variable_selector: []
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desc: ''
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model:
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completion_params:
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temperature: 0.7
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mode: chat
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name: deepseek-coder
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provider: deepseek
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prompt_template:
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- id: fb5ec31d-3aef-4b55-bb45-b5e0910ab916
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role: system
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text: '你是一个数据处理专家，擅长需求分析和数据处理，你可以和用户进行基本的对话，习惯使用 pandas 编写代码处理数据处理的需求。
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'
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- id: ab066e1b-b7c0-4984-991d-fe2972312bd4
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role: user
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text: "<UserMessage>\n{{#1733304372042.query#}}\n</UserMessage>\n\n\n##\
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\ 步骤\n1、首先，根据 xml 标签 <UserMessage></UserMessage> 中的内容，思考这是否是一个数据处理需求？如果这不是处理数据的需求，你可以请求用户澄清需要如何处理数据。如果这是一个数据处理请求，请结合下面的背景信息，给出处理步骤。\n\
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2、根据处理步骤，使用 pandas 生成代码，无需注释，直接输出代码即可。\n3、csv的数据路径为：{{#1733304400929.file_path#}}\n\
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4、python代码最终输出的内容为markdown的文本\n\n\n### 背景\n下面我会用 xml 的格式给你提供每个 DataSample\
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\ 的数据样本。\n\n<DATA>\n <DataFrame>\n <DataSample>{{#1733364687479.result#}}</DataSample>\n\
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\ </DataFrame>\n</DATA>"
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selected: false
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title: LLM
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type: llm
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variables: []
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vision:
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enabled: false
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height: 98
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id: '1733308734162'
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position:
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x: 942
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y: 258
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positionAbsolute:
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x: 942
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y: 258
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selected: false
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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code: "import re\nimport json\n\ndef main(markdown_text):\n \"\"\"\n \
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\ 从Markdown文本中提取Python代码块。\n :param markdown_text: Markdown内容字符串\n \
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\ :return: 提取的Python代码列表\n \"\"\"\n # 使用正则表达式匹配Markdown中的Python代码块\n\
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\ code_blocks = re.findall(r'```python(.*?)```', markdown_text, re.DOTALL)\n\
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\ code_string = [code.strip() for code in code_blocks][0]\n return\
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\ {\"code\":json.dumps(code_string)}\n"
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code_language: python3
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desc: ''
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outputs:
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code:
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children: null
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type: string
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selected: false
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title: 提取LLM中都代码
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type: code
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variables:
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- value_selector:
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- '1733308734162'
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- text
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variable: markdown_text
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height: 54
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id: '1733309556954'
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position:
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positionAbsolute:
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selected: false
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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code: "import json\n\ndef main(response):\n data= json.loads(response)\n\
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\ return {\n \"result\": data['data']['stdout'],\n }\n"
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code_language: python3
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desc: ''
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outputs:
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result:
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children: null
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type: string
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selected: false
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title: 提取输出内容
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type: code
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variables:
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- value_selector:
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- '1733305706063'
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- body
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variable: response
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height: 54
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id: '1733310096303'
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position:
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x: 942
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y: 698
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positionAbsolute:
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x: 942
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y: 698
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selected: false
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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- data:
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code: "import pandas as pd\nfile_path = '{{file_path}}'\ndef main(file_path):\n\
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\ try:\n # 读取CSV文件\n df = pd.read_csv(file_path)\n \
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\ \n # 获取前5行数据作为样本\n sample_df = df.head()\n \n\
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\ # 生成markdown表格\n markdown = \"### 数据样本预览\\n\\n\"\n \
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\ \n # 添加表头\n headers = \"|\" + \"|\".join(str(col) for\
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\ col in sample_df.columns) + \"|\"\n separator = \"|\" + \"|\".join([\"\
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---\" for _ in sample_df.columns]) + \"|\"\n \n markdown +=\
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\ headers + \"\\n\" + separator + \"\\n\"\n \n # 添加数据行\n \
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\ for _, row in sample_df.iterrows():\n markdown += \"\
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|\" + \"|\".join(str(val) for val in row.values) + \"|\\n\"\n \
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\ \n # 添加数据集信息\n markdown += f\"\\n### 数据集信息\\n\"\n \
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\ markdown += f\"- 总行数: {len(df)}\\n\"\n markdown += f\"- 总列数:\
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\ {len(df.columns)}\\n\"\n markdown += f\"- 列名: {', '.join(df.columns.tolist())}\\\
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n\"\n \n return {\"result\": markdown}\n \n except\
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\ Exception as e:\n return {\"result\": f\"错误: {str(e)}\"}"
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code_language: python3
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desc: ''
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outputs:
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result:
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children: null
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type: string
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selected: false
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title: 读取csv
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type: code
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||||
variables:
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||||
- value_selector:
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||||
- '1733304400929'
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- file_path
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variable: file_path
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||||
height: 54
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||||
id: '1733364687479'
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||||
position:
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||||
x: 638
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y: 258
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positionAbsolute:
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||||
x: 638
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||||
y: 258
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sourcePosition: right
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targetPosition: left
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type: custom
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width: 244
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||||
viewport:
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||||
x: 4
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||||
y: -51.09999999999991
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zoom: 0.7
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@@ -113,6 +113,7 @@ sandbox 运行pandas，numpy>2.0，matplotlib，scikit-learn 代码老报错，
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| 文件 | 描述 | 来源 |
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| ---------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | ------------------------------------------------------------------- |
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| `File_read.yml` | 使用sandbox读取文件并解析，需要使用[dify-sandbox-py](https://github.com/svcvit/dify-sandbox-py)，挂在上传目录，这是一个pandas读取csv的示例，具体方法参考右侧来源链接  | [@svcvit](https://blog.vcvit.me/2024/12/05/use-dify-sandbox-to-parse-files/) |
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| `runLLMCode.yml` | 使用LLM生成的Code，再通过sandbox去执行。因为code节点无法直接引用LLM的代码，所以通过HTTP请求的方式执行，这里有一个分析csv的示例。   | [@svcvit](https://vcvit.me/) |
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After Width: | Height: | Size: 435 KiB |
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