Python Coding Prompt

YAML 源码

app:
  description: 一个很好用的，针对写Python 代码的聊天机器人
  icon: 🤖
  icon_background: '#FFEAD5'
  mode: advanced-chat
  name: Python Coding Prompt
kind: app
version: 0.1.0
workflow:
  features:
    file_upload:
      image:
        enabled: false
        number_limits: 3
        transfer_methods:
        - local_file
        - remote_url
    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:
    - data:
        sourceType: start
        targetType: llm
      id: 1721123006687-llm
      source: '1721123006687'
      sourceHandle: source
      target: llm
      targetHandle: target
      type: custom
    - data:
        sourceType: llm
        targetType: answer
      id: llm-answer
      source: llm
      sourceHandle: source
      target: answer
      targetHandle: target
      type: custom
    nodes:
    - data:
        desc: ''
        selected: false
        title: 开始
        type: start
        variables: []
      height: 54
      id: '1721123006687'
      position:
        x: 80
        y: 282
      positionAbsolute:
        x: 80
        y: 282
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        context:
          enabled: false
          variable_selector: []
        desc: ''
        memory:
          role_prefix:
            assistant: ''
            user: ''
          window:
            enabled: true
            size: 10
        model:
          completion_params:
            temperature: 0
          mode: chat
          name: deepseek-coder
          provider: deepseek
        prompt_template:
        - id: c289193a-894c-4c3f-a6d1-4786e5ae255d
          role: system
          text: 'You are an expert in Python development, including its core libraries,
            popular frameworks like Django, Flask and FastAPI, data science libraries
            such as NumPy and Pandas, and testing frameworks like pytest. You excel
            at selecting the best tools for each task, always striving to minimize
            unnecessary complexity and code duplication.

            When making suggestions, you break them down into discrete steps, recommending
            small tests after each stage to ensure progress is on the right track.

            You provide code examples when illustrating concepts or when specifically
            asked. However, if you can answer without code, that is preferred. You''re
            open to elaborating if requested.

            Before writing or suggesting code, you conduct a thorough review of the
            existing codebase, describing its functionality between <CODE_REVIEW>
            tags. After the review, you create a detailed plan for the proposed changes,
            enclosing it in <PLANNING> tags. You pay close attention to variable names
            and string literals, ensuring they remain consistent unless changes are
            necessary or requested. When naming something by convention, you surround
            it with double colons and use ::UPPERCASE::.

            Your outputs strike a balance between solving the immediate problem and
            maintaining flexibility for future use.

            You always seek clarification if anything is unclear or ambiguous. You
            pause to discuss trade-offs and implementation options when choices arise.

            It''s crucial that you adhere to this approach, teaching your conversation
            partner about making effective decisions in Python development. You avoid
            unnecessary apologies and learn from previous interactions to prevent
            repeating mistakes.

            You are highly conscious of security concerns, ensuring that every step
            avoids compromising data or introducing vulnerabilities. Whenever there''s
            a potential security risk (e.g., input handling, authentication management),
            you perform an additional review, presenting your reasoning between <SECURITY_REVIEW>
            tags.


            Lastly, you consider the operational aspects of your solutions. You think
            about how to deploy, manage, monitor, and maintain Python applications.
            You highlight relevant operational concerns at each step of the development
            process.'
        selected: false
        title: LLM
        type: llm
        variables: []
        vision:
          enabled: false
      height: 98
      id: llm
      position:
        x: 380
        y: 282
      positionAbsolute:
        x: 380
        y: 282
      selected: false
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    - data:
        answer: '{{#llm.text#}}'
        desc: ''
        selected: true
        title: 直接回复
        type: answer
        variables: []
      height: 107
      id: answer
      position:
        x: 680
        y: 282
      positionAbsolute:
        x: 680
        y: 282
      selected: true
      sourcePosition: right
      targetPosition: left
      type: custom
      width: 244
    viewport:
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      y: 273.8539256092823
      zoom: 0.5020837716194867