meta prompt generator
1.0.0
메타 프롬프트 생성기는 작업 설명 또는 기존 프롬프트를 기반으로 언어 모델에 대한 자세한 시스템 프롬프트를 생성하는 파이썬 패키지입니다. OpenAI의 GPT 모델을 활용하여 AI 모델이 다양한 작업을 효과적으로 완료하는 데 사용할 수있는 잘 구조화 된 작업 별 프롬프트를 만듭니다. CLI에서도 사용할 수도 있습니다.
메타 프롬프트 생성기를 설치하려면 복제 또는 PIP를 사용할 수 있습니다.
저장소 복제 :
git clone https://github.com/Zakk-Yang/meta-prompt-generator.git
cd meta-prompt-generator
PIP를 통해 패키지 설치 :
pip install meta-prompt-generator --upgrade
메타 프롬프트 발전기 사용 방법의 기본 예는 다음과 같습니다.
from meta_prompt_generator import generate_prompt
# Generate a prompt
task = "Create a prompt for generating creative short stories"
prompt = generate_prompt ( task )
print ( prompt )생성 된 프롬프트는 Markdown 코드 블록으로 래핑됩니다.
자신의 템플릿을 사용자 정의하기 전에 현재 템플릿을 확인하는 것이 좋습니다.
from meta_prompt_generator . prompts import META_PROMPT
print ( META_PROMPT )산출:
Given a task description or existing prompt, produce a detailed system prompt to guide a language model in completing the task effectively.
# Guidelines
- Understand the Task: Grasp the main objective, goals, requirements, constraints, and expected output.
- Minimal Changes: If an existing prompt is provided, improve it only if it's simple. For complex prompts, enhance clarity and add missing elements without altering the original structure.
- Reasoning Before Conclusions: Encourage reasoning steps before any conclusions are reached. ATTENTION! If the user provides examples where the reasoning happens afterward, REVERSE the order! NEVER START EXAMPLES WITH CONCLUSIONS!
- Reasoning Order: Call out reasoning portions of the prompt and conclusion parts (specific fields by name). For each, determine the ORDER in which this is done, and whether it needs to be reversed.
- Conclusion, classifications, or results should ALWAYS appear last.
- Examples: Include high-quality examples if helpful, using placeholders [ in brackets ] for complex elements.
- What kinds of examples may need to be included, how many, and whether they are complex enough to benefit from placeholders.
- Clarity and Conciseness: Use clear, specific language. Avoid unnecessary instructions or bland statements.
- Formatting: Use markdown features for readability. DO NOT USE ``` CODE BLOCKS UNLESS SPECIFICALLY REQUESTED.
- Preserve User Content: If the input task or prompt includes extensive guidelines or examples, preserve them entirely, or as closely as possible. If they are vague, consider breaking down into sub-steps. Keep any details, guidelines, examples, variables, or placeholders provided by the user.
- Constants: DO include constants in the prompt, as they are not susceptible to prompt injection. Such as guides, rubrics, and examples.
- Output Format: Explicitly the most appropriate output format, in detail. This should include length and syntax (e.g. short sentence, paragraph, JSON, etc.)
- For tasks outputting well-defined or structured data (classification, JSON, etc.) bias toward outputting a JSON.
- JSON should never be wrapped in code blocks (```) unless explicitly requested.
The final prompt you output should adhere to the following structure below. Do not include any additional commentary, only output the completed system prompt. SPECIFICALLY, do not include any additional messages at the start or end of the prompt. (e.g. no "---")
[ Concise instruction describing the task - this should be the first line in the prompt, no section header ]
[ Additional details as needed. ]
[ Optional sections with headings or bullet points for detailed steps. ]
# Steps [ optional ]
[ optional: a detailed breakdown of the steps necessary to accomplish the task ]
# Output Format
[ Specifically call out how the output should be formatted, be it response length, structure e.g. JSON, markdown, etc ]
# Examples [ optional ]
[ Optional: 1-3 well-defined examples with placeholders if necessary. Clearly mark where examples start and end, and what the input and output are. User placeholders as necessary. ]
[ If the examples are shorter than what a realistic example is expected to be, make a reference with () explaining how real examples should be longer / shorter / different. AND USE PLACEHOLDERS! ]
# Notes [ optional ]
[ optional: edge cases, details, and an area to call or repeat out specific important considerations ]
그런 다음 자신의 템플릿을 변경하고 적용 할 수 있습니다.
my_meta_prompt = """ Customize your own template here """
task = "Create a prompt for generating creative short stories"
prompt = generate_prompt ( task , prompt_template = my_meta_prompt )
print ( prompt )JSON 형식의 출력 : 스키마 템플릿을 먼저 확인합니다.
from meta_prompt_generator . prompts import META_SCHEMA_PROMPT , META_SCHEMA
print ( META_SCHEMA_PROMPT )
print ( META_SCHEMA )JSON 출력 생성 :
from meta_prompt_generator . generator import generate_meta_schema
print ( generate_meta_schema ( 'generate KPIs for a data team' ))산출:
{
"name" : " kpis_data_team " ,
"type" : " object " ,
"properties" : {
"kpi_list" : {
"type" : " array " ,
"description" : " A list of KPIs defined for the data team. " ,
"items" : {
"type" : " object " ,
"properties" : {
"name" : {
"type" : " string " ,
"description" : " The name of the KPI. "
},
"description" : {
"type" : " string " ,
"description" : " A brief description of what the KPI measures. "
},
"target" : {
"type" : " string " ,
"description" : " The target value or goal for the KPI. "
},
"frequency" : {
"type" : " string " ,
"description" : " The frequency of measuring this KPI (e.g., weekly, monthly). "
},
"owner" : {
"type" : " string " ,
"description" : " The individual or role responsible for this KPI. "
}
},
"required" : [
" name " ,
" description " ,
" target " ,
" frequency " ,
" owner "
],
"additionalProperties" : false
}
}
},
"required" : [
" kpi_list "
],
"additionalProperties" : false
} 아래 예제에 따라 META_SCHEMA_PROMPT 및 META_SCHEMA 또는 기타 매개 변수를 모두 변경하십시오 : task_or_prompt : str, api_key : 옵션 [str] = none, schema_template : dict = meta_schema, prfust_template : 옵션 [str] = meta_schema_prompt, model _name : 옵션 [gpt-yo-mom-a-moc-momecmp.
from meta_prompt_generator . generator import generate_meta_schema
print ( generate_meta_schema ( task_or_prompt = 'generate KPIs for a data team' ,
schema_template = 'your schema template' ,
prompt_template = 'your prompt template' ,
model_name = 'your preferred openai model name' # default is gpt-4o-mini
))CLI에서 사용 :
기본적으로 gpt-4o-mini 모델을 사용하고 있습니다.
meta-prompt " Create a prompt for generating creative short stories "다른 모델을 사용하도록 선택할 수 있습니다.
meta-prompt " Design a system to classify customer feedback " --model-name gpt-4o매우 길어질 수 있으므로 여기에 사용자 정의 된 프롬프트 템플릿을 추가하는 것이 좋습니다.
패키지에는 OpenAI API 키가 필요합니다. 세 가지 방법으로 제공 할 수 있습니다.
generate_prompt 함수에 대한 인수 :
prompt = generate_prompt ( task , api_key = "your-api-key-here" ) OPENAI_API_KEY 라는 환경 변수로 :
export OPENAI_API_KEY= " your-api-key-here "포함 할 루트에 .env를 작성하십시오
OPENAI_API_KEY = 'sk-xxx'
참고 : 실수로 API 키를 커밋하지 않도록 .gitignore 파일에 .env를 추가하십시오.
메타 프롬프트 생성기에 대한 기여를 환영합니다! 풀 요청을 제출하십시오.
이 프로젝트는 MIT 라이센스에 따라 라이센스가 부여됩니다. 자세한 내용은 라이센스 파일을 참조하십시오.
