meta prompt generator
1.0.0
مولد Meta Product هو حزمة Python التي تنشئ مطالبات نظام مفصلة لنماذج اللغة بناءً على وصف المهمة أو المطالبات الموجودة. إنه يعزز نماذج GPT الخاصة بـ Openai لإنشاء مطالبات جيدة التنظيم خاصة يمكن استخدامها لتوجيه نماذج الذكاء الاصطناعى في إكمال المهام المختلفة بشكل فعال. يمكنك أيضا استخدامه في CLI.
لتثبيت مولد مطالبة meta ، يمكنك إما استنساخ أو استخدام PIP:
استنساخ المستودع:
git clone https://github.com/Zakk-Yang/meta-prompt-generator.git
cd meta-prompt-generator
قم بتثبيت الحزمة من خلال PIP:
pip install meta-prompt-generator --upgrade
فيما يلي مثال أساسي على كيفية استخدام مولد موجه META:
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 )يتم لف المطالبة التي تم إنشاؤها في كتل رمز Markdown.
قبل تخصيص القالب الخاص بك ، يوصى بالتحقق من القالب الحالي.
from meta_prompt_generator . prompts import META_PROMPT
print ( META_PROMPT )الإخراج:
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 ]
ثم ، يمكنك تغيير القالب الخاص بك والتطبيق:
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 )الإخراج في تنسيق JSON: يمكنك التحقق من قالب المخطط أولاً:
from meta_prompt_generator . prompts import META_SCHEMA_PROMPT , META_SCHEMA
print ( META_SCHEMA_PROMPT )
print ( META_SCHEMA )إنشاء إخراج JSON:
from meta_prompt_generator . generator import generate_meta_schema
print ( generate_meta_schema ( 'generate KPIs for a data team' ))الإخراج:
{
"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
} لا تتردد في تغيير كل من META_SCHEMA_PROMPT و META_SCHEMA أو غيرها من المعلمات من خلال المثال أدناه: Task_or_prompt: str ، api_key: اختياري [str] = لا شيء ، schema_template: dict = meta_schema ، report_template: اختياري = meta_schema_prompt ، model_name:
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
))استخدم في CLI:
بشكل افتراضي ، يستخدم نموذج gpt-4o-mini .
meta-prompt " Create a prompt for generating creative short stories "يمكنك اختيار استخدام نموذج مختلف.
meta-prompt " Design a system to classify customer feedback " --model-name gpt-4oلا ينصح بإضافة قالب الموجه المخصص الخاص بك هنا لأنه يمكن أن يكون طويلًا جدًا.
تتطلب الحزمة مفتاح Openai API. يمكنك تقديمه بثلاث طرق:
كوسيطة لدالة generate_prompt :
prompt = generate_prompt ( task , api_key = "your-api-key-here" ) كمتغير بيئة يسمى OPENAI_API_KEY :
export OPENAI_API_KEY= " your-api-key-here "إنشاء .env في الجذر لتضمينه
OPENAI_API_KEY = 'sk-xxx'
ملاحظة: تأكد من إضافة .env إلى ملف .gitignore الخاص بك لتجنب ارتكاب مفتاح API عن طريق الخطأ.
المساهمات في مولد Meta Propert موضع ترحيب! لا تتردد في تقديم طلب سحب.
تم ترخيص هذا المشروع بموجب ترخيص معهد ماساتشوستس للتكنولوجيا - راجع ملف الترخيص للحصول على التفاصيل.
