meta prompt generator
1.0.0
Meta resmpt Generator - это пакет Python, который генерирует подробные системы системы для языковых моделей на основе описаний задач или существующих подсказок. Он использует модели GPT OpenAI для создания хорошо структурированных, конкретных задач подсказок, которые можно использовать для руководства моделями искусственного интеллекта при эффективном выполнении различных задач. Вы также можете использовать его в CLI.
Чтобы установить генератор Meta Ridse, вы можете либо клонировать, либо использовать PIP:
Клонировать репозиторий:
git clone https://github.com/Zakk-Yang/meta-prompt-generator.git
cd meta-prompt-generator
Установите пакет через PIP:
pip install meta-prompt-generator --upgrade
Вот основной пример того, как использовать генератор Meta rample:
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 )Сгенерированная подсказка обернута в блоки кода разметки.
Перед настройкой собственного шаблона рекомендуется проверить текущий шаблон.
from meta_prompt_generator . prompts import META_PROMPT
print ( META_PROMPT )Выход:
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 ]
Затем вы можете изменить свой собственный шаблон и применить:
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 )Вывод в формате JSON: вы сначала проверьте шаблон схемы:
from meta_prompt_generator . prompts import META_SCHEMA_PROMPT , META_SCHEMA
print ( META_SCHEMA_PROMPT )
print ( META_SCHEMA )Создать вывод JSON:
from meta_prompt_generator . generator import generate_meta_schema
print ( generate_meta_schema ( 'generate KPIs for a data team' ))Выход:
{
"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
} Не стесняйтесь изменять как META_SCHEMA_PROMPT , так и META_SCHEMA или другие параметры по примеру ниже: task_or_prompt: str, api_key: необязательно [str] = none, schema_template: dict = meta_schema, rample_template: опциональный [str] = meta_schema_prompt, model_name: ratperalate [str] = grpt-meta_schema_prompt, model_name: strabletal [gr] = grpt-meta_schema_prompt, model_name: stryptalate [str] = meta_schema.
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
))Используйте в CLI:
По умолчанию он использует модель gpt-4o-mini .
meta-prompt " Create a prompt for generating creative short stories "Вы можете использовать другую модель.
meta-prompt " Design a system to classify customer feedback " --model-name gpt-4oНе рекомендуется добавлять ваш настраиваемый шаблон приглашения здесь, так как он может быть очень длинным.
Пакет требует ключа API OpenAI. Вы можете предоставить его тремя способами:
В качестве аргумента функции generate_prompt :
prompt = generate_prompt ( task , api_key = "your-api-key-here" ) В качестве переменной среды с именем OPENAI_API_KEY :
export OPENAI_API_KEY= " your-api-key-here "Создать .env в корне, чтобы включить
OPENAI_API_KEY = 'sk-xxx'
ПРИМЕЧАНИЕ. Обязательно добавьте .env в свой файл .gitignore, чтобы избежать случайного совершения вашего ключа API.
Взносы в генератор Meta Quick Generator приветствуются! Пожалуйста, не стесняйтесь отправить запрос на привлечение.
Этот проект лицензирован по лицензии MIT - для получения подробной информации см. Файл лицензии.
