meta prompt generator
1.0.0
El generador de meta rápido es un paquete Python que genera indicaciones detalladas del sistema para modelos de lenguaje basados en descripciones de tareas o indicaciones existentes. Aprovecha los modelos GPT de OpenAI para crear indicaciones bien estructuradas y específicas de tareas que se pueden usar para guiar los modelos de IA para completar varias tareas de manera efectiva. También puedes usarlo en CLI.
Para instalar el generador de meta indicador, puede clonar o usar PIP:
Clon el repositorio:
git clone https://github.com/Zakk-Yang/meta-prompt-generator.git
cd meta-prompt-generator
Instale el paquete a través de PIP:
pip install meta-prompt-generator --upgrade
Aquí hay un ejemplo básico de cómo usar el generador de meta indicador:
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 )El aviso generado se envuelve en bloques de código Markdown.
Antes de personalizar su propia plantilla, se recomienda verificar la plantilla actual.
from meta_prompt_generator . prompts import META_PROMPT
print ( META_PROMPT )Producción:
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 ]
Luego, puede cambiar su propia plantilla y aplicar:
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 )Salida en formato JSON: primero verifica la plantilla de esquema:
from meta_prompt_generator . prompts import META_SCHEMA_PROMPT , META_SCHEMA
print ( META_SCHEMA_PROMPT )
print ( META_SCHEMA )Crear salida JSON:
from meta_prompt_generator . generator import generate_meta_schema
print ( generate_meta_schema ( 'generate KPIs for a data team' ))Producción:
{
"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
} Siéntase libre de cambiar tanto META_SCHEMA_PROMPT como META_SCHEMA u otros parámetros por el ejemplo a continuación: task_or_prompt: str, api_key: opcional [str] = none, schema_template: dict = meta_schema, aprop_template: opcional [str] = meta_schema_prompt, model_name: opcional [opcional [opcional] = "
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
))Use en CLI:
Por defecto, está utilizando el modelo gpt-4o-mini .
meta-prompt " Create a prompt for generating creative short stories "Puede elegir usar un modelo diferente.
meta-prompt " Design a system to classify customer feedback " --model-name gpt-4oNo se recomienda agregar su plantilla de aviso personalizada aquí, ya que puede ser muy largo.
El paquete requiere una tecla API de OpenAI. Puede proporcionarlo de tres maneras:
Como argumento para la función generate_prompt :
prompt = generate_prompt ( task , api_key = "your-api-key-here" ) Como una variable de entorno llamada OPENAI_API_KEY :
export OPENAI_API_KEY= " your-api-key-here "Crear .env en la raíz para incluir
OPENAI_API_KEY = 'sk-xxx'
Nota: asegúrese de agregar .env a su archivo .Gitignore para evitar cometer accidentalmente su clave API.
¡Las contribuciones al generador meta rápido son bienvenidas! No dude en enviar una solicitud de extracción.
Este proyecto tiene licencia bajo la licencia MIT; consulte el archivo de licencia para obtener más detalles.
