meta prompt generator
1.0.0
Meta Prompt Generator adalah paket Python yang menghasilkan permintaan sistem terperinci untuk model bahasa berdasarkan deskripsi tugas atau petunjuk yang ada. Ini memanfaatkan model GPT Openai untuk membuat petunjuk khusus yang terstruktur dengan baik yang dapat digunakan untuk memandu model AI dalam menyelesaikan berbagai tugas secara efektif. Anda juga dapat menggunakannya di CLI.
Untuk memasang generator meta prompt, Anda dapat mengkloning atau menggunakan pip:
Klon Repositori:
git clone https://github.com/Zakk-Yang/meta-prompt-generator.git
cd meta-prompt-generator
Instal paket melalui PIP:
pip install meta-prompt-generator --upgrade
Berikut adalah contoh dasar tentang cara menggunakan generator meta prompt:
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 )Prompt yang dihasilkan dibungkus dalam blok kode Markdown.
Sebelum menyesuaikan template Anda sendiri, disarankan untuk memeriksa templat saat ini.
from meta_prompt_generator . prompts import META_PROMPT
print ( META_PROMPT )Keluaran:
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 ]
Kemudian, Anda dapat mengubah templat Anda sendiri dan menerapkan:
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 )Output dalam format JSON: Anda chan memeriksa templat skema terlebih dahulu:
from meta_prompt_generator . prompts import META_SCHEMA_PROMPT , META_SCHEMA
print ( META_SCHEMA_PROMPT )
print ( META_SCHEMA )Buat Output JSON:
from meta_prompt_generator . generator import generate_meta_schema
print ( generate_meta_schema ( 'generate KPIs for a data team' ))Keluaran:
{
"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
} Jangan ragu untuk mengubah kedua META_SCHEMA_PROMPT dan META_SCHEMA atau parameter lainnya dengan contoh di bawah ini: Tugas_or_promppt: str, api_key: opsional [str] = tidak ada, schema_template: Dict = meta_schema, prompt_template: opsional [str] = meta_schema_prom, prompt, prompt, opsional: opsional [str] = meta_schema_prom. "GPT-4O-Mini",
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
))Gunakan di CLI:
Secara default, menggunakan model gpt-4o-mini .
meta-prompt " Create a prompt for generating creative short stories "Anda dapat memilih untuk menggunakan model yang berbeda.
meta-prompt " Design a system to classify customer feedback " --model-name gpt-4oTidak disarankan untuk menambahkan template prompt khusus Anda di sini karena bisa sangat panjang.
Paket membutuhkan kunci API OpenAI. Anda dapat memberikannya dalam tiga cara:
Sebagai argumen untuk fungsi generate_prompt :
prompt = generate_prompt ( task , api_key = "your-api-key-here" ) Sebagai variabel lingkungan bernama OPENAI_API_KEY :
export OPENAI_API_KEY= " your-api-key-here "Buat .env di root untuk dimasukkan
OPENAI_API_KEY = 'sk-xxx'
Catatan: Pastikan untuk menambahkan .env ke file .gitignore Anda untuk menghindari secara tidak sengaja melakukan kunci API Anda.
Kontribusi untuk Meta Prompt Generator dipersilakan! Silakan mengirimkan permintaan tarik.
Proyek ini dilisensikan di bawah lisensi MIT - lihat file lisensi untuk detailnya.
