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# Written by YORKI MINAKO🤡
CONVERSATION_TITLE_PROMPT = """ You need to decompose the user ' s input into " subject " and " intention " in order to accurately figure out what the user ' s input language actually is.
Notice : the language type user using is abundant , can be English , Chinese , Español , Arabic , Japanese , and etc .
MAKE SURE your output is the SAME language as the user ' s input!
Your output is restricted only to : ( Input language ) Intention + Subject ( short as possible )
Tip : When the user ' s question is directed at you (the language model), you can add an emoji to make it more fun.
example 1 :
User Input : hi , yesterday i had some burgers .
{
" Language Type " : " The user ' s input is pure English " ,
" Your Reasoning " : " The language of my output must be pure English. " ,
" Your Output " : " sharing yesterday ' s food "
}
example 2 :
User Input : hello
{
" Language Type " : " The user ' s input is written in pure English " ,
" Your Reasoning " : " The language of my output must be pure English. " ,
" Your Output " : " Greeting myself☺️ "
}
example 3 :
User Input : why mmap file : oom
{
" Language Type " : " The user ' s input is written in pure English " ,
" Your Reasoning " : " The language of my output must be pure English. " ,
" Your Output " : " Asking about the reason for mmap file: oom "
}
example 4 :
User Input : www . convinceme . yesterday - you - ate - seafood . tv讲了什么 ?
{
" Language Type " : " The user ' s input English-Chinese mixed " ,
" Your Reasoning " : " The English-part is an URL, the main intention is still written in Chinese, so the language of my output must be using Chinese. " ,
" Your Output " : " 询问网站www.convinceme.yesterday-you-ate-seafood.tv "
}
example 5 :
User Input : why小红的年龄is老than小明 ?
{
" Language Type " : " The user ' s input is English-Chinese mixed " ,
" Your Reasoning " : " The English parts are subjective particles, the main intention is written in Chinese, besides, Chinese occupies a greater \" actual meaning \" than English, so the language of my output must be using Chinese. " ,
" Your Output " : " 询问小红和小明的年龄 "
}
example 6 :
User Input : yo , 你今天咋样 ?
{
" Language Type " : " The user ' s input is English-Chinese mixed " ,
" Your Reasoning " : " The English-part is a subjective particle, the main intention is written in Chinese, so the language of my output must be using Chinese. " ,
" Your Output " : " 查询今日我的状态☺️ "
}
User Input :
"""
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CONVERSATION_SUMMARY_PROMPT = (
" Please generate a short summary of the following conversation. \n "
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" If the following conversation communicating in English, you should only return an English summary. \n "
" If the following conversation communicating in Chinese, you should only return a Chinese summary. \n "
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" [Conversation Start] \n "
" {context} \n "
" [Conversation End] \n \n "
" summary: "
)
INTRODUCTION_GENERATE_PROMPT = (
" I am designing a product for users to interact with an AI through dialogue. "
" The Prompt given to the AI before the conversation is: \n \n "
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" ``` \n {prompt} \n ``` \n \n "
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" Please generate a brief introduction of no more than 50 words that greets the user, based on this Prompt. "
" Do not reveal the developer ' s motivation or deep logic behind the Prompt, "
" but focus on building a relationship with the user: \n "
)
MORE_LIKE_THIS_GENERATE_PROMPT = (
" ----- \n "
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" {original_completion} \n "
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" ----- \n \n "
" Please use the above content as a sample for generating the result, "
" and include key information points related to the original sample in the result. "
" Try to rephrase this information in different ways and predict according to the rules below. \n \n "
" ----- \n "
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" {prompt} \n "
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)
SUGGESTED_QUESTIONS_AFTER_ANSWER_INSTRUCTION_PROMPT = (
" Please help me predict the three most likely questions that human would ask, "
" and keeping each question under 20 characters. \n "
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" The output must be an array in JSON format following the specified schema: \n "
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" [ \" question1 \" , \" question2 \" , \" question3 \" ] \n "
)
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GENERATOR_QA_PROMPT = (
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' The user will send a long text. Please think step by step. '
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' Step 1: Understand and summarize the main content of this text. \n '
' Step 2: What key information or concepts are mentioned in this text? \n '
' Step 3: Decompose or combine multiple pieces of information and concepts. \n '
' Step 4: Generate 20 questions and answers based on these key information and concepts. '
' The questions should be clear and detailed, and the answers should be detailed and complete. \n '
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" Answer must be the language: {language} and in the following format: Q1: \n A1: \n Q2: \n A2:... \n "
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)
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RULE_CONFIG_GENERATE_TEMPLATE = """ Given MY INTENDED AUDIENCES and HOPING TO SOLVE using a language model, please select \
the model prompt that best suits the input .
You will be provided with the prompt , variables , and an opening statement .
Only the content enclosed in double curly braces , such as { { variable } } , in the prompt can be considered as a variable ; \
otherwise , it cannot exist as a variable in the variables .
If you believe revising the original input will result in a better response from the language model , you may \
suggest revisions .
<< FORMATTING >>
Return a markdown code snippet with a JSON object formatted to look like , \
no any other string out of markdown code snippet :
` ` ` json
{ { { {
" prompt " : string \\ generated prompt
" variables " : list of string \\ variables
" opening_statement " : string \\ an opening statement to guide users on how to ask questions with generated prompt \
and fill in variables , with a welcome sentence , and keep TLDR .
} } } }
` ` `
<< EXAMPLES >>
[ EXAMPLE A ]
` ` ` json
{
" prompt " : " Write a letter about love " ,
" variables " : [ ] ,
" opening_statement " : " Hi! I ' m your love letter writer AI. "
}
` ` `
[ EXAMPLE B ]
` ` ` json
{
" prompt " : " Translate from {{ lanA}} to {{ lanB}} " ,
" variables " : [ " lanA " , " lanB " ] ,
" opening_statement " : " Welcome to use translate app "
}
` ` `
[ EXAMPLE C ]
` ` ` json
{
" prompt " : " Write a story about {{ topic}} " ,
" variables " : [ " topic " ] ,
" opening_statement " : " I ' m your story writer "
}
` ` `
<< MY INTENDED AUDIENCES >>
{ audiences }
<< HOPING TO SOLVE >>
{ hoping_to_solve }
<< OUTPUT >>
"""