auto prompt lang

This commit is contained in:
binary-husky
2024-01-21 13:47:11 +08:00
parent 06b0e800a2
commit b55d573819
3 changed files with 94 additions and 19 deletions

View File

@@ -4,18 +4,27 @@
import importlib
from toolbox import clear_line_break
from toolbox import build_gpt_academic_masked_string
from toolbox import apply_gpt_academic_string_mask_langbased
from toolbox import build_gpt_academic_masked_string_langbased
from textwrap import dedent
def get_core_functions():
return {
"英语学术润色": {
# [1*] 前缀,会被加在你的输入之前。例如,用来描述你的要求,例如翻译、解释代码、润色等等
"Prefix": r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, "
r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. "
r"Firstly, you should provide the polished paragraph. "
r"Secondly, you should list all your modification and explain the reasons to do so in markdown table." + "\n\n",
# [2*] 后缀,会被加在你的输入之后。例如,配合前缀可以把你的输入内容用引号圈起来
"学术语料润色": {
# [1*] 前缀字符串,会被加在你的输入之前。例如,用来描述你的要求,例如翻译、解释代码、润色等等
# 这里填一个提示词字符串就行了,这里为了区分中英文情景搞复杂了一点
"Prefix": build_gpt_academic_masked_string_langbased(
text_show_english=
r"Below is a paragraph from an academic paper. Polish the writing to meet the academic style, "
r"improve the spelling, grammar, clarity, concision and overall readability. When necessary, rewrite the whole sentence. "
r"Firstly, you should provide the polished paragraph. "
r"Secondly, you should list all your modification and explain the reasons to do so in markdown table.",
text_show_chinese=
r"作为一名中文学术论文写作改进助理,你的任务是改进所提供文本的拼写、语法、清晰、简洁和整体可读性,"
r"同时分解长句减少重复并提供改进建议。请先提供文本的更正版本然后在markdown表格中列出修改的内容并给出修改的理由:"
) + "\n\n",
# [2*] 后缀字符串,会被加在你的输入之后。例如,配合前缀可以把你的输入内容用引号圈起来
"Suffix": r"",
# [3] 按钮颜色 (可选参数,默认 secondary)
"Color": r"secondary",
@@ -33,6 +42,7 @@ def get_core_functions():
"Prefix": r"",
# 后缀,会被加在你的输入之后。例如,配合前缀可以把你的输入内容用引号圈起来
"Suffix":
# dedent() 函数用于去除多行字符串的缩进
dedent("\n"+f'''
==============================
@@ -85,14 +95,22 @@ def get_core_functions():
"学术英中互译": {
"Prefix": r"I want you to act as a scientific English-Chinese translator, " +
r"I will provide you with some paragraphs in one language " +
r"and your task is to accurately and academically translate the paragraphs only into the other language. " +
r"Do not repeat the original provided paragraphs after translation. " +
r"You should use artificial intelligence tools, " +
r"such as natural language processing, and rhetorical knowledge " +
r"and experience about effective writing techniques to reply. " +
r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:" + "\n\n",
"Prefix": build_gpt_academic_masked_string_langbased(
text_show_chinese=
r"I want you to act as a scientific English-Chinese translator, "
r"I will provide you with some paragraphs in one language "
r"and your task is to accurately and academically translate the paragraphs only into the other language. "
r"Do not repeat the original provided paragraphs after translation. "
r"You should use artificial intelligence tools, "
r"such as natural language processing, and rhetorical knowledge "
r"and experience about effective writing techniques to reply. "
r"I'll give you my paragraphs as follows, tell me what language it is written in, and then translate:",
text_show_english=
r"你是经验丰富的翻译,请把以下学术文章段落翻译成中文,"
r"并同时充分考虑中文的语法、清晰、简洁和整体可读性,"
r"必要时,你可以修改整个句子的顺序以确保翻译后的段落符合中文的语言习惯。"
r"你需要翻译的文本如下:"
) + "\n\n",
"Suffix": r"",
},
@@ -142,7 +160,11 @@ def handle_core_functionality(additional_fn, inputs, history, chatbot):
if "PreProcess" in core_functional[additional_fn]:
if core_functional[additional_fn]["PreProcess"] is not None:
inputs = core_functional[additional_fn]["PreProcess"](inputs) # 获取预处理函数(如果有的话)
inputs = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"]
# 为字符串加上上面定义的前缀和后缀。
inputs = apply_gpt_academic_string_mask_langbased(
string = core_functional[additional_fn]["Prefix"] + inputs + core_functional[additional_fn]["Suffix"],
lang_reference = inputs,
)
if core_functional[additional_fn].get("AutoClearHistory", False):
history = []
return inputs, history