68a49d3758
相当于将“批量总结PDF文档”插件拆成了两部分，目的在于使用廉价的模型干粗活，再将关键的最终总结交给GPT-4，降低使用成本 批量总结PDF文档_初步：初步总结PDF，每个PDF输出一个md文档 批量总结Markdown文档_进阶：将所有md文档高度凝练并汇总至一个md文档，可直接使用“批量总结PDF文档_初步”的输出结果作为输入
128 lines
5.9 KiB
Python
128 lines
5.9 KiB
Python
import logging, os
|
||
from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str, get_log_folder
|
||
from toolbox import CatchException, report_exception, trimmed_format_exc
|
||
from toolbox import write_history_to_file, promote_file_to_downloadzone
|
||
from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
|
||
from .crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency
|
||
from .crazy_utils import input_clipping
|
||
|
||
|
||
def 总结Markdown(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
|
||
file_write_buffer = []
|
||
SUMMARY_WORD_LIMIT = 800
|
||
meta_inputs_array = []
|
||
meta_inputs_show_user_array = []
|
||
meta_sys_prompt_array = []
|
||
inputs_array = []
|
||
inputs_show_user_array = []
|
||
sys_prompt_array = []
|
||
file_name_array = []
|
||
for idx, file_name in enumerate(file_manifest):
|
||
print('begin analysis on:', file_name)
|
||
file_name_array.append(f'# {idx}.{os.path.basename(file_name)}')
|
||
|
||
with open(file_name, 'r', encoding='utf-8', errors='replace') as f:
|
||
file_content = f.read()
|
||
|
||
_ = file_content.split('## metadata')
|
||
if len(_) >= 2:
|
||
file_meta = _[-2]
|
||
file_content = _[-1]
|
||
else:
|
||
file_meta = file_name
|
||
|
||
meta_inputs_array.append(
|
||
"我需要你从一段文本中识别并提取出这篇文章的1.标题、2.作者、3.作者单位、4.关键词。"
|
||
"其中，1.标题和4.关键词需要给出中文和英文的双语结果，2.作者和3.作者单位按原文语言给出。"
|
||
"以下是需要你识别的文本: " + file_meta
|
||
)
|
||
meta_inputs_show_user_array.append(
|
||
'开始分析元数据：' + file_name
|
||
)
|
||
meta_sys_prompt_array.append("As an academic professional, you need to extract basic informations of the paper from its metadata")
|
||
|
||
inputs_array.append(
|
||
"我需要你根据我提供的文本总结一份Markdown文档，分为四个部分：1.研究背景，2.文章主要内容，3.主要创新点，4.结论。"
|
||
+ f"各部分的题目采用二级标题前缀(## ),内容可适当的分为若干条，总字数不超过{SUMMARY_WORD_LIMIT}个中文字符."
|
||
+ "以下是需要你处理的文本: " + file_content)
|
||
inputs_show_user_array.append('开始总结：' + file_name)
|
||
sys_prompt_array.append(f"As an academic professional, you need to summarize the text with less than {SUMMARY_WORD_LIMIT} Chinese characters")
|
||
|
||
gpt_meta_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||
inputs_array=meta_inputs_array,
|
||
inputs_show_user_array=meta_inputs_show_user_array,
|
||
llm_kwargs=llm_kwargs,
|
||
chatbot=chatbot,
|
||
history_array=[[""] for _ in range(len(inputs_array))],
|
||
sys_prompt_array=meta_sys_prompt_array,
|
||
# max_workers=5, # OpenAI所允许的最大并行过载
|
||
scroller_max_len=80
|
||
)
|
||
|
||
gpt_response_collection = yield from request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency(
|
||
inputs_array=inputs_array,
|
||
inputs_show_user_array=inputs_show_user_array,
|
||
llm_kwargs=llm_kwargs,
|
||
chatbot=chatbot,
|
||
history_array=[[""] for _ in range(len(inputs_array))],
|
||
sys_prompt_array=sys_prompt_array,
|
||
# max_workers=5, # OpenAI所允许的最大并行过载
|
||
scroller_max_len=80
|
||
)
|
||
try:
|
||
for idx, (gpt_say_meta, gpt_say) in enumerate(zip(gpt_meta_response_collection[1::2], gpt_response_collection[1::2])):
|
||
file_write_buffer.append(file_name_array[idx])
|
||
file_write_buffer.append("## 元数据\n\n" + gpt_say_meta)
|
||
file_write_buffer.append(gpt_say)
|
||
except:
|
||
logging.error(trimmed_format_exc())
|
||
|
||
res = write_history_to_file(file_write_buffer, file_basename="result.md", auto_caption=False)
|
||
promote_file_to_downloadzone(res, chatbot=chatbot)
|
||
yield from update_ui(chatbot=chatbot, history=gpt_response_collection) # 刷新界面
|
||
|
||
|
||
@CatchException
|
||
def 批量总结Markdown文档_进阶(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
|
||
import glob, os
|
||
|
||
# 基本信息：功能、贡献者
|
||
chatbot.append([
|
||
"函数插件功能？",
|
||
"批量总结Markdown文档。函数插件贡献者: ValeriaWong，Eralien，Joshua Reed"])
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
|
||
# 尝试导入依赖，如果缺少依赖，则给出安装建议
|
||
try:
|
||
import fitz
|
||
except:
|
||
report_exception(chatbot, history,
|
||
a = f"解析项目: {txt}",
|
||
b = f"导入软件依赖失败。使用该模块需要额外依赖，安装方法```pip install --upgrade pymupdf```。")
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
return
|
||
|
||
# 清空历史，以免输入溢出
|
||
history = []
|
||
|
||
# 检测输入参数，如没有给定输入参数，直接退出
|
||
if os.path.exists(txt):
|
||
project_folder = txt
|
||
else:
|
||
if txt == "": txt = '空空如也的输入栏'
|
||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到本地项目或无权访问: {txt}")
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
return
|
||
|
||
# 搜索需要处理的文件清单
|
||
file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.md', recursive=True)]
|
||
|
||
# 如果没找到任何文件
|
||
if len(file_manifest) == 0:
|
||
report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.md文件: {txt}")
|
||
yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
|
||
return
|
||
|
||
# 开始正式执行任务
|
||
yield from 总结Markdown(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
|