镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-09 16:06:48 +00:00
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
132 行
7.3 KiB
Python
132 行
7.3 KiB
Python
import zipfile
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import os
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from toolbox import update_ui, promote_file_to_downloadzone, gen_time_str, get_log_folder
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from toolbox import CatchException, report_exception
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from toolbox import write_history_to_file, promote_file_to_downloadzone
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from .crazy_utils import request_gpt_model_in_new_thread_with_ui_alive
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from .crazy_utils import read_and_clean_pdf_text
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from .crazy_utils import input_clipping
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pj = os.path.join
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def move_file_to_zip(file_path, zip_file):
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zip_file.write(file_path, os.path.basename(file_path))
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os.remove(file_path)
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def 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt):
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zip_file_path = pj(get_log_folder(), 'result.zip')
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with zipfile.ZipFile(zip_file_path, 'w') as zip_file:
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for file_name in file_manifest:
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file_write_buffer = []
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print('begin analysis on:', file_name)
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############################## <第 0 步，切割PDF> ##################################
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# 递归地切割PDF文件，每一块（尽量是完整的一个section，比如introduction，experiment等，必要时再进行切割）
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# 的长度必须小于 2500 个 Token
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file_content, page_one = read_and_clean_pdf_text(file_name) # （尝试）按照章节切割PDF
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file_content = file_content.encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
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page_one = str(page_one).encode('utf-8', 'ignore').decode() # avoid reading non-utf8 chars
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TOKEN_LIMIT_PER_FRAGMENT = 2500
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from .crazy_utils import breakdown_txt_to_satisfy_token_limit_for_pdf
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from request_llms.bridge_all import model_info
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enc = model_info["gpt-3.5-turbo"]['tokenizer']
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def get_token_num(txt): return len(enc.encode(txt, disallowed_special=()))
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paper_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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txt=file_content, get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT)
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page_one_fragments = breakdown_txt_to_satisfy_token_limit_for_pdf(
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txt=str(page_one), get_token_fn=get_token_num, limit=TOKEN_LIMIT_PER_FRAGMENT//4)
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# 为了更好的效果，我们剥离Introduction之后的部分（如果有）
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paper_meta = page_one_fragments[0].split('introduction')[0].split('Introduction')[0].split('INTRODUCTION')[0]
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############################## <第 1 步，从摘要中提取高价值信息，放到history中> ##################################
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final_results = []
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final_results.append("## metadata\n\n" + paper_meta + "\n\n## metadata")
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############################## <第 2 步，迭代地历遍整个文章，提取精炼信息> ##################################
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i_say_show_user = f'首先你在中文语境下通读整篇论文。'; gpt_say = "[Local Message] 收到。" # 用户提示
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chatbot.append([i_say_show_user, gpt_say]); yield from update_ui(chatbot=chatbot, history=[]) # 更新UI
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iteration_results = []
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last_iteration_result = paper_meta # 初始值是摘要
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MAX_WORD_TOTAL = 4096 * 0.7
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n_fragment = len(paper_fragments)
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if n_fragment >= 20: print('文章极长，不能达到预期效果')
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for i in range(n_fragment):
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NUM_OF_WORD = MAX_WORD_TOTAL // n_fragment
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i_say = f"Read this section, recapitulate the content of this section in Chinese with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i]}"
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i_say_show_user = f"[{i+1}/{n_fragment}] Read this section, recapitulate the content of this section with less than {NUM_OF_WORD} Chinese characters: {paper_fragments[i][:200]}"
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gpt_say = yield from request_gpt_model_in_new_thread_with_ui_alive(i_say, i_say_show_user, # i_say=真正给chatgpt的提问， i_say_show_user=给用户看的提问
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llm_kwargs, chatbot,
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history=["The main idea of the previous section is?", last_iteration_result], # 迭代上一次的结果
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sys_prompt="Extract the main idea of this section with Chinese." # 提示
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)
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iteration_results.append(gpt_say)
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last_iteration_result = gpt_say
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############################## <第 3 步，整理history，提取总结> ##################################
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final_results.extend(iteration_results)
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file_write_buffer.extend(final_results)
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############################## <第 4 步，设置一个token上限> ##################################
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_, final_results = input_clipping("", final_results, max_token_limit=3200)
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yield from update_ui(chatbot=chatbot, history=final_results) # 注意这里的历史记录被替代了
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res = write_history_to_file(
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file_write_buffer,
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file_basename=os.path.splitext(os.path.basename(file_name))[0] + '.md',
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auto_caption=False
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)
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if len(file_manifest) == 1:
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promote_file_to_downloadzone(res, chatbot=chatbot)
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return
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move_file_to_zip(res, zip_file)
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promote_file_to_downloadzone(zip_file_path, chatbot=chatbot)
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@CatchException
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def 批量总结PDF文档_初步(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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import glob, os
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# 基本信息：功能、贡献者
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chatbot.append([
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"函数插件功能？",
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"批量总结PDF文档。函数插件贡献者: ValeriaWong，Eralien，Joshua Reed"])
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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# 尝试导入依赖，如果缺少依赖，则给出安装建议
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try:
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import fitz
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except:
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report_exception(chatbot, history,
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a = f"解析项目: {txt}",
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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```。")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 清空历史，以免输入溢出
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history = []
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# 检测输入参数，如没有给定输入参数，直接退出
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if os.path.exists(txt):
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project_folder = txt
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else:
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if txt == "": txt = '空空如也的输入栏'
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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}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 搜索需要处理的文件清单
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file_manifest = [f for f in glob.glob(f'{project_folder}/**/*.pdf', recursive=True)]
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# 如果没找到任何文件
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if len(file_manifest) == 0:
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report_exception(chatbot, history, a = f"解析项目: {txt}", b = f"找不到任何.tex或.pdf文件: {txt}")
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yield from update_ui(chatbot=chatbot, history=history) # 刷新界面
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return
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# 开始正式执行任务
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yield from 解析PDF(file_manifest, project_folder, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt)
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