83 lines
3.1 KiB
Python
83 lines
3.1 KiB
Python
import os.path
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from toolbox import CatchException, update_ui
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from crazy_functions.rag_essay_fns.paper_processing import ArxivPaperProcessor
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import asyncio
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@CatchException
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def Rag论文对话(txt, llm_kwargs, plugin_kwargs, chatbot, history, system_prompt, web_port):
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"""
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txt: 用户输入，通常是arxiv论文链接
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功能：RAG论文总结和对话
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"""
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if_project, if_arxiv = False, False
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if os.path.exists(txt):
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from crazy_functions.rag_essay_fns.document_splitter import SmartDocumentSplitter
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splitter = SmartDocumentSplitter(
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char_range=(1000, 1200),
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max_workers=32 # 可选，默认会根据CPU核心数自动设置
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)
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if_project = True
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else:
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from crazy_functions.rag_essay_fns.arxiv_splitter import SmartArxivSplitter
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splitter = SmartArxivSplitter(
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char_range=(1000, 1200),
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root_dir="gpt_log/arxiv_cache"
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)
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if_arxiv = True
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for fragment in splitter.process(txt):
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pass
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# 初始化处理器
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processor = ArxivPaperProcessor()
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rag_handler = RagHandler()
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# Step 1: 下载和提取论文
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download_result = processor.download_and_extract(txt, chatbot, history)
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project_folder, arxiv_id = None, None
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for result in download_result:
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if isinstance(result, tuple) and len(result) == 2:
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project_folder, arxiv_id = result
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break
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if not project_folder or not arxiv_id:
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return
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# Step 2: 合并TEX文件
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paper_content = processor.merge_tex_files(project_folder, chatbot, history)
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if not paper_content:
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return
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# Step 3: RAG处理
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chatbot.append(["正在构建知识图谱...", "处理中..."])
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yield from update_ui(chatbot=chatbot, history=history)
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# 处理论文内容
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rag_handler.process_paper_content(paper_content)
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# 生成初始摘要
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summary = rag_handler.query("请总结这篇论文的主要内容，包括研究目的、方法、结果和结论。")
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chatbot.append(["论文摘要", summary])
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yield from update_ui(chatbot=chatbot, history=history)
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# 交互式问答
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chatbot.append(["知识图谱构建完成", "您可以开始提问了。支持以下类型的问题：\n1. 论文的具体内容\n2. 研究方法的细节\n3. 实验结果分析\n4. 与其他工作的比较"])
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yield from update_ui(chatbot=chatbot, history=history)
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# 等待用户提问并回答
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while True:
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question = yield from wait_user_input()
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if not question:
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break
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# 根据问题类型选择不同的查询模式
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if "比较" in question or "关系" in question:
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mode = "global" # 使用全局模式处理比较类问题
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elif "具体" in question or "细节" in question:
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mode = "local" # 使用局部模式处理细节问题
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else:
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mode = "hybrid" # 默认使用混合模式
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response = rag_handler.query(question, mode=mode)
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chatbot.append([question, response])
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yield from update_ui(chatbot=chatbot, history=history) |