From 73f573092b379acabb1c1e8a8568c90d9075a6b0 Mon Sep 17 00:00:00 2001 From: binary-husky Date: Sun, 22 Jun 2025 18:31:41 +0800 Subject: [PATCH] stage academic conversation --- config.py | 30 +- crazy_functions/Academic_Conversation.py | 290 ++++++++ crazy_functions/pdf_fns/breakdown_pdf_txt.py | 275 +++++++ crazy_functions/review_fns/__init__.py | 0 .../conversation_doc/endnote_doc.py | 68 ++ .../review_fns/conversation_doc/excel_doc.py | 211 ++++++ .../review_fns/conversation_doc/html_doc.py | 472 ++++++++++++ .../conversation_doc/markdown_doc.py | 47 ++ .../conversation_doc/reference_formatter.py | 174 +++++ .../review_fns/conversation_doc/word2pdf.py | 138 ++++ .../review_fns/conversation_doc/word_doc.py | 246 ++++++ .../review_fns/data_sources/__init__.py | 0 .../review_fns/data_sources/adsabs_source.py | 279 +++++++ .../review_fns/data_sources/arxiv_source.py | 636 ++++++++++++++++ .../review_fns/data_sources/base_source.py | 102 +++ .../review_fns/data_sources/cas_if.json | 1 + .../data_sources/crossref_source.py | 400 ++++++++++ .../data_sources/elsevier_source.py | 449 +++++++++++ .../review_fns/data_sources/github_source.py | 698 ++++++++++++++++++ .../data_sources/journal_metrics.py | 142 ++++ .../data_sources/openalex_source.py | 163 ++++ .../review_fns/data_sources/pubmed_source.py | 458 ++++++++++++ .../review_fns/data_sources/scihub_source.py | 326 ++++++++ .../review_fns/data_sources/scopus_source.py | 400 ++++++++++ .../data_sources/semantic_source.py | 480 ++++++++++++ .../data_sources/unpaywall_source.py | 46 ++ .../review_fns/handlers/base_handler.py | 412 +++++++++++ .../review_fns/handlers/latest_handler.py | 106 +++ .../review_fns/handlers/paper_handler.py | 344 +++++++++ .../review_fns/handlers/qa_handler.py | 147 ++++ .../review_fns/handlers/recommend_handler.py | 185 +++++ .../review_fns/handlers/review_handler.py | 193 +++++ .../paper_processor/paper_llm_ranker.py | 452 ++++++++++++ .../review_fns/prompts/adsabs_prompts.py | 76 ++ .../review_fns/prompts/arxiv_prompts.py | 341 +++++++++ .../review_fns/prompts/crossref_prompts.py | 55 ++ .../review_fns/prompts/paper_prompts.py | 47 ++ .../review_fns/prompts/pubmed_prompts.py | 108 +++ .../review_fns/prompts/semantic_prompts.py | 276 +++++++ crazy_functions/review_fns/query_analyzer.py | 493 +++++++++++++ crazy_functions/review_fns/query_processor.py | 64 ++ request_llms/embed_models/bge_llm.py | 109 +++ request_llms/embed_models/bridge_all_embed.py | 5 +- requirements.txt | 1 + tests/test_academic_conversation.py | 64 ++ 45 files changed, 9992 insertions(+), 17 deletions(-) create mode 100644 crazy_functions/Academic_Conversation.py create mode 100644 crazy_functions/pdf_fns/breakdown_pdf_txt.py create mode 100644 crazy_functions/review_fns/__init__.py create mode 100644 crazy_functions/review_fns/conversation_doc/endnote_doc.py create mode 100644 crazy_functions/review_fns/conversation_doc/excel_doc.py create mode 100644 crazy_functions/review_fns/conversation_doc/html_doc.py create mode 100644 crazy_functions/review_fns/conversation_doc/markdown_doc.py create mode 100644 crazy_functions/review_fns/conversation_doc/reference_formatter.py create mode 100644 crazy_functions/review_fns/conversation_doc/word2pdf.py create mode 100644 crazy_functions/review_fns/conversation_doc/word_doc.py create mode 100644 crazy_functions/review_fns/data_sources/__init__.py create mode 100644 crazy_functions/review_fns/data_sources/adsabs_source.py create mode 100644 crazy_functions/review_fns/data_sources/arxiv_source.py create mode 100644 crazy_functions/review_fns/data_sources/base_source.py create mode 100644 crazy_functions/review_fns/data_sources/cas_if.json create mode 100644 crazy_functions/review_fns/data_sources/crossref_source.py create mode 100644 crazy_functions/review_fns/data_sources/elsevier_source.py create mode 100644 crazy_functions/review_fns/data_sources/github_source.py create mode 100644 crazy_functions/review_fns/data_sources/journal_metrics.py create mode 100644 crazy_functions/review_fns/data_sources/openalex_source.py create mode 100644 crazy_functions/review_fns/data_sources/pubmed_source.py create mode 100644 crazy_functions/review_fns/data_sources/scihub_source.py create mode 100644 crazy_functions/review_fns/data_sources/scopus_source.py create mode 100644 crazy_functions/review_fns/data_sources/semantic_source.py create mode 100644 crazy_functions/review_fns/data_sources/unpaywall_source.py create mode 100644 crazy_functions/review_fns/handlers/base_handler.py create mode 100644 crazy_functions/review_fns/handlers/latest_handler.py create mode 100644 crazy_functions/review_fns/handlers/paper_handler.py create mode 100644 crazy_functions/review_fns/handlers/qa_handler.py create mode 100644 crazy_functions/review_fns/handlers/recommend_handler.py create mode 100644 crazy_functions/review_fns/handlers/review_handler.py create mode 100644 crazy_functions/review_fns/paper_processor/paper_llm_ranker.py create mode 100644 crazy_functions/review_fns/prompts/adsabs_prompts.py create mode 100644 crazy_functions/review_fns/prompts/arxiv_prompts.py create mode 100644 crazy_functions/review_fns/prompts/crossref_prompts.py create mode 100644 crazy_functions/review_fns/prompts/paper_prompts.py create mode 100644 crazy_functions/review_fns/prompts/pubmed_prompts.py create mode 100644 crazy_functions/review_fns/prompts/semantic_prompts.py create mode 100644 crazy_functions/review_fns/query_analyzer.py create mode 100644 crazy_functions/review_fns/query_processor.py create mode 100644 request_llms/embed_models/bge_llm.py create mode 100644 tests/test_academic_conversation.py diff --git a/config.py b/config.py index 4af8ffd3..acc1e94a 100644 --- a/config.py +++ b/config.py @@ -43,7 +43,7 @@ AVAIL_LLM_MODELS = ["qwen-max", "o1-mini", "o1-mini-2024-09-12", "o1", "o1-2024- "gpt-3.5-turbo-1106", "gpt-3.5-turbo-16k", "gpt-3.5-turbo", "azure-gpt-3.5", "gpt-4", "gpt-4-32k", "azure-gpt-4", "glm-4", "glm-4v", "glm-3-turbo", "gemini-1.5-pro", "chatglm3", "chatglm4", - "deepseek-chat", "deepseek-coder", "deepseek-reasoner", + "deepseek-chat", "deepseek-coder", "deepseek-reasoner", "volcengine-deepseek-r1-250120", "volcengine-deepseek-v3-241226", "dashscope-deepseek-r1", "dashscope-deepseek-v3", "dashscope-qwen3-14b", "dashscope-qwen3-235b-a22b", "dashscope-qwen3-32b", @@ -94,19 +94,19 @@ AVAIL_THEMES = ["Default", "Chuanhu-Small-and-Beautiful", "High-Contrast", "Gsta FONT = "Theme-Default-Font" AVAIL_FONTS = [ - "默认值(Theme-Default-Font)", - "宋体(SimSun)", - "黑体(SimHei)", - "楷体(KaiTi)", - "仿宋(FangSong)", + "默认值(Theme-Default-Font)", + "宋体(SimSun)", + "黑体(SimHei)", + "楷体(KaiTi)", + "仿宋(FangSong)", "华文细黑(STHeiti Light)", - "华文楷体(STKaiti)", - "华文仿宋(STFangsong)", - "华文宋体(STSong)", - "华文中宋(STZhongsong)", - "华文新魏(STXinwei)", - "华文隶书(STLiti)", - # 备注：以下字体需要网络支持，您可以自定义任意您喜欢的字体，如下所示，需要满足的格式为 "字体昵称(字体英文真名@字体css下载链接)" + "华文楷体(STKaiti)", + "华文仿宋(STFangsong)", + "华文宋体(STSong)", + "华文中宋(STZhongsong)", + "华文新魏(STXinwei)", + "华文隶书(STLiti)", + # 备注：以下字体需要网络支持，您可以自定义任意您喜欢的字体，如下所示，需要满足的格式为 "字体昵称(字体英文真名@字体css下载链接)" "思源宋体(Source Han Serif CN VF@https://chinese-fonts-cdn.deno.dev/packages/syst/dist/SourceHanSerifCN/result.css)", "月星楷(Moon Stars Kai HW@https://chinese-fonts-cdn.deno.dev/packages/moon-stars-kai/dist/MoonStarsKaiHW-Regular/result.css)", "珠圆体(MaokenZhuyuanTi@https://chinese-fonts-cdn.deno.dev/packages/mkzyt/dist/猫啃珠圆体/result.css)", @@ -355,6 +355,10 @@ DAAS_SERVER_URLS = [ f"https://niuziniu-biligpt{i}.hf.space/stream" for i in ran JINA_API_KEY = "" +# SEMANTIC SCHOLAR API KEY +SEMANTIC_SCHOLAR_KEY = "" + + # 是否自动裁剪上下文长度（是否启动，默认不启动） AUTO_CONTEXT_CLIP_ENABLE = False # 目标裁剪上下文的token长度（如果超过这个长度，则会自动裁剪） diff --git a/crazy_functions/Academic_Conversation.py b/crazy_functions/Academic_Conversation.py new file mode 100644 index 00000000..99be6a6a --- /dev/null +++ b/crazy_functions/Academic_Conversation.py @@ -0,0 +1,290 @@ +import re +import os +import asyncio +from typing import List, Dict, Tuple +from dataclasses import dataclass +from textwrap import dedent +from toolbox import CatchException, get_conf, update_ui, promote_file_to_downloadzone, get_log_folder, get_user +from toolbox import update_ui, CatchException, report_exception, write_history_to_file +from crazy_functions.review_fns.data_sources.semantic_source import SemanticScholarSource +from crazy_functions.review_fns.data_sources.arxiv_source import ArxivSource +from crazy_functions.review_fns.query_analyzer import QueryAnalyzer +from crazy_functions.review_fns.handlers.review_handler import 文献综述功能 +from crazy_functions.review_fns.handlers.recommend_handler import 论文推荐功能 +from crazy_functions.review_fns.handlers.qa_handler import 学术问答功能 +from crazy_functions.review_fns.handlers.paper_handler import 单篇论文分析功能 +from crazy_functions.Conversation_To_File import write_chat_to_file +from crazy_functions.crazy_utils import request_gpt_model_in_new_thread_with_ui_alive +from crazy_functions.review_fns.handlers.latest_handler import Arxiv最新论文推荐功能 +from datetime import datetime + +@CatchException +def 学术对话(txt: str, llm_kwargs: Dict, plugin_kwargs: Dict, chatbot: List, + history: List, system_prompt: str, user_request: str): + """主函数""" + + # 初始化数据源 + arxiv_source = ArxivSource() + semantic_source = SemanticScholarSource( + api_key=get_conf("SEMANTIC_SCHOLAR_KEY") + ) + + # 初始化处理器 + handlers = { + "review": 文献综述功能(arxiv_source, semantic_source, llm_kwargs), + "recommend": 论文推荐功能(arxiv_source, semantic_source, llm_kwargs), + "qa": 学术问答功能(arxiv_source, semantic_source, llm_kwargs), + "paper": 单篇论文分析功能(arxiv_source, semantic_source, llm_kwargs), + "latest": Arxiv最新论文推荐功能(arxiv_source, semantic_source, llm_kwargs), + } + + # 分析查询意图 + chatbot.append([None, "正在分析研究主题和查询要求..."]) + yield from update_ui(chatbot=chatbot, history=history) + + query_analyzer = QueryAnalyzer() + search_criteria = yield from query_analyzer.analyze_query(txt, chatbot, llm_kwargs) + handler = handlers.get(search_criteria.query_type) + if not handler: + handler = handlers["qa"] # 默认使用QA处理器 + + # 处理查询 + chatbot.append([None, f"使用{handler.__class__.__name__}处理...，可能需要您耐心等待3～5分钟..."]) + yield from update_ui(chatbot=chatbot, history=history) + + final_prompt = asyncio.run(handler.handle( + criteria=search_criteria, + chatbot=chatbot, + history=history, + system_prompt=system_prompt, + llm_kwargs=llm_kwargs, + plugin_kwargs=plugin_kwargs + )) + + if final_prompt: + # 检查是否是道歉提示 + if "很抱歉，我们未能找到" in final_prompt: + chatbot.append([txt, final_prompt]) + yield from update_ui(chatbot=chatbot, history=history) + return + # 在 final_prompt 末尾添加用户原始查询要求 + final_prompt += dedent(f""" + Original user query: "{txt}" + + IMPORTANT NOTE : + - Your response must directly address the user's original user query above + - While following the previous guidelines, prioritize answering what the user specifically asked + - Make sure your response format and content align with the user's expectations + - Do not translate paper titles, keep them in their original language + - Do not generate a reference list in your response - references will be handled separately + """) + + # 使用最终的prompt生成回答 + response = yield from request_gpt_model_in_new_thread_with_ui_alive( + inputs=final_prompt, + inputs_show_user=txt, + llm_kwargs=llm_kwargs, + chatbot=chatbot, + history=[], + sys_prompt=f"You are a helpful academic assistant. Response in Chinese by default unless specified language is required in the user's query." + ) + + # 1. 获取文献列表 + papers_list = handler.ranked_papers # 直接使用原始论文数据 + + # 在新的对话中添加格式化的参考文献列表 + if papers_list: + references = "" + for idx, paper in enumerate(papers_list, 1): + # 构建作者列表 + authors = paper.authors[:3] + if len(paper.authors) > 3: + authors.append("et al.") + authors_str = ", ".join(authors) + + # 构建期刊指标信息 + metrics = [] + if hasattr(paper, 'if_factor') and paper.if_factor: + metrics.append(f"IF: {paper.if_factor}") + if hasattr(paper, 'jcr_division') and paper.jcr_division: + metrics.append(f"JCR: {paper.jcr_division}") + if hasattr(paper, 'cas_division') and paper.cas_division: + metrics.append(f"中科院分区: {paper.cas_division}") + metrics_str = f" [{', '.join(metrics)}]" if metrics else "" + + # 构建DOI链接 + doi_link = "" + if paper.doi: + if "arxiv.org" in str(paper.doi): + doi_url = paper.doi + else: + doi_url = f"https://doi.org/{paper.doi}" + doi_link = f" DOI: {paper.doi}" + + # 构建完整的引用 + reference = f"[{idx}] {authors_str}. *{paper.title}*" + if paper.venue_name: + reference += f". {paper.venue_name}" + if paper.year: + reference += f", {paper.year}" + reference += metrics_str + if doi_link: + reference += f".{doi_link}" + reference += " \n" + + references += reference + + # 添加新的对话显示参考文献 + chatbot.append(["参考文献如下：", references]) + yield from update_ui(chatbot=chatbot, history=history) + + + # 2. 保存为不同格式 + from .review_fns.conversation_doc.word_doc import WordFormatter + from .review_fns.conversation_doc.word2pdf import WordToPdfConverter + from .review_fns.conversation_doc.markdown_doc import MarkdownFormatter + from .review_fns.conversation_doc.html_doc import HtmlFormatter + + # 创建保存目录 + save_dir = get_log_folder(get_user(chatbot), plugin_name='chatscholar') + + if not os.path.exists(save_dir): + os.makedirs(save_dir) + + # 生成文件名 + def get_safe_filename(txt, max_length=10): + # 获取文本前max_length个字符作为文件名 + filename = txt[:max_length].strip() + # 移除不安全的文件名字符 + filename = re.sub(r'[\\/:*?"<>|]', '', filename) + # 如果文件名为空，使用时间戳 + if not filename: + filename = datetime.now().strftime("%Y-%m-%d_%H-%M-%S") + return filename + + base_filename = get_safe_filename(txt) + + result_files = [] # 收集所有生成的文件 + pdf_path = None # 用于跟踪PDF是否成功生成 + + # 保存为Markdown + try: + md_formatter = MarkdownFormatter() + md_content = md_formatter.create_document(txt, response, papers_list) + result_file_md = write_history_to_file( + history=[md_content], + file_basename=f"markdown_{base_filename}.md" + ) + result_files.append(result_file_md) + except Exception as e: + print(f"Markdown保存失败: {str(e)}") + + # 保存为HTML + try: + html_formatter = HtmlFormatter() + html_content = html_formatter.create_document(txt, response, papers_list) + result_file_html = write_history_to_file( + history=[html_content], + file_basename=f"html_{base_filename}.html" + ) + result_files.append(result_file_html) + except Exception as e: + print(f"HTML保存失败: {str(e)}") + + # 保存为Word + try: + word_formatter = WordFormatter() + try: + doc = word_formatter.create_document(txt, response, papers_list) + except Exception as e: + print(f"Word文档内容生成失败: {str(e)}") + raise e + + try: + result_file_docx = os.path.join( + os.path.dirname(result_file_md) if result_file_md else save_dir, + f"docx_{base_filename}.docx" + ) + doc.save(result_file_docx) + result_files.append(result_file_docx) + print(f"Word文档已保存到: {result_file_docx}") + + # 转换为PDF + try: + pdf_path = WordToPdfConverter.convert_to_pdf(result_file_docx) + if pdf_path: + result_files.append(pdf_path) + print(f"PDF文档已生成: {pdf_path}") + except Exception as e: + print(f"PDF转换失败: {str(e)}") + + except Exception as e: + print(f"Word文档保存失败: {str(e)}") + raise e + + except Exception as e: + print(f"Word格式化失败: {str(e)}") + import traceback + print(f"详细错误信息: {traceback.format_exc()}") + + # 保存为BibTeX格式 + try: + from .review_fns.conversation_doc.reference_formatter import ReferenceFormatter + ref_formatter = ReferenceFormatter() + bibtex_content = ref_formatter.create_document(papers_list) + + # 在与其他文件相同目录下创建BibTeX文件 + result_file_bib = os.path.join( + os.path.dirname(result_file_md) if result_file_md else save_dir, + f"references_{base_filename}.bib" + ) + + # 直接写入文件 + with open(result_file_bib, 'w', encoding='utf-8') as f: + f.write(bibtex_content) + + result_files.append(result_file_bib) + print(f"BibTeX文件已保存到: {result_file_bib}") + except Exception as e: + print(f"BibTeX格式保存失败: {str(e)}") + + # 保存为EndNote格式 + try: + from .review_fns.conversation_doc.endnote_doc import EndNoteFormatter + endnote_formatter = EndNoteFormatter() + endnote_content = endnote_formatter.create_document(papers_list) + + # 在与其他文件相同目录下创建EndNote文件 + result_file_enw = os.path.join( + os.path.dirname(result_file_md) if result_file_md else save_dir, + f"references_{base_filename}.enw" + ) + + # 直接写入文件 + with open(result_file_enw, 'w', encoding='utf-8') as f: + f.write(endnote_content) + + result_files.append(result_file_enw) + print(f"EndNote文件已保存到: {result_file_enw}") + except Exception as e: + print(f"EndNote格式保存失败: {str(e)}") + + # 添加所有文件到下载区 + success_files = [] + for file in result_files: + try: + promote_file_to_downloadzone(file, chatbot=chatbot) + success_files.append(os.path.basename(file)) + except Exception as e: + print(f"文件添加到下载区失败: {str(e)}") + + # 更新成功提示消息 + if success_files: + chatbot.append(["保存对话记录成功，bib和enw文件支持导入到EndNote、Zotero、JabRef、Mendeley等文献管理软件，HTML文件支持在浏览器中打开，里面包含详细论文源信息", "对话已保存并添加到下载区，可以在下载区找到相关文件"]) + else: + chatbot.append(["保存对话记录", "所有格式的保存都失败了，请检查错误日志。"]) + + yield from update_ui(chatbot=chatbot, history=history) + else: + report_exception(chatbot, history, a=f"处理失败", b=f"请尝试其他查询") + yield from update_ui(chatbot=chatbot, history=history) \ No newline at end of file diff --git a/crazy_functions/pdf_fns/breakdown_pdf_txt.py b/crazy_functions/pdf_fns/breakdown_pdf_txt.py new file mode 100644 index 00000000..8334f626 --- /dev/null +++ b/crazy_functions/pdf_fns/breakdown_pdf_txt.py @@ -0,0 +1,275 @@ +from crazy_functions.ipc_fns.mp import run_in_subprocess_with_timeout +from loguru import logger +import time +import re + +def force_breakdown(txt, limit, get_token_fn): + """ 当无法用标点、空行分割时，我们用最暴力的方法切割 + """ + for i in reversed(range(len(txt))): + if get_token_fn(txt[:i]) < limit: + return txt[:i], txt[i:] + return "Tiktoken未知错误", "Tiktoken未知错误" + + +def maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage): + """ 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage + 当 remain_txt_to_cut < `_min` 时，我们再把 remain_txt_to_cut_storage 中的部分文字取出 + """ + _min = int(5e4) + _max = int(1e5) + # print(len(remain_txt_to_cut), len(remain_txt_to_cut_storage)) + if len(remain_txt_to_cut) < _min and len(remain_txt_to_cut_storage) > 0: + remain_txt_to_cut = remain_txt_to_cut + remain_txt_to_cut_storage + remain_txt_to_cut_storage = "" + if len(remain_txt_to_cut) > _max: + remain_txt_to_cut_storage = remain_txt_to_cut[_max:] + remain_txt_to_cut_storage + remain_txt_to_cut = remain_txt_to_cut[:_max] + return remain_txt_to_cut, remain_txt_to_cut_storage + + +def cut(limit, get_token_fn, txt_tocut, must_break_at_empty_line, break_anyway=False): + """ 文本切分 + """ + res = [] + total_len = len(txt_tocut) + fin_len = 0 + remain_txt_to_cut = txt_tocut + remain_txt_to_cut_storage = "" + # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage + remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage) + + while True: + if get_token_fn(remain_txt_to_cut) <= limit: + # 如果剩余文本的token数小于限制，那么就不用切了 + res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut) + break + else: + # 如果剩余文本的token数大于限制，那么就切 + lines = remain_txt_to_cut.split('\n') + + # 估计一个切分点 + estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines) + estimated_line_cut = int(estimated_line_cut) + + # 开始查找合适切分点的偏移（cnt） + cnt = 0 + for cnt in reversed(range(estimated_line_cut)): + if must_break_at_empty_line: + # 首先尝试用双空行（\n\n）作为切分点 + if lines[cnt] != "": + continue + prev = "\n".join(lines[:cnt]) + post = "\n".join(lines[cnt:]) + if get_token_fn(prev) < limit: + break + + if cnt == 0: + # 如果没有找到合适的切分点 + if break_anyway: + # 是否允许暴力切分 + prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn) + else: + # 不允许直接报错 + raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}") + + # 追加列表 + res.append(prev); fin_len+=len(prev) + # 准备下一次迭代 + remain_txt_to_cut = post + remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage) + process = fin_len/total_len + logger.info(f'正在文本切分 {int(process*100)}%') + if len(remain_txt_to_cut.strip()) == 0: + break + return res + + +def breakdown_text_to_satisfy_token_limit_(txt, limit, llm_model="gpt-3.5-turbo"): + """ 使用多种方式尝试切分文本，以满足 token 限制 + """ + from request_llms.bridge_all import model_info + enc = model_info[llm_model]['tokenizer'] + def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=())) + try: + # 第1次尝试，将双空行（\n\n）作为切分点 + return cut(limit, get_token_fn, txt, must_break_at_empty_line=True) + except RuntimeError: + try: + # 第2次尝试，将单空行（\n）作为切分点 + return cut(limit, get_token_fn, txt, must_break_at_empty_line=False) + except RuntimeError: + try: + # 第3次尝试，将英文句号（.）作为切分点 + res = cut(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在 + return [r.replace('。\n', '.') for r in res] + except RuntimeError as e: + try: + # 第4次尝试，将中文句号（。）作为切分点 + res = cut(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False) + return [r.replace('。。\n', '。') for r in res] + except RuntimeError as e: + # 第5次尝试，没办法了，随便切一下吧 + return cut(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True) + +breakdown_text_to_satisfy_token_limit = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_, timeout=60) + +def cut_new(limit, get_token_fn, txt_tocut, must_break_at_empty_line, must_break_at_one_empty_line=False, break_anyway=False): + """ 文本切分 + """ + res = [] + res_empty_line = [] + total_len = len(txt_tocut) + fin_len = 0 + remain_txt_to_cut = txt_tocut + remain_txt_to_cut_storage = "" + # 为了加速计算，我们采样一个特殊的手段。当 remain_txt_to_cut > `_max` 时， 我们把 _max 后的文字转存至 remain_txt_to_cut_storage + remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage) + empty=0 + + while True: + if get_token_fn(remain_txt_to_cut) <= limit: + # 如果剩余文本的token数小于限制，那么就不用切了 + res.append(remain_txt_to_cut); fin_len+=len(remain_txt_to_cut) + res_empty_line.append(empty) + break + else: + # 如果剩余文本的token数大于限制，那么就切 + lines = remain_txt_to_cut.split('\n') + + # 估计一个切分点 + estimated_line_cut = limit / get_token_fn(remain_txt_to_cut) * len(lines) + estimated_line_cut = int(estimated_line_cut) + + # 开始查找合适切分点的偏移（cnt） + cnt = 0 + for cnt in reversed(range(estimated_line_cut)): + if must_break_at_empty_line: + # 首先尝试用双空行（\n\n）作为切分点 + if lines[cnt] != "": + continue + if must_break_at_empty_line or must_break_at_one_empty_line: + empty=1 + prev = "\n".join(lines[:cnt]) + post = "\n".join(lines[cnt:]) + if get_token_fn(prev) < limit : + break + # empty=0 + if get_token_fn(prev)>limit: + if '.' not in prev or '。' not in prev: + # empty = 0 + break + + # if cnt + if cnt == 0: + # 如果没有找到合适的切分点 + if break_anyway: + # 是否允许暴力切分 + prev, post = force_breakdown(remain_txt_to_cut, limit, get_token_fn) + empty =0 + else: + # 不允许直接报错 + raise RuntimeError(f"存在一行极长的文本！{remain_txt_to_cut}") + + # 追加列表 + res.append(prev); fin_len+=len(prev) + res_empty_line.append(empty) + # 准备下一次迭代 + remain_txt_to_cut = post + remain_txt_to_cut, remain_txt_to_cut_storage = maintain_storage(remain_txt_to_cut, remain_txt_to_cut_storage) + process = fin_len/total_len + logger.info(f'正在文本切分 {int(process*100)}%') + if len(remain_txt_to_cut.strip()) == 0: + break + return res,res_empty_line + + +def breakdown_text_to_satisfy_token_limit_new_(txt, limit, llm_model="gpt-3.5-turbo"): + """ 使用多种方式尝试切分文本，以满足 token 限制 + """ + from request_llms.bridge_all import model_info + enc = model_info[llm_model]['tokenizer'] + def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=())) + try: + # 第1次尝试，将双空行（\n\n）作为切分点 + res, empty_line =cut_new(limit, get_token_fn, txt, must_break_at_empty_line=True) + return res,empty_line + except RuntimeError: + try: + # 第2次尝试，将单空行（\n）作为切分点 + res, _ = cut_new(limit, get_token_fn, txt, must_break_at_empty_line=False,must_break_at_one_empty_line=True) + return res, _ + except RuntimeError: + try: + # 第3次尝试，将英文句号（.）作为切分点 + res, _ = cut_new(limit, get_token_fn, txt.replace('.', '。\n'), must_break_at_empty_line=False) # 这个中文的句号是故意的，作为一个标识而存在 + return [r.replace('。\n', '.') for r in res],_ + + except RuntimeError as e: + try: + # 第4次尝试，将中文句号（。）作为切分点 + res,_ = cut_new(limit, get_token_fn, txt.replace('。', '。。\n'), must_break_at_empty_line=False) + return [r.replace('。。\n', '。') for r in res], _ + except RuntimeError as e: + # 第5次尝试，没办法了，随便切一下吧 + res, _ = cut_new(limit, get_token_fn, txt, must_break_at_empty_line=False, break_anyway=True) + return res,_ +breakdown_text_to_satisfy_token_limit_new = run_in_subprocess_with_timeout(breakdown_text_to_satisfy_token_limit_new_, timeout=60) + +def cut_from_end_to_satisfy_token_limit_(txt, limit, reserve_token=500, llm_model="gpt-3.5-turbo"): + """从后往前裁剪文本，以论文为单位进行裁剪 + + 参数： + txt: 要处理的文本（格式化后的论文列表字符串） + limit: token数量上限 + reserve_token: 需要预留的token数量，默认500 + llm_model: 使用的模型名称 + 返回： + 裁剪后的文本 + """ + from request_llms.bridge_all import model_info + enc = model_info[llm_model]['tokenizer'] + def get_token_fn(txt): return len(enc.encode(txt, disallowed_special=())) + + # 计算当前文本的token数 + current_tokens = get_token_fn(txt) + target_limit = limit - reserve_token + + # 如果当前token数已经在限制范围内，直接返回 + if current_tokens <= target_limit: + return txt + + # 按论文编号分割文本 + papers = re.split(r'\n(?=\d+\. \*\*)', txt) + if not papers: + return txt + + # 从前往后累加论文，直到达到token限制 + result = papers[0] # 保留第一篇 + current_tokens = get_token_fn(result) + + for paper in papers[1:]: + paper_tokens = get_token_fn(paper) + if current_tokens + paper_tokens <= target_limit: + result += "\n" + paper + current_tokens += paper_tokens + else: + break + + return result + +# 添加超时保护 +cut_from_end_to_satisfy_token_limit = run_in_subprocess_with_timeout(cut_from_end_to_satisfy_token_limit_, timeout=20) + +if __name__ == '__main__': + from crazy_functions.crazy_utils import read_and_clean_pdf_text + file_content, page_one = read_and_clean_pdf_text("build/assets/at.pdf") + + from request_llms.bridge_all import model_info + for i in range(5): + file_content += file_content + + logger.info(len(file_content)) + TOKEN_LIMIT_PER_FRAGMENT = 2500 + res = breakdown_text_to_satisfy_token_limit(file_content, TOKEN_LIMIT_PER_FRAGMENT) + diff --git a/crazy_functions/review_fns/__init__.py b/crazy_functions/review_fns/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/crazy_functions/review_fns/conversation_doc/endnote_doc.py b/crazy_functions/review_fns/conversation_doc/endnote_doc.py new file mode 100644 index 00000000..a3bb1331 --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/endnote_doc.py @@ -0,0 +1,68 @@ +from typing import List +from crazy_functions.review_fns.data_sources.base_source import PaperMetadata + +class EndNoteFormatter: + """EndNote参考文献格式生成器""" + + def __init__(self): + pass + + def create_document(self, papers: List[PaperMetadata]) -> str: + """生成EndNote格式的参考文献文本 + + Args: + papers: 论文列表 + + Returns: + str: EndNote格式的参考文献文本 + """ + endnote_text = "" + + for paper in papers: + # 开始一个新条目 + endnote_text += "%0 Journal Article\n" # 默认类型为期刊文章 + + # 根据venue_type调整条目类型 + if hasattr(paper, 'venue_type') and paper.venue_type: + if paper.venue_type.lower() == 'conference': + endnote_text = endnote_text.replace("Journal Article", "Conference Paper") + elif paper.venue_type.lower() == 'preprint': + endnote_text = endnote_text.replace("Journal Article", "Electronic Article") + + # 添加标题 + endnote_text += f"%T {paper.title}\n" + + # 添加作者 + for author in paper.authors: + endnote_text += f"%A {author}\n" + + # 添加年份 + if paper.year: + endnote_text += f"%D {paper.year}\n" + + # 添加期刊/会议名称 + if hasattr(paper, 'venue_name') and paper.venue_name: + endnote_text += f"%J {paper.venue_name}\n" + elif paper.venue: + endnote_text += f"%J {paper.venue}\n" + + # 添加DOI + if paper.doi: + endnote_text += f"%R {paper.doi}\n" + endnote_text += f"%U https://doi.org/{paper.doi}\n" + elif paper.url: + endnote_text += f"%U {paper.url}\n" + + # 添加摘要 + if paper.abstract: + endnote_text += f"%X {paper.abstract}\n" + + # 添加机构 + if hasattr(paper, 'institutions'): + for institution in paper.institutions: + endnote_text += f"%I {institution}\n" + + # 条目之间添加空行 + endnote_text += "\n" + + return endnote_text \ No newline at end of file diff --git a/crazy_functions/review_fns/conversation_doc/excel_doc.py b/crazy_functions/review_fns/conversation_doc/excel_doc.py new file mode 100644 index 00000000..755d18f3 --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/excel_doc.py @@ -0,0 +1,211 @@ +import re +import os +import pandas as pd +from datetime import datetime + + +class ExcelTableFormatter: + """聊天记录中Markdown表格转Excel生成器""" + + def __init__(self): + """初始化Excel文档对象""" + from openpyxl import Workbook + self.workbook = Workbook() + self._table_count = 0 + self._current_sheet = None + + def _normalize_table_row(self, row): + """标准化表格行，处理不同的分隔符情况""" + row = row.strip() + if row.startswith('|'): + row = row[1:] + if row.endswith('|'): + row = row[:-1] + return [cell.strip() for cell in row.split('|')] + + def _is_separator_row(self, row): + """检查是否是分隔行（由 - 或 : 组成）""" + clean_row = re.sub(r'[\s|]', '', row) + return bool(re.match(r'^[-:]+$', clean_row)) + + def _extract_tables_from_text(self, text): + """从文本中提取所有表格内容""" + if not isinstance(text, str): + return [] + + tables = [] + current_table = [] + is_in_table = False + + for line in text.split('\n'): + line = line.strip() + if not line: + if is_in_table and current_table: + if len(current_table) >= 2: + tables.append(current_table) + current_table = [] + is_in_table = False + continue + + if '|' in line: + if not is_in_table: + is_in_table = True + current_table.append(line) + else: + if is_in_table and current_table: + if len(current_table) >= 2: + tables.append(current_table) + current_table = [] + is_in_table = False + + if is_in_table and current_table and len(current_table) >= 2: + tables.append(current_table) + + return tables + + def _parse_table(self, table_lines): + """解析表格内容为结构化数据""" + try: + headers = self._normalize_table_row(table_lines[0]) + + separator_index = next( + (i for i, line in enumerate(table_lines) if self._is_separator_row(line)), + 1 + ) + + data_rows = [] + for line in table_lines[separator_index + 1:]: + cells = self._normalize_table_row(line) + # 确保单元格数量与表头一致 + while len(cells) < len(headers): + cells.append('') + cells = cells[:len(headers)] + data_rows.append(cells) + + if headers and data_rows: + return { + 'headers': headers, + 'data': data_rows + } + except Exception as e: + print(f"解析表格时发生错误: {str(e)}") + + return None + + def _create_sheet(self, question_num, table_num): + """创建新的工作表""" + sheet_name = f'Q{question_num}_T{table_num}' + if len(sheet_name) > 31: + sheet_name = f'Table{self._table_count}' + + if sheet_name in self.workbook.sheetnames: + sheet_name = f'{sheet_name}_{datetime.now().strftime("%H%M%S")}' + + return self.workbook.create_sheet(title=sheet_name) + + def create_document(self, history): + """ + 处理聊天历史中的所有表格并创建Excel文档 + + Args: + history: 聊天历史列表 + + Returns: + Workbook: 处理完成的Excel工作簿对象，如果没有表格则返回None + """ + has_tables = False + + # 删除默认创建的工作表 + default_sheet = self.workbook['Sheet'] + self.workbook.remove(default_sheet) + + # 遍历所有回答 + for i in range(1, len(history), 2): + answer = history[i] + tables = self._extract_tables_from_text(answer) + + for table_lines in tables: + parsed_table = self._parse_table(table_lines) + if parsed_table: + self._table_count += 1 + sheet = self._create_sheet(i // 2 + 1, self._table_count) + + # 写入表头 + for col, header in enumerate(parsed_table['headers'], 1): + sheet.cell(row=1, column=col, value=header) + + # 写入数据 + for row_idx, row_data in enumerate(parsed_table['data'], 2): + for col_idx, value in enumerate(row_data, 1): + sheet.cell(row=row_idx, column=col_idx, value=value) + + has_tables = True + + return self.workbook if has_tables else None + + +def save_chat_tables(history, save_dir, base_name): + """ + 保存聊天历史中的表格到Excel文件 + + Args: + history: 聊天历史列表 + save_dir: 保存目录 + base_name: 基础文件名 + + Returns: + list: 保存的文件路径列表 + """ + result_files = [] + + try: + # 创建Excel格式 + excel_formatter = ExcelTableFormatter() + workbook = excel_formatter.create_document(history) + + if workbook is not None: + # 确保保存目录存在 + os.makedirs(save_dir, exist_ok=True) + + # 生成Excel文件路径 + excel_file = os.path.join(save_dir, base_name + '.xlsx') + + # 保存Excel文件 + workbook.save(excel_file) + result_files.append(excel_file) + print(f"已保存表格到Excel文件: {excel_file}") + except Exception as e: + print(f"保存Excel格式失败: {str(e)}") + + return result_files + + +# 使用示例 +if __name__ == "__main__": + # 示例聊天历史 + history = [ + "问题1", + """这是第一个表格： + | A | B | C | + |---|---|---| + | 1 | 2 | 3 |""", + + "问题2", + "这是没有表格的回答", + + "问题3", + """回答包含多个表格： + | Name | Age | + |------|-----| + | Tom | 20 | + + 第二个表格： + | X | Y | + |---|---| + | 1 | 2 |""" + ] + + # 保存表格 + save_dir = "output" + base_name = "chat_tables" + saved_files = save_chat_tables(history, save_dir, base_name) \ No newline at end of file diff --git a/crazy_functions/review_fns/conversation_doc/html_doc.py b/crazy_functions/review_fns/conversation_doc/html_doc.py new file mode 100644 index 00000000..52b39162 --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/html_doc.py @@ -0,0 +1,472 @@ +class HtmlFormatter: + """聊天记录HTML格式生成器""" + + def __init__(self): + self.css_styles = """ + :root { + --primary-color: #2563eb; + --primary-light: #eff6ff; + --secondary-color: #1e293b; + --background-color: #f8fafc; + --text-color: #334155; + --border-color: #e2e8f0; + --card-shadow: 0 4px 6px -1px rgb(0 0 0 / 0.1), 0 2px 4px -2px rgb(0 0 0 / 0.1); + } + + body { + font-family: system-ui, -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; + line-height: 1.8; + margin: 0; + padding: 2rem; + color: var(--text-color); + background-color: var(--background-color); + } + + .container { + max-width: 1200px; + margin: 0 auto; + background: white; + padding: 2rem; + border-radius: 16px; + box-shadow: var(--card-shadow); + } + ::selection { + background: var(--primary-light); + color: var(--primary-color); + } + @keyframes fadeIn { + from { opacity: 0; transform: translateY(20px); } + to { opacity: 1; transform: translateY(0); } + } + + @keyframes slideIn { + from { transform: translateX(-20px); opacity: 0; } + to { transform: translateX(0); opacity: 1; } + } + + .container { + animation: fadeIn 0.6s ease-out; + } + + .QaBox { + animation: slideIn 0.5s ease-out; + transition: all 0.3s ease; + } + + .QaBox:hover { + transform: translateX(5px); + } + .Question, .Answer, .historyBox { + transition: all 0.3s ease; + } + .chat-title { + color: var(--primary-color); + font-size: 2em; + text-align: center; + margin: 1rem 0 2rem; + padding-bottom: 1rem; + border-bottom: 2px solid var(--primary-color); + } + + .chat-body { + display: flex; + flex-direction: column; + gap: 1.5rem; + margin: 2rem 0; + } + + .QaBox { + background: white; + padding: 1.5rem; + border-radius: 8px; + border-left: 4px solid var(--primary-color); + box-shadow: 0 2px 4px rgba(0, 0, 0, 0.1); + margin-bottom: 1.5rem; + } + + .Question { + color: var(--secondary-color); + font-weight: 500; + margin-bottom: 1rem; + } + + .Answer { + color: var(--text-color); + background: var(--primary-light); + padding: 1rem; + border-radius: 6px; + } + + .history-section { + margin-top: 3rem; + padding-top: 2rem; + border-top: 2px solid var(--border-color); + } + + .history-title { + color: var(--secondary-color); + font-size: 1.5em; + margin-bottom: 1.5rem; + text-align: center; + } + + .historyBox { + background: white; + padding: 1rem; + margin: 0.5rem 0; + border-radius: 6px; + border: 1px solid var(--border-color); + } + + @media (prefers-color-scheme: dark) { + :root { + --background-color: #0f172a; + --text-color: #e2e8f0; + --border-color: #1e293b; + } + + .container, .QaBox { + background: #1e293b; + } + } + """ + + def create_document(self, question: str, answer: str, ranked_papers: list = None) -> str: + """生成完整的HTML文档 + Args: + question: str, 用户问题 + answer: str, AI回答 + ranked_papers: list, 排序后的论文列表 + Returns: + str: 完整的HTML文档字符串 + """ + chat_content = f''' +
+
{question}
+
{answer}
+
+ ''' + + references_content = "" + if ranked_papers: + references_content = '

参考文献

' + for idx, paper in enumerate(ranked_papers, 1): + authors = ', '.join(paper.authors) + + # 构建引用信息 + citations_info = f"被引用次数：{paper.citations}" if paper.citations is not None else "引用信息未知" + + # 构建下载链接 + download_links = [] + if paper.doi: + # 检查是否是arXiv链接 + if 'arxiv.org' in paper.doi: + # 如果DOI中包含完整的arXiv URL，直接使用 + arxiv_url = paper.doi if paper.doi.startswith('http') else f'http://{paper.doi}' + download_links.append(f'arXiv链接') + # 提取arXiv ID并添加PDF链接 + arxiv_id = arxiv_url.split('abs/')[-1].split('v')[0] + download_links.append(f'PDF下载') + else: + # 非arXiv的DOI使用标准格式 + download_links.append(f'DOI: {paper.doi}') + + if hasattr(paper, 'url') and paper.url and 'arxiv.org' not in str(paper.url): + # 只有当URL不是arXiv链接时才添加 + download_links.append(f'原文链接') + download_section = ' | '.join(download_links) if download_links else "无直接下载链接" + + # 构建来源信息 + source_info = [] + if paper.venue_type: + source_info.append(f"类型：{paper.venue_type}") + if paper.venue_name: + source_info.append(f"来源：{paper.venue_name}") + + # 添加期刊指标信息 + if hasattr(paper, 'if_factor') and paper.if_factor: + source_info.append(f"IF: {paper.if_factor}") + if hasattr(paper, 'jcr_division') and paper.jcr_division: + source_info.append(f"JCR分区: {paper.jcr_division}") + if hasattr(paper, 'cas_division') and paper.cas_division: + source_info.append(f"中科院分区: {paper.cas_division}") + + if hasattr(paper, 'venue_info') and paper.venue_info: + if paper.venue_info.get('journal_ref'): + source_info.append(f"期刊参考：{paper.venue_info['journal_ref']}") + if paper.venue_info.get('publisher'): + source_info.append(f"出版商：{paper.venue_info['publisher']}") + source_section = ' | '.join(source_info) if source_info else "" + + # 构建标准引用格式 + standard_citation = f"[{idx}] " + # 添加作者（最多3个，超过则添加et al.） + author_list = paper.authors[:3] + if len(paper.authors) > 3: + author_list.append("et al.") + standard_citation += ", ".join(author_list) + ". " + # 添加标题 + standard_citation += f"{paper.title}" + # 添加期刊/会议名称 + if paper.venue_name: + standard_citation += f". {paper.venue_name}" + # 添加年份 + if paper.year: + standard_citation += f", {paper.year}" + # 添加DOI + if paper.doi: + if 'arxiv.org' in paper.doi: + # 如果是arXiv链接，直接使用arXiv URL + arxiv_url = paper.doi if paper.doi.startswith('http') else f'http://{paper.doi}' + standard_citation += f". {arxiv_url}" + else: + # 非arXiv的DOI使用标准格式 + standard_citation += f". DOI: {paper.doi}" + standard_citation += "." + + references_content += f''' +
+
+

[{idx}] {paper.title}

+

作者：{authors}

+

发表年份：{paper.year if paper.year else "未知"}

+

{citations_info}

+ {f'

{source_section}

' if source_section else ""} +

摘要：{paper.abstract if paper.abstract else "无摘要"}

+

链接：{download_section}

+
+

标准引用格式：

+

{standard_citation}

+ +
+
+
+ ''' + references_content += '
' + + # 添加新的CSS样式 + css_additions = """ + .paper-title { + font-size: 1.1em; + margin-bottom: 0.5em; + } + .paper-authors { + color: var(--secondary-color); + margin: 0.3em 0; + } + .paper-year, .paper-citations { + color: var(--text-color); + margin: 0.3em 0; + } + .paper-source { + color: var(--text-color); + font-style: italic; + margin: 0.3em 0; + } + .paper-abstract { + margin: 0.8em 0; + padding: 0.8em; + background: var(--primary-light); + border-radius: 4px; + } + .paper-links { + margin-top: 0.5em; + } + .paper-links a { + color: var(--primary-color); + text-decoration: none; + margin-right: 1em; + } + .paper-links a:hover { + text-decoration: underline; + } + .standard-citation { + margin-top: 1em; + padding: 1em; + background: #f8fafc; + border-radius: 4px; + border: 1px solid var(--border-color); + } + + .citation-title { + font-weight: bold; + margin-bottom: 0.5em; + color: var(--secondary-color); + } + + .citation-text { + font-family: 'Times New Roman', Times, serif; + line-height: 1.6; + margin-bottom: 0.5em; + padding: 0.5em; + background: white; + border-radius: 4px; + border: 1px solid var(--border-color); + } + + .copy-btn { + background: var(--primary-color); + color: white; + border: none; + padding: 0.5em 1em; + border-radius: 4px; + cursor: pointer; + font-size: 0.9em; + transition: background-color 0.2s; + } + + .copy-btn:hover { + background: #1e40af; + } + + @media (prefers-color-scheme: dark) { + .standard-citation { + background: #1e293b; + } + .citation-text { + background: #0f172a; + } + } + + /* 添加期刊指标样式 */ + .journal-metric { + display: inline-block; + padding: 0.2em 0.6em; + margin: 0 0.3em; + background: var(--primary-light); + border-radius: 4px; + font-weight: 500; + color: var(--primary-color); + } + + @media (prefers-color-scheme: dark) { + .journal-metric { + background: #1e293b; + color: #60a5fa; + } + } + """ + + # 修改 js_code 部分，添加 markdown 解析功能 + js_code = """ + + """ + + # 将新的CSS样式添加到现有样式中 + self.css_styles += css_additions + + return f""" + + + + + + 学术对话存档 + + + + + + + +
+

学术对话存档

+
+ {chat_content} + {references_content} +
+
+ {js_code} + + + """ \ No newline at end of file diff --git a/crazy_functions/review_fns/conversation_doc/markdown_doc.py b/crazy_functions/review_fns/conversation_doc/markdown_doc.py new file mode 100644 index 00000000..778c19e3 --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/markdown_doc.py @@ -0,0 +1,47 @@ +class MarkdownFormatter: + """Markdown格式文档生成器 - 用于生成对话记录的markdown文档""" + + def __init__(self): + self.content = [] + + def _add_content(self, text: str): + """添加正文内容""" + if text: + self.content.append(f"\n{text}\n") + + def create_document(self, question: str, answer: str, ranked_papers: list = None) -> str: + """创建完整的Markdown文档 + Args: + question: str, 用户问题 + answer: str, AI回答 + ranked_papers: list, 排序后的论文列表 + Returns: + str: 生成的Markdown文本 + """ + content = [] + + # 添加问答部分 + content.append("## 问题") + content.append(question) + content.append("\n## 回答") + content.append(answer) + + # 添加参考文献 + if ranked_papers: + content.append("\n## 参考文献") + for idx, paper in enumerate(ranked_papers, 1): + authors = ', '.join(paper.authors[:3]) + if len(paper.authors) > 3: + authors += ' et al.' + + ref = f"[{idx}] {authors}. *{paper.title}*" + if paper.venue_name: + ref += f". {paper.venue_name}" + if paper.year: + ref += f", {paper.year}" + if paper.doi: + ref += f". [DOI: {paper.doi}](https://doi.org/{paper.doi})" + + content.append(ref) + + return "\n\n".join(content) diff --git a/crazy_functions/review_fns/conversation_doc/reference_formatter.py b/crazy_functions/review_fns/conversation_doc/reference_formatter.py new file mode 100644 index 00000000..96344768 --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/reference_formatter.py @@ -0,0 +1,174 @@ +from typing import List +from crazy_functions.review_fns.data_sources.base_source import PaperMetadata +import re + +class ReferenceFormatter: + """通用参考文献格式生成器""" + + def __init__(self): + pass + + def _sanitize_bibtex(self, text: str) -> str: + """清理BibTeX字符串，处理特殊字符""" + if not text: + return "" + + # 替换特殊字符 + replacements = { + '&': '\\&', + '%': '\\%', + '$': '\\$', + '#': '\\#', + '_': '\\_', + '{': '\\{', + '}': '\\}', + '~': '\\textasciitilde{}', + '^': '\\textasciicircum{}', + '\\': '\\textbackslash{}', + '<': '\\textless{}', + '>': '\\textgreater{}', + '"': '``', + "'": "'", + '-': '--', + '—': '---', + } + + for char, replacement in replacements.items(): + text = text.replace(char, replacement) + + return text + + def _generate_cite_key(self, paper: PaperMetadata) -> str: + """生成引用键 + 格式: 第一作者姓氏_年份_第一个实词 + """ + # 获取第一作者姓氏 + first_author = "" + if paper.authors and len(paper.authors) > 0: + first_author = paper.authors[0].split()[-1].lower() + + # 获取年份 + year = str(paper.year) if paper.year else "0000" + + # 从标题中获取第一个实词 + title_word = "" + if paper.title: + # 移除特殊字符，分割成单词 + words = re.findall(r'\w+', paper.title.lower()) + # 过滤掉常见的停用词 + stop_words = {'a', 'an', 'the', 'in', 'on', 'at', 'to', 'for', 'of', 'with', 'by'} + for word in words: + if word not in stop_words and len(word) > 2: + title_word = word + break + + # 组合cite key + cite_key = f"{first_author}{year}{title_word}" + + # 确保cite key只包含合法字符 + cite_key = re.sub(r'[^a-z0-9]', '', cite_key.lower()) + + return cite_key + + def _get_entry_type(self, paper: PaperMetadata) -> str: + """确定BibTeX条目类型""" + if hasattr(paper, 'venue_type') and paper.venue_type: + venue_type = paper.venue_type.lower() + if venue_type == 'conference': + return 'inproceedings' + elif venue_type == 'preprint': + return 'unpublished' + elif venue_type == 'journal': + return 'article' + elif venue_type == 'book': + return 'book' + elif venue_type == 'thesis': + return 'phdthesis' + return 'article' # 默认为期刊文章 + + + def create_document(self, papers: List[PaperMetadata]) -> str: + """生成BibTeX格式的参考文献文本""" + bibtex_text = "% This file was automatically generated by GPT-Academic\n" + bibtex_text += "% Compatible with: EndNote, Zotero, JabRef, and LaTeX\n\n" + + for paper in papers: + entry_type = self._get_entry_type(paper) + cite_key = self._generate_cite_key(paper) + + bibtex_text += f"@{entry_type}{{{cite_key},\n" + + # 添加标题 + if paper.title: + bibtex_text += f" title = {{{self._sanitize_bibtex(paper.title)}}},\n" + + # 添加作者 + if paper.authors: + # 确保每个作者的姓和名正确分隔 + processed_authors = [] + for author in paper.authors: + names = author.split() + if len(names) > 1: + # 假设最后一个词是姓，其他的是名 + surname = names[-1] + given_names = ' '.join(names[:-1]) + processed_authors.append(f"{surname}, {given_names}") + else: + processed_authors.append(author) + + authors = " and ".join([self._sanitize_bibtex(author) for author in processed_authors]) + bibtex_text += f" author = {{{authors}}},\n" + + # 添加年份 + if paper.year: + bibtex_text += f" year = {{{paper.year}}},\n" + + # 添加期刊/会议名称 + if hasattr(paper, 'venue_name') and paper.venue_name: + if entry_type == 'inproceedings': + bibtex_text += f" booktitle = {{{self._sanitize_bibtex(paper.venue_name)}}},\n" + elif entry_type == 'article': + bibtex_text += f" journal = {{{self._sanitize_bibtex(paper.venue_name)}}},\n" + # 添加期刊相关信息 + if hasattr(paper, 'venue_info'): + if 'volume' in paper.venue_info: + bibtex_text += f" volume = {{{paper.venue_info['volume']}}},\n" + if 'number' in paper.venue_info: + bibtex_text += f" number = {{{paper.venue_info['number']}}},\n" + if 'pages' in paper.venue_info: + bibtex_text += f" pages = {{{paper.venue_info['pages']}}},\n" + elif paper.venue: + venue_field = "booktitle" if entry_type == "inproceedings" else "journal" + bibtex_text += f" {venue_field} = {{{self._sanitize_bibtex(paper.venue)}}},\n" + + # 添加DOI + if paper.doi: + bibtex_text += f" doi = {{{paper.doi}}},\n" + + # 添加URL + if paper.url: + bibtex_text += f" url = {{{paper.url}}},\n" + elif paper.doi: + bibtex_text += f" url = {{https://doi.org/{paper.doi}}},\n" + + # 添加摘要 + if paper.abstract: + bibtex_text += f" abstract = {{{self._sanitize_bibtex(paper.abstract)}}},\n" + + # 添加机构 + if hasattr(paper, 'institutions') and paper.institutions: + institutions = " and ".join([self._sanitize_bibtex(inst) for inst in paper.institutions]) + bibtex_text += f" institution = {{{institutions}}},\n" + + # 添加月份 + if hasattr(paper, 'month'): + bibtex_text += f" month = {{{paper.month}}},\n" + + # 添加注释字段 + if hasattr(paper, 'note'): + bibtex_text += f" note = {{{self._sanitize_bibtex(paper.note)}}},\n" + + # 移除最后一个逗号并关闭条目 + bibtex_text = bibtex_text.rstrip(',\n') + "\n}\n\n" + + return bibtex_text \ No newline at end of file diff --git a/crazy_functions/review_fns/conversation_doc/word2pdf.py b/crazy_functions/review_fns/conversation_doc/word2pdf.py new file mode 100644 index 00000000..3c499b5e --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/word2pdf.py @@ -0,0 +1,138 @@ +from docx2pdf import convert +import os +import platform +from typing import Union +from pathlib import Path +from datetime import datetime + +class WordToPdfConverter: + """Word文档转PDF转换器""" + + @staticmethod + def _replace_docx_in_filename(filename: Union[str, Path]) -> Path: + """ + 将文件名中的'docx'替换为'pdf' + 例如: 'docx_test.pdf' -> 'pdf_test.pdf' + """ + path = Path(filename) + new_name = path.stem.replace('docx', 'pdf') + return path.parent / f"{new_name}{path.suffix}" + + @staticmethod + def convert_to_pdf(word_path: Union[str, Path], pdf_path: Union[str, Path] = None) -> str: + """ + 将Word文档转换为PDF + + 参数: + word_path: Word文档的路径 + pdf_path: 可选，PDF文件的输出路径。如果未指定，将使用与Word文档相同的名称和位置 + + 返回: + 生成的PDF文件路径 + + 异常: + 如果转换失败，将抛出相应异常 + """ + try: + word_path = Path(word_path) + + if pdf_path is None: + # 创建新的pdf路径，同时替换文件名中的docx + pdf_path = WordToPdfConverter._replace_docx_in_filename(word_path).with_suffix('.pdf') + else: + pdf_path = WordToPdfConverter._replace_docx_in_filename(Path(pdf_path)) + + # 检查操作系统 + if platform.system() == 'Linux': + # Linux系统需要安装libreoffice + if not os.system('which libreoffice') == 0: + raise RuntimeError("请先安装LibreOffice: sudo apt-get install libreoffice") + + # 使用libreoffice进行转换 + os.system(f'libreoffice --headless --convert-to pdf "{word_path}" --outdir "{pdf_path.parent}"') + + # 如果输出路径与默认生成的不同，则重命名 + default_pdf = word_path.with_suffix('.pdf') + if default_pdf != pdf_path: + os.rename(default_pdf, pdf_path) + else: + # Windows和MacOS使用 docx2pdf + convert(word_path, pdf_path) + + return str(pdf_path) + + except Exception as e: + raise Exception(f"转换PDF失败: {str(e)}") + + @staticmethod + def batch_convert(word_dir: Union[str, Path], pdf_dir: Union[str, Path] = None) -> list: + """ + 批量转换目录下的所有Word文档 + + 参数: + word_dir: 包含Word文档的目录路径 + pdf_dir: 可选，PDF文件的输出目录。如果未指定，将使用与Word文档相同的目录 + + 返回: + 生成的PDF文件路径列表 + """ + word_dir = Path(word_dir) + if pdf_dir: + pdf_dir = Path(pdf_dir) + pdf_dir.mkdir(parents=True, exist_ok=True) + + converted_files = [] + + for word_file in word_dir.glob("*.docx"): + try: + if pdf_dir: + pdf_path = pdf_dir / WordToPdfConverter._replace_docx_in_filename( + word_file.with_suffix('.pdf') + ).name + else: + pdf_path = WordToPdfConverter._replace_docx_in_filename( + word_file.with_suffix('.pdf') + ) + + pdf_file = WordToPdfConverter.convert_to_pdf(word_file, pdf_path) + converted_files.append(pdf_file) + + except Exception as e: + print(f"转换 {word_file} 失败: {str(e)}") + + return converted_files + + @staticmethod + def convert_doc_to_pdf(doc, output_dir: Union[str, Path] = None) -> str: + """ + 将docx对象直接转换为PDF + + 参数: + doc: python-docx的Document对象 + output_dir: 可选，输出目录。如果未指定，将使用当前目录 + + 返回: + 生成的PDF文件路径 + """ + try: + # 设置临时文件路径和输出路径 + output_dir = Path(output_dir) if output_dir else Path.cwd() + output_dir.mkdir(parents=True, exist_ok=True) + + # 生成临时word文件 + temp_docx = output_dir / f"temp_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx" + doc.save(temp_docx) + + # 转换为PDF + pdf_path = temp_docx.with_suffix('.pdf') + WordToPdfConverter.convert_to_pdf(temp_docx, pdf_path) + + # 删除临时word文件 + temp_docx.unlink() + + return str(pdf_path) + + except Exception as e: + if temp_docx.exists(): + temp_docx.unlink() + raise Exception(f"转换PDF失败: {str(e)}") \ No newline at end of file diff --git a/crazy_functions/review_fns/conversation_doc/word_doc.py b/crazy_functions/review_fns/conversation_doc/word_doc.py new file mode 100644 index 00000000..6bae7d9a --- /dev/null +++ b/crazy_functions/review_fns/conversation_doc/word_doc.py @@ -0,0 +1,246 @@ +import re +from docx import Document +from docx.shared import Cm, Pt +from docx.enum.text import WD_PARAGRAPH_ALIGNMENT, WD_LINE_SPACING +from docx.enum.style import WD_STYLE_TYPE +from docx.oxml.ns import qn +from datetime import datetime +import docx +from docx.oxml import shared +from crazy_functions.doc_fns.conversation_doc.word_doc import convert_markdown_to_word + + +class WordFormatter: + """聊天记录Word文档生成器 - 符合中国政府公文格式规范(GB/T 9704-2012)""" + + def __init__(self): + self.doc = Document() + self._setup_document() + self._create_styles() + + def _setup_document(self): + """设置文档基本格式，包括页面设置和页眉""" + sections = self.doc.sections + for section in sections: + # 设置页面大小为A4 + section.page_width = Cm(21) + section.page_height = Cm(29.7) + # 设置页边距 + section.top_margin = Cm(3.7) # 上边距37mm + section.bottom_margin = Cm(3.5) # 下边距35mm + section.left_margin = Cm(2.8) # 左边距28mm + section.right_margin = Cm(2.6) # 右边距26mm + # 设置页眉页脚距离 + section.header_distance = Cm(2.0) + section.footer_distance = Cm(2.0) + + # 修改页眉 + header = section.header + header_para = header.paragraphs[0] + header_para.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER + header_run = header_para.add_run("GPT-Academic学术对话 (体验地址：https://auth.gpt-academic.top/)") + header_run.font.name = '仿宋' + header_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋') + header_run.font.size = Pt(9) + + def _create_styles(self): + """创建文档样式""" + # 创建正文样式 + style = self.doc.styles.add_style('Normal_Custom', WD_STYLE_TYPE.PARAGRAPH) + style.font.name = '仿宋' + style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋') + style.font.size = Pt(12) + style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE + style.paragraph_format.space_after = Pt(0) + + # 创建问题样式 + question_style = self.doc.styles.add_style('Question_Style', WD_STYLE_TYPE.PARAGRAPH) + question_style.font.name = '黑体' + question_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体') + question_style.font.size = Pt(14) # 调整为14磅 + question_style.font.bold = True + question_style.paragraph_format.space_before = Pt(12) # 减小段前距 + question_style.paragraph_format.space_after = Pt(6) + question_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE + question_style.paragraph_format.left_indent = Pt(0) # 移除左缩进 + + # 创建回答样式 + answer_style = self.doc.styles.add_style('Answer_Style', WD_STYLE_TYPE.PARAGRAPH) + answer_style.font.name = '仿宋' + answer_style._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋') + answer_style.font.size = Pt(12) # 调整为12磅 + answer_style.paragraph_format.space_before = Pt(6) + answer_style.paragraph_format.space_after = Pt(12) + answer_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE + answer_style.paragraph_format.left_indent = Pt(0) # 移除左缩进 + + # 创建标题样式 + title_style = self.doc.styles.add_style('Title_Custom', WD_STYLE_TYPE.PARAGRAPH) + title_style.font.name = '黑体' # 改用黑体 + title_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体') + title_style.font.size = Pt(22) # 调整为22磅 + title_style.font.bold = True + title_style.paragraph_format.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER + title_style.paragraph_format.space_before = Pt(0) + title_style.paragraph_format.space_after = Pt(24) + title_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE + + # 添加参考文献样式 + ref_style = self.doc.styles.add_style('Reference_Style', WD_STYLE_TYPE.PARAGRAPH) + ref_style.font.name = '宋体' + ref_style._element.rPr.rFonts.set(qn('w:eastAsia'), '宋体') + ref_style.font.size = Pt(10.5) # 参考文献使用小号字体 + ref_style.paragraph_format.space_before = Pt(3) + ref_style.paragraph_format.space_after = Pt(3) + ref_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.SINGLE + ref_style.paragraph_format.left_indent = Pt(21) + ref_style.paragraph_format.first_line_indent = Pt(-21) + + # 添加参考文献标题样式 + ref_title_style = self.doc.styles.add_style('Reference_Title_Style', WD_STYLE_TYPE.PARAGRAPH) + ref_title_style.font.name = '黑体' + ref_title_style._element.rPr.rFonts.set(qn('w:eastAsia'), '黑体') + ref_title_style.font.size = Pt(16) # 参考文献标题与问题同样大小 + ref_title_style.font.bold = True + ref_title_style.paragraph_format.space_before = Pt(24) # 增加段前距 + ref_title_style.paragraph_format.space_after = Pt(12) + ref_title_style.paragraph_format.line_spacing_rule = WD_LINE_SPACING.ONE_POINT_FIVE + + def create_document(self, question: str, answer: str, ranked_papers: list = None): + """写入聊天历史 + Args: + question: str, 用户问题 + answer: str, AI回答 + ranked_papers: list, 排序后的论文列表 + """ + try: + # 添加标题 + title_para = self.doc.add_paragraph(style='Title_Custom') + title_run = title_para.add_run('GPT-Academic 对话记录') + + # 添加日期 + try: + date_para = self.doc.add_paragraph() + date_para.alignment = WD_PARAGRAPH_ALIGNMENT.CENTER + date_run = date_para.add_run(datetime.now().strftime('%Y年%m月%d日')) + date_run.font.name = '仿宋' + date_run._element.rPr.rFonts.set(qn('w:eastAsia'), '仿宋') + date_run.font.size = Pt(16) + except Exception as e: + print(f"添加日期失败: {str(e)}") + raise + + self.doc.add_paragraph() # 添加空行 + + # 添加问答对话 + try: + q_para = self.doc.add_paragraph(style='Question_Style') + q_para.add_run('问题：').bold = True + q_para.add_run(str(question)) + + a_para = self.doc.add_paragraph(style='Answer_Style') + a_para.add_run('回答：').bold = True + a_para.add_run(convert_markdown_to_word(str(answer))) + except Exception as e: + print(f"添加问答对话失败: {str(e)}") + raise + + # 添加参考文献部分 + if ranked_papers: + try: + ref_title = self.doc.add_paragraph(style='Reference_Title_Style') + ref_title.add_run("参考文献") + + for idx, paper in enumerate(ranked_papers, 1): + try: + ref_para = self.doc.add_paragraph(style='Reference_Style') + ref_para.add_run(f'[{idx}] ').bold = True + + # 添加作者 + authors = ', '.join(paper.authors[:3]) + if len(paper.authors) > 3: + authors += ' et al.' + ref_para.add_run(f'{authors}. ') + + # 添加标题 + title_run = ref_para.add_run(paper.title) + title_run.italic = True + if hasattr(paper, 'url') and paper.url: + try: + title_run._element.rPr.rStyle = self._create_hyperlink_style() + self._add_hyperlink(ref_para, paper.title, paper.url) + except Exception as e: + print(f"添加超链接失败: {str(e)}") + + # 添加期刊/会议信息 + if paper.venue_name: + ref_para.add_run(f'. {paper.venue_name}') + + # 添加年份 + if paper.year: + ref_para.add_run(f', {paper.year}') + + # 添加DOI + if paper.doi: + ref_para.add_run('. ') + if "arxiv" in paper.url: + doi_url = paper.doi + else: + doi_url = f'https://doi.org/{paper.doi}' + self._add_hyperlink(ref_para, f'DOI: {paper.doi}', doi_url) + + ref_para.add_run('.') + except Exception as e: + print(f"添加第 {idx} 篇参考文献失败: {str(e)}") + continue + except Exception as e: + print(f"添加参考文献部分失败: {str(e)}") + raise + + return self.doc + + except Exception as e: + print(f"Word文档创建失败: {str(e)}") + import traceback + print(f"详细错误信息: {traceback.format_exc()}") + raise + + def _create_hyperlink_style(self): + """创建超链接样式""" + styles = self.doc.styles + if 'Hyperlink' not in styles: + hyperlink_style = styles.add_style('Hyperlink', WD_STYLE_TYPE.CHARACTER) + # 使用科技蓝 (#0066CC) + hyperlink_style.font.color.rgb = 0x0066CC # 科技蓝 + hyperlink_style.font.underline = True + return styles['Hyperlink'] + + def _add_hyperlink(self, paragraph, text, url): + """添加超链接到段落""" + # 这个是在XML级别添加超链接 + part = paragraph.part + r_id = part.relate_to(url, docx.opc.constants.RELATIONSHIP_TYPE.HYPERLINK, is_external=True) + + # 创建超链接XML元素 + hyperlink = docx.oxml.shared.OxmlElement('w:hyperlink') + hyperlink.set(docx.oxml.shared.qn('r:id'), r_id) + + # 创建文本运行 + new_run = docx.oxml.shared.OxmlElement('w:r') + rPr = docx.oxml.shared.OxmlElement('w:rPr') + + # 应用超链接样式 + rStyle = docx.oxml.shared.OxmlElement('w:rStyle') + rStyle.set(docx.oxml.shared.qn('w:val'), 'Hyperlink') + rPr.append(rStyle) + + # 添加文本 + t = docx.oxml.shared.OxmlElement('w:t') + t.text = text + new_run.append(rPr) + new_run.append(t) + hyperlink.append(new_run) + + # 将超链接添加到段落 + paragraph._p.append(hyperlink) + diff --git a/crazy_functions/review_fns/data_sources/__init__.py b/crazy_functions/review_fns/data_sources/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/crazy_functions/review_fns/data_sources/adsabs_source.py b/crazy_functions/review_fns/data_sources/adsabs_source.py new file mode 100644 index 00000000..17fa6ba2 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/adsabs_source.py @@ -0,0 +1,279 @@ +from typing import List, Optional, Dict, Union +from datetime import datetime +import aiohttp +import asyncio +from crazy_functions.review_fns.data_sources.base_source import DataSource, PaperMetadata +import json +from tqdm import tqdm +import random + +class AdsabsSource(DataSource): + """ADS (Astrophysics Data System) API实现""" + + # 定义API密钥列表 + API_KEYS = [ + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" + ] + + def __init__(self, api_key: str = None): + """初始化 + + Args: + api_key: ADS API密钥，如果不提供则从预定义列表中随机选择 + """ + self.api_key = api_key or random.choice(self.API_KEYS) # 随机选择一个API密钥 + self._initialize() + + def _initialize(self) -> None: + """初始化基础URL和请求头""" + self.base_url = "https://api.adsabs.harvard.edu/v1" + self.headers = { + "Authorization": f"Bearer {self.api_key}", + "Content-Type": "application/json" + } + + async def _make_request(self, url: str, method: str = "GET", data: dict = None) -> Optional[dict]: + """发送HTTP请求 + + Args: + url: 请求URL + method: HTTP方法 + data: POST请求数据 + + Returns: + 响应内容 + """ + try: + async with aiohttp.ClientSession(headers=self.headers) as session: + if method == "GET": + async with session.get(url) as response: + if response.status == 200: + return await response.json() + elif method == "POST": + async with session.post(url, json=data) as response: + if response.status == 200: + return await response.json() + return None + except Exception as e: + print(f"请求发生错误: {str(e)}") + return None + + def _parse_paper(self, doc: dict) -> PaperMetadata: + """解析ADS文献数据 + + Args: + doc: ADS文献数据 + + Returns: + 解析后的论文数据 + """ + try: + return PaperMetadata( + title=doc.get('title', [''])[0] if doc.get('title') else '', + authors=doc.get('author', []), + abstract=doc.get('abstract', ''), + year=doc.get('year'), + doi=doc.get('doi', [''])[0] if doc.get('doi') else None, + url=f"https://ui.adsabs.harvard.edu/abs/{doc.get('bibcode')}/abstract" if doc.get('bibcode') else None, + citations=doc.get('citation_count'), + venue=doc.get('pub', ''), + institutions=doc.get('aff', []), + venue_type="journal", + venue_name=doc.get('pub', ''), + venue_info={ + 'volume': doc.get('volume'), + 'issue': doc.get('issue'), + 'pub_date': doc.get('pubdate', '') + }, + source='adsabs' + ) + except Exception as e: + print(f"解析文章时发生错误: {str(e)}") + return None + + async def search( + self, + query: str, + limit: int = 100, + sort_by: str = "relevance", + start_year: int = None + ) -> List[PaperMetadata]: + """搜索论文 + + Args: + query: 搜索关键词 + limit: 返回结果数量限制 + sort_by: 排序方式 ('relevance', 'date', 'citations') + start_year: 起始年份 + + Returns: + 论文列表 + """ + try: + # 构建查询 + if start_year: + query = f"{query} year:{start_year}-" + + # 设置排序 + sort_mapping = { + 'relevance': 'score desc', + 'date': 'date desc', + 'citations': 'citation_count desc' + } + sort = sort_mapping.get(sort_by, 'score desc') + + # 构建搜索请求 + search_url = f"{self.base_url}/search/query" + params = { + "q": query, + "rows": limit, + "sort": sort, + "fl": "title,author,abstract,year,doi,bibcode,citation_count,pub,aff,volume,issue,pubdate" + } + + response = await self._make_request(f"{search_url}?{self._build_query_string(params)}") + if not response or 'response' not in response: + return [] + + # 解析结果 + papers = [] + for doc in response['response']['docs']: + paper = self._parse_paper(doc) + if paper: + papers.append(paper) + + return papers + + except Exception as e: + print(f"搜索论文时发生错误: {str(e)}") + return [] + + def _build_query_string(self, params: dict) -> str: + """构建查询字符串""" + return "&".join([f"{k}={v}" for k, v in params.items()]) + + async def get_paper_details(self, bibcode: str) -> Optional[PaperMetadata]: + """获取指定bibcode的论文详情""" + search_url = f"{self.base_url}/search/query" + params = { + "q": f"identifier:{bibcode}", + "fl": "title,author,abstract,year,doi,bibcode,citation_count,pub,aff,volume,issue,pubdate" + } + + response = await self._make_request(f"{search_url}?{self._build_query_string(params)}") + if response and 'response' in response and response['response']['docs']: + return self._parse_paper(response['response']['docs'][0]) + return None + + async def get_related_papers(self, bibcode: str, limit: int = 100) -> List[PaperMetadata]: + """获取相关论文""" + url = f"{self.base_url}/search/query" + params = { + "q": f"citations(identifier:{bibcode}) OR references(identifier:{bibcode})", + "rows": limit, + "fl": "title,author,abstract,year,doi,bibcode,citation_count,pub,aff,volume,issue,pubdate" + } + + response = await self._make_request(f"{url}?{self._build_query_string(params)}") + if not response or 'response' not in response: + return [] + + papers = [] + for doc in response['response']['docs']: + paper = self._parse_paper(doc) + if paper: + papers.append(paper) + return papers + + async def search_by_author( + self, + author: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按作者搜索论文""" + query = f"author:\"{author}\"" + return await self.search(query, limit=limit, start_year=start_year) + + async def search_by_journal( + self, + journal: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按期刊搜索论文""" + query = f"pub:\"{journal}\"" + return await self.search(query, limit=limit, start_year=start_year) + + async def get_latest_papers( + self, + days: int = 7, + limit: int = 100 + ) -> List[PaperMetadata]: + """获取最新论文""" + query = f"entdate:[NOW-{days}DAYS TO NOW]" + return await self.search(query, limit=limit, sort_by="date") + + async def get_citations(self, bibcode: str) -> List[PaperMetadata]: + """获取引用该论文的文献""" + url = f"{self.base_url}/search/query" + params = { + "q": f"citations(identifier:{bibcode})", + "fl": "title,author,abstract,year,doi,bibcode,citation_count,pub,aff,volume,issue,pubdate" + } + + response = await self._make_request(f"{url}?{self._build_query_string(params)}") + if not response or 'response' not in response: + return [] + + papers = [] + for doc in response['response']['docs']: + paper = self._parse_paper(doc) + if paper: + papers.append(paper) + return papers + + async def get_references(self, bibcode: str) -> List[PaperMetadata]: + """获取该论文引用的文献""" + url = f"{self.base_url}/search/query" + params = { + "q": f"references(identifier:{bibcode})", + "fl": "title,author,abstract,year,doi,bibcode,citation_count,pub,aff,volume,issue,pubdate" + } + + response = await self._make_request(f"{url}?{self._build_query_string(params)}") + if not response or 'response' not in response: + return [] + + papers = [] + for doc in response['response']['docs']: + paper = self._parse_paper(doc) + if paper: + papers.append(paper) + return papers + +async def example_usage(): + """AdsabsSource使用示例""" + ads = AdsabsSource() + + try: + # 示例1：基本搜索 + print("\n=== 示例1：搜索黑洞相关论文 ===") + papers = await ads.search("black hole", limit=3) + for i, paper in enumerate(papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + + # 其他示例... + + except Exception as e: + print(f"发生错误: {str(e)}") + +if __name__ == "__main__": + # python -m crazy_functions.review_fns.data_sources.adsabs_source + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/arxiv_source.py b/crazy_functions/review_fns/data_sources/arxiv_source.py new file mode 100644 index 00000000..e4fb492d --- /dev/null +++ b/crazy_functions/review_fns/data_sources/arxiv_source.py @@ -0,0 +1,636 @@ +import arxiv +from typing import List, Optional, Union, Literal, Dict +from datetime import datetime +from .base_source import DataSource, PaperMetadata +import os +from urllib.request import urlretrieve +import feedparser +from tqdm import tqdm + +class ArxivSource(DataSource): + """arXiv API实现""" + + CATEGORIES = { + # 物理学 + "Physics": { + "astro-ph": "天体物理学", + "cond-mat": "凝聚态物理", + "gr-qc": "广义相对论与量子宇宙学", + "hep-ex": "高能物理实验", + "hep-lat": "格点场论", + "hep-ph": "高能物理理论", + "hep-th": "高能物理理论", + "math-ph": "数学物理", + "nlin": "非线性科学", + "nucl-ex": "核实验", + "nucl-th": "核理论", + "physics": "物理学", + "quant-ph": "量子物理", + }, + + # 数学 + "Mathematics": { + "math.AG": "代数几何", + "math.AT": "代数拓扑", + "math.AP": "分析与偏微分方程", + "math.CT": "范畴论", + "math.CA": "复分析", + "math.CO": "组合数学", + "math.AC": "交换代数", + "math.CV": "复变函数", + "math.DG": "微分几何", + "math.DS": "动力系统", + "math.FA": "泛函分析", + "math.GM": "一般数学", + "math.GN": "一般拓扑", + "math.GT": "几何拓扑", + "math.GR": "群论", + "math.HO": "数学史与数学概述", + "math.IT": "信息论", + "math.KT": "K理论与同调", + "math.LO": "逻辑", + "math.MP": "数学物理", + "math.MG": "度量几何", + "math.NT": "数论", + "math.NA": "数值分析", + "math.OA": "算子代数", + "math.OC": "最优化与控制", + "math.PR": "概率论", + "math.QA": "量子代数", + "math.RT": "表示论", + "math.RA": "环与代数", + "math.SP": "谱理论", + "math.ST": "统计理论", + "math.SG": "辛几何", + }, + + # 计算机科学 + "Computer Science": { + "cs.AI": "人工智能", + "cs.CL": "计算语言学", + "cs.CC": "计算复杂性", + "cs.CE": "计算工程", + "cs.CG": "计算几何", + "cs.GT": "计算机博弈论", + "cs.CV": "计算机视觉", + "cs.CY": "计算机与社会", + "cs.CR": "密码学与安全", + "cs.DS": "数据结构与算法", + "cs.DB": "数据库", + "cs.DL": "数字图书馆", + "cs.DM": "离散数学", + "cs.DC": "分布式计算", + "cs.ET": "新兴技术", + "cs.FL": "形式语言与自动机理论", + "cs.GL": "一般文献", + "cs.GR": "图形学", + "cs.AR": "硬件架构", + "cs.HC": "人机交互", + "cs.IR": "信息检索", + "cs.IT": "信息论", + "cs.LG": "机器学习", + "cs.LO": "逻辑与计算机", + "cs.MS": "数学软件", + "cs.MA": "多智能体系统", + "cs.MM": "多媒体", + "cs.NI": "网络与互联网架构", + "cs.NE": "神经与进化计算", + "cs.NA": "数值分析", + "cs.OS": "操作系统", + "cs.OH": "其他计算机科学", + "cs.PF": "性能评估", + "cs.PL": "编程语言", + "cs.RO": "机器人学", + "cs.SI": "社会与信息网络", + "cs.SE": "软件工程", + "cs.SD": "声音", + "cs.SC": "符号计算", + "cs.SY": "系统与控制", + }, + + # 定量生物学 + "Quantitative Biology": { + "q-bio.BM": "生物分子", + "q-bio.CB": "细胞行为", + "q-bio.GN": "基因组学", + "q-bio.MN": "分子网络", + "q-bio.NC": "神经计算", + "q-bio.OT": "其他", + "q-bio.PE": "群体与进化", + "q-bio.QM": "定量方法", + "q-bio.SC": "亚细胞过程", + "q-bio.TO": "组织与器官", + }, + + # 定量金融 + "Quantitative Finance": { + "q-fin.CP": "计算金融", + "q-fin.EC": "经济学", + "q-fin.GN": "一般金融", + "q-fin.MF": "数学金融", + "q-fin.PM": "投资组合管理", + "q-fin.PR": "定价理论", + "q-fin.RM": "风险管理", + "q-fin.ST": "统计金融", + "q-fin.TR": "交易与市场微观结构", + }, + + # 统计学 + "Statistics": { + "stat.AP": "应用统计", + "stat.CO": "计算统计", + "stat.ML": "机器学习", + "stat.ME": "方法论", + "stat.OT": "其他统计", + "stat.TH": "统计理论", + }, + + # 电气工程与系统科学 + "Electrical Engineering and Systems Science": { + "eess.AS": "音频与语音处理", + "eess.IV": "图像与视频处理", + "eess.SP": "信号处理", + "eess.SY": "系统与控制", + }, + + # 经济学 + "Economics": { + "econ.EM": "计量经济学", + "econ.GN": "一般经济学", + "econ.TH": "理论经济学", + } + } + + def __init__(self): + """初始化""" + self._initialize() # 调用初始化方法 + # 修改排序选项映射 + self.sort_options = { + 'relevance': arxiv.SortCriterion.Relevance, # arXiv的相关性排序 + 'lastUpdatedDate': arxiv.SortCriterion.LastUpdatedDate, # 最后更新日期 + 'submittedDate': arxiv.SortCriterion.SubmittedDate, # 提交日期 + } + + self.sort_order_options = { + 'ascending': arxiv.SortOrder.Ascending, + 'descending': arxiv.SortOrder.Descending + } + + self.default_sort = 'lastUpdatedDate' + self.default_order = 'descending' + + def _initialize(self) -> None: + """初始化客户端，设置默认参数""" + self.client = arxiv.Client() + + async def search( + self, + query: str, + limit: int = 10, + sort_by: str = None, + sort_order: str = None, + start_year: int = None + ) -> List[Dict]: + """搜索论文""" + try: + # 使用默认排序如果提供的排序选项无效 + if not sort_by or sort_by not in self.sort_options: + sort_by = self.default_sort + + # 使用默认排序顺序如果提供的顺序无效 + if not sort_order or sort_order not in self.sort_order_options: + sort_order = self.default_order + + # 如果指定了起始年份，添加到查询中 + if start_year: + query = f"{query} AND submittedDate:[{start_year}0101 TO 99991231]" + + search = arxiv.Search( + query=query, + max_results=limit, + sort_by=self.sort_options[sort_by], + sort_order=self.sort_order_options[sort_order] + ) + + results = list(self.client.results(search)) + return [self._parse_paper_data(result) for result in results] + except Exception as e: + print(f"搜索论文时发生错误: {str(e)}") + return [] + + async def search_by_id(self, paper_id: Union[str, List[str]]) -> List[PaperMetadata]: + """按ID搜索论文 + + Args: + paper_id: 单个arXiv ID或ID列表，例如：'2005.14165' 或 ['2005.14165', '2103.14030'] + """ + if isinstance(paper_id, str): + paper_id = [paper_id] + + search = arxiv.Search( + id_list=paper_id, + max_results=len(paper_id) + ) + results = list(self.client.results(search)) + return [self._parse_paper_data(result) for result in results] + + async def search_by_category( + self, + category: str, + limit: int = 100, + sort_by: str = 'relevance', + sort_order: str = 'descending', + start_year: int = None + ) -> List[PaperMetadata]: + """按类别搜索论文""" + query = f"cat:{category}" + + # 如果指定了起始年份，添加到查询中 + if start_year: + query = f"{query} AND submittedDate:[{start_year}0101 TO 99991231]" + + return await self.search( + query=query, + limit=limit, + sort_by=sort_by, + sort_order=sort_order + ) + + async def search_by_authors( + self, + authors: List[str], + limit: int = 100, + sort_by: str = 'relevance', + start_year: int = None + ) -> List[PaperMetadata]: + """按作者搜索论文""" + query = " AND ".join([f"au:\"{author}\"" for author in authors]) + + # 如果指定了起始年份，添加到查询中 + if start_year: + query = f"{query} AND submittedDate:[{start_year}0101 TO 99991231]" + + return await self.search( + query=query, + limit=limit, + sort_by=sort_by + ) + + async def search_by_date_range( + self, + start_date: datetime, + end_date: datetime, + limit: int = 100, + sort_by: Literal['relevance', 'updated', 'submitted'] = 'submitted', + sort_order: Literal['ascending', 'descending'] = 'descending' + ) -> List[PaperMetadata]: + """按日期范围搜索论文""" + query = f"submittedDate:[{start_date.strftime('%Y%m%d')} TO {end_date.strftime('%Y%m%d')}]" + return await self.search( + query, + limit=limit, + sort_by=sort_by, + sort_order=sort_order + ) + + async def download_pdf(self, paper_id: str, dirpath: str = "./", filename: str = "") -> str: + """下载论文PDF + + Args: + paper_id: arXiv ID + dirpath: 保存目录 + filename: 文件名，如果为空则使用默认格式：{paper_id}_{标题}.pdf + + Returns: + 保存的文件路径 + """ + papers = await self.search_by_id(paper_id) + if not papers: + raise ValueError(f"未找到ID为 {paper_id} 的论文") + paper = papers[0] + + if not filename: + # 清理标题中的非法字符 + safe_title = "".join(c if c.isalnum() else "_" for c in paper.title) + filename = f"{paper_id}_{safe_title}.pdf" + + filepath = os.path.join(dirpath, filename) + urlretrieve(paper.url, filepath) + return filepath + + async def download_source(self, paper_id: str, dirpath: str = "./", filename: str = "") -> str: + """下载论文源文件（通常是LaTeX源码） + + Args: + paper_id: arXiv ID + dirpath: 保存目录 + filename: 文件名，如果为空则使用默认格式：{paper_id}_{标题}.tar.gz + + Returns: + 保存的文件路径 + """ + papers = await self.search_by_id(paper_id) + if not papers: + raise ValueError(f"未找到ID为 {paper_id} 的论文") + paper = papers[0] + + if not filename: + safe_title = "".join(c if c.isalnum() else "_" for c in paper.title) + filename = f"{paper_id}_{safe_title}.tar.gz" + + filepath = os.path.join(dirpath, filename) + source_url = paper.url.replace("/pdf/", "/src/") + urlretrieve(source_url, filepath) + return filepath + + async def get_citations(self, paper_id: str) -> List[PaperMetadata]: + # arXiv API不直接提供引用信息 + return [] + + async def get_references(self, paper_id: str) -> List[PaperMetadata]: + # arXiv API不直接提供引用信息 + return [] + + async def get_paper_details(self, paper_id: str) -> Optional[PaperMetadata]: + """获取论文详情 + + Args: + paper_id: arXiv ID 或 DOI + + Returns: + 论文详细信息，如果未找到返回 None + """ + try: + # 如果是完整的 arXiv URL，提取 ID + if "arxiv.org" in paper_id: + paper_id = paper_id.split("/")[-1] + # 如果是 DOI 格式且是 arXiv 论文，提取 ID + elif paper_id.startswith("10.48550/arXiv."): + paper_id = paper_id.split(".")[-1] + + papers = await self.search_by_id(paper_id) + return papers[0] if papers else None + except Exception as e: + print(f"获取论文详情时发生错误: {str(e)}") + return None + + def _parse_paper_data(self, result: arxiv.Result) -> PaperMetadata: + """解析arXiv API返回的数据""" + # 解析主要类别和次要类别 + primary_category = result.primary_category + categories = result.categories + + # 构建venue信息 + venue_info = { + 'primary_category': primary_category, + 'categories': categories, + 'comments': getattr(result, 'comment', None), + 'journal_ref': getattr(result, 'journal_ref', None) + } + + return PaperMetadata( + title=result.title, + authors=[author.name for author in result.authors], + abstract=result.summary, + year=result.published.year, + doi=result.entry_id, + url=result.pdf_url, + citations=None, + venue=f"arXiv:{primary_category}", + institutions=[], + venue_type='preprint', # arXiv论文都是预印本 + venue_name='arXiv', + venue_info=venue_info, + source='arxiv' # 添加来源标记 + ) + + async def get_latest_papers( + self, + category: str, + debug: bool = False, + batch_size: int = 50 + ) -> List[PaperMetadata]: + """获取指定类别的最新论文 + + 通过 RSS feed 获取最新发布的论文，然后批量获取详细信息 + + Args: + category: arXiv类别，例如： + - 整个领域: 'cs' + - 具体方向: 'cs.AI' + - 多个类别: 'cs.AI+q-bio.NC' + debug: 是否为调试模式，如果为True则只返回5篇最新论文 + batch_size: 批量获取论文的数量，默认50 + + Returns: + 论文列表 + + Raises: + ValueError: 如果类别无效 + """ + try: + # 处理类别格式 + # 1. 转换为小写 + # 2. 确保多个类别之间使用+连接 + category = category.lower().replace(' ', '+') + + # 构建RSS feed URL + feed_url = f"https://rss.arxiv.org/rss/{category}" + print(f"正在获取RSS feed: {feed_url}") # 添加调试信息 + + feed = feedparser.parse(feed_url) + + # 检查feed是否有效 + if hasattr(feed, 'status') and feed.status != 200: + raise ValueError(f"获取RSS feed失败，状态码: {feed.status}") + + if not feed.entries: + print(f"警告：未在feed中找到任何条目") # 添加调试信息 + print(f"Feed标题: {feed.feed.title if hasattr(feed, 'feed') else '无标题'}") + raise ValueError(f"无效的arXiv类别或未找到论文: {category}") + + if debug: + # 调试模式：只获取5篇最新论文 + search = arxiv.Search( + query=f'cat:{category}', + sort_by=arxiv.SortCriterion.SubmittedDate, + sort_order=arxiv.SortOrder.Descending, + max_results=5 + ) + results = list(self.client.results(search)) + return [self._parse_paper_data(result) for result in results] + + # 正常模式：获取所有新论文 + # 从RSS条目中提取arXiv ID + paper_ids = [] + for entry in feed.entries: + try: + # RSS链接格式可能是以下几种： + # - http://arxiv.org/abs/2403.xxxxx + # - http://arxiv.org/pdf/2403.xxxxx + # - https://arxiv.org/abs/2403.xxxxx + link = entry.link or entry.id + arxiv_id = link.split('/')[-1].replace('.pdf', '') + if arxiv_id: + paper_ids.append(arxiv_id) + except Exception as e: + print(f"警告：处理条目时出错: {str(e)}") # 添加调试信息 + continue + + if not paper_ids: + print("未能从feed中提取到任何论文ID") # 添加调试信息 + return [] + + print(f"成功提取到 {len(paper_ids)} 个论文ID") # 添加调试信息 + + # 批量获取论文详情 + papers = [] + with tqdm(total=len(paper_ids), desc="获取arXiv论文") as pbar: + for i in range(0, len(paper_ids), batch_size): + batch_ids = paper_ids[i:i + batch_size] + search = arxiv.Search( + id_list=batch_ids, + max_results=len(batch_ids) + ) + batch_results = list(self.client.results(search)) + papers.extend([self._parse_paper_data(result) for result in batch_results]) + pbar.update(len(batch_results)) + + return papers + + except Exception as e: + print(f"获取最新论文时发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) # 添加完整的错误追踪 + return [] + +async def example_usage(): + """ArxivSource使用示例""" + arxiv_source = ArxivSource() + + try: + # 示例1：基本搜索，使用不同的排序方式 + # print("\n=== 示例1：搜索最新的机器学习论文（按提交时间排序）===") + # papers = await arxiv_source.search( + # "ti:\"machine learning\"", + # limit=3, + # sort_by='submitted', + # sort_order='descending' + # ) + # print(f"找到 {len(papers)} 篇论文") + + # for i, paper in enumerate(papers, 1): + # print(f"\n--- 论文 {i} ---") + # print(f"标题: {paper.title}") + # print(f"作者: {', '.join(paper.authors)}") + # print(f"发表年份: {paper.year}") + # print(f"arXiv ID: {paper.doi}") + # print(f"PDF URL: {paper.url}") + # if paper.abstract: + # print(f"\n摘要:") + # print(paper.abstract) + # print(f"发表venue: {paper.venue}") + + # # 示例2：按ID搜索 + # print("\n=== 示例2：按ID搜索论文 ===") + # paper_id = "2005.14165" # GPT-3论文 + # papers = await arxiv_source.search_by_id(paper_id) + # if papers: + # paper = papers[0] + # print(f"标题: {paper.title}") + # print(f"作者: {', '.join(paper.authors)}") + # print(f"发表年份: {paper.year}") + + # # 示例3：按类别搜索 + # print("\n=== 示例3：搜索人工智能领域最新论文 ===") + # ai_papers = await arxiv_source.search_by_category( + # "cs.AI", + # limit=2, + # sort_by='updated', + # sort_order='descending' + # ) + # for i, paper in enumerate(ai_papers, 1): + # print(f"\n--- AI论文 {i} ---") + # print(f"标题: {paper.title}") + # print(f"作者: {', '.join(paper.authors)}") + # print(f"发表venue: {paper.venue}") + + # # 示例4：按作者搜索 + # print("\n=== 示例4：搜索特定作者的论文 ===") + # author_papers = await arxiv_source.search_by_authors( + # ["Bengio"], + # limit=2, + # sort_by='relevance' + # ) + # for i, paper in enumerate(author_papers, 1): + # print(f"\n--- Bengio的论文 {i} ---") + # print(f"标题: {paper.title}") + # print(f"作者: {', '.join(paper.authors)}") + # print(f"发表venue: {paper.venue}") + + # # 示例5：按日期范围搜索 + # print("\n=== 示例5：搜索特定日期范围的论文 ===") + # from datetime import datetime, timedelta + # end_date = datetime.now() + # start_date = end_date - timedelta(days=7) # 最近一周 + # recent_papers = await arxiv_source.search_by_date_range( + # start_date, + # end_date, + # limit=2 + # ) + # for i, paper in enumerate(recent_papers, 1): + # print(f"\n--- 最近论文 {i} ---") + # print(f"标题: {paper.title}") + # print(f"作者: {', '.join(paper.authors)}") + # print(f"发表年份: {paper.year}") + + # # 示例6：下载PDF + # print("\n=== 示例6：下载论文PDF ===") + # if papers: # 使用之前搜索到的GPT-3论文 + # pdf_path = await arxiv_source.download_pdf(paper_id) + # print(f"PDF已下载到: {pdf_path}") + + # # 示例7：下载源文件 + # print("\n=== 示例7：下载论文源文件 ===") + # if papers: + # source_path = await arxiv_source.download_source(paper_id) + # print(f"源文件已下载到: {source_path}") + + # 示例6：获取最新论文 + print("\n=== 示例8：获取最新论文 ===") + + # 获取CS.AI领域的最新论文 + print("\n--- 获取AI领域最新论文 ---") + ai_latest = await arxiv_source.get_latest_papers("cs.AI", debug=True) + for i, paper in enumerate(ai_latest, 1): + print(f"\n论文 {i}:") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + + # 获取整个计算机科学领域的最新论文 + print("\n--- 获取整个CS领域最新论文 ---") + cs_latest = await arxiv_source.get_latest_papers("cs", debug=True) + for i, paper in enumerate(cs_latest, 1): + print(f"\n论文 {i}:") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + + # 获取多个类别的最新论文 + print("\n--- 获取AI和机器学习领域最新论文 ---") + multi_latest = await arxiv_source.get_latest_papers("cs.AI+cs.LG", debug=True) + for i, paper in enumerate(multi_latest, 1): + print(f"\n论文 {i}:") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + import asyncio + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/base_source.py b/crazy_functions/review_fns/data_sources/base_source.py new file mode 100644 index 00000000..fea46e36 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/base_source.py @@ -0,0 +1,102 @@ +from abc import ABC, abstractmethod +from typing import List, Dict, Optional +from dataclasses import dataclass + +class PaperMetadata: + """论文元数据""" + def __init__( + self, + title: str, + authors: List[str], + abstract: str, + year: int, + doi: str = None, + url: str = None, + citations: int = None, + venue: str = None, + institutions: List[str] = None, + venue_type: str = None, # 来源类型(journal/conference/preprint等) + venue_name: str = None, # 具体的期刊/会议名称 + venue_info: Dict = None, # 更多来源详细信息(如影响因子、分区等) + source: str = None # 新增: 论文来源标记 + ): + self.title = title + self.authors = authors + self.abstract = abstract + self.year = year + self.doi = doi + self.url = url + self.citations = citations + self.venue = venue + self.institutions = institutions or [] + self.venue_type = venue_type # 新增 + self.venue_name = venue_name # 新增 + self.venue_info = venue_info or {} # 新增 + self.source = source # 新增: 存储论文来源 + + # 新增：影响因子和分区信息，初始化为None + self._if_factor = None + self._cas_division = None + self._jcr_division = None + + @property + def if_factor(self) -> Optional[float]: + """获取影响因子""" + return self._if_factor + + @if_factor.setter + def if_factor(self, value: float): + """设置影响因子""" + self._if_factor = value + + @property + def cas_division(self) -> Optional[str]: + """获取中科院分区""" + return self._cas_division + + @cas_division.setter + def cas_division(self, value: str): + """设置中科院分区""" + self._cas_division = value + + @property + def jcr_division(self) -> Optional[str]: + """获取JCR分区""" + return self._jcr_division + + @jcr_division.setter + def jcr_division(self, value: str): + """设置JCR分区""" + self._jcr_division = value + +class DataSource(ABC): + """数据源基类""" + + def __init__(self, api_key: Optional[str] = None): + self.api_key = api_key + self._initialize() + + @abstractmethod + def _initialize(self) -> None: + """初始化数据源""" + pass + + @abstractmethod + async def search(self, query: str, limit: int = 100) -> List[PaperMetadata]: + """搜索论文""" + pass + + @abstractmethod + async def get_paper_details(self, paper_id: str) -> PaperMetadata: + """获取论文详细信息""" + pass + + @abstractmethod + async def get_citations(self, paper_id: str) -> List[PaperMetadata]: + """获取引用该论文的文献""" + pass + + @abstractmethod + async def get_references(self, paper_id: str) -> List[PaperMetadata]: + """获取该论文引用的文献""" + pass \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/cas_if.json b/crazy_functions/review_fns/data_sources/cas_if.json new file mode 100644 index 00000000..a51c3355 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/cas_if.json @@ -0,0 +1 @@ +[{"journal":"CA-A CANCER JOURNAL FOR CLINICIANS","jabb":"CA-CANCER J CLIN","issn":"0007-9235","eissn":"1542-4863","IF":"503.1","Q":"Q1","B":"B1","T":"0"},{"journal":"NATURE REVIEWS DRUG DISCOVERY","jabb":"NAT REV DRUG DISCOV","issn":"1474-1776","eissn":"1474-1784","IF":"122.7","Q":"Q1","B":"B1","T":"1"},{"journal":"LANCET","jabb":"LANCET","issn":"0140-6736","eissn":"1474-547X","IF":"98.4","Q":"Q1","B":"B1","T":"1"},{"journal":"NEW ENGLAND JOURNAL OF MEDICINE","jabb":"NEW ENGL J MED","issn":"0028-4793","eissn":"1533-4406","IF":"96.2","Q":"Q1","B":"B1","T":"1"},{"journal":"BMJ-British Medical Journal","jabb":"BMJ-BRIT MED J","issn":"0959-535X","eissn":"1756-1833","IF":"93.6","Q":"Q1","B":"B1","T":"1"},{"journal":"NATURE REVIEWS MOLECULAR CELL BIOLOGY","jabb":"NAT REV MOL CELL 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OPT","issn":"1609-1833","eissn":"N/A","IF":"N/A","Q":"N/A","B":"B4","T":"0"},{"journal":"INFORMATION SCIENCES","jabb":"INFORM SCIENCES","issn":"0020-0255","eissn":"1872-6291","IF":"N/A","Q":"N/A","B":"B1","T":"1"},{"journal":"LIBRARY HI TECH","jabb":"LIBR HI TECH","issn":"0737-8831","eissn":"0737-8831","IF":"N/A","Q":"N/A","B":"B3","T":"0"},{"journal":"Climate Change Economics","jabb":"CLIM CHANG ECON","issn":"2010-0078","eissn":"2010-0086","IF":"N/A","Q":"N/A","B":"B4","T":"0"},{"journal":"ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH","jabb":"ENVIRON SCI POLLUT R","issn":"0944-1344","eissn":"1614-7499","IF":"N/A","Q":"N/A","B":"B3","T":"0"},{"journal":"MINERVA MEDICA","jabb":"MINERVA MED","issn":"0026-4806","eissn":"1827-1669","IF":"N/A","Q":"N/A","B":"B4","T":"0"},{"journal":"PANMINERVA MEDICA","jabb":"PANMINERVA MED","issn":"0031-0808","eissn":"1827-1898","IF":"N/A","Q":"N/A","B":"B4","T":"0"}] diff --git a/crazy_functions/review_fns/data_sources/crossref_source.py b/crazy_functions/review_fns/data_sources/crossref_source.py new file mode 100644 index 00000000..dc53946a --- /dev/null +++ b/crazy_functions/review_fns/data_sources/crossref_source.py @@ -0,0 +1,400 @@ +import aiohttp +from typing import List, Dict, Optional +from datetime import datetime +from crazy_functions.review_fns.data_sources.base_source import DataSource, PaperMetadata +import random + +class CrossrefSource(DataSource): + """Crossref API实现""" + + CONTACT_EMAILS = [ + "gpt_abc_academic@163.com", + "gpt_abc_newapi@163.com", + "gpt_abc_academic_pwd@163.com" + ] + + def _initialize(self) -> None: + """初始化客户端，设置默认参数""" + self.base_url = "https://api.crossref.org" + # 随机选择一个邮箱 + contact_email = random.choice(self.CONTACT_EMAILS) + self.headers = { + "Accept": "application/json", + "User-Agent": f"Mozilla/5.0 (compatible; PythonScript/1.0; mailto:{contact_email})", + } + if self.api_key: + self.headers["Crossref-Plus-API-Token"] = f"Bearer {self.api_key}" + + async def search( + self, + query: str, + limit: int = 100, + sort_by: str = None, + sort_order: str = None, + start_year: int = None + ) -> List[PaperMetadata]: + """搜索论文 + + Args: + query: 搜索关键词 + limit: 返回结果数量限制 + sort_by: 排序字段 + sort_order: 排序顺序 + start_year: 起始年份 + """ + async with aiohttp.ClientSession(headers=self.headers) as session: + # 请求更多的结果以补偿可能被过滤掉的文章 + adjusted_limit = min(limit * 3, 1000) # 设置上限以避免请求过多 + params = { + "query": query, + "rows": adjusted_limit, + "select": ( + "DOI,title,author,published-print,abstract,reference," + "container-title,is-referenced-by-count,type," + "publisher,ISSN,ISBN,issue,volume,page" + ) + } + + # 添加年份过滤 + if start_year: + params["filter"] = f"from-pub-date:{start_year}" + + # 添加排序 + if sort_by: + params["sort"] = sort_by + if sort_order: + params["order"] = sort_order + + async with session.get( + f"{self.base_url}/works", + params=params + ) as response: + if response.status != 200: + print(f"API请求失败: HTTP {response.status}") + print(f"响应内容: {await response.text()}") + return [] + + data = await response.json() + items = data.get("message", {}).get("items", []) + if not items: + print(f"未找到相关论文") + return [] + + # 过滤掉没有摘要的文章 + papers = [] + filtered_count = 0 + for work in items: + paper = self._parse_work(work) + if paper.abstract and paper.abstract.strip(): + papers.append(paper) + if len(papers) >= limit: # 达到原始请求的限制后停止 + break + else: + filtered_count += 1 + + print(f"找到 {len(items)} 篇相关论文，其中 {filtered_count} 篇因缺少摘要被过滤") + print(f"返回 {len(papers)} 篇包含摘要的论文") + return papers + + async def get_paper_details(self, doi: str) -> PaperMetadata: + """获取指定DOI的论文详情""" + async with aiohttp.ClientSession(headers=self.headers) as session: + async with session.get( + f"{self.base_url}/works/{doi}", + params={ + "select": ( + "DOI,title,author,published-print,abstract,reference," + "container-title,is-referenced-by-count,type," + "publisher,ISSN,ISBN,issue,volume,page" + ) + } + ) as response: + if response.status != 200: + print(f"获取论文详情失败: HTTP {response.status}") + print(f"响应内容: {await response.text()}") + return None + + try: + data = await response.json() + return self._parse_work(data.get("message", {})) + except Exception as e: + print(f"解析论文详情时发生错误: {str(e)}") + return None + + async def get_references(self, doi: str) -> List[PaperMetadata]: + """获取指定DOI论文的参考文献列表""" + async with aiohttp.ClientSession(headers=self.headers) as session: + async with session.get( + f"{self.base_url}/works/{doi}", + params={"select": "reference"} + ) as response: + if response.status != 200: + print(f"获取参考文献失败: HTTP {response.status}") + return [] + + try: + data = await response.json() + # 确保我们正确处理返回的数据结构 + if not isinstance(data, dict): + print(f"API返回了意外的数据格式: {type(data)}") + return [] + + references = data.get("message", {}).get("reference", []) + if not references: + print(f"未找到参考文献") + return [] + + return [ + PaperMetadata( + title=ref.get("article-title", ""), + authors=[ref.get("author", "")], + year=ref.get("year"), + doi=ref.get("DOI"), + url=f"https://doi.org/{ref.get('DOI')}" if ref.get("DOI") else None, + abstract="", + citations=None, + venue=ref.get("journal-title", ""), + institutions=[] + ) + for ref in references + ] + except Exception as e: + print(f"解析参考文献数据时发生错误: {str(e)}") + return [] + + async def get_citations(self, doi: str) -> List[PaperMetadata]: + """获取引用指定DOI论文的文献列表""" + async with aiohttp.ClientSession(headers=self.headers) as session: + async with session.get( + f"{self.base_url}/works", + params={ + "filter": f"reference.DOI:{doi}", + "select": "DOI,title,author,published-print,abstract" + } + ) as response: + if response.status != 200: + print(f"获取引用信息失败: HTTP {response.status}") + print(f"响应内容: {await response.text()}") + return [] + + try: + data = await response.json() + # 检查返回的数据结构 + if isinstance(data, dict): + items = data.get("message", {}).get("items", []) + return [self._parse_work(work) for work in items] + else: + print(f"API返回了意外的数据格式: {type(data)}") + return [] + except Exception as e: + print(f"解析引用数据时发生错误: {str(e)}") + return [] + + def _parse_work(self, work: Dict) -> PaperMetadata: + """解析Crossref返回的数据""" + # 获取摘要 - 处理可能的不同格式 + abstract = "" + if isinstance(work.get("abstract"), str): + abstract = work.get("abstract", "") + elif isinstance(work.get("abstract"), dict): + abstract = work.get("abstract", {}).get("value", "") + + if not abstract: + print(f"警告: 论文 '{work.get('title', [''])[0]}' 没有可用的摘要") + + # 获取机构信息 + institutions = [] + for author in work.get("author", []): + if "affiliation" in author: + for affiliation in author["affiliation"]: + if "name" in affiliation and affiliation["name"] not in institutions: + institutions.append(affiliation["name"]) + + # 获取venue信息 + venue_name = work.get("container-title", [None])[0] + venue_type = work.get("type", "unknown") # 文献类型 + venue_info = { + "publisher": work.get("publisher"), + "issn": work.get("ISSN", []), + "isbn": work.get("ISBN", []), + "issue": work.get("issue"), + "volume": work.get("volume"), + "page": work.get("page") + } + + return PaperMetadata( + title=work.get("title", [None])[0] or "", + authors=[ + author.get("given", "") + " " + author.get("family", "") + for author in work.get("author", []) + ], + institutions=institutions, # 添加机构信息 + abstract=abstract, + year=work.get("published-print", {}).get("date-parts", [[None]])[0][0], + doi=work.get("DOI"), + url=f"https://doi.org/{work.get('DOI')}" if work.get("DOI") else None, + citations=work.get("is-referenced-by-count"), + venue=venue_name, + venue_type=venue_type, # 添加venue类型 + venue_name=venue_name, # 添加venue名称 + venue_info=venue_info, # 添加venue详细信息 + source='crossref' # 添加来源标记 + ) + + async def search_by_authors( + self, + authors: List[str], + limit: int = 100, + sort_by: str = None, + start_year: int = None + ) -> List[PaperMetadata]: + """按作者搜索论文""" + query = " ".join([f"author:\"{author}\"" for author in authors]) + return await self.search( + query=query, + limit=limit, + sort_by=sort_by, + start_year=start_year + ) + + async def search_by_date_range( + self, + start_date: datetime, + end_date: datetime, + limit: int = 100, + sort_by: str = None, + sort_order: str = None + ) -> List[PaperMetadata]: + """按日期范围搜索论文""" + query = f"from-pub-date:{start_date.strftime('%Y-%m-%d')} until-pub-date:{end_date.strftime('%Y-%m-%d')}" + return await self.search( + query=query, + limit=limit, + sort_by=sort_by, + sort_order=sort_order + ) + +async def example_usage(): + """CrossrefSource使用示例""" + crossref = CrossrefSource(api_key=None) + + try: + # 示例1：基本搜索，使用不同的排序方式 + print("\n=== 示例1：搜索最新的机器学习论文 ===") + papers = await crossref.search( + query="machine learning", + limit=3, + sort_by="published", + sort_order="desc", + start_year=2023 + ) + + for i, paper in enumerate(papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"URL: {paper.url}") + if paper.abstract: + print(f"摘要: {paper.abstract[:200]}...") + if paper.institutions: + print(f"机构: {', '.join(paper.institutions)}") + print(f"引用次数: {paper.citations}") + print(f"发表venue: {paper.venue}") + print(f"venue类型: {paper.venue_type}") + if paper.venue_info: + print("Venue详细信息:") + for key, value in paper.venue_info.items(): + if value: + print(f" - {key}: {value}") + + # 示例2：按DOI获取论文详情 + print("\n=== 示例2：获取特定论文详情 ===") + # 使用BERT论文的DOI + doi = "10.18653/v1/N19-1423" + paper = await crossref.get_paper_details(doi) + if paper: + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + if paper.abstract: + print(f"摘要: {paper.abstract[:200]}...") + print(f"引用次数: {paper.citations}") + + # 示例3：按作者搜索 + print("\n=== 示例3：搜索特定作者的论文 ===") + author_papers = await crossref.search_by_authors( + authors=["Yoshua Bengio"], + limit=3, + sort_by="published", + start_year=2020 + ) + for i, paper in enumerate(author_papers, 1): + print(f"\n--- {i}. {paper.title} ---") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"引用次数: {paper.citations}") + + # 示例4：按日期范围搜索 + print("\n=== 示例4：搜索特定日期范围的论文 ===") + from datetime import datetime, timedelta + end_date = datetime.now() + start_date = end_date - timedelta(days=30) # 最近一个月 + recent_papers = await crossref.search_by_date_range( + start_date=start_date, + end_date=end_date, + limit=3, + sort_by="published", + sort_order="desc" + ) + for i, paper in enumerate(recent_papers, 1): + print(f"\n--- 最近发表的论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + + # 示例5：获取论文引用信息 + print("\n=== 示例5：获取论文引用信息 ===") + if paper: # 使用之前获取的BERT论文 + print("\n获取引用该论文的文献:") + citations = await crossref.get_citations(paper.doi) + for i, citing_paper in enumerate(citations[:3], 1): + print(f"\n--- 引用论文 {i} ---") + print(f"标题: {citing_paper.title}") + print(f"作者: {', '.join(citing_paper.authors)}") + print(f"发表年份: {citing_paper.year}") + + print("\n获取该论文引用的参考文献:") + references = await crossref.get_references(paper.doi) + for i, ref_paper in enumerate(references[:3], 1): + print(f"\n--- 参考文献 {i} ---") + print(f"标题: {ref_paper.title}") + print(f"作者: {', '.join(ref_paper.authors)}") + print(f"发表年份: {ref_paper.year if ref_paper.year else '未知'}") + + # 示例6：展示venue信息的使用 + print("\n=== 示例6：展示期刊/会议详细信息 ===") + if papers: + paper = papers[0] + print(f"文献类型: {paper.venue_type}") + print(f"发表venue: {paper.venue_name}") + if paper.venue_info: + print("Venue详细信息:") + for key, value in paper.venue_info.items(): + if value: + print(f" - {key}: {value}") + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + import asyncio + + # 运行示例 + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/elsevier_source.py b/crazy_functions/review_fns/data_sources/elsevier_source.py new file mode 100644 index 00000000..6a157c32 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/elsevier_source.py @@ -0,0 +1,449 @@ +from typing import List, Optional, Dict, Union +from datetime import datetime +import aiohttp +import asyncio +from crazy_functions.review_fns.data_sources.base_source import DataSource, PaperMetadata +import json +from tqdm import tqdm +import random + +class ElsevierSource(DataSource): + """Elsevier (Scopus) API实现""" + + # 定义API密钥列表 + API_KEYS = [ + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxx" + ] + + def __init__(self, api_key: str = None): + """初始化 + + Args: + api_key: Elsevier API密钥，如果不提供则从预定义列表中随机选择 + """ + self.api_key = api_key or random.choice(self.API_KEYS) + self._initialize() + + def _initialize(self) -> None: + """初始化基础URL和请求头""" + self.base_url = "https://api.elsevier.com/content" + self.headers = { + "X-ELS-APIKey": self.api_key, + "Accept": "application/json", + "Content-Type": "application/json", + # 添加更多必要的头部信息 + "X-ELS-Insttoken": "", # 如果有机构令牌 + } + + async def _make_request(self, url: str, params: Dict = None) -> Optional[Dict]: + """发送HTTP请求 + + Args: + url: 请求URL + params: 查询参数 + + Returns: + JSON响应 + """ + try: + async with aiohttp.ClientSession(headers=self.headers) as session: + async with session.get(url, params=params) as response: + if response.status == 200: + return await response.json() + else: + # 添加更详细的错误信息 + error_text = await response.text() + print(f"请求失败: {response.status}") + print(f"错误详情: {error_text}") + if response.status == 401: + print(f"使用的API密钥: {self.api_key}") + # 尝试切换到另一个API密钥 + new_key = random.choice([k for k in self.API_KEYS if k != self.api_key]) + print(f"尝试切换到新的API密钥: {new_key}") + self.api_key = new_key + self.headers["X-ELS-APIKey"] = new_key + # 重试请求 + return await self._make_request(url, params) + return None + except Exception as e: + print(f"请求发生错误: {str(e)}") + return None + + async def search( + self, + query: str, + limit: int = 100, + sort_by: str = "relevance", + start_year: int = None + ) -> List[PaperMetadata]: + """搜索论文""" + try: + params = { + "query": query, + "count": min(limit, 100), + "view": "STANDARD", + # 移除dc:description字段，因为它在STANDARD视图中不可用 + "field": "dc:title,dc:creator,prism:doi,prism:coverDate,citedby-count,prism:publicationName" + } + + # 添加年份过滤 + if start_year: + params["date"] = f"{start_year}-present" + + # 添加排序 + if sort_by == "date": + params["sort"] = "-coverDate" + elif sort_by == "cited": + params["sort"] = "-citedby-count" + + # 发送搜索请求 + response = await self._make_request( + f"{self.base_url}/search/scopus", + params=params + ) + + if not response or "search-results" not in response: + return [] + + # 解析搜索结果 + entries = response["search-results"].get("entry", []) + papers = [paper for paper in (self._parse_entry(entry) for entry in entries) if paper is not None] + + # 尝试为每篇论文获取摘要 + for paper in papers: + if paper.doi: + paper.abstract = await self.fetch_abstract(paper.doi) or "" + + return papers + + except Exception as e: + print(f"搜索论文时发生错误: {str(e)}") + return [] + + def _parse_entry(self, entry: Dict) -> Optional[PaperMetadata]: + """解析Scopus API返回的条目""" + try: + # 获取作者列表 + authors = [] + creator = entry.get("dc:creator") + if creator: + authors = [creator] + + # 获取发表年份 + year = None + if "prism:coverDate" in entry: + try: + year = int(entry["prism:coverDate"][:4]) + except: + pass + + # 简化venue信息 + venue_info = { + 'source_id': entry.get("source-id"), + 'issn': entry.get("prism:issn") + } + + return PaperMetadata( + title=entry.get("dc:title", ""), + authors=authors, + abstract=entry.get("dc:description", ""), # 从响应中获取摘要 + year=year, + doi=entry.get("prism:doi"), + url=entry.get("prism:url"), + citations=int(entry.get("citedby-count", 0)), + venue=entry.get("prism:publicationName"), + institutions=[], # 移除机构信息 + venue_type="", + venue_name=entry.get("prism:publicationName"), + venue_info=venue_info + ) + + except Exception as e: + print(f"解析条目时发生错误: {str(e)}") + return None + + async def get_citations(self, doi: str, limit: int = 100) -> List[PaperMetadata]: + """获取引用该论文的文献""" + try: + params = { + "query": f"REF({doi})", + "count": min(limit, 100), + "view": "STANDARD" + } + + response = await self._make_request( + f"{self.base_url}/search/scopus", + params=params + ) + + if not response or "search-results" not in response: + return [] + + entries = response["search-results"].get("entry", []) + return [self._parse_entry(entry) for entry in entries] + + except Exception as e: + print(f"获取引用文献时发生错误: {str(e)}") + return [] + + async def get_references(self, doi: str) -> List[PaperMetadata]: + """获取该论文引用的文献""" + try: + response = await self._make_request( + f"{self.base_url}/abstract/doi/{doi}/references", + params={"view": "STANDARD"} + ) + + if not response or "references" not in response: + return [] + + references = response["references"].get("reference", []) + papers = [paper for paper in (self._parse_reference(ref) for ref in references) if paper is not None] + return papers + + except Exception as e: + print(f"获取参考文献时发生错误: {str(e)}") + return [] + + def _parse_reference(self, ref: Dict) -> Optional[PaperMetadata]: + """解析参考文献数据""" + try: + authors = [] + if "author-list" in ref: + author_list = ref["author-list"].get("author", []) + if isinstance(author_list, list): + authors = [f"{author.get('ce:given-name', '')} {author.get('ce:surname', '')}" + for author in author_list] + else: + authors = [f"{author_list.get('ce:given-name', '')} {author_list.get('ce:surname', '')}"] + + year = None + if "prism:coverDate" in ref: + try: + year = int(ref["prism:coverDate"][:4]) + except: + pass + + return PaperMetadata( + title=ref.get("ce:title", ""), + authors=authors, + abstract="", # 参考文献通常不包含摘要 + year=year, + doi=ref.get("prism:doi"), + url=None, + citations=None, + venue=ref.get("prism:publicationName"), + institutions=[], + venue_type="unknown", + venue_name=ref.get("prism:publicationName"), + venue_info={} + ) + + except Exception as e: + print(f"解析参考文献时发生错误: {str(e)}") + return None + + async def search_by_author( + self, + author: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按作者搜索论文""" + query = f"AUTHOR-NAME({author})" + return await self.search(query, limit=limit, start_year=start_year) + + async def search_by_affiliation( + self, + affiliation: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按机构搜索论文""" + query = f"AF-ID({affiliation})" + return await self.search(query, limit=limit, start_year=start_year) + + async def search_by_venue( + self, + venue: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按期刊/会议搜索论文""" + query = f"SRCTITLE({venue})" + return await self.search(query, limit=limit, start_year=start_year) + + async def test_api_access(self): + """测试API访问权限""" + print(f"\n测试API密钥: {self.api_key}") + + # 测试1: 基础搜索 + basic_params = { + "query": "test", + "count": 1, + "view": "STANDARD" + } + print("\n1. 测试基础搜索...") + response = await self._make_request( + f"{self.base_url}/search/scopus", + params=basic_params + ) + if response: + print("基础搜索成功") + print("可用字段:", list(response.get("search-results", {}).get("entry", [{}])[0].keys())) + + # 测试2: 测试单篇文章访问 + print("\n2. 测试文章详情访问...") + test_doi = "10.1016/j.artint.2021.103535" # 一个示例DOI + response = await self._make_request( + f"{self.base_url}/abstract/doi/{test_doi}", + params={"view": "STANDARD"} # 改为STANDARD视图 + ) + if response: + print("文章详情访问成功") + else: + print("文章详情访问失败") + + async def get_paper_details(self, paper_id: str) -> Optional[PaperMetadata]: + """获取论文详细信息 + + 注意：当前API权限不支持获取详细信息，返回None + + Args: + paper_id: 论文ID + + Returns: + None，因为当前API权限不支持此功能 + """ + return None + + async def fetch_abstract(self, doi: str) -> Optional[str]: + """获取论文摘要 + + 使用Scopus Abstract API获取论文摘要 + + Args: + doi: 论文的DOI + + Returns: + 摘要文本，如果获取失败则返回None + """ + try: + # 使用Abstract API而不是Search API + response = await self._make_request( + f"{self.base_url}/abstract/doi/{doi}", + params={ + "view": "FULL" # 使用FULL视图 + } + ) + + if response and "abstracts-retrieval-response" in response: + # 从coredata中获取摘要 + coredata = response["abstracts-retrieval-response"].get("coredata", {}) + return coredata.get("dc:description", "") + + return None + + except Exception as e: + print(f"获取摘要时发生错误: {str(e)}") + return None + +async def example_usage(): + """ElsevierSource使用示例""" + elsevier = ElsevierSource() + + try: + # 首先测试API访问权限 + print("\n=== 测试API访问权限 ===") + await elsevier.test_api_access() + + # 示例1：基本搜索 + print("\n=== 示例1：搜索机器学习相关论文 ===") + papers = await elsevier.search("machine learning", limit=3) + for i, paper in enumerate(papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"URL: {paper.url}") + print(f"引用次数: {paper.citations}") + print(f"期刊/会议: {paper.venue}") + print("期刊信息:") + for key, value in paper.venue_info.items(): + if value: # 只打印非空值 + print(f" - {key}: {value}") + + # 示例2：获取引用信息 + if papers and papers[0].doi: + print("\n=== 示例2：获取引用该论文的文献 ===") + citations = await elsevier.get_citations(papers[0].doi, limit=3) + for i, paper in enumerate(citations, 1): + print(f"\n--- 引用论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"引用次数: {paper.citations}") + print(f"期刊/会议: {paper.venue}") + + # 示例3：获取参考文献 + if papers and papers[0].doi: + print("\n=== 示例3：获取论文的参考文献 ===") + references = await elsevier.get_references(papers[0].doi) + for i, paper in enumerate(references[:3], 1): + print(f"\n--- 参考文献 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"期刊/会议: {paper.venue}") + + # 示例4：按作者搜索 + print("\n=== 示例4：按作者搜索 ===") + author_papers = await elsevier.search_by_author("Hinton G", limit=3) + for i, paper in enumerate(author_papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"引用次数: {paper.citations}") + print(f"期刊/会议: {paper.venue}") + + # 示例5：按机构搜索 + print("\n=== 示例5：按机构搜索 ===") + affiliation_papers = await elsevier.search_by_affiliation("60027950", limit=3) # MIT的机构ID + for i, paper in enumerate(affiliation_papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"引用次数: {paper.citations}") + print(f"期刊/会议: {paper.venue}") + + # 示例6：获取论文摘要 + print("\n=== 示例6：获取论文摘要 ===") + test_doi = "10.1016/j.artint.2021.103535" + abstract = await elsevier.fetch_abstract(test_doi) + if abstract: + print(f"摘要: {abstract[:200]}...") # 只显示前200个字符 + else: + print("无法获取摘要") + + # 在搜索结果中显示摘要 + print("\n=== 示例7：搜索结果中的摘要 ===") + papers = await elsevier.search("machine learning", limit=1) + for paper in papers: + print(f"标题: {paper.title}") + print(f"摘要: {paper.abstract[:200]}..." if paper.abstract else "摘要: 无") + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/github_source.py b/crazy_functions/review_fns/data_sources/github_source.py new file mode 100644 index 00000000..65965fb2 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/github_source.py @@ -0,0 +1,698 @@ +import aiohttp +import asyncio +import base64 +import json +import random +from datetime import datetime +from typing import List, Dict, Optional, Union, Any + +class GitHubSource: + """GitHub API实现""" + + # 默认API密钥列表 - 可以放置多个GitHub令牌 + API_KEYS = [ + "github_pat_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "github_pat_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + ] + + def __init__(self, api_key: Optional[Union[str, List[str]]] = None): + """初始化GitHub API客户端 + + Args: + api_key: GitHub个人访问令牌或令牌列表 + """ + if api_key is None: + self.api_keys = self.API_KEYS + elif isinstance(api_key, str): + self.api_keys = [api_key] + else: + self.api_keys = api_key + + self._initialize() + + def _initialize(self) -> None: + """初始化客户端，设置默认参数""" + self.base_url = "https://api.github.com" + self.headers = { + "Accept": "application/vnd.github+json", + "X-GitHub-Api-Version": "2022-11-28", + "User-Agent": "GitHub-API-Python-Client" + } + + # 如果有可用的API密钥，随机选择一个 + if self.api_keys: + selected_key = random.choice(self.api_keys) + self.headers["Authorization"] = f"Bearer {selected_key}" + print(f"已随机选择API密钥进行认证") + else: + print("警告: 未提供API密钥，将受到GitHub API请求限制") + + async def _request(self, method: str, endpoint: str, params: Dict = None, data: Dict = None) -> Any: + """发送API请求 + + Args: + method: HTTP方法 (GET, POST, PUT, DELETE等) + endpoint: API端点 + params: URL参数 + data: 请求体数据 + + Returns: + 解析后的响应JSON + """ + async with aiohttp.ClientSession(headers=self.headers) as session: + url = f"{self.base_url}{endpoint}" + + # 为调试目的打印请求信息 + print(f"请求: {method} {url}") + if params: + print(f"参数: {params}") + + # 发送请求 + request_kwargs = {} + if params: + request_kwargs["params"] = params + if data: + request_kwargs["json"] = data + + async with session.request(method, url, **request_kwargs) as response: + response_text = await response.text() + + # 检查HTTP状态码 + if response.status >= 400: + print(f"API请求失败: HTTP {response.status}") + print(f"响应内容: {response_text}") + return None + + # 解析JSON响应 + try: + return json.loads(response_text) + except json.JSONDecodeError: + print(f"JSON解析错误: {response_text}") + return None + + # ===== 用户相关方法 ===== + + async def get_user(self, username: Optional[str] = None) -> Dict: + """获取用户信息 + + Args: + username: 指定用户名，不指定则获取当前授权用户 + + Returns: + 用户信息字典 + """ + endpoint = "/user" if username is None else f"/users/{username}" + return await self._request("GET", endpoint) + + async def get_user_repos(self, username: Optional[str] = None, sort: str = "updated", + direction: str = "desc", per_page: int = 30, page: int = 1) -> List[Dict]: + """获取用户的仓库列表 + + Args: + username: 指定用户名，不指定则获取当前授权用户 + sort: 排序方式 (created, updated, pushed, full_name) + direction: 排序方向 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + 仓库列表 + """ + endpoint = "/user/repos" if username is None else f"/users/{username}/repos" + params = { + "sort": sort, + "direction": direction, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def get_user_starred(self, username: Optional[str] = None, + per_page: int = 30, page: int = 1) -> List[Dict]: + """获取用户星标的仓库 + + Args: + username: 指定用户名，不指定则获取当前授权用户 + per_page: 每页结果数量 + page: 页码 + + Returns: + 星标仓库列表 + """ + endpoint = "/user/starred" if username is None else f"/users/{username}/starred" + params = { + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + # ===== 仓库相关方法 ===== + + async def get_repo(self, owner: str, repo: str) -> Dict: + """获取仓库信息 + + Args: + owner: 仓库所有者 + repo: 仓库名 + + Returns: + 仓库信息 + """ + endpoint = f"/repos/{owner}/{repo}" + return await self._request("GET", endpoint) + + async def get_repo_branches(self, owner: str, repo: str, per_page: int = 30, page: int = 1) -> List[Dict]: + """获取仓库的分支列表 + + Args: + owner: 仓库所有者 + repo: 仓库名 + per_page: 每页结果数量 + page: 页码 + + Returns: + 分支列表 + """ + endpoint = f"/repos/{owner}/{repo}/branches" + params = { + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def get_repo_commits(self, owner: str, repo: str, sha: Optional[str] = None, + path: Optional[str] = None, per_page: int = 30, page: int = 1) -> List[Dict]: + """获取仓库的提交历史 + + Args: + owner: 仓库所有者 + repo: 仓库名 + sha: 特定提交SHA或分支名 + path: 文件路径筛选 + per_page: 每页结果数量 + page: 页码 + + Returns: + 提交列表 + """ + endpoint = f"/repos/{owner}/{repo}/commits" + params = { + "per_page": per_page, + "page": page + } + if sha: + params["sha"] = sha + if path: + params["path"] = path + + return await self._request("GET", endpoint, params=params) + + async def get_commit_details(self, owner: str, repo: str, commit_sha: str) -> Dict: + """获取特定提交的详情 + + Args: + owner: 仓库所有者 + repo: 仓库名 + commit_sha: 提交SHA + + Returns: + 提交详情 + """ + endpoint = f"/repos/{owner}/{repo}/commits/{commit_sha}" + return await self._request("GET", endpoint) + + # ===== 内容相关方法 ===== + + async def get_file_content(self, owner: str, repo: str, path: str, ref: Optional[str] = None) -> Dict: + """获取文件内容 + + Args: + owner: 仓库所有者 + repo: 仓库名 + path: 文件路径 + ref: 分支名、标签名或提交SHA + + Returns: + 文件内容信息 + """ + endpoint = f"/repos/{owner}/{repo}/contents/{path}" + params = {} + if ref: + params["ref"] = ref + + response = await self._request("GET", endpoint, params=params) + if response and isinstance(response, dict) and "content" in response: + try: + # 解码Base64编码的文件内容 + content = base64.b64decode(response["content"].encode()).decode() + response["decoded_content"] = content + except Exception as e: + print(f"解码文件内容时出错: {str(e)}") + + return response + + async def get_directory_content(self, owner: str, repo: str, path: str, ref: Optional[str] = None) -> List[Dict]: + """获取目录内容 + + Args: + owner: 仓库所有者 + repo: 仓库名 + path: 目录路径 + ref: 分支名、标签名或提交SHA + + Returns: + 目录内容列表 + """ + # 注意：此方法与get_file_content使用相同的端点，但对于目录会返回列表 + endpoint = f"/repos/{owner}/{repo}/contents/{path}" + params = {} + if ref: + params["ref"] = ref + + return await self._request("GET", endpoint, params=params) + + # ===== Issues相关方法 ===== + + async def get_issues(self, owner: str, repo: str, state: str = "open", + sort: str = "created", direction: str = "desc", + per_page: int = 30, page: int = 1) -> List[Dict]: + """获取仓库的Issues列表 + + Args: + owner: 仓库所有者 + repo: 仓库名 + state: Issue状态 (open, closed, all) + sort: 排序方式 (created, updated, comments) + direction: 排序方向 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + Issues列表 + """ + endpoint = f"/repos/{owner}/{repo}/issues" + params = { + "state": state, + "sort": sort, + "direction": direction, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def get_issue(self, owner: str, repo: str, issue_number: int) -> Dict: + """获取特定Issue的详情 + + Args: + owner: 仓库所有者 + repo: 仓库名 + issue_number: Issue编号 + + Returns: + Issue详情 + """ + endpoint = f"/repos/{owner}/{repo}/issues/{issue_number}" + return await self._request("GET", endpoint) + + async def get_issue_comments(self, owner: str, repo: str, issue_number: int) -> List[Dict]: + """获取Issue的评论 + + Args: + owner: 仓库所有者 + repo: 仓库名 + issue_number: Issue编号 + + Returns: + 评论列表 + """ + endpoint = f"/repos/{owner}/{repo}/issues/{issue_number}/comments" + return await self._request("GET", endpoint) + + # ===== Pull Requests相关方法 ===== + + async def get_pull_requests(self, owner: str, repo: str, state: str = "open", + sort: str = "created", direction: str = "desc", + per_page: int = 30, page: int = 1) -> List[Dict]: + """获取仓库的Pull Request列表 + + Args: + owner: 仓库所有者 + repo: 仓库名 + state: PR状态 (open, closed, all) + sort: 排序方式 (created, updated, popularity, long-running) + direction: 排序方向 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + Pull Request列表 + """ + endpoint = f"/repos/{owner}/{repo}/pulls" + params = { + "state": state, + "sort": sort, + "direction": direction, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def get_pull_request(self, owner: str, repo: str, pr_number: int) -> Dict: + """获取特定Pull Request的详情 + + Args: + owner: 仓库所有者 + repo: 仓库名 + pr_number: Pull Request编号 + + Returns: + Pull Request详情 + """ + endpoint = f"/repos/{owner}/{repo}/pulls/{pr_number}" + return await self._request("GET", endpoint) + + async def get_pull_request_files(self, owner: str, repo: str, pr_number: int) -> List[Dict]: + """获取Pull Request中修改的文件 + + Args: + owner: 仓库所有者 + repo: 仓库名 + pr_number: Pull Request编号 + + Returns: + 修改文件列表 + """ + endpoint = f"/repos/{owner}/{repo}/pulls/{pr_number}/files" + return await self._request("GET", endpoint) + + # ===== 搜索相关方法 ===== + + async def search_repositories(self, query: str, sort: str = "stars", + order: str = "desc", per_page: int = 30, page: int = 1) -> Dict: + """搜索仓库 + + Args: + query: 搜索关键词 + sort: 排序方式 (stars, forks, updated) + order: 排序顺序 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + 搜索结果 + """ + endpoint = "/search/repositories" + params = { + "q": query, + "sort": sort, + "order": order, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def search_code(self, query: str, sort: str = "indexed", + order: str = "desc", per_page: int = 30, page: int = 1) -> Dict: + """搜索代码 + + Args: + query: 搜索关键词 + sort: 排序方式 (indexed) + order: 排序顺序 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + 搜索结果 + """ + endpoint = "/search/code" + params = { + "q": query, + "sort": sort, + "order": order, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def search_issues(self, query: str, sort: str = "created", + order: str = "desc", per_page: int = 30, page: int = 1) -> Dict: + """搜索Issues和Pull Requests + + Args: + query: 搜索关键词 + sort: 排序方式 (created, updated, comments) + order: 排序顺序 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + 搜索结果 + """ + endpoint = "/search/issues" + params = { + "q": query, + "sort": sort, + "order": order, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def search_users(self, query: str, sort: str = "followers", + order: str = "desc", per_page: int = 30, page: int = 1) -> Dict: + """搜索用户 + + Args: + query: 搜索关键词 + sort: 排序方式 (followers, repositories, joined) + order: 排序顺序 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + 搜索结果 + """ + endpoint = "/search/users" + params = { + "q": query, + "sort": sort, + "order": order, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + # ===== 组织相关方法 ===== + + async def get_organization(self, org: str) -> Dict: + """获取组织信息 + + Args: + org: 组织名称 + + Returns: + 组织信息 + """ + endpoint = f"/orgs/{org}" + return await self._request("GET", endpoint) + + async def get_organization_repos(self, org: str, type: str = "all", + sort: str = "created", direction: str = "desc", + per_page: int = 30, page: int = 1) -> List[Dict]: + """获取组织的仓库列表 + + Args: + org: 组织名称 + type: 仓库类型 (all, public, private, forks, sources, member, internal) + sort: 排序方式 (created, updated, pushed, full_name) + direction: 排序方向 (asc, desc) + per_page: 每页结果数量 + page: 页码 + + Returns: + 仓库列表 + """ + endpoint = f"/orgs/{org}/repos" + params = { + "type": type, + "sort": sort, + "direction": direction, + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + async def get_organization_members(self, org: str, per_page: int = 30, page: int = 1) -> List[Dict]: + """获取组织成员列表 + + Args: + org: 组织名称 + per_page: 每页结果数量 + page: 页码 + + Returns: + 成员列表 + """ + endpoint = f"/orgs/{org}/members" + params = { + "per_page": per_page, + "page": page + } + return await self._request("GET", endpoint, params=params) + + # ===== 更复杂的操作 ===== + + async def get_repository_languages(self, owner: str, repo: str) -> Dict: + """获取仓库使用的编程语言及其比例 + + Args: + owner: 仓库所有者 + repo: 仓库名 + + Returns: + 语言使用情况 + """ + endpoint = f"/repos/{owner}/{repo}/languages" + return await self._request("GET", endpoint) + + async def get_repository_stats_contributors(self, owner: str, repo: str) -> List[Dict]: + """获取仓库的贡献者统计 + + Args: + owner: 仓库所有者 + repo: 仓库名 + + Returns: + 贡献者统计信息 + """ + endpoint = f"/repos/{owner}/{repo}/stats/contributors" + return await self._request("GET", endpoint) + + async def get_repository_stats_commit_activity(self, owner: str, repo: str) -> List[Dict]: + """获取仓库的提交活动 + + Args: + owner: 仓库所有者 + repo: 仓库名 + + Returns: + 提交活动统计 + """ + endpoint = f"/repos/{owner}/{repo}/stats/commit_activity" + return await self._request("GET", endpoint) + +async def example_usage(): + """GitHubSource使用示例""" + # 创建客户端实例（可选传入API令牌） + # github = GitHubSource(api_key="your_github_token") + github = GitHubSource() + + try: + # 示例1：搜索热门Python仓库 + print("\n=== 示例1：搜索热门Python仓库 ===") + repos = await github.search_repositories( + query="language:python stars:>1000", + sort="stars", + order="desc", + per_page=5 + ) + + if repos and "items" in repos: + for i, repo in enumerate(repos["items"], 1): + print(f"\n--- 仓库 {i} ---") + print(f"名称: {repo['full_name']}") + print(f"描述: {repo['description']}") + print(f"星标数: {repo['stargazers_count']}") + print(f"Fork数: {repo['forks_count']}") + print(f"最近更新: {repo['updated_at']}") + print(f"URL: {repo['html_url']}") + + # 示例2：获取特定仓库的详情 + print("\n=== 示例2：获取特定仓库的详情 ===") + repo_details = await github.get_repo("microsoft", "vscode") + if repo_details: + print(f"名称: {repo_details['full_name']}") + print(f"描述: {repo_details['description']}") + print(f"星标数: {repo_details['stargazers_count']}") + print(f"Fork数: {repo_details['forks_count']}") + print(f"默认分支: {repo_details['default_branch']}") + print(f"开源许可: {repo_details.get('license', {}).get('name', '无')}") + print(f"语言: {repo_details['language']}") + print(f"Open Issues数: {repo_details['open_issues_count']}") + + # 示例3：获取仓库的提交历史 + print("\n=== 示例3：获取仓库的最近提交 ===") + commits = await github.get_repo_commits("tensorflow", "tensorflow", per_page=5) + if commits: + for i, commit in enumerate(commits, 1): + print(f"\n--- 提交 {i} ---") + print(f"SHA: {commit['sha'][:7]}") + print(f"作者: {commit['commit']['author']['name']}") + print(f"日期: {commit['commit']['author']['date']}") + print(f"消息: {commit['commit']['message'].splitlines()[0]}") + + # 示例4：搜索代码 + print("\n=== 示例4：搜索代码 ===") + code_results = await github.search_code( + query="filename:README.md language:markdown pytorch in:file", + per_page=3 + ) + if code_results and "items" in code_results: + print(f"共找到: {code_results['total_count']} 个结果") + for i, item in enumerate(code_results["items"], 1): + print(f"\n--- 代码 {i} ---") + print(f"仓库: {item['repository']['full_name']}") + print(f"文件: {item['path']}") + print(f"URL: {item['html_url']}") + + # 示例5：获取文件内容 + print("\n=== 示例5：获取文件内容 ===") + file_content = await github.get_file_content("python", "cpython", "README.rst") + if file_content and "decoded_content" in file_content: + content = file_content["decoded_content"] + print(f"文件名: {file_content['name']}") + print(f"大小: {file_content['size']} 字节") + print(f"内容预览: {content[:200]}...") + + # 示例6：获取仓库使用的编程语言 + print("\n=== 示例6：获取仓库使用的编程语言 ===") + languages = await github.get_repository_languages("facebook", "react") + if languages: + print(f"React仓库使用的编程语言:") + for lang, bytes_of_code in languages.items(): + print(f"- {lang}: {bytes_of_code} 字节") + + # 示例7：获取组织信息 + print("\n=== 示例7：获取组织信息 ===") + org_info = await github.get_organization("google") + if org_info: + print(f"名称: {org_info['name']}") + print(f"描述: {org_info.get('description', '无')}") + print(f"位置: {org_info.get('location', '未指定')}") + print(f"公共仓库数: {org_info['public_repos']}") + print(f"成员数: {org_info.get('public_members', 0)}") + print(f"URL: {org_info['html_url']}") + + # 示例8：获取用户信息 + print("\n=== 示例8：获取用户信息 ===") + user_info = await github.get_user("torvalds") + if user_info: + print(f"名称: {user_info['name']}") + print(f"公司: {user_info.get('company', '无')}") + print(f"博客: {user_info.get('blog', '无')}") + print(f"位置: {user_info.get('location', '未指定')}") + print(f"公共仓库数: {user_info['public_repos']}") + print(f"关注者数: {user_info['followers']}") + print(f"URL: {user_info['html_url']}") + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + import asyncio + + # 运行示例 + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/journal_metrics.py b/crazy_functions/review_fns/data_sources/journal_metrics.py new file mode 100644 index 00000000..2b4ff413 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/journal_metrics.py @@ -0,0 +1,142 @@ +import json +import os +from typing import Dict, Optional + +class JournalMetrics: + """期刊指标管理类""" + + def __init__(self): + self.journal_data: Dict = {} # 期刊名称到指标的映射 + self.issn_map: Dict = {} # ISSN到指标的映射 + self.name_map: Dict = {} # 标准化名称到指标的映射 + self._load_journal_data() + + def _normalize_journal_name(self, name: str) -> str: + """标准化期刊名称 + + Args: + name: 原始期刊名称 + + Returns: + 标准化后的期刊名称 + """ + if not name: + return "" + + # 转换为小写 + name = name.lower() + + # 移除常见的前缀和后缀 + prefixes = ['the ', 'proceedings of ', 'journal of '] + suffixes = [' journal', ' proceedings', ' magazine', ' review', ' letters'] + + for prefix in prefixes: + if name.startswith(prefix): + name = name[len(prefix):] + + for suffix in suffixes: + if name.endswith(suffix): + name = name[:-len(suffix)] + + # 移除特殊字符，保留字母、数字和空格 + name = ''.join(c for c in name if c.isalnum() or c.isspace()) + + # 移除多余的空格 + name = ' '.join(name.split()) + + return name + + def _convert_if_value(self, if_str: str) -> Optional[float]: + """转换IF值为float，处理特殊情况""" + try: + if if_str.startswith('<'): + # 对于<0.1这样的值，返回0.1 + return float(if_str.strip('<')) + return float(if_str) + except (ValueError, AttributeError): + return None + + def _load_journal_data(self): + """加载期刊数据""" + try: + file_path = os.path.join(os.path.dirname(__file__), 'cas_if.json') + with open(file_path, 'r', encoding='utf-8') as f: + data = json.load(f) + + # 建立期刊名称到指标的映射 + for journal in data: + # 准备指标数据 + metrics = { + 'if_factor': self._convert_if_value(journal.get('IF')), + 'jcr_division': journal.get('Q'), + 'cas_division': journal.get('B') + } + + # 存储期刊名称映射（使用标准化名称） + if journal.get('journal'): + normalized_name = self._normalize_journal_name(journal['journal']) + self.journal_data[normalized_name] = metrics + self.name_map[normalized_name] = metrics + + # 存储期刊缩写映射 + if journal.get('jabb'): + normalized_abbr = self._normalize_journal_name(journal['jabb']) + self.journal_data[normalized_abbr] = metrics + self.name_map[normalized_abbr] = metrics + + # 存储ISSN映射 + if journal.get('issn'): + self.issn_map[journal['issn']] = metrics + if journal.get('eissn'): + self.issn_map[journal['eissn']] = metrics + + except Exception as e: + print(f"加载期刊数据时出错: {str(e)}") + self.journal_data = {} + self.issn_map = {} + self.name_map = {} + + def get_journal_metrics(self, venue_name: str, venue_info: dict) -> dict: + """获取期刊指标 + + Args: + venue_name: 期刊名称 + venue_info: 期刊详细信息 + + Returns: + 包含期刊指标的字典 + """ + try: + metrics = {} + + # 1. 首先尝试通过ISSN匹配 + if venue_info and 'issn' in venue_info: + issn_value = venue_info['issn'] + # 处理ISSN可能是列表的情况 + if isinstance(issn_value, list): + # 尝试每个ISSN + for issn in issn_value: + metrics = self.issn_map.get(issn, {}) + if metrics: # 如果找到匹配的指标，就停止搜索 + break + else: # ISSN是字符串的情况 + metrics = self.issn_map.get(issn_value, {}) + + # 2. 如果ISSN匹配失败，尝试通过期刊名称匹配 + if not metrics and venue_name: + # 标准化期刊名称 + normalized_name = self._normalize_journal_name(venue_name) + metrics = self.name_map.get(normalized_name, {}) + + # 如果完全匹配失败，尝试部分匹配 + # if not metrics: + # for db_name, db_metrics in self.name_map.items(): + # if normalized_name in db_name: + # metrics = db_metrics + # break + + return metrics + + except Exception as e: + print(f"获取期刊指标时出错: {str(e)}") + return {} \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/openalex_source.py b/crazy_functions/review_fns/data_sources/openalex_source.py new file mode 100644 index 00000000..e3630886 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/openalex_source.py @@ -0,0 +1,163 @@ +import aiohttp +from typing import List, Dict, Optional +from datetime import datetime +from .base_source import DataSource, PaperMetadata +import os +from urllib.parse import quote + +class OpenAlexSource(DataSource): + """OpenAlex API实现""" + + def _initialize(self) -> None: + self.base_url = "https://api.openalex.org" + self.mailto = "xxxxxxxxxxxxxxxxxxxxxxxx@163.com" # 直接写入邮件地址 + + async def search(self, query: str, limit: int = 100) -> List[PaperMetadata]: + params = {"mailto": self.mailto} if self.mailto else {} + params.update({ + "filter": f"title.search:{query}", + "per-page": limit + }) + + async with aiohttp.ClientSession() as session: + async with session.get( + f"{self.base_url}/works", + params=params + ) as response: + try: + response.raise_for_status() + data = await response.json() + results = data.get("results", []) + return [self._parse_work(work) for work in results] + except Exception as e: + print(f"搜索出错: {str(e)}") + return [] + + def _parse_work(self, work: Dict) -> PaperMetadata: + """解析OpenAlex返回的数据""" + # 获取作者信息 + raw_author_names = [ + authorship.get("raw_author_name", "") + for authorship in work.get("authorships", []) + if authorship + ] + # 处理作者名字格式 + authors = [ + self._reformat_name(author) + for author in raw_author_names + ] + + # 获取机构信息 + institutions = [ + inst.get("display_name", "") + for authorship in work.get("authorships", []) + for inst in authorship.get("institutions", []) + if inst + ] + + # 获取主要发表位置信息 + primary_location = work.get("primary_location") or {} + source = primary_location.get("source") or {} + venue = source.get("display_name") + + # 获取发表日期 + year = work.get("publication_year") + + return PaperMetadata( + title=work.get("title", ""), + authors=authors, + institutions=institutions, + abstract=work.get("abstract", ""), + year=year, + doi=work.get("doi"), + url=work.get("doi"), # OpenAlex 使用 DOI 作为 URL + citations=work.get("cited_by_count"), + venue=venue + ) + + def _reformat_name(self, name: str) -> str: + """重新格式化作者名字""" + if "," not in name: + return name + family, given_names = (x.strip() for x in name.split(",", maxsplit=1)) + return f"{given_names} {family}" + + async def get_paper_details(self, doi: str) -> PaperMetadata: + """获取指定DOI的论文详情""" + params = {"mailto": self.mailto} if self.mailto else {} + async with aiohttp.ClientSession() as session: + async with session.get( + f"{self.base_url}/works/https://doi.org/{quote(doi, safe='')}", + params=params + ) as response: + data = await response.json() + return self._parse_work(data) + + async def get_references(self, doi: str) -> List[PaperMetadata]: + """获取指定DOI论文的参考文献列表""" + params = {"mailto": self.mailto} if self.mailto else {} + async with aiohttp.ClientSession() as session: + async with session.get( + f"{self.base_url}/works/https://doi.org/{quote(doi, safe='')}/references", + params=params + ) as response: + data = await response.json() + return [self._parse_work(work) for work in data.get("results", [])] + + async def get_citations(self, doi: str) -> List[PaperMetadata]: + """获取引用指定DOI论文的文献列表""" + params = {"mailto": self.mailto} if self.mailto else {} + params.update({ + "filter": f"cites:doi:{doi}", + "per-page": 100 + }) + + async with aiohttp.ClientSession() as session: + async with session.get( + f"{self.base_url}/works", + params=params + ) as response: + data = await response.json() + return [self._parse_work(work) for work in data.get("results", [])] + +async def example_usage(): + """OpenAlexSource使用示例""" + # 初始化OpenAlexSource + openalex = OpenAlexSource() + + try: + print("正在搜索论文...") + # 搜索与"artificial intelligence"相关的论文，限制返回5篇 + papers = await openalex.search(query="artificial intelligence", limit=5) + + if not papers: + print("未获取到任何论文信息") + return + + print(f"找到 {len(papers)} 篇论文") + + # 打印搜索结果 + for i, paper in enumerate(papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors) if paper.authors else '未知'}") + if paper.institutions: + print(f"机构: {', '.join(paper.institutions)}") + print(f"发表年份: {paper.year if paper.year else '未知'}") + print(f"DOI: {paper.doi if paper.doi else '未知'}") + print(f"URL: {paper.url if paper.url else '未知'}") + if paper.abstract: + print(f"摘要: {paper.abstract[:200]}...") + print(f"引用次数: {paper.citations if paper.citations is not None else '未知'}") + print(f"发表venue: {paper.venue if paper.venue else '未知'}") + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +# 如果直接运行此文件，执行示例代码 +if __name__ == "__main__": + import asyncio + + # 运行示例 + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/pubmed_source.py b/crazy_functions/review_fns/data_sources/pubmed_source.py new file mode 100644 index 00000000..1b492417 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/pubmed_source.py @@ -0,0 +1,458 @@ +from typing import List, Optional, Dict, Union +from datetime import datetime +import aiohttp +import asyncio +from crazy_functions.review_fns.data_sources.base_source import DataSource, PaperMetadata +import xml.etree.ElementTree as ET +from urllib.parse import quote +import json +from tqdm import tqdm +import random + +class PubMedSource(DataSource): + """PubMed API实现""" + + # 定义API密钥列表 + API_KEYS = [ + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" + ] + + def __init__(self, api_key: str = None): + """初始化 + + Args: + api_key: PubMed API密钥，如果不提供则从预定义列表中随机选择 + """ + self.api_key = api_key or random.choice(self.API_KEYS) # 随机选择一个API密钥 + self._initialize() + + def _initialize(self) -> None: + """初始化基础URL和请求头""" + self.base_url = "https://eutils.ncbi.nlm.nih.gov/entrez/eutils" + self.headers = { + "User-Agent": "Mozilla/5.0 PubMedDataSource/1.0", + "Accept": "application/json" + } + + async def _make_request(self, url: str) -> Optional[str]: + """发送HTTP请求 + + Args: + url: 请求URL + + Returns: + 响应内容 + """ + try: + async with aiohttp.ClientSession(headers=self.headers) as session: + async with session.get(url) as response: + if response.status == 200: + return await response.text() + else: + print(f"请求失败: {response.status}") + return None + except Exception as e: + print(f"请求发生错误: {str(e)}") + return None + + async def search( + self, + query: str, + limit: int = 100, + sort_by: str = "relevance", + start_year: int = None + ) -> List[PaperMetadata]: + """搜索论文 + + Args: + query: 搜索关键词 + limit: 返回结果数量限制 + sort_by: 排序方式 ('relevance', 'date', 'citations') + start_year: 起始年份 + + Returns: + 论文列表 + """ + try: + # 添加年份过滤 + if start_year: + query = f"{query} AND {start_year}:3000[dp]" + + # 构建搜索URL + search_url = ( + f"{self.base_url}/esearch.fcgi?" + f"db=pubmed&term={quote(query)}&retmax={limit}" + f"&usehistory=y&api_key={self.api_key}" + ) + + if sort_by == "date": + search_url += "&sort=date" + + # 获取搜索结果 + response = await self._make_request(search_url) + if not response: + return [] + + # 解析XML响应 + root = ET.fromstring(response) + id_list = root.findall(".//Id") + pmids = [id_elem.text for id_elem in id_list] + + if not pmids: + return [] + + # 批量获取论文详情 + papers = [] + batch_size = 50 + for i in range(0, len(pmids), batch_size): + batch = pmids[i:i + batch_size] + batch_papers = await self._fetch_papers_batch(batch) + papers.extend(batch_papers) + + return papers + + except Exception as e: + print(f"搜索论文时发生错误: {str(e)}") + return [] + + async def _fetch_papers_batch(self, pmids: List[str]) -> List[PaperMetadata]: + """批量获取论文详情 + + Args: + pmids: PubMed ID列表 + + Returns: + 论文详情列表 + """ + try: + # 构建批量获取URL + fetch_url = ( + f"{self.base_url}/efetch.fcgi?" + f"db=pubmed&id={','.join(pmids)}" + f"&retmode=xml&api_key={self.api_key}" + ) + + response = await self._make_request(fetch_url) + if not response: + return [] + + # 解析XML响应 + root = ET.fromstring(response) + articles = root.findall(".//PubmedArticle") + + return [self._parse_article(article) for article in articles] + + except Exception as e: + print(f"获取论文批次时发生错误: {str(e)}") + return [] + + def _parse_article(self, article: ET.Element) -> PaperMetadata: + """解析PubMed文章XML + + Args: + article: XML元素 + + Returns: + 解析后的论文数据 + """ + try: + # 提取基本信息 + pmid = article.find(".//PMID").text + article_meta = article.find(".//Article") + + # 获取标题 + title = article_meta.find(".//ArticleTitle") + title = title.text if title is not None else "" + + # 获取作者列表 + authors = [] + author_list = article_meta.findall(".//Author") + for author in author_list: + last_name = author.find("LastName") + fore_name = author.find("ForeName") + if last_name is not None and fore_name is not None: + authors.append(f"{fore_name.text} {last_name.text}") + elif last_name is not None: + authors.append(last_name.text) + + # 获取摘要 + abstract = article_meta.find(".//Abstract/AbstractText") + abstract = abstract.text if abstract is not None else "" + + # 获取发表年份 + pub_date = article_meta.find(".//PubDate/Year") + year = int(pub_date.text) if pub_date is not None else None + + # 获取DOI + doi = article.find(".//ELocationID[@EIdType='doi']") + doi = doi.text if doi is not None else None + + # 获取期刊信息 + journal = article_meta.find(".//Journal") + if journal is not None: + journal_title = journal.find(".//Title") + venue = journal_title.text if journal_title is not None else None + + # 获取期刊详细信息 + venue_info = { + 'issn': journal.findtext(".//ISSN"), + 'volume': journal.findtext(".//Volume"), + 'issue': journal.findtext(".//Issue"), + 'pub_date': journal.findtext(".//PubDate/MedlineDate") or + f"{journal.findtext('.//PubDate/Year', '')}-{journal.findtext('.//PubDate/Month', '')}" + } + else: + venue = None + venue_info = {} + + # 获取机构信息 + institutions = [] + affiliations = article_meta.findall(".//Affiliation") + for affiliation in affiliations: + if affiliation is not None and affiliation.text: + institutions.append(affiliation.text) + + return PaperMetadata( + title=title, + authors=authors, + abstract=abstract, + year=year, + doi=doi, + url=f"https://pubmed.ncbi.nlm.nih.gov/{pmid}/" if pmid else None, + citations=None, # PubMed API不直接提供引用数据 + venue=venue, + institutions=institutions, + venue_type="journal", + venue_name=venue, + venue_info=venue_info, + source='pubmed' # 添加来源标记 + ) + + except Exception as e: + print(f"解析文章时发生错误: {str(e)}") + return None + + async def get_paper_details(self, pmid: str) -> Optional[PaperMetadata]: + """获取指定PMID的论文详情""" + papers = await self._fetch_papers_batch([pmid]) + return papers[0] if papers else None + + async def get_related_papers(self, pmid: str, limit: int = 100) -> List[PaperMetadata]: + """获取相关论文 + + 使用PubMed的相关文章功能 + + Args: + pmid: PubMed ID + limit: 返回结果数量限制 + + Returns: + 相关论文列表 + """ + try: + # 构建相关文章URL + link_url = ( + f"{self.base_url}/elink.fcgi?" + f"db=pubmed&id={pmid}&cmd=neighbor&api_key={self.api_key}" + ) + + response = await self._make_request(link_url) + if not response: + return [] + + # 解析XML响应 + root = ET.fromstring(response) + related_ids = root.findall(".//Link/Id") + pmids = [id_elem.text for id_elem in related_ids][:limit] + + if not pmids: + return [] + + # 获取相关论文详情 + return await self._fetch_papers_batch(pmids) + + except Exception as e: + print(f"获取相关论文时发生错误: {str(e)}") + return [] + + async def search_by_author( + self, + author: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按作者搜索论文""" + query = f"{author}[Author]" + if start_year: + query += f" AND {start_year}:3000[dp]" + return await self.search(query, limit=limit) + + async def search_by_journal( + self, + journal: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按期刊搜索论文""" + query = f"{journal}[Journal]" + if start_year: + query += f" AND {start_year}:3000[dp]" + return await self.search(query, limit=limit) + + async def get_latest_papers( + self, + days: int = 7, + limit: int = 100 + ) -> List[PaperMetadata]: + """获取最新论文 + + Args: + days: 最近几天的论文 + limit: 返回结果数量限制 + + Returns: + 最新论文列表 + """ + query = f"last {days} days[dp]" + return await self.search(query, limit=limit, sort_by="date") + + async def get_citations(self, paper_id: str) -> List[PaperMetadata]: + """获取引用该论文的文献 + + 注意：PubMed API本身不提供引用数据，此方法将返回空列表 + 未来可以考虑集成其他数据源(如CrossRef)来获取引用信息 + + Args: + paper_id: PubMed ID + + Returns: + 空列表，因为PubMed不提供引用数据 + """ + return [] + + async def get_references(self, paper_id: str) -> List[PaperMetadata]: + """获取该论文引用的文献 + + 从PubMed文章的参考文献列表获取引用的文献 + + Args: + paper_id: PubMed ID + + Returns: + 引用的文献列表 + """ + try: + # 构建获取参考文献的URL + refs_url = ( + f"{self.base_url}/elink.fcgi?" + f"dbfrom=pubmed&db=pubmed&id={paper_id}" + f"&cmd=neighbor_history&linkname=pubmed_pubmed_refs" + f"&api_key={self.api_key}" + ) + + response = await self._make_request(refs_url) + if not response: + return [] + + # 解析XML响应 + root = ET.fromstring(response) + ref_ids = root.findall(".//Link/Id") + pmids = [id_elem.text for id_elem in ref_ids] + + if not pmids: + return [] + + # 获取参考文献详情 + return await self._fetch_papers_batch(pmids) + + except Exception as e: + print(f"获取参考文献时发生错误: {str(e)}") + return [] + +async def example_usage(): + """PubMedSource使用示例""" + pubmed = PubMedSource() + + try: + # 示例1：基本搜索 + print("\n=== 示例1：搜索COVID-19相关论文 ===") + papers = await pubmed.search("COVID-19", limit=3) + for i, paper in enumerate(papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + if paper.abstract: + print(f"摘要: {paper.abstract[:200]}...") + + # 示例2：获取论文详情 + if papers: + print("\n=== 示例2：获取论文详情 ===") + paper_id = papers[0].url.split("/")[-2] + paper = await pubmed.get_paper_details(paper_id) + if paper: + print(f"标题: {paper.title}") + print(f"期刊: {paper.venue}") + print(f"机构: {', '.join(paper.institutions)}") + + # 示例3：获取相关论文 + if papers: + print("\n=== 示例3：获取相关论文 ===") + related = await pubmed.get_related_papers(paper_id, limit=3) + for i, paper in enumerate(related, 1): + print(f"\n--- 相关论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + + # 示例4：按作者搜索 + print("\n=== 示例4：按作者搜索 ===") + author_papers = await pubmed.search_by_author("Fauci AS", limit=3) + for i, paper in enumerate(author_papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"发表年份: {paper.year}") + + # 示例5：按期刊搜索 + print("\n=== 示例5：按期刊搜索 ===") + journal_papers = await pubmed.search_by_journal("Nature", limit=3) + for i, paper in enumerate(journal_papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"发表年份: {paper.year}") + + # 示例6：获取最新论文 + print("\n=== 示例6：获取最新论文 ===") + latest = await pubmed.get_latest_papers(days=7, limit=3) + for i, paper in enumerate(latest, 1): + print(f"\n--- 最新论文 {i} ---") + print(f"标题: {paper.title}") + print(f"发表日期: {paper.venue_info.get('pub_date')}") + + # 示例7：获取论文的参考文献 + if papers: + print("\n=== 示例7：获取论文的参考文献 ===") + paper_id = papers[0].url.split("/")[-2] + references = await pubmed.get_references(paper_id) + for i, paper in enumerate(references[:3], 1): + print(f"\n--- 参考文献 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + + # 示例8：尝试获取引用信息（将返回空列表） + if papers: + print("\n=== 示例8：获取论文的引用信息 ===") + paper_id = papers[0].url.split("/")[-2] + citations = await pubmed.get_citations(paper_id) + print(f"引用数据：{len(citations)} (PubMed API不提供引用信息)") + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + asyncio.run(example_usage()) diff --git a/crazy_functions/review_fns/data_sources/scihub_source.py b/crazy_functions/review_fns/data_sources/scihub_source.py new file mode 100644 index 00000000..f56030c7 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/scihub_source.py @@ -0,0 +1,326 @@ +from pathlib import Path +import requests +from bs4 import BeautifulSoup +import time +from loguru import logger +import PyPDF2 +import io + + +class SciHub: + # 更新的镜像列表，包含更多可用的镜像 + MIRRORS = [ + 'https://sci-hub.se/', + 'https://sci-hub.st/', + 'https://sci-hub.ru/', + 'https://sci-hub.wf/', + 'https://sci-hub.ee/', + 'https://sci-hub.ren/', + 'https://sci-hub.tf/', + 'https://sci-hub.si/', + 'https://sci-hub.do/', + 'https://sci-hub.hkvisa.net/', + 'https://sci-hub.mksa.top/', + 'https://sci-hub.shop/', + 'https://sci-hub.yncjkj.com/', + 'https://sci-hub.41610.org/', + 'https://sci-hub.automic.us/', + 'https://sci-hub.et-fine.com/', + 'https://sci-hub.pooh.mu/', + 'https://sci-hub.bban.top/', + 'https://sci-hub.usualwant.com/', + 'https://sci-hub.unblockit.kim/' + ] + + def __init__(self, doi: str, path: Path, url=None, timeout=60, use_proxy=True): + self.timeout = timeout + self.path = path + self.doi = str(doi) + self.use_proxy = use_proxy + self.headers = { + 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/120.0.0.0 Safari/537.36', + } + self.payload = { + 'sci-hub-plugin-check': '', + 'request': self.doi + } + self.url = url if url else self.MIRRORS[0] + self.proxies = { + "http": "socks5h://localhost:10880", + "https": "socks5h://localhost:10880", + } if use_proxy else None + + def _test_proxy_connection(self): + """测试代理连接是否可用""" + if not self.use_proxy: + return True + + try: + # 测试代理连接 + test_response = requests.get( + 'https://httpbin.org/ip', + proxies=self.proxies, + timeout=10 + ) + if test_response.status_code == 200: + logger.info("代理连接测试成功") + return True + except Exception as e: + logger.warning(f"代理连接测试失败: {str(e)}") + return False + return False + + def _check_pdf_validity(self, content): + """检查PDF文件是否有效""" + try: + # 使用PyPDF2检查PDF是否可以正常打开和读取 + pdf = PyPDF2.PdfReader(io.BytesIO(content)) + if len(pdf.pages) > 0: + return True + return False + except Exception as e: + logger.error(f"PDF文件无效: {str(e)}") + return False + + def _send_request(self): + """发送请求到Sci-Hub镜像站点""" + # 首先测试代理连接 + if self.use_proxy and not self._test_proxy_connection(): + logger.warning("代理连接不可用，切换到直连模式") + self.use_proxy = False + self.proxies = None + + last_exception = None + working_mirrors = [] + + # 先测试哪些镜像可用 + logger.info("正在测试镜像站点可用性...") + for mirror in self.MIRRORS: + try: + test_response = requests.get( + mirror, + headers=self.headers, + proxies=self.proxies, + timeout=10 + ) + if test_response.status_code == 200: + working_mirrors.append(mirror) + logger.info(f"镜像 {mirror} 可用") + if len(working_mirrors) >= 5: # 找到5个可用镜像就够了 + break + except Exception as e: + logger.debug(f"镜像 {mirror} 不可用: {str(e)}") + continue + + if not working_mirrors: + raise Exception("没有找到可用的镜像站点") + + logger.info(f"找到 {len(working_mirrors)} 个可用镜像，开始尝试下载...") + + # 使用可用的镜像进行下载 + for mirror in working_mirrors: + try: + res = requests.post( + mirror, + headers=self.headers, + data=self.payload, + proxies=self.proxies, + timeout=self.timeout + ) + if res.ok: + logger.info(f"成功使用镜像站点: {mirror}") + self.url = mirror # 更新当前使用的镜像 + time.sleep(1) # 降低等待时间以提高效率 + return res + except Exception as e: + logger.error(f"尝试镜像 {mirror} 失败: {str(e)}") + last_exception = e + continue + + if last_exception: + raise last_exception + raise Exception("所有可用镜像站点均无法完成下载") + + def _extract_url(self, response): + """从响应中提取PDF下载链接""" + soup = BeautifulSoup(response.content, 'html.parser') + try: + # 尝试多种方式提取PDF链接 + pdf_element = soup.find(id='pdf') + if pdf_element: + content_url = pdf_element.get('src') + else: + # 尝试其他可能的选择器 + pdf_element = soup.find('iframe') + if pdf_element: + content_url = pdf_element.get('src') + else: + # 查找直接的PDF链接 + pdf_links = soup.find_all('a', href=lambda x: x and '.pdf' in x) + if pdf_links: + content_url = pdf_links[0].get('href') + else: + raise AttributeError("未找到PDF链接") + + if content_url: + content_url = content_url.replace('#navpanes=0&view=FitH', '').replace('//', '/') + if not content_url.endswith('.pdf') and 'pdf' not in content_url.lower(): + raise AttributeError("找到的链接不是PDF文件") + except AttributeError: + logger.error(f"未找到论文 {self.doi}") + return None + + current_mirror = self.url.rstrip('/') + if content_url.startswith('/'): + return current_mirror + content_url + elif content_url.startswith('http'): + return content_url + else: + return 'https:/' + content_url + + def _download_pdf(self, pdf_url): + """下载PDF文件并验证其完整性""" + try: + # 尝试不同的下载方式 + download_methods = [ + # 方法1：直接下载 + lambda: requests.get(pdf_url, proxies=self.proxies, timeout=self.timeout), + # 方法2：添加 Referer 头 + lambda: requests.get(pdf_url, proxies=self.proxies, timeout=self.timeout, + headers={**self.headers, 'Referer': self.url}), + # 方法3：使用原始域名作为 Referer + lambda: requests.get(pdf_url, proxies=self.proxies, timeout=self.timeout, + headers={**self.headers, 'Referer': pdf_url.split('/downloads')[0] if '/downloads' in pdf_url else self.url}) + ] + + for i, download_method in enumerate(download_methods): + try: + logger.info(f"尝试下载方式 {i+1}/3...") + response = download_method() + if response.status_code == 200: + content = response.content + if len(content) > 1000 and self._check_pdf_validity(content): # 确保文件不是太小 + logger.info(f"PDF下载成功，文件大小: {len(content)} bytes") + return content + else: + logger.warning("下载的文件可能不是有效的PDF") + elif response.status_code == 403: + logger.warning(f"访问被拒绝 (403 Forbidden)，尝试其他下载方式") + continue + else: + logger.warning(f"下载失败，状态码: {response.status_code}") + continue + except Exception as e: + logger.warning(f"下载方式 {i+1} 失败: {str(e)}") + continue + + # 如果所有方法都失败，尝试构造替代URL + try: + logger.info("尝试使用替代镜像下载...") + # 从原始URL提取关键信息 + if '/downloads/' in pdf_url: + file_part = pdf_url.split('/downloads/')[-1] + alternative_mirrors = [ + f"https://sci-hub.se/downloads/{file_part}", + f"https://sci-hub.st/downloads/{file_part}", + f"https://sci-hub.ru/downloads/{file_part}", + f"https://sci-hub.wf/downloads/{file_part}", + f"https://sci-hub.ee/downloads/{file_part}", + f"https://sci-hub.ren/downloads/{file_part}", + f"https://sci-hub.tf/downloads/{file_part}" + ] + + for alt_url in alternative_mirrors: + try: + response = requests.get( + alt_url, + proxies=self.proxies, + timeout=self.timeout, + headers={**self.headers, 'Referer': alt_url.split('/downloads')[0]} + ) + if response.status_code == 200: + content = response.content + if len(content) > 1000 and self._check_pdf_validity(content): + logger.info(f"使用替代镜像成功下载: {alt_url}") + return content + except Exception as e: + logger.debug(f"替代镜像 {alt_url} 下载失败: {str(e)}") + continue + + except Exception as e: + logger.error(f"所有下载方式都失败: {str(e)}") + + return None + + except Exception as e: + logger.error(f"下载PDF文件失败: {str(e)}") + return None + + def fetch(self): + """获取论文PDF，包含重试和验证机制""" + for attempt in range(2): # 最多重试3次 + try: + logger.info(f"开始第 {attempt + 1} 次尝试下载论文: {self.doi}") + + # 获取PDF下载链接 + response = self._send_request() + pdf_url = self._extract_url(response) + if pdf_url is None: + logger.warning(f"第 {attempt + 1} 次尝试：未找到PDF下载链接") + continue + + logger.info(f"找到PDF下载链接: {pdf_url}") + + # 下载并验证PDF + pdf_content = self._download_pdf(pdf_url) + if pdf_content is None: + logger.warning(f"第 {attempt + 1} 次尝试：PDF下载失败") + continue + + # 保存PDF文件 + pdf_name = f"{self.doi.replace('/', '_').replace(':', '_')}.pdf" + pdf_path = self.path.joinpath(pdf_name) + pdf_path.write_bytes(pdf_content) + + logger.info(f"成功下载论文: {pdf_name}，文件大小: {len(pdf_content)} bytes") + return str(pdf_path) + + except Exception as e: + logger.error(f"第 {attempt + 1} 次尝试失败: {str(e)}") + if attempt < 2: # 不是最后一次尝试 + wait_time = (attempt + 1) * 3 # 递增等待时间 + logger.info(f"等待 {wait_time} 秒后重试...") + time.sleep(wait_time) + continue + + raise Exception(f"无法下载论文 {self.doi}，所有重试都失败了") + +# Usage Example +if __name__ == '__main__': + # 创建一个用于保存PDF的目录 + save_path = Path('./downloaded_papers') + save_path.mkdir(exist_ok=True) + + # DOI示例 + sample_doi = '10.3897/rio.7.e67379' # 这是一篇Nature的论文DOI + + try: + # 初始化SciHub下载器，先尝试使用代理 + logger.info("尝试使用代理模式...") + downloader = SciHub(doi=sample_doi, path=save_path, use_proxy=True) + + # 开始下载 + result = downloader.fetch() + print(f"论文已保存到: {result}") + + except Exception as e: + print(f"使用代理模式失败: {str(e)}") + try: + # 如果代理模式失败，尝试直连模式 + logger.info("尝试直连模式...") + downloader = SciHub(doi=sample_doi, path=save_path, use_proxy=False) + result = downloader.fetch() + print(f"论文已保存到: {result}") + except Exception as e2: + print(f"直连模式也失败: {str(e2)}") + print("建议检查网络连接或尝试其他DOI") \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/scopus_source.py b/crazy_functions/review_fns/data_sources/scopus_source.py new file mode 100644 index 00000000..e1943964 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/scopus_source.py @@ -0,0 +1,400 @@ +from typing import List, Optional, Dict, Union +from datetime import datetime +import aiohttp +import random +from .base_source import DataSource, PaperMetadata +from tqdm import tqdm + +class ScopusSource(DataSource): + """Scopus API实现""" + + # 定义API密钥列表 + API_KEYS = [ + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" + ] + + def __init__(self, api_key: str = None): + """初始化 + + Args: + api_key: Scopus API密钥，如果不提供则从预定义列表中随机选择 + """ + self.api_key = api_key or random.choice(self.API_KEYS) + self._initialize() + + def _initialize(self) -> None: + """初始化基础URL和请求头""" + self.base_url = "https://api.elsevier.com/content" + self.headers = { + "X-ELS-APIKey": self.api_key, + "Accept": "application/json" + } + + async def _make_request(self, url: str, params: Dict = None) -> Optional[Dict]: + """发送HTTP请求 + + Args: + url: 请求URL + params: 查询参数 + + Returns: + 响应JSON数据 + """ + try: + async with aiohttp.ClientSession(headers=self.headers) as session: + async with session.get(url, params=params) as response: + if response.status == 200: + return await response.json() + else: + print(f"请求失败: {response.status}") + return None + except Exception as e: + print(f"请求发生错误: {str(e)}") + return None + + def _parse_paper_data(self, data: Dict) -> PaperMetadata: + """解析Scopus API返回的数据 + + Args: + data: Scopus API返回的论文数据 + + Returns: + 解析后的论文元数据 + """ + try: + # 提取基本信息 + title = data.get("dc:title", "") + + # 提取作者信息 + authors = [] + if "author" in data: + if isinstance(data["author"], list): + for author in data["author"]: + if "given-name" in author and "surname" in author: + authors.append(f"{author['given-name']} {author['surname']}") + elif "indexed-name" in author: + authors.append(author["indexed-name"]) + elif isinstance(data["author"], dict): + if "given-name" in data["author"] and "surname" in data["author"]: + authors.append(f"{data['author']['given-name']} {data['author']['surname']}") + elif "indexed-name" in data["author"]: + authors.append(data["author"]["indexed-name"]) + + # 提取摘要 + abstract = data.get("dc:description", "") + + # 提取年份 + year = None + if "prism:coverDate" in data: + try: + year = int(data["prism:coverDate"][:4]) + except: + pass + + # 提取DOI + doi = data.get("prism:doi") + + # 提取引用次数 + citations = data.get("citedby-count") + if citations: + try: + citations = int(citations) + except: + citations = None + + # 提取期刊信息 + venue = data.get("prism:publicationName") + + # 提取机构信息 + institutions = [] + if "affiliation" in data: + if isinstance(data["affiliation"], list): + for aff in data["affiliation"]: + if "affilname" in aff: + institutions.append(aff["affilname"]) + elif isinstance(data["affiliation"], dict): + if "affilname" in data["affiliation"]: + institutions.append(data["affiliation"]["affilname"]) + + # 构建venue信息 + venue_info = { + "issn": data.get("prism:issn"), + "eissn": data.get("prism:eIssn"), + "volume": data.get("prism:volume"), + "issue": data.get("prism:issueIdentifier"), + "page_range": data.get("prism:pageRange"), + "article_number": data.get("article-number"), + "publication_date": data.get("prism:coverDate") + } + + return PaperMetadata( + title=title, + authors=authors, + abstract=abstract, + year=year, + doi=doi, + url=data.get("link", [{}])[0].get("@href"), + citations=citations, + venue=venue, + institutions=institutions, + venue_type="journal", + venue_name=venue, + venue_info=venue_info + ) + + except Exception as e: + print(f"解析论文数据时发生错误: {str(e)}") + return None + + async def search( + self, + query: str, + limit: int = 100, + sort_by: str = None, + start_year: int = None + ) -> List[PaperMetadata]: + """搜索论文 + + Args: + query: 搜索关键词 + limit: 返回结果数量限制 + sort_by: 排序方式 ('relevance', 'date', 'citations') + start_year: 起始年份 + + Returns: + 论文列表 + """ + try: + # 构建查询参数 + params = { + "query": query, + "count": min(limit, 100), # Scopus API单次请求限制 + "start": 0 + } + + # 添加年份过滤 + if start_year: + params["date"] = f"{start_year}-present" + + # 添加排序 + if sort_by: + sort_map = { + "relevance": "-score", + "date": "-coverDate", + "citations": "-citedby-count" + } + if sort_by in sort_map: + params["sort"] = sort_map[sort_by] + + # 发送请求 + url = f"{self.base_url}/search/scopus" + response = await self._make_request(url, params) + + if not response or "search-results" not in response: + return [] + + # 解析结果 + results = response["search-results"].get("entry", []) + papers = [] + + for result in results: + paper = self._parse_paper_data(result) + if paper: + papers.append(paper) + + return papers + + except Exception as e: + print(f"搜索论文时发生错误: {str(e)}") + return [] + + async def get_paper_details(self, paper_id: str) -> Optional[PaperMetadata]: + """获取论文详情 + + Args: + paper_id: Scopus ID或DOI + + Returns: + 论文详情 + """ + try: + # 判断是否为DOI + if "/" in paper_id: + url = f"{self.base_url}/article/doi/{paper_id}" + else: + url = f"{self.base_url}/abstract/scopus_id/{paper_id}" + + response = await self._make_request(url) + + if not response or "abstracts-retrieval-response" not in response: + return None + + data = response["abstracts-retrieval-response"] + return self._parse_paper_data(data) + + except Exception as e: + print(f"获取论文详情时发生错误: {str(e)}") + return None + + async def get_citations(self, paper_id: str) -> List[PaperMetadata]: + """获取引用该论文的文献 + + Args: + paper_id: Scopus ID + + Returns: + 引用论文列表 + """ + try: + url = f"{self.base_url}/abstract/citations/{paper_id}" + response = await self._make_request(url) + + if not response or "citing-papers" not in response: + return [] + + results = response["citing-papers"].get("papers", []) + papers = [] + + for result in results: + paper = self._parse_paper_data(result) + if paper: + papers.append(paper) + + return papers + + except Exception as e: + print(f"获取引用信息时发生错误: {str(e)}") + return [] + + async def get_references(self, paper_id: str) -> List[PaperMetadata]: + """获取该论文引用的文献 + + Args: + paper_id: Scopus ID + + Returns: + 参考文献列表 + """ + try: + url = f"{self.base_url}/abstract/references/{paper_id}" + response = await self._make_request(url) + + if not response or "references" not in response: + return [] + + results = response["references"].get("reference", []) + papers = [] + + for result in results: + paper = self._parse_paper_data(result) + if paper: + papers.append(paper) + + return papers + + except Exception as e: + print(f"获取参考文献时发生错误: {str(e)}") + return [] + + async def search_by_author( + self, + author: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按作者搜索论文""" + query = f"AUTHOR-NAME({author})" + if start_year: + query += f" AND PUBYEAR > {start_year}" + return await self.search(query, limit=limit) + + async def search_by_journal( + self, + journal: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """按期刊搜索论文""" + query = f"SRCTITLE({journal})" + if start_year: + query += f" AND PUBYEAR > {start_year}" + return await self.search(query, limit=limit) + + async def get_latest_papers( + self, + days: int = 7, + limit: int = 100 + ) -> List[PaperMetadata]: + """获取最新论文""" + query = f"LOAD-DATE > NOW() - {days}d" + return await self.search(query, limit=limit, sort_by="date") + +async def example_usage(): + """ScopusSource使用示例""" + scopus = ScopusSource() + + try: + # 示例1：基本搜索 + print("\n=== 示例1：搜索机器学习相关论文 ===") + papers = await scopus.search("machine learning", limit=3) + print(f"\n找到 {len(papers)} 篇相关论文:") + for i, paper in enumerate(papers, 1): + print(f"\n论文 {i}:") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"发表期刊: {paper.venue}") + print(f"引用次数: {paper.citations}") + print(f"DOI: {paper.doi}") + if paper.abstract: + print(f"摘要:\n{paper.abstract}") + print("-" * 80) + + # 示例2：按作者搜索 + print("\n=== 示例2：搜索特定作者的论文 ===") + author_papers = await scopus.search_by_author("Hinton G.", limit=3) + print(f"\n找到 {len(author_papers)} 篇 Hinton 的论文:") + for i, paper in enumerate(author_papers, 1): + print(f"\n论文 {i}:") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"发表期刊: {paper.venue}") + print(f"引用次数: {paper.citations}") + print(f"DOI: {paper.doi}") + if paper.abstract: + print(f"摘要:\n{paper.abstract}") + print("-" * 80) + + # 示例3：根据关键词搜索相关论文 + print("\n=== 示例3：搜索人工智能相关论文 ===") + keywords = "artificial intelligence AND deep learning" + papers = await scopus.search( + query=keywords, + limit=5, + sort_by="citations", # 按引用次数排序 + start_year=2020 # 只搜索2020年之后的论文 + ) + + print(f"\n找到 {len(papers)} 篇相关论文:") + for i, paper in enumerate(papers, 1): + print(f"\n论文 {i}:") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"发表期刊: {paper.venue}") + print(f"引用次数: {paper.citations}") + print(f"DOI: {paper.doi}") + if paper.abstract: + print(f"摘要:\n{paper.abstract}") + print("-" * 80) + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + import asyncio + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/semantic_source.py b/crazy_functions/review_fns/data_sources/semantic_source.py new file mode 100644 index 00000000..254e2435 --- /dev/null +++ b/crazy_functions/review_fns/data_sources/semantic_source.py @@ -0,0 +1,480 @@ +from typing import List, Optional +from datetime import datetime +from crazy_functions.review_fns.data_sources.base_source import DataSource, PaperMetadata +import random + +class SemanticScholarSource(DataSource): + """Semantic Scholar API实现,使用官方Python包""" + + def __init__(self, api_key: str = None): + """初始化 + + Args: + api_key: Semantic Scholar API密钥(可选) + """ + self.api_key = api_key + self._initialize() # 调用初始化方法 + + def _initialize(self) -> None: + """初始化API客户端""" + if not self.api_key: + # 默认API密钥列表 + default_api_keys = [ + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx", + "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" + ] + self.api_key = random.choice(default_api_keys) + + self.client = None # 延迟初始化 + self.fields = [ + "title", + "authors", + "abstract", + "year", + "externalIds", + "citationCount", + "venue", + "openAccessPdf", + "publicationVenue" + ] + + async def _ensure_client(self): + """确保客户端已初始化""" + if self.client is None: + from semanticscholar import AsyncSemanticScholar + self.client = AsyncSemanticScholar(api_key=self.api_key) + + async def search( + self, + query: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """搜索论文""" + try: + await self._ensure_client() + + # 如果指定了起始年份，添加到查询中 + if start_year: + query = f"{query} year>={start_year}" + + # 直接使用 search_paper 的结果 + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/paper/search", + f"query={query}&limit={min(limit, 100)}&fields={','.join(self.fields)}", + self.client.auth_header + ) + papers = response.get('data', []) + return [self._parse_paper_data(paper) for paper in papers] + except Exception as e: + print(f"搜索论文时发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + return [] + + async def get_paper_details(self, doi: str) -> Optional[PaperMetadata]: + """获取指定DOI的论文详情""" + try: + await self._ensure_client() + paper = await self.client.get_paper(f"DOI:{doi}", fields=self.fields) + return self._parse_paper_data(paper) + except Exception as e: + print(f"获取论文详情时发生错误: {str(e)}") + return None + + async def get_citations( + self, + doi: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """获取引用指定DOI论文的文献列表""" + try: + await self._ensure_client() + # 构建查询参数 + fields_param = f"fields={','.join(self.fields)}" + limit_param = f"limit={limit}" + year_param = f"year>={start_year}" if start_year else "" + params = "&".join(filter(None, [fields_param, limit_param, year_param])) + + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/paper/DOI:{doi}/citations", + params, + self.client.auth_header + ) + citations = response.get('data', []) + return [self._parse_paper_data(citation.get('citingPaper', {})) for citation in citations] + except Exception as e: + print(f"获取引用列表时发生错误: {str(e)}") + return [] + + async def get_references( + self, + doi: str, + limit: int = 100, + start_year: int = None + ) -> List[PaperMetadata]: + """获取指定DOI论文的参考文献列表""" + try: + await self._ensure_client() + # 构建查询参数 + fields_param = f"fields={','.join(self.fields)}" + limit_param = f"limit={limit}" + year_param = f"year>={start_year}" if start_year else "" + params = "&".join(filter(None, [fields_param, limit_param, year_param])) + + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/paper/DOI:{doi}/references", + params, + self.client.auth_header + ) + references = response.get('data', []) + return [self._parse_paper_data(reference.get('citedPaper', {})) for reference in references] + except Exception as e: + print(f"获取参考文献列表时发生错误: {str(e)}") + return [] + + async def get_recommended_papers(self, doi: str, limit: int = 100) -> List[PaperMetadata]: + """获取论文推荐 + + 根据一篇论文获取相关的推荐论文 + + Args: + doi: 论文的DOI + limit: 返回结果数量限制，最大500 + + Returns: + 推荐论文列表 + """ + try: + await self._ensure_client() + papers = await self.client.get_recommended_papers( + f"DOI:{doi}", + fields=self.fields, + limit=min(limit, 500) + ) + return [self._parse_paper_data(paper) for paper in papers] + except Exception as e: + print(f"获取论文推荐时发生错误: {str(e)}") + return [] + + async def get_recommended_papers_from_lists( + self, + positive_dois: List[str], + negative_dois: List[str] = None, + limit: int = 100 + ) -> List[PaperMetadata]: + """基于正负例论文列表获取推荐 + + Args: + positive_dois: 正例论文DOI列表（想要获取类似的论文） + negative_dois: 负例论文DOI列表（不想要类似的论文） + limit: 返回结果数量限制，最大500 + + Returns: + 推荐论文列表 + """ + try: + await self._ensure_client() + positive_ids = [f"DOI:{doi}" for doi in positive_dois] + negative_ids = [f"DOI:{doi}" for doi in negative_dois] if negative_dois else None + + papers = await self.client.get_recommended_papers_from_lists( + positive_paper_ids=positive_ids, + negative_paper_ids=negative_ids, + fields=self.fields, + limit=min(limit, 500) + ) + return [self._parse_paper_data(paper) for paper in papers] + except Exception as e: + print(f"获取论文推荐列表时发生错误: {str(e)}") + return [] + + async def search_author(self, query: str, limit: int = 100) -> List[dict]: + """搜索作者""" + try: + await self._ensure_client() + # 直接使用 API 请求而不是 search_author 方法 + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/author/search", + f"query={query}&fields=name,paperCount,citationCount&limit={min(limit, 1000)}", + self.client.auth_header + ) + authors = response.get('data', []) + return [ + { + 'author_id': author.get('authorId'), + 'name': author.get('name'), + 'paper_count': author.get('paperCount'), + 'citation_count': author.get('citationCount'), + } + for author in authors + ] + except Exception as e: + print(f"搜索作者时发生错误: {str(e)}") + return [] + + async def get_author_details(self, author_id: str) -> Optional[dict]: + """获取作者详细信息""" + try: + await self._ensure_client() + # 直接使用 API 请求 + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/author/{author_id}", + "fields=name,paperCount,citationCount,hIndex", + self.client.auth_header + ) + return { + 'author_id': response.get('authorId'), + 'name': response.get('name'), + 'paper_count': response.get('paperCount'), + 'citation_count': response.get('citationCount'), + 'h_index': response.get('hIndex'), + } + except Exception as e: + print(f"获取作者详情时发生错误: {str(e)}") + return None + + async def get_author_papers(self, author_id: str, limit: int = 100) -> List[PaperMetadata]: + """获取作者的论文列表""" + try: + await self._ensure_client() + # 直接使用 API 请求 + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/author/{author_id}/papers", + f"fields={','.join(self.fields)}&limit={min(limit, 1000)}", + self.client.auth_header + ) + papers = response.get('data', []) + return [self._parse_paper_data(paper) for paper in papers] + except Exception as e: + print(f"获取作者论文列表时发生错误: {str(e)}") + return [] + + async def get_paper_autocomplete(self, query: str) -> List[dict]: + """论文标题自动补全""" + try: + await self._ensure_client() + # 直接使用 API 请求 + response = await self.client._requester.get_data_async( + f"{self.client.api_url}{self.client.BASE_PATH_GRAPH}/paper/autocomplete", + f"query={query}", + self.client.auth_header + ) + suggestions = response.get('matches', []) + return [ + { + 'title': suggestion.get('title'), + 'paper_id': suggestion.get('paperId'), + 'year': suggestion.get('year'), + 'venue': suggestion.get('venue'), + } + for suggestion in suggestions + ] + except Exception as e: + print(f"获取标题自动补全时发生错误: {str(e)}") + return [] + + def _parse_paper_data(self, paper) -> PaperMetadata: + """解析论文数据""" + # 获取DOI + doi = None + external_ids = paper.get('externalIds', {}) if isinstance(paper, dict) else paper.externalIds + if external_ids: + if isinstance(external_ids, dict): + doi = external_ids.get('DOI') + if not doi and 'ArXiv' in external_ids: + doi = f"10.48550/arXiv.{external_ids['ArXiv']}" + else: + doi = external_ids.DOI if hasattr(external_ids, 'DOI') else None + if not doi and hasattr(external_ids, 'ArXiv'): + doi = f"10.48550/arXiv.{external_ids.ArXiv}" + + # 获取PDF URL + pdf_url = None + pdf_info = paper.get('openAccessPdf', {}) if isinstance(paper, dict) else paper.openAccessPdf + if pdf_info: + pdf_url = pdf_info.get('url') if isinstance(pdf_info, dict) else pdf_info.url + + # 获取发表场所详细信息 + venue_type = None + venue_name = None + venue_info = {} + + venue = paper.get('publicationVenue', {}) if isinstance(paper, dict) else paper.publicationVenue + if venue: + if isinstance(venue, dict): + venue_name = venue.get('name') + venue_type = venue.get('type') + # 提取更多venue信息 + venue_info = { + 'issn': venue.get('issn'), + 'publisher': venue.get('publisher'), + 'url': venue.get('url'), + 'alternate_names': venue.get('alternate_names', []) + } + else: + venue_name = venue.name if hasattr(venue, 'name') else None + venue_type = venue.type if hasattr(venue, 'type') else None + venue_info = { + 'issn': getattr(venue, 'issn', None), + 'publisher': getattr(venue, 'publisher', None), + 'url': getattr(venue, 'url', None), + 'alternate_names': getattr(venue, 'alternate_names', []) + } + + # 获取标题 + title = paper.get('title', '') if isinstance(paper, dict) else getattr(paper, 'title', '') + + # 获取作者 + authors = paper.get('authors', []) if isinstance(paper, dict) else getattr(paper, 'authors', []) + author_names = [] + for author in authors: + if isinstance(author, dict): + author_names.append(author.get('name', '')) + else: + author_names.append(author.name if hasattr(author, 'name') else str(author)) + + # 获取摘要 + abstract = paper.get('abstract', '') if isinstance(paper, dict) else getattr(paper, 'abstract', '') + + # 获取年份 + year = paper.get('year') if isinstance(paper, dict) else getattr(paper, 'year', None) + + # 获取引用次数 + citations = paper.get('citationCount') if isinstance(paper, dict) else getattr(paper, 'citationCount', None) + + return PaperMetadata( + title=title, + authors=author_names, + abstract=abstract, + year=year, + doi=doi, + url=pdf_url or (f"https://doi.org/{doi}" if doi else None), + citations=citations, + venue=venue_name, + institutions=[], + venue_type=venue_type, + venue_name=venue_name, + venue_info=venue_info, + source='semantic' # 添加来源标记 + ) + +async def example_usage(): + """SemanticScholarSource使用示例""" + semantic = SemanticScholarSource() + + try: + # 示例1：使用DOI直接获取论文 + print("\n=== 示例1：通过DOI获取论文 ===") + doi = "10.18653/v1/N19-1423" # BERT论文 + print(f"获取DOI为 {doi} 的论文信息...") + + paper = await semantic.get_paper_details(doi) + if paper: + print("\n--- 论文信息 ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + print(f"DOI: {paper.doi}") + print(f"URL: {paper.url}") + if paper.abstract: + print(f"\n摘要:") + print(paper.abstract) + print(f"\n引用次数: {paper.citations}") + print(f"发表venue: {paper.venue}") + + # 示例2：搜索论文 + print("\n=== 示例2：搜索论文 ===") + query = "BERT pre-training" + print(f"搜索关键词 '{query}' 相关的论文...") + papers = await semantic.search(query=query, limit=3) + + for i, paper in enumerate(papers, 1): + print(f"\n--- 搜索结果 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + if paper.abstract: + print(f"\n摘要:") + print(paper.abstract) + print(f"\nDOI: {paper.doi}") + print(f"引用次数: {paper.citations}") + + # 示例3：获取论文推荐 + print("\n=== 示例3：获取论文推荐 ===") + print(f"获取与论文 {doi} 相关的推荐论文...") + recommendations = await semantic.get_recommended_papers(doi, limit=3) + for i, paper in enumerate(recommendations, 1): + print(f"\n--- 推荐论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + print(f"发表年份: {paper.year}") + + # 示例4：基于多篇论文的推荐 + print("\n=== 示例4：基于多篇论文的推荐 ===") + positive_dois = ["10.18653/v1/N19-1423", "10.18653/v1/P19-1285"] + print(f"基于 {len(positive_dois)} 篇论文获取推荐...") + multi_recommendations = await semantic.get_recommended_papers_from_lists( + positive_dois=positive_dois, + limit=3 + ) + for i, paper in enumerate(multi_recommendations, 1): + print(f"\n--- 推荐论文 {i} ---") + print(f"标题: {paper.title}") + print(f"作者: {', '.join(paper.authors)}") + + # 示例5：搜索作者 + print("\n=== 示例5：搜索作者 ===") + author_query = "Yann LeCun" + print(f"搜索作者: '{author_query}'") + authors = await semantic.search_author(author_query, limit=3) + for i, author in enumerate(authors, 1): + print(f"\n--- 作者 {i} ---") + print(f"姓名: {author['name']}") + print(f"论文数量: {author['paper_count']}") + print(f"总引用次数: {author['citation_count']}") + + # 示例6：获取作者详情 + print("\n=== 示例6：获取作者详情 ===") + if authors: # 使用第一个搜索结果的作者ID + author_id = authors[0]['author_id'] + print(f"获取作者ID {author_id} 的详细信息...") + author_details = await semantic.get_author_details(author_id) + if author_details: + print(f"姓名: {author_details['name']}") + print(f"H指数: {author_details['h_index']}") + print(f"总引用次数: {author_details['citation_count']}") + print(f"发表论文数: {author_details['paper_count']}") + + # 示例7：获取作者论文 + print("\n=== 示例7：获取作者论文 ===") + if authors: # 使用第一个搜索结果的作者ID + author_id = authors[0]['author_id'] + print(f"获取作者 {authors[0]['name']} 的论文列表...") + author_papers = await semantic.get_author_papers(author_id, limit=3) + for i, paper in enumerate(author_papers, 1): + print(f"\n--- 论文 {i} ---") + print(f"标题: {paper.title}") + print(f"发表年份: {paper.year}") + print(f"引用次数: {paper.citations}") + + # 示例8：论文标题自动补全 + print("\n=== 示例8：论文标题自动补全 ===") + title_query = "Attention is all" + print(f"搜索标题: '{title_query}'") + suggestions = await semantic.get_paper_autocomplete(title_query) + for i, suggestion in enumerate(suggestions[:3], 1): + print(f"\n--- 建议 {i} ---") + print(f"标题: {suggestion['title']}") + print(f"发表年份: {suggestion['year']}") + print(f"发表venue: {suggestion['venue']}") + + except Exception as e: + print(f"发生错误: {str(e)}") + import traceback + print(traceback.format_exc()) + +if __name__ == "__main__": + import asyncio + asyncio.run(example_usage()) \ No newline at end of file diff --git a/crazy_functions/review_fns/data_sources/unpaywall_source.py b/crazy_functions/review_fns/data_sources/unpaywall_source.py new file mode 100644 index 00000000..0c841dda --- /dev/null +++ b/crazy_functions/review_fns/data_sources/unpaywall_source.py @@ -0,0 +1,46 @@ +import aiohttp +from typing import List, Dict, Optional +from datetime import datetime +from .base_source import DataSource, PaperMetadata + +class UnpaywallSource(DataSource): + """Unpaywall API实现""" + + def _initialize(self) -> None: + self.base_url = "https://api.unpaywall.org/v2" + self.email = self.api_key # Unpaywall使用email作为API key + + async def search(self, query: str, limit: int = 100) -> List[PaperMetadata]: + async with aiohttp.ClientSession() as session: + async with session.get( + f"{self.base_url}/search", + params={ + "query": query, + "email": self.email, + "limit": limit + } + ) as response: + data = await response.json() + return [self._parse_response(item.response) + for item in data.get("results", [])] + + def _parse_response(self, data: Dict) -> PaperMetadata: + """解析Unpaywall返回的数据""" + return PaperMetadata( + title=data.get("title", ""), + authors=[ + f"{author.get('given', '')} {author.get('family', '')}" + for author in data.get("z_authors", []) + ], + institutions=[ + aff.get("name", "") + for author in data.get("z_authors", []) + for aff in author.get("affiliation", []) + ], + abstract="", # Unpaywall不提供摘要 + year=data.get("year"), + doi=data.get("doi"), + url=data.get("doi_url"), + citations=None, # Unpaywall不提供引用计数 + venue=data.get("journal_name") + ) \ No newline at end of file diff --git a/crazy_functions/review_fns/handlers/base_handler.py b/crazy_functions/review_fns/handlers/base_handler.py new file mode 100644 index 00000000..70a350d8 --- /dev/null +++ b/crazy_functions/review_fns/handlers/base_handler.py @@ -0,0 +1,412 @@ +import asyncio +from datetime import datetime +from abc import ABC, abstractmethod +from typing import List, Dict, Any +from crazy_functions.review_fns.query_analyzer import SearchCriteria +from crazy_functions.review_fns.data_sources.arxiv_source import ArxivSource +from crazy_functions.review_fns.data_sources.semantic_source import SemanticScholarSource +from crazy_functions.review_fns.data_sources.pubmed_source import PubMedSource +from crazy_functions.review_fns.paper_processor.paper_llm_ranker import PaperLLMRanker +from crazy_functions.pdf_fns.breakdown_pdf_txt import cut_from_end_to_satisfy_token_limit +from request_llms.bridge_all import model_info +from crazy_functions.review_fns.data_sources.crossref_source import CrossrefSource +from crazy_functions.review_fns.data_sources.adsabs_source import AdsabsSource +from toolbox import get_conf + + +class BaseHandler(ABC): + """处理器基类""" + + def __init__(self, arxiv: ArxivSource, semantic: SemanticScholarSource, llm_kwargs: Dict = None): + self.arxiv = arxiv + self.semantic = semantic + self.pubmed = PubMedSource() + self.crossref = CrossrefSource() # 添加 Crossref 实例 + self.adsabs = AdsabsSource() # 添加 ADS 实例 + self.paper_ranker = PaperLLMRanker(llm_kwargs=llm_kwargs) + self.ranked_papers = [] # 存储排序后的论文列表 + self.llm_kwargs = llm_kwargs or {} # 保存llm_kwargs + + def _get_search_params(self, plugin_kwargs: Dict) -> Dict: + """获取搜索参数""" + return { + 'max_papers': plugin_kwargs.get('max_papers', 100), # 最大论文数量 + 'min_year': plugin_kwargs.get('min_year', 2015), # 最早年份 + 'search_multiplier': plugin_kwargs.get('search_multiplier', 3), # 检索倍数 + } + + @abstractmethod + async def handle( + self, + criteria: SearchCriteria, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> List[List[str]]: + """处理查询""" + pass + + async def _search_arxiv(self, params: Dict, limit_multiplier: int = 1, min_year: int = 2015) -> List: + """使用arXiv专用参数搜索""" + try: + original_limit = params.get("limit", 20) + params["limit"] = original_limit * limit_multiplier + papers = [] + + # 首先尝试基础搜索 + query = params.get("query", "") + if query: + papers = await self.arxiv.search( + query, + limit=params["limit"], + sort_by=params.get("sort_by", "relevance"), + sort_order=params.get("sort_order", "descending"), + start_year=min_year + ) + + # 如果基础搜索没有结果，尝试分类搜索 + if not papers: + categories = params.get("categories", []) + for category in categories: + category_papers = await self.arxiv.search_by_category( + category, + limit=params["limit"], + sort_by=params.get("sort_by", "relevance"), + sort_order=params.get("sort_order", "descending"), + ) + if category_papers: + papers.extend(category_papers) + + return papers or [] + + except Exception as e: + print(f"arXiv搜索出错: {str(e)}") + return [] + + async def _search_semantic(self, params: Dict, limit_multiplier: int = 1, min_year: int = 2015) -> List: + """使用Semantic Scholar专用参数搜索""" + try: + original_limit = params.get("limit", 20) + params["limit"] = original_limit * limit_multiplier + + # 只使用基本的搜索参数 + papers = await self.semantic.search( + query=params.get("query", ""), + limit=params["limit"] + ) + + # 在内存中进行过滤 + if papers and min_year: + papers = [p for p in papers if getattr(p, 'year', 0) and p.year >= min_year] + + return papers or [] + + except Exception as e: + print(f"Semantic Scholar搜索出错: {str(e)}") + return [] + + async def _search_pubmed(self, params: Dict, limit_multiplier: int = 1, min_year: int = 2015) -> List: + """使用PubMed专用参数搜索""" + try: + # 如果不需要PubMed搜索，直接返回空列表 + if params.get("search_type") == "none": + return [] + + original_limit = params.get("limit", 20) + params["limit"] = original_limit * limit_multiplier + papers = [] + + # 根据搜索类型选择搜索方法 + if params.get("search_type") == "basic": + papers = await self.pubmed.search( + query=params.get("query", ""), + limit=params["limit"], + start_year=min_year + ) + elif params.get("search_type") == "author": + papers = await self.pubmed.search_by_author( + author=params.get("query", ""), + limit=params["limit"], + start_year=min_year + ) + elif params.get("search_type") == "journal": + papers = await self.pubmed.search_by_journal( + journal=params.get("query", ""), + limit=params["limit"], + start_year=min_year + ) + + return papers or [] + + except Exception as e: + print(f"PubMed搜索出错: {str(e)}") + return [] + + async def _search_crossref(self, params: Dict, limit_multiplier: int = 1, min_year: int = 2015) -> List: + """使用Crossref专用参数搜索""" + try: + original_limit = params.get("limit", 20) + params["limit"] = original_limit * limit_multiplier + papers = [] + + # 根据搜索类型选择搜索方法 + if params.get("search_type") == "basic": + papers = await self.crossref.search( + query=params.get("query", ""), + limit=params["limit"], + start_year=min_year + ) + elif params.get("search_type") == "author": + papers = await self.crossref.search_by_authors( + authors=[params.get("query", "")], + limit=params["limit"], + start_year=min_year + ) + elif params.get("search_type") == "journal": + # 实现期刊搜索逻辑 + pass + + return papers or [] + + except Exception as e: + print(f"Crossref搜索出错: {str(e)}") + return [] + + async def _search_adsabs(self, params: Dict, limit_multiplier: int = 1, min_year: int = 2015) -> List: + """使用ADS专用参数搜索""" + try: + original_limit = params.get("limit", 20) + params["limit"] = original_limit * limit_multiplier + papers = [] + + # 执行搜索 + if params.get("search_type") == "basic": + papers = await self.adsabs.search( + query=params.get("query", ""), + limit=params["limit"], + start_year=min_year + ) + + return papers or [] + + except Exception as e: + print(f"ADS搜索出错: {str(e)}") + return [] + + async def _search_all_sources(self, criteria: SearchCriteria, search_params: Dict) -> List: + """从所有数据源搜索论文""" + search_tasks = [] + + # # 检查是否需要执行PubMed搜索 + # is_using_pubmed = criteria.pubmed_params.get("search_type") != "none" and criteria.pubmed_params.get("query") != "none" + is_using_pubmed = False # 开源版本不再搜索pubmed + + # 如果使用PubMed，则只执行PubMed和Semantic Scholar搜索 + if is_using_pubmed: + search_tasks.append( + self._search_pubmed( + criteria.pubmed_params, + limit_multiplier=search_params['search_multiplier'], + min_year=criteria.start_year + ) + ) + + # Semantic Scholar总是执行搜索 + search_tasks.append( + self._search_semantic( + criteria.semantic_params, + limit_multiplier=search_params['search_multiplier'], + min_year=criteria.start_year + ) + ) + else: + + # 如果不使用ADS，则执行Crossref搜索 + if criteria.crossref_params.get("search_type") != "none" and criteria.crossref_params.get("query") != "none": + search_tasks.append( + self._search_crossref( + criteria.crossref_params, + limit_multiplier=search_params['search_multiplier'], + min_year=criteria.start_year + ) + ) + + search_tasks.append( + self._search_arxiv( + criteria.arxiv_params, + limit_multiplier=search_params['search_multiplier'], + min_year=criteria.start_year + ) + ) + if get_conf("SEMANTIC_SCHOLAR_KEY"): + search_tasks.append( + self._search_semantic( + criteria.semantic_params, + limit_multiplier=search_params['search_multiplier'], + min_year=criteria.start_year + ) + ) + + # 执行所有需要的搜索任务 + papers = await asyncio.gather(*search_tasks) + + # 合并所有来源的论文并统计各来源的数量 + all_papers = [] + source_counts = { + 'arxiv': 0, + 'semantic': 0, + 'pubmed': 0, + 'crossref': 0, + 'adsabs': 0 + } + + for source_papers in papers: + if source_papers: + for paper in source_papers: + source = getattr(paper, 'source', 'unknown') + if source in source_counts: + source_counts[source] += 1 + all_papers.extend(source_papers) + + # 打印各来源的论文数量 + print("\n=== 各数据源找到的论文数量 ===") + for source, count in source_counts.items(): + if count > 0: # 只打印有论文的来源 + print(f"{source.capitalize()}: {count} 篇") + print(f"总计: {len(all_papers)} 篇") + print("===========================\n") + + return all_papers + + def _format_paper_time(self, paper) -> str: + """格式化论文时间信息""" + year = getattr(paper, 'year', None) + if not year: + return "" + + # 如果有具体的发表日期，使用具体日期 + if hasattr(paper, 'published') and paper.published: + return f"(发表于 {paper.published.strftime('%Y-%m')})" + # 如果只有年份，只显示年份 + return f"({year})" + + def _format_papers(self, papers: List) -> str: + """格式化论文列表，使用token限制控制长度""" + formatted = [] + + for i, paper in enumerate(papers, 1): + # 只保留前三个作者 + authors = paper.authors[:3] + if len(paper.authors) > 3: + authors.append("et al.") + + # 构建所有可能的下载链接 + download_links = [] + + # 添加arXiv链接 + if hasattr(paper, 'doi') and paper.doi: + if paper.doi.startswith("10.48550/arXiv."): + # 从DOI中提取完整的arXiv ID + arxiv_id = paper.doi.split("arXiv.")[-1] + # 移除多余的点号并确保格式正确 + arxiv_id = arxiv_id.replace("..", ".") # 移除重复的点号 + if arxiv_id.startswith("."): # 移除开头的点号 + arxiv_id = arxiv_id[1:] + if arxiv_id.endswith("."): # 移除结尾的点号 + arxiv_id = arxiv_id[:-1] + + download_links.append(f"[arXiv PDF](https://arxiv.org/pdf/{arxiv_id}.pdf)") + download_links.append(f"[arXiv Page](https://arxiv.org/abs/{arxiv_id})") + elif "arxiv.org/abs/" in paper.doi: + # 直接从URL中提取arXiv ID + arxiv_id = paper.doi.split("arxiv.org/abs/")[-1] + if "v" in arxiv_id: # 移除版本号 + arxiv_id = arxiv_id.split("v")[0] + + download_links.append(f"[arXiv PDF](https://arxiv.org/pdf/{arxiv_id}.pdf)") + download_links.append(f"[arXiv Page](https://arxiv.org/abs/{arxiv_id})") + else: + download_links.append(f"[DOI](https://doi.org/{paper.doi})") + + # 添加直接URL链接（如果存在且不同于前面的链接） + if hasattr(paper, 'url') and paper.url: + if not any(paper.url in link for link in download_links): + download_links.append(f"[Source]({paper.url})") + + # 构建下载链接字符串 + download_section = " | ".join(download_links) if download_links else "No direct download link available" + + # 构建来源信息 + source_info = [] + if hasattr(paper, 'venue_type') and paper.venue_type and paper.venue_type != 'preprint': + source_info.append(f"Type: {paper.venue_type}") + if hasattr(paper, 'venue_name') and paper.venue_name: + source_info.append(f"Venue: {paper.venue_name}") + + # 添加IF指数和分区信息 + if hasattr(paper, 'if_factor') and paper.if_factor: + source_info.append(f"IF: {paper.if_factor}") + if hasattr(paper, 'cas_division') and paper.cas_division: + source_info.append(f"中科院分区: {paper.cas_division}") + if hasattr(paper, 'jcr_division') and paper.jcr_division: + source_info.append(f"JCR分区: {paper.jcr_division}") + + if hasattr(paper, 'venue_info') and paper.venue_info: + if paper.venue_info.get('journal_ref'): + source_info.append(f"Journal Reference: {paper.venue_info['journal_ref']}") + if paper.venue_info.get('publisher'): + source_info.append(f"Publisher: {paper.venue_info['publisher']}") + + # 构建当前论文的格式化文本 + paper_text = ( + f"{i}. **{paper.title}**\n" + + f" Authors: {', '.join(authors)}\n" + + f" Year: {paper.year}\n" + + f" Citations: {paper.citations if paper.citations else 'N/A'}\n" + + (f" Source: {'; '.join(source_info)}\n" if source_info else "") + + # 添加PubMed特有信息 + (f" MeSH Terms: {'; '.join(paper.mesh_terms)}\n" if hasattr(paper, + 'mesh_terms') and paper.mesh_terms else "") + + f" 📥 PDF Downloads: {download_section}\n" + + f" Abstract: {paper.abstract}\n" + ) + + formatted.append(paper_text) + + full_text = "\n".join(formatted) + + # 根据不同模型设置不同的token限制 + model_name = getattr(self, 'llm_kwargs', {}).get('llm_model', 'gpt-3.5-turbo') + + token_limit = model_info[model_name]['max_token'] * 3 // 4 + # 使用token限制控制长度 + return cut_from_end_to_satisfy_token_limit(full_text, limit=token_limit, reserve_token=0, llm_model=model_name) + + def _get_current_time(self) -> str: + """获取当前时间信息""" + now = datetime.now() + return now.strftime("%Y年%m月%d日") + + def _generate_apology_prompt(self, criteria: SearchCriteria) -> str: + """生成道歉提示""" + return f"""很抱歉，我们未能找到与"{criteria.main_topic}"相关的有效文献。 + +可能的原因： +1. 搜索词过于具体或专业 +2. 时间范围限制过严 + +建议解决方案： + 1. 尝试使用更通用的关键词 + 2. 扩大搜索时间范围 + 3. 使用同义词或相关术语 +请根据以上建议调整后重试。""" + + def get_ranked_papers(self) -> str: + """获取排序后的论文列表的格式化字符串""" + return self._format_papers(self.ranked_papers) if self.ranked_papers else "" + + def _is_pubmed_paper(self, paper) -> bool: + """判断是否为PubMed论文""" + return (paper.url and 'pubmed.ncbi.nlm.nih.gov' in paper.url) \ No newline at end of file diff --git a/crazy_functions/review_fns/handlers/latest_handler.py b/crazy_functions/review_fns/handlers/latest_handler.py new file mode 100644 index 00000000..5617c688 --- /dev/null +++ b/crazy_functions/review_fns/handlers/latest_handler.py @@ -0,0 +1,106 @@ +from typing import List, Dict, Any +from .base_handler import BaseHandler +from crazy_functions.review_fns.query_analyzer import SearchCriteria +import asyncio + +class Arxiv最新论文推荐功能(BaseHandler): + """最新论文推荐处理器""" + + def __init__(self, arxiv, semantic, llm_kwargs=None): + super().__init__(arxiv, semantic, llm_kwargs) + + async def handle( + self, + criteria: SearchCriteria, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> str: + """处理最新论文推荐请求""" + + # 获取搜索参数 + search_params = self._get_search_params(plugin_kwargs) + + # 获取最新论文 + papers = [] + for category in criteria.arxiv_params["categories"]: + latest_papers = await self.arxiv.get_latest_papers( + category=category, + debug=False, + batch_size=50 + ) + papers.extend(latest_papers) + + if not papers: + return self._generate_apology_prompt(criteria) + + # 使用embedding模型对论文进行排序 + self.ranked_papers = self.paper_ranker.rank_papers( + query=criteria.original_query, + papers=papers, + search_criteria=criteria + ) + + # 构建最终的prompt + current_time = self._get_current_time() + final_prompt = f"""Current time: {current_time} + +Based on your interest in {criteria.main_topic}, here are the latest papers from arXiv in relevant categories: +{', '.join(criteria.arxiv_params["categories"])} + +Latest papers available: +{self._format_papers(self.ranked_papers)} + +Please provide: +1. A clear list of latext papers, organized by themes or approaches + + +2. Group papers by sub-topics or themes if applicable + +3. For each paper: + - Publication time + - The key contributions and main findings + - Why it's relevant to the user's interests + - How it relates to other latest papers + - The paper's citation count and citation impact + - The paper's download link + +4. A suggested reading order based on: + - Paper relationships and dependencies + - Difficulty level + - Significance + +5. Future Directions + - Emerging venues and research streams + - Novel methodological approaches + - Cross-disciplinary opportunities + - Research gaps by publication type + +IMPORTANT: +- Focus on explaining why each paper is interesting +- Highlight the novelty and potential impact +- Consider the credibility and stage of each publication +- Use the provided paper titles with their links when referring to specific papers +- Base recommendations ONLY on the explicitly provided paper information +- Do not make ANY assumptions about papers beyond the given data +- When information is missing or unclear, acknowledge the limitation +- Never speculate about: + * Paper quality or rigor not evidenced in the data + * Research impact beyond citation counts + * Implementation details not mentioned + * Author expertise or background + * Future research directions not stated +- For each paper, cite only verifiable information +- Clearly distinguish between facts and potential implications +- Each paper includes download links in its 📥 PDF Downloads section + +Format your response in markdown with clear sections. + +Language requirement: +- If the query explicitly specifies a language, use that language +- Otherwise, match the language of the original user query +""" + + return final_prompt \ No newline at end of file diff --git a/crazy_functions/review_fns/handlers/paper_handler.py b/crazy_functions/review_fns/handlers/paper_handler.py new file mode 100644 index 00000000..7f55b585 --- /dev/null +++ b/crazy_functions/review_fns/handlers/paper_handler.py @@ -0,0 +1,344 @@ +from typing import List, Dict, Any, Optional, Tuple +from .base_handler import BaseHandler +from crazy_functions.review_fns.query_analyzer import SearchCriteria +import asyncio +from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency as request_gpt + +class 单篇论文分析功能(BaseHandler): + """论文分析处理器""" + + def __init__(self, arxiv, semantic, llm_kwargs=None): + super().__init__(arxiv, semantic, llm_kwargs) + + async def handle( + self, + criteria: SearchCriteria, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> str: + """处理论文分析请求，返回最终的prompt""" + + # 1. 获取论文详情 + paper = await self._get_paper_details(criteria) + if not paper: + return self._generate_apology_prompt(criteria) + + # 保存为ranked_papers以便统一接口 + self.ranked_papers = [paper] + + # 2. 构建最终的prompt + current_time = self._get_current_time() + + # 获取论文信息 + title = getattr(paper, "title", "Unknown Title") + authors = getattr(paper, "authors", []) + year = getattr(paper, "year", "Unknown Year") + abstract = getattr(paper, "abstract", "No abstract available") + citations = getattr(paper, "citations", "N/A") + + # 添加论文ID信息 + paper_id = "" + if criteria.paper_source == "arxiv": + paper_id = f"arXiv ID: {criteria.paper_id}\n" + elif criteria.paper_source == "doi": + paper_id = f"DOI: {criteria.paper_id}\n" + + # 格式化作者列表 + authors_str = ', '.join(authors) if isinstance(authors, list) else authors + + final_prompt = f"""Current time: {current_time} + +Please provide a comprehensive analysis of the following paper: + +{paper_id}Title: {title} +Authors: {authors_str} +Year: {year} +Citations: {citations} +Publication Venue: {paper.venue_name} ({paper.venue_type}) +{f"Publisher: {paper.venue_info.get('publisher')}" if paper.venue_info.get('publisher') else ""} +{f"Journal Reference: {paper.venue_info.get('journal_ref')}" if paper.venue_info.get('journal_ref') else ""} +Abstract: {abstract} + +Please provide: +1. Publication Context + - Publication venue analysis and impact factor (if available) + - Paper type (journal article, conference paper, preprint) + - Publication timeline and peer review status + - Publisher reputation and venue prestige + +2. Research Context + - Field positioning and significance + - Historical context and prior work + - Related research streams + - Cross-venue impact analysis + +3. Technical Analysis + - Detailed methodology review + - Implementation details + - Experimental setup and results + - Technical innovations + +4. Impact Analysis + - Citation patterns and influence + - Cross-venue recognition + - Industry vs. academic impact + - Practical applications + +5. Critical Review + - Methodological rigor assessment + - Result reliability and reproducibility + - Venue-appropriate evaluation standards + - Limitations and potential improvements + +IMPORTANT: +- Strictly use ONLY the information provided above about the paper +- Do not make ANY assumptions or inferences beyond the given data +- If certain information is not provided, explicitly state that it is unknown +- For any unclear or missing details, acknowledge the limitation rather than speculating +- When discussing methodology or results, only describe what is explicitly stated in the abstract +- Never fabricate or assume any details about: + * Publication venues or status + * Implementation details not mentioned + * Results or findings not stated + * Impact or influence not supported by the citation count + * Authors' affiliations or backgrounds + * Future work or implications not mentioned +- You can find the paper's download options in the 📥 PDF Downloads section +- Available download formats include arXiv PDF, DOI links, and source URLs + +Format your response in markdown with clear sections. + +Language requirement: +- If the query explicitly specifies a language, use that language +- Otherwise, match the language of the original user query +""" + + return final_prompt + + async def _get_paper_details(self, criteria: SearchCriteria): + """获取论文详情""" + try: + if criteria.paper_source == "arxiv": + # 使用 arxiv ID 搜索 + papers = await self.arxiv.search_by_id(criteria.paper_id) + return papers[0] if papers else None + + elif criteria.paper_source == "doi": + # 尝试从所有来源获取 + paper = await self.semantic.get_paper_by_doi(criteria.paper_id) + if not paper: + # 如果Semantic Scholar没有找到，尝试PubMed + papers = await self.pubmed.search( + f"{criteria.paper_id}[doi]", + limit=1 + ) + if papers: + return papers[0] + return paper + + elif criteria.paper_source == "title": + # 使用_search_all_sources搜索 + search_params = { + 'max_papers': 1, + 'min_year': 1900, # 不限制年份 + 'search_multiplier': 1 + } + + # 设置搜索参数 + criteria.arxiv_params = { + "search_type": "basic", + "query": f'ti:"{criteria.paper_title}"', + "limit": 1 + } + criteria.semantic_params = { + "query": criteria.paper_title, + "limit": 1 + } + criteria.pubmed_params = { + "search_type": "basic", + "query": f'"{criteria.paper_title}"[Title]', + "limit": 1 + } + + papers = await self._search_all_sources(criteria, search_params) + return papers[0] if papers else None + + # 如果都没有找到，尝试使用 main_topic 作为标题搜索 + if not criteria.paper_title and not criteria.paper_id: + search_params = { + 'max_papers': 1, + 'min_year': 1900, + 'search_multiplier': 1 + } + + # 设置搜索参数 + criteria.arxiv_params = { + "search_type": "basic", + "query": f'ti:"{criteria.main_topic}"', + "limit": 1 + } + criteria.semantic_params = { + "query": criteria.main_topic, + "limit": 1 + } + criteria.pubmed_params = { + "search_type": "basic", + "query": f'"{criteria.main_topic}"[Title]', + "limit": 1 + } + + papers = await self._search_all_sources(criteria, search_params) + return papers[0] if papers else None + + return None + + except Exception as e: + print(f"获取论文详情时出错: {str(e)}") + return None + + async def _get_citation_context(self, paper: Dict, plugin_kwargs: Dict) -> Tuple[List, List]: + """获取引用上下文""" + search_params = self._get_search_params(plugin_kwargs) + + # 使用论文标题构建搜索参数 + title_query = f'ti:"{getattr(paper, "title", "")}"' + arxiv_params = { + "query": title_query, + "limit": search_params['max_papers'], + "search_type": "basic", + "sort_by": "relevance", + "sort_order": "descending" + } + semantic_params = { + "query": getattr(paper, "title", ""), + "limit": search_params['max_papers'] + } + + citations, references = await asyncio.gather( + self._search_semantic( + semantic_params, + limit_multiplier=search_params['search_multiplier'], + min_year=search_params['min_year'] + ), + self._search_arxiv( + arxiv_params, + limit_multiplier=search_params['search_multiplier'], + min_year=search_params['min_year'] + ) + ) + + return citations, references + + async def _generate_analysis( + self, + paper: Dict, + citations: List, + references: List, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any] + ) -> List[List[str]]: + """生成论文分析""" + + # 构建提示 + analysis_prompt = f"""Please provide a comprehensive analysis of the following paper: + +Paper details: +{self._format_paper(paper)} + +Key references (papers cited by this paper): +{self._format_papers(references)} + +Important citations (papers that cite this paper): +{self._format_papers(citations)} + +Please provide: +1. Paper Overview + - Main research question/objective + - Key methodology/approach + - Main findings/contributions + +2. Technical Analysis + - Detailed methodology review + - Technical innovations + - Implementation details + - Experimental setup and results + +3. Impact Analysis + - Significance in the field + - Influence on subsequent research (based on citing papers) + - Relationship to prior work (based on cited papers) + - Practical applications + +4. Critical Review + - Strengths and limitations + - Potential improvements + - Open questions and future directions + - Alternative approaches + +5. Related Research Context + - How it builds on previous work + - How it has influenced subsequent research + - Comparison with alternative approaches + +Format your response in markdown with clear sections.""" + + # 并行生成概述和技术分析 + for response_chunk in request_gpt( + inputs_array=[ + analysis_prompt, + self._get_technical_prompt(paper) + ], + inputs_show_user_array=[ + "Generating paper analysis...", + "Analyzing technical details..." + ], + llm_kwargs=llm_kwargs, + chatbot=chatbot, + history_array=[history, []], + sys_prompt_array=[ + system_prompt, + "You are an expert at analyzing technical details in research papers." + ] + ): + pass # 等待生成完成 + + # 获取最后的两个回答 + if chatbot and len(chatbot[-2:]) == 2: + analysis = chatbot[-2][1] + technical = chatbot[-1][1] + full_analysis = f"""# Paper Analysis: {paper.title} + +## General Analysis +{analysis} + +## Technical Deep Dive +{technical} +""" + chatbot.append(["Here is the paper analysis:", full_analysis]) + else: + chatbot.append(["Here is the paper analysis:", "Failed to generate analysis."]) + + return chatbot + + def _get_technical_prompt(self, paper: Dict) -> str: + """生成技术分析提示""" + return f"""Please provide a detailed technical analysis of the following paper: + +{self._format_paper(paper)} + +Focus on: +1. Mathematical formulations and their implications +2. Algorithm design and complexity analysis +3. Architecture details and design choices +4. Implementation challenges and solutions +5. Performance analysis and bottlenecks +6. Technical limitations and potential improvements + +Format your response in markdown, focusing purely on technical aspects.""" + + diff --git a/crazy_functions/review_fns/handlers/qa_handler.py b/crazy_functions/review_fns/handlers/qa_handler.py new file mode 100644 index 00000000..08e0c3fc --- /dev/null +++ b/crazy_functions/review_fns/handlers/qa_handler.py @@ -0,0 +1,147 @@ +from typing import List, Dict, Any +from .base_handler import BaseHandler +from crazy_functions.review_fns.query_analyzer import SearchCriteria +from textwrap import dedent +from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency as request_gpt + +class 学术问答功能(BaseHandler): + """学术问答处理器""" + + def __init__(self, arxiv, semantic, llm_kwargs=None): + super().__init__(arxiv, semantic, llm_kwargs) + + async def handle( + self, + criteria: SearchCriteria, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> str: + """处理学术问答请求，返回最终的prompt""" + + # 1. 获取搜索参数 + search_params = self._get_search_params(plugin_kwargs) + + # 2. 搜索相关论文 + papers = await self._search_relevant_papers(criteria, search_params) + if not papers: + return self._generate_apology_prompt(criteria) + + # 构建最终的prompt + current_time = self._get_current_time() + final_prompt = dedent(f"""Current time: {current_time} + + Based on the following paper abstracts, please answer this academic question: {criteria.original_query} + + Available papers for reference: + {self._format_papers(self.ranked_papers)} + + Please structure your response in the following format: + + 1. Core Answer (2-3 paragraphs) + - Provide a clear, direct answer synthesizing key findings + - Support main points with citations [1,2,etc.] + - Focus on consensus and differences across papers + + 2. Key Evidence (2-3 paragraphs) + - Present supporting evidence from abstracts + - Compare methodologies and results + - Highlight significant findings with citations + + 3. Research Context (1-2 paragraphs) + - Discuss current trends and developments + - Identify research gaps or limitations + - Suggest potential future directions + + Guidelines: + - Base your answer ONLY on the provided abstracts + - Use numbered citations [1], [2,3], etc. for every claim + - Maintain academic tone and objectivity + - Synthesize findings across multiple papers + - Focus on the most relevant information to the question + + Constraints: + - Do not include information beyond the provided abstracts + - Avoid speculation or personal opinions + - Do not elaborate on technical details unless directly relevant + - Keep citations concise and focused + - Use [N] citations for every major claim or finding + - Cite multiple papers [1,2,3] when showing consensus + - Place citations immediately after the relevant statements + + Note: Provide citations for every major claim to ensure traceability to source papers. + Language requirement: + - If the query explicitly specifies a language, use that language. Use Chinese to answer if no language is specified. + - Otherwise, match the language of the original user query + """ + ) + + return final_prompt + + async def _search_relevant_papers(self, criteria: SearchCriteria, search_params: Dict) -> List: + """搜索相关论文""" + # 使用_search_all_sources替代原来的并行搜索 + all_papers = await self._search_all_sources(criteria, search_params) + + if not all_papers: + return [] + + # 使用BGE重排序 + self.ranked_papers = self.paper_ranker.rank_papers( + query=criteria.main_topic, + papers=all_papers, + search_criteria=criteria + ) + + return self.ranked_papers or [] + + async def _generate_answer( + self, + criteria: SearchCriteria, + papers: List, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any] + ) -> List[List[str]]: + """生成答案""" + + # 构建提示 + qa_prompt = dedent(f"""Please answer the following academic question based on recent research papers. + + Question: {criteria.main_topic} + + Relevant papers: + {self._format_papers(papers)} + + Please provide: + 1. A direct answer to the question + 2. Supporting evidence from the papers + 3. Different perspectives or approaches if applicable + 4. Current limitations and open questions + 5. References to specific papers + + Format your response in markdown with clear sections.""" + ) + # 调用LLM生成答案 + for response_chunk in request_gpt( + inputs_array=[qa_prompt], + inputs_show_user_array=["Generating answer..."], + llm_kwargs=llm_kwargs, + chatbot=chatbot, + history_array=[history], + sys_prompt_array=[system_prompt] + ): + pass # 等待生成完成 + + # 获取最后的回答 + if chatbot and len(chatbot[-1]) >= 2: + answer = chatbot[-1][1] + chatbot.append(["Here is the answer:", answer]) + else: + chatbot.append(["Here is the answer:", "Failed to generate answer."]) + + return chatbot + diff --git a/crazy_functions/review_fns/handlers/recommend_handler.py b/crazy_functions/review_fns/handlers/recommend_handler.py new file mode 100644 index 00000000..c6286b88 --- /dev/null +++ b/crazy_functions/review_fns/handlers/recommend_handler.py @@ -0,0 +1,185 @@ +from typing import List, Dict, Any +from .base_handler import BaseHandler +from textwrap import dedent +from crazy_functions.review_fns.query_analyzer import SearchCriteria +from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency as request_gpt + +class 论文推荐功能(BaseHandler): + """论文推荐处理器""" + + def __init__(self, arxiv, semantic, llm_kwargs=None): + super().__init__(arxiv, semantic, llm_kwargs) + + async def handle( + self, + criteria: SearchCriteria, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> str: + """处理论文推荐请求，返回最终的prompt""" + + search_params = self._get_search_params(plugin_kwargs) + + # 1. 先搜索种子论文 + seed_papers = await self._search_seed_papers(criteria, search_params) + if not seed_papers: + return self._generate_apology_prompt(criteria) + + # 使用BGE重排序 + all_papers = seed_papers + + if not all_papers: + return self._generate_apology_prompt(criteria) + + self.ranked_papers = self.paper_ranker.rank_papers( + query=criteria.original_query, + papers=all_papers, + search_criteria=criteria + ) + + if not self.ranked_papers: + return self._generate_apology_prompt(criteria) + + # 构建最终的prompt + current_time = self._get_current_time() + final_prompt = dedent(f"""Current time: {current_time} + + Based on the user's interest in {criteria.main_topic}, here are relevant papers. + + Available papers for recommendation: + {self._format_papers(self.ranked_papers)} + + Please provide: + 1. Group papers by sub-topics or themes if applicable + + 2. For each paper: + - Publication time and venue (when available) + - Journal metrics (when available): + * Impact Factor (IF) + * JCR Quartile + * Chinese Academy of Sciences (CAS) Division + - The key contributions and main findings + - Why it's relevant to the user's interests + - How it relates to other recommended papers + - The paper's citation count and citation impact + - The paper's download link + + 3. A suggested reading order based on: + - Journal impact and quality metrics + - Chronological development of ideas + - Paper relationships and dependencies + - Difficulty level + - Impact and significance + + 4. Future Directions + - Emerging venues and research streams + - Novel methodological approaches + - Cross-disciplinary opportunities + - Research gaps by publication type + + + IMPORTANT: + - Focus on explaining why each paper is valuable + - Highlight connections between papers + - Consider both citation counts AND journal metrics when discussing impact + - When available, use IF, JCR quartile, and CAS division to assess paper quality + - Mention publication timing when discussing paper relationships + - When referring to papers, use HTML links in this format: + * For DOIs: DOI: DOI_HERE + * For titles: PAPER_TITLE + - Present papers in a way that shows the evolution of ideas over time + - Base recommendations ONLY on the explicitly provided paper information + - Do not make ANY assumptions about papers beyond the given data + - When information is missing or unclear, acknowledge the limitation + - Never speculate about: + * Paper quality or rigor not evidenced in the data + * Research impact beyond citation counts and journal metrics + * Implementation details not mentioned + * Author expertise or background + * Future research directions not stated + - For each recommendation, cite only verifiable information + - Clearly distinguish between facts and potential implications + + Format your response in markdown with clear sections. + Language requirement: + - If the query explicitly specifies a language, use that language + - Otherwise, match the language of the original user query + """ + ) + return final_prompt + + async def _search_seed_papers(self, criteria: SearchCriteria, search_params: Dict) -> List: + """搜索种子论文""" + try: + # 使用_search_all_sources替代原来的并行搜索 + all_papers = await self._search_all_sources(criteria, search_params) + + if not all_papers: + return [] + + return all_papers + + except Exception as e: + print(f"搜索种子论文时出错: {str(e)}") + return [] + + async def _get_recommendations(self, seed_papers: List, multiplier: int = 1) -> List: + """获取推荐论文""" + recommendations = [] + base_limit = 3 * multiplier + + # 将种子论文添加到推荐列表中 + recommendations.extend(seed_papers) + + # 只使用前5篇论文作为种子 + seed_papers = seed_papers[:5] + + for paper in seed_papers: + try: + if paper.doi and paper.doi.startswith("10.48550/arXiv."): + # arXiv论文 + arxiv_id = paper.doi.split(".")[-1] + paper_details = await self.arxiv.get_paper_details(arxiv_id) + if paper_details and hasattr(paper_details, 'venue'): + category = paper_details.venue.split(":")[-1] + similar_papers = await self.arxiv.search_by_category( + category, + limit=base_limit, + sort_by='relevance' + ) + recommendations.extend(similar_papers) + elif paper.doi: # 只对有DOI的论文获取推荐 + # Semantic Scholar论文 + similar_papers = await self.semantic.get_recommended_papers( + paper.doi, + limit=base_limit + ) + if similar_papers: # 只添加成功获取的推荐 + recommendations.extend(similar_papers) + else: + # 对于没有DOI的论文，使用标题进行相关搜索 + if paper.title: + similar_papers = await self.semantic.search( + query=paper.title, + limit=base_limit + ) + recommendations.extend(similar_papers) + + except Exception as e: + print(f"获取论文 '{paper.title}' 的推荐时发生错误: {str(e)}") + continue + + # 去重处理 + seen_dois = set() + unique_recommendations = [] + for paper in recommendations: + if paper.doi and paper.doi not in seen_dois: + seen_dois.add(paper.doi) + unique_recommendations.append(paper) + elif not paper.doi and paper not in unique_recommendations: + unique_recommendations.append(paper) + + return unique_recommendations diff --git a/crazy_functions/review_fns/handlers/review_handler.py b/crazy_functions/review_fns/handlers/review_handler.py new file mode 100644 index 00000000..a04a41f6 --- /dev/null +++ b/crazy_functions/review_fns/handlers/review_handler.py @@ -0,0 +1,193 @@ +from typing import List, Dict, Any, Tuple +from .base_handler import BaseHandler +from crazy_functions.review_fns.query_analyzer import SearchCriteria +import asyncio + +class 文献综述功能(BaseHandler): + """文献综述处理器""" + + def __init__(self, arxiv, semantic, llm_kwargs=None): + super().__init__(arxiv, semantic, llm_kwargs) + + async def handle( + self, + criteria: SearchCriteria, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> str: + """处理文献综述请求，返回最终的prompt""" + + # 获取搜索参数 + search_params = self._get_search_params(plugin_kwargs) + + # 使用_search_all_sources替代原来的并行搜索 + all_papers = await self._search_all_sources(criteria, search_params) + + if not all_papers: + return self._generate_apology_prompt(criteria) + + self.ranked_papers = self.paper_ranker.rank_papers( + query=criteria.original_query, + papers=all_papers, + search_criteria=criteria + ) + + # 检查排序后的论文数量 + if not self.ranked_papers: + return self._generate_apology_prompt(criteria) + + # 检查是否包含PubMed论文 + has_pubmed_papers = any(paper.url and 'pubmed.ncbi.nlm.nih.gov' in paper.url + for paper in self.ranked_papers) + + if has_pubmed_papers: + return self._generate_medical_review_prompt(criteria) + else: + return self._generate_general_review_prompt(criteria) + + def _generate_medical_review_prompt(self, criteria: SearchCriteria) -> str: + """生成医学文献综述prompt""" + return f"""Current time: {self._get_current_time()} + +Conduct a systematic medical literature review on {criteria.main_topic} based STRICTLY on the provided articles. + +Available literature for review: +{self._format_papers(self.ranked_papers)} + +IMPORTANT: If the user query contains specific requirements for the review structure or format, those requirements take precedence over the following guidelines. + +Please structure your medical review following these guidelines: + +1. Research Overview + - Main research questions and objectives from the studies + - Types of studies included (clinical trials, observational studies, etc.) + - Study populations and settings + - Time period of the research + +2. Key Findings + - Main outcomes and results reported in abstracts + - Primary endpoints and their measurements + - Statistical significance when reported + - Observed trends across studies + +3. Methods Summary + - Study designs used + - Major interventions or treatments studied + - Key outcome measures + - Patient populations studied + +4. Clinical Relevance + - Reported clinical implications + - Main conclusions from authors + - Reported benefits and risks + - Treatment responses when available + +5. Research Status + - Current research focus areas + - Reported limitations + - Gaps identified in abstracts + - Authors' suggested future directions + +CRITICAL REQUIREMENTS: + +Citation Rules (MANDATORY): +- EVERY finding or statement MUST be supported by citations [N], where N is the number matching the paper in the provided literature list +- When reporting outcomes, ALWAYS cite the source studies using the exact paper numbers from the literature list +- For findings supported by multiple studies, use consecutive numbers as shown in the literature list [1,2,3] +- Use ONLY the papers provided in the available literature list above +- Citations must appear immediately after each statement +- Citation numbers MUST match the numbers assigned to papers in the literature list above (e.g., if a finding comes from the first paper in the list, cite it as [1]) +- DO NOT change or reorder the citation numbers - they must exactly match the paper numbers in the literature list + +Content Guidelines: +- Present only information available in the provided papers +- If certain information is not available, simply omit that aspect rather than explicitly stating its absence +- Focus on synthesizing and presenting available findings +- Maintain professional medical writing style +- Present limitations and gaps as research opportunities rather than missing information + +Writing Style: +- Use precise medical terminology +- Maintain objective reporting +- Use consistent terminology throughout +- Present a cohesive narrative without referencing data limitations + +Language requirement: +- If the query explicitly specifies a language, use that language +- Otherwise, match the language of the original user query +""" + + def _generate_general_review_prompt(self, criteria: SearchCriteria) -> str: + """生成通用文献综述prompt""" + current_time = self._get_current_time() + final_prompt = f"""Current time: {current_time} + +Conduct a comprehensive literature review on {criteria.main_topic} focusing on the following aspects: +{', '.join(criteria.sub_topics)} + +Available literature for review: +{self._format_papers(self.ranked_papers)} + +IMPORTANT: If the user query contains specific requirements for the review structure or format, those requirements take precedence over the following guidelines. + +Please structure your review following these guidelines: + +1. Introduction and Research Background + - Current state and significance of the research field + - Key research problems and challenges + - Research development timeline and evolution + +2. Research Directions and Classifications + - Major research directions and their relationships + - Different technical approaches and their characteristics + - Comparative analysis of various solutions + +3. Core Technologies and Methods + - Key technological breakthroughs + - Advantages and limitations of different methods + - Technical challenges and solutions + +4. Applications and Impact + - Real-world applications and use cases + - Industry influence and practical value + - Implementation challenges and solutions + +5. Future Trends and Prospects + - Emerging research directions + - Unsolved problems and challenges + - Potential breakthrough points + +CRITICAL REQUIREMENTS: + +Citation Rules (MANDATORY): +- EVERY finding or statement MUST be supported by citations [N], where N is the number matching the paper in the provided literature list +- When reporting outcomes, ALWAYS cite the source studies using the exact paper numbers from the literature list +- For findings supported by multiple studies, use consecutive numbers as shown in the literature list [1,2,3] +- Use ONLY the papers provided in the available literature list above +- Citations must appear immediately after each statement +- Citation numbers MUST match the numbers assigned to papers in the literature list above (e.g., if a finding comes from the first paper in the list, cite it as [1]) +- DO NOT change or reorder the citation numbers - they must exactly match the paper numbers in the literature list + +Writing Style: +- Maintain academic and professional tone +- Focus on objective analysis with proper citations +- Ensure logical flow and clear structure + +Content Requirements: +- Base ALL analysis STRICTLY on the provided papers with explicit citations +- When introducing any concept, method, or finding, immediately follow with [N] +- For each research direction or approach, cite the specific papers [N] that proposed or developed it +- When discussing limitations or challenges, cite the papers [N] that identified them +- DO NOT include information from sources outside the provided paper list +- DO NOT make unsupported claims or statements + +Language requirement: +- If the query explicitly specifies a language, use that language +- Otherwise, match the language of the original user query +""" + + return final_prompt + diff --git a/crazy_functions/review_fns/paper_processor/paper_llm_ranker.py b/crazy_functions/review_fns/paper_processor/paper_llm_ranker.py new file mode 100644 index 00000000..a70f2df8 --- /dev/null +++ b/crazy_functions/review_fns/paper_processor/paper_llm_ranker.py @@ -0,0 +1,452 @@ +from typing import List, Dict +from crazy_functions.review_fns.data_sources.base_source import PaperMetadata +from request_llms.embed_models.bge_llm import BGELLMRanker +from crazy_functions.review_fns.query_analyzer import SearchCriteria +import random +from crazy_functions.review_fns.data_sources.journal_metrics import JournalMetrics + +class PaperLLMRanker: + """使用LLM进行论文重排序""" + + def __init__(self, llm_kwargs: Dict = None): + self.ranker = BGELLMRanker(llm_kwargs=llm_kwargs) + self.journal_metrics = JournalMetrics() + + def _update_paper_metrics(self, papers: List[PaperMetadata]) -> None: + """更新论文的期刊指标""" + for paper in papers: + # 跳过arXiv来源的论文 + if getattr(paper, 'source', '') == 'arxiv': + continue + + if hasattr(paper, 'venue_name') or hasattr(paper, 'venue_info'): + # 获取venue_name和venue_info + venue_name = getattr(paper, 'venue_name', '') + venue_info = getattr(paper, 'venue_info', {}) + + # 使用改进的匹配逻辑获取指标 + metrics = self.journal_metrics.get_journal_metrics(venue_name, venue_info) + + # 更新论文的指标 + paper.if_factor = metrics.get('if_factor') + paper.jcr_division = metrics.get('jcr_division') + paper.cas_division = metrics.get('cas_division') + + def _get_year_as_int(self, paper) -> int: + """统一获取论文年份为整数格式 + + Args: + paper: 论文对象或直接是年份值 + + Returns: + 整数格式的年份，如果无法转换则返回0 + """ + try: + # 如果输入直接是年份而不是论文对象 + if isinstance(paper, int): + return paper + elif isinstance(paper, str): + try: + return int(paper) + except ValueError: + import re + year_match = re.search(r'\d{4}', paper) + if year_match: + return int(year_match.group()) + return 0 + elif isinstance(paper, float): + return int(paper) + + # 处理论文对象 + year = getattr(paper, 'year', None) + if year is None: + return 0 + + # 如果是字符串，尝试转换为整数 + if isinstance(year, str): + # 首先尝试直接转换整个字符串 + try: + return int(year) + except ValueError: + # 如果直接转换失败，尝试提取第一个数字序列 + import re + year_match = re.search(r'\d{4}', year) + if year_match: + return int(year_match.group()) + return 0 + # 如果是浮点数，转换为整数 + elif isinstance(year, float): + return int(year) + # 如果已经是整数，直接返回 + elif isinstance(year, int): + return year + return 0 + except (ValueError, TypeError): + return 0 + + def rank_papers( + self, + query: str, + papers: List[PaperMetadata], + search_criteria: SearchCriteria = None, + top_k: int = 40, + use_rerank: bool = False, + pre_filter_ratio: float = 0.5, + max_papers: int = 150 + ) -> List[PaperMetadata]: + """对论文进行重排序""" + initial_count = len(papers) if papers else 0 + stats = {'initial': initial_count} + + if not papers or not query: + return [] + + # 更新论文的期刊指标 + self._update_paper_metrics(papers) + + # 构建增强查询 + # enhanced_query = self._build_enhanced_query(query, search_criteria) if search_criteria else query + enhanced_query = query + # 首先过滤不满足年份要求的论文 + if search_criteria and search_criteria.start_year and search_criteria.end_year: + before_year_filter = len(papers) + filtered_papers = [] + start_year = int(search_criteria.start_year) + end_year = int(search_criteria.end_year) + + for paper in papers: + paper_year = self._get_year_as_int(paper) + if paper_year == 0 or start_year <= paper_year <= end_year: + filtered_papers.append(paper) + + papers = filtered_papers + stats['after_year_filter'] = len(papers) + + if not papers: # 如果过滤后没有论文，直接返回空列表 + return [] + + # 新增：对少量论文的快速处理 + SMALL_PAPER_THRESHOLD = 10 # 定义"少量"论文的阈值 + if len(papers) <= SMALL_PAPER_THRESHOLD: + # 对于少量论文，直接根据查询类型进行简单排序 + if search_criteria: + if search_criteria.query_type == "latest": + papers.sort(key=lambda x: getattr(x, 'year', 0) or 0, reverse=True) + elif search_criteria.query_type == "recommend": + papers.sort(key=lambda x: getattr(x, 'citations', 0) or 0, reverse=True) + elif search_criteria.query_type == "review": + papers.sort(key=lambda x: + 1 if any(keyword in (getattr(x, 'title', '') or '').lower() or + keyword in (getattr(x, 'abstract', '') or '').lower() + for keyword in ['review', 'survey', 'overview']) + else 0, + reverse=True + ) + return papers[:top_k] + + # 1. 优先处理最新的论文 + if search_criteria and search_criteria.query_type == "latest": + papers = sorted(papers, key=lambda x: self._get_year_as_int(x), reverse=True) + + # 2. 如果是综述类查询，优先处理可能的综述论文 + if search_criteria and search_criteria.query_type == "review": + papers = sorted(papers, key=lambda x: + 1 if any(keyword in (getattr(x, 'title', '') or '').lower() or + keyword in (getattr(x, 'abstract', '') or '').lower() + for keyword in ['review', 'survey', 'overview']) + else 0, + reverse=True + ) + + # 3. 如果论文数量超过限制，采用分层采样而不是完全随机 + if len(papers) > max_papers: + before_max_limit = len(papers) + papers = self._select_papers_strategically(papers, search_criteria, max_papers) + stats['after_max_limit'] = len(papers) + + try: + paper_texts = [] + valid_papers = [] # 4. 跟踪有效论文 + + for paper in papers: + if paper is None: + continue + # 5. 预先过滤明显不相关的论文 + if search_criteria and search_criteria.start_year: + if getattr(paper, 'year', 0) and self._get_year_as_int(paper.year) < search_criteria.start_year: + continue + + doc = self._build_enhanced_document(paper, search_criteria) + paper_texts.append(doc) + valid_papers.append(paper) # 记录对应的论文 + + stats['after_valid_check'] = len(valid_papers) + + if not paper_texts: + return [] + + # 使用LLM判断相关性 + relevance_results = self.ranker.batch_check_relevance( + query=enhanced_query, # 使用增强的查询 + paper_texts=paper_texts, + show_progress=True + ) + + # 6. 优化相关论文的选择策略 + relevant_papers = [] + for paper, is_relevant in zip(valid_papers, relevance_results): + if is_relevant: + relevant_papers.append(paper) + + stats['after_llm_filter'] = len(relevant_papers) + + # 打印统计信息 + print(f"论文筛选统计: 初始数量={stats['initial']}, " + + f"年份过滤后={stats.get('after_year_filter', stats['initial'])}, " + + f"数量限制后={stats.get('after_max_limit', stats.get('after_year_filter', stats['initial']))}, " + + f"有效性检查后={stats['after_valid_check']}, " + + f"LLM筛选后={stats['after_llm_filter']}") + + # 7. 改进回退策略 + if len(relevant_papers) < min(5, len(papers)): + # 如果相关论文太少，返回按引用量排序的论文 + return sorted( + papers[:top_k], + key=lambda x: getattr(x, 'citations', 0) or 0, + reverse=True + ) + + # 8. 对最终结果进行排序 + if search_criteria: + if search_criteria.query_type == "latest": + # 最新论文优先，但同年份按IF排序 + relevant_papers.sort(key=lambda x: ( + self._get_year_as_int(x), + getattr(x, 'if_factor', 0) or 0 + ), reverse=True) + elif search_criteria.query_type == "recommend": + # IF指数优先，其次是引用量 + relevant_papers.sort(key=lambda x: ( + getattr(x, 'if_factor', 0) or 0, + getattr(x, 'citations', 0) or 0 + ), reverse=True) + else: + # 默认按IF指数排序 + relevant_papers.sort(key=lambda x: getattr(x, 'if_factor', 0) or 0, reverse=True) + + return relevant_papers[:top_k] + + except Exception as e: + print(f"论文排序时出错: {str(e)}") + # 9. 改进错误处理的回退策略 + try: + return sorted( + papers[:top_k], + key=lambda x: getattr(x, 'citations', 0) or 0, + reverse=True + ) + except: + return papers[:top_k] if papers else [] + + def _build_enhanced_query(self, query: str, criteria: SearchCriteria) -> str: + """构建增强的查询文本""" + components = [] + + # 强调这是用户的原始查询，是最重要的匹配依据 + components.append(f"Original user query that must be primarily matched: {query}") + + if criteria: + # 添加主题（如果与原始查询不同） + if criteria.main_topic and criteria.main_topic != query: + components.append(f"Additional context - The main topic is about: {criteria.main_topic}") + + # 添加子主题 + if criteria.sub_topics: + components.append(f"Secondary aspects to consider: {', '.join(criteria.sub_topics)}") + + # 添加查询类型相关信息 + if criteria.query_type == "review": + components.append("Paper type preference: Looking for comprehensive review papers, survey papers, or overview papers") + elif criteria.query_type == "latest": + components.append("Temporal preference: Focus on the most recent developments and latest papers") + elif criteria.query_type == "recommend": + components.append("Impact preference: Consider influential and fundamental papers") + + # 直接连接所有组件，保持语序 + enhanced_query = ' '.join(components) + + # 限制长度但不打乱顺序 + if len(enhanced_query) > 1000: + enhanced_query = enhanced_query[:997] + "..." + + return enhanced_query + + def _build_enhanced_document(self, paper: PaperMetadata, criteria: SearchCriteria) -> str: + """构建增强的文档表示""" + components = [] + + # 基本信息 + title = getattr(paper, 'title', '') + authors = ', '.join(getattr(paper, 'authors', [])) + abstract = getattr(paper, 'abstract', '') + year = getattr(paper, 'year', '') + venue = getattr(paper, 'venue', '') + + components.extend([ + f"Title: {title}", + f"Authors: {authors}", + f"Year: {year}", + f"Venue: {venue}", + f"Abstract: {abstract}" + ]) + + # 根据查询类型添加额外信息 + if criteria: + if criteria.query_type == "review": + # 对于综述类查询，强调论文的综述性质 + title_lower = (title or '').lower() + abstract_lower = (abstract or '').lower() + if any(keyword in title_lower or keyword in abstract_lower + for keyword in ['review', 'survey', 'overview']): + components.append("This is a review/survey paper") + + elif criteria.query_type == "latest": + # 对于最新论文查询，强调时间信息 + if year and int(year) >= criteria.start_year: + components.append(f"This is a recent paper from {year}") + + elif criteria.query_type == "recommend": + # 对于推荐类查询，添加主题相关性信息 + if criteria.main_topic: + title_lower = (title or '').lower() + abstract_lower = (abstract or '').lower() + topic_relevance = any(topic.lower() in title_lower or topic.lower() in abstract_lower + for topic in [criteria.main_topic] + (criteria.sub_topics or [])) + if topic_relevance: + components.append(f"This paper is directly related to {criteria.main_topic}") + + return '\n'.join(components) + + def _select_papers_strategically( + self, + papers: List[PaperMetadata], + search_criteria: SearchCriteria, + max_papers: int = 150 + ) -> List[PaperMetadata]: + """战略性地选择论文子集，优先选择非Crossref来源的论文， + 当ADS论文充足时排除arXiv论文""" + if len(papers) <= max_papers: + return papers + + # 1. 首先按来源分组 + papers_by_source = { + 'crossref': [], + 'adsabs': [], + 'arxiv': [], + 'others': [] # semantic, pubmed等其他来源 + } + + for paper in papers: + source = getattr(paper, 'source', '') + if source == 'crossref': + papers_by_source['crossref'].append(paper) + elif source == 'adsabs': + papers_by_source['adsabs'].append(paper) + elif source == 'arxiv': + papers_by_source['arxiv'].append(paper) + else: + papers_by_source['others'].append(paper) + + # 2. 计算分数的通用函数 + def calculate_paper_score(paper): + score = 0 + title = (getattr(paper, 'title', '') or '').lower() + abstract = (getattr(paper, 'abstract', '') or '').lower() + year = self._get_year_as_int(paper) + citations = getattr(paper, 'citations', 0) or 0 + + # 安全地获取搜索条件 + main_topic = (getattr(search_criteria, 'main_topic', '') or '').lower() + sub_topics = getattr(search_criteria, 'sub_topics', []) or [] + query_type = getattr(search_criteria, 'query_type', '') + start_year = getattr(search_criteria, 'start_year', 0) or 0 + + # 主题相关性得分 + if main_topic and main_topic in title: + score += 10 + if main_topic and main_topic in abstract: + score += 5 + + # 子主题相关性得分 + for sub_topic in sub_topics: + if sub_topic and sub_topic.lower() in title: + score += 5 + if sub_topic and sub_topic.lower() in abstract: + score += 2.5 + + # 根据查询类型调整分数 + if query_type == "review": + review_keywords = ['review', 'survey', 'overview'] + if any(keyword in title for keyword in review_keywords): + score *= 1.5 + if any(keyword in abstract for keyword in review_keywords): + score *= 1.2 + elif query_type == "latest": + if year and start_year: + year_int = year if isinstance(year, int) else self._get_year_as_int(paper) + start_year_int = start_year if isinstance(start_year, int) else int(start_year) + if year_int >= start_year_int: + recency_bonus = min(5, (year_int - start_year_int)) + score += recency_bonus * 2 + elif query_type == "recommend": + citation_score = min(10, citations / 100) + score += citation_score + + return score + + result = [] + + # 3. 处理ADS和arXiv论文 + non_crossref_papers = papers_by_source['others'] # 首先添加其他来源的论文 + + # 添加ADS论文 + if papers_by_source['adsabs']: + non_crossref_papers.extend(papers_by_source['adsabs']) + + # 只有当ADS论文不足20篇时，才添加arXiv论文 + if len(papers_by_source['adsabs']) <= 20: + non_crossref_papers.extend(papers_by_source['arxiv']) + elif not papers_by_source['adsabs'] and papers_by_source['arxiv']: + # 如果没有ADS论文但有arXiv论文，也使用arXiv论文 + non_crossref_papers.extend(papers_by_source['arxiv']) + + # 4. 对非Crossref论文评分和排序 + scored_non_crossref = [(p, calculate_paper_score(p)) for p in non_crossref_papers] + scored_non_crossref.sort(key=lambda x: x[1], reverse=True) + + # 5. 先添加高分的非Crossref论文 + non_crossref_limit = max_papers * 0.9 # 90%的配额给非Crossref论文 + if len(scored_non_crossref) >= non_crossref_limit: + result.extend([p[0] for p in scored_non_crossref[:int(non_crossref_limit)]]) + else: + result.extend([p[0] for p in scored_non_crossref]) + + # 6. 如果还有剩余空间，考虑添加Crossref论文 + remaining_slots = max_papers - len(result) + if remaining_slots > 0 and papers_by_source['crossref']: + # 计算Crossref论文的最大数量（不超过总数的10%） + max_crossref = min(remaining_slots, max_papers * 0.1) + + # 对Crossref论文评分和排序 + scored_crossref = [(p, calculate_paper_score(p)) for p in papers_by_source['crossref']] + scored_crossref.sort(key=lambda x: x[1], reverse=True) + + # 添加最高分的Crossref论文 + result.extend([p[0] for p in scored_crossref[:int(max_crossref)]]) + + # 7. 如果使用了Crossref论文后还有空位，继续使用非Crossref论文填充 + if len(result) < max_papers and len(scored_non_crossref) > len(result): + remaining_non_crossref = [p[0] for p in scored_non_crossref[len(result):]] + result.extend(remaining_non_crossref[:max_papers - len(result)]) + + return result \ No newline at end of file diff --git a/crazy_functions/review_fns/prompts/adsabs_prompts.py b/crazy_functions/review_fns/prompts/adsabs_prompts.py new file mode 100644 index 00000000..bc60d430 --- /dev/null +++ b/crazy_functions/review_fns/prompts/adsabs_prompts.py @@ -0,0 +1,76 @@ +# ADS query optimization prompt +ADSABS_QUERY_PROMPT = """Analyze and optimize the following query for NASA ADS search. +If the query is not related to astronomy, astrophysics, or physics, return none. +If the query contains non-English terms, translate them to English first. + +Query: {query} + +Task: Transform the natural language query into an optimized ADS search query. +Always generate English search terms regardless of the input language. + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements. Focus only on the core research topic for the search query. + +Relevant research areas for ADS: +- Astronomy and astrophysics +- Physics (theoretical and experimental) +- Space science and exploration +- Planetary science +- Cosmology +- Astrobiology +- Related instrumentation and methods + +Available search fields and filters: +1. Basic fields: + - title: Search in title (title:"term") + - abstract: Search in abstract (abstract:"term") + - author: Search for author names (author:"lastname, firstname") + - year: Filter by year (year:2020-2023) + - bibstem: Search by journal abbreviation (bibstem:ApJ) + +2. Boolean operators: + - AND + - OR + - NOT + - (): Group terms + - "": Exact phrase match + +3. Special filters: + - citations(identifier:paper): Papers citing a specific paper + - references(identifier:paper): References of a specific paper + - citation_count: Filter by citation count + - database: Filter by database (database:astronomy) + +Examples: + +1. Query: "Black holes in galaxy centers after 2020" +title:"black hole" AND abstract:"galaxy center" AND year:2020- + +2. Query: "Papers by Neil deGrasse Tyson about exoplanets" +author:"Tyson, Neil deGrasse" AND title:exoplanet + +3. Query: "Most cited papers about dark matter in ApJ" +title:"dark matter" AND bibstem:ApJ AND citation_count:[100 TO *] + +4. Query: "Latest research on diabetes treatment" +none + +5. Query: "Machine learning for galaxy classification" +title:("machine learning" OR "deep learning") AND (title:galaxy OR abstract:galaxy) AND abstract:classification + +Please analyze the query and respond ONLY with XML tags: +Provide the optimized ADS search query using appropriate fields and operators, or "none" if not relevant""" + +# System prompt +ADSABS_QUERY_SYSTEM_PROMPT = """You are an expert at crafting NASA ADS search queries. +Your task is to: +1. First determine if the query is relevant to astronomy, astrophysics, or physics research +2. If relevant, optimize the natural language query for the ADS API +3. If not relevant, return "none" to indicate the query should be handled by other databases + +Focus on creating precise queries that will return relevant astronomical and physics literature. +Always generate English search terms regardless of the input language. +Consider using field-specific search terms and appropriate filters to improve search accuracy. + +Remember: ADS is specifically for astronomy, astrophysics, and physics research. +Medical, biological, or general research queries should return "none".""" \ No newline at end of file diff --git a/crazy_functions/review_fns/prompts/arxiv_prompts.py b/crazy_functions/review_fns/prompts/arxiv_prompts.py new file mode 100644 index 00000000..e0590b54 --- /dev/null +++ b/crazy_functions/review_fns/prompts/arxiv_prompts.py @@ -0,0 +1,341 @@ +# Basic type analysis prompt +ARXIV_TYPE_PROMPT = """Analyze the research query and determine if arXiv search is needed and its type. + +Query: {query} + +Task 1: Determine if this query requires arXiv search +- arXiv is suitable for: + * Computer science and AI/ML + * Physics and mathematics + * Quantitative biology and finance + * Electrical engineering + * Recent preprints in these fields +- arXiv is NOT needed for: + * Medical research (unless ML/AI applications) + * Social sciences + * Business studies + * Humanities + * Industry reports + +Task 2: If arXiv search is needed, determine the most appropriate search type +Available types: +1. basic: Keyword-based search across all fields + - For specific technical queries + - When looking for particular methods or applications +2. category: Category-based search within specific fields + - For broad topic exploration + - When surveying a research area +3. none: arXiv search not needed for this query + - When topic is outside arXiv's scope + - For non-technical or clinical research + +Examples: + +1. Query: "BERT transformer architecture" +basic + +2. Query: "latest developments in machine learning" +category + +3. Query: "COVID-19 clinical trials" +none + +4. Query: "psychological effects of social media" +none + +Please analyze the query and respond ONLY with XML tags: +Choose either 'basic', 'category', or 'none'""" + +# Query optimization prompt +ARXIV_QUERY_PROMPT = """Optimize the following query for arXiv search. + +Query: {query} + +Task: Transform the natural language query into an optimized arXiv search query using boolean operators and field tags. +Always generate English search terms regardless of the input language. + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements. Focus only on the core research topic for the search query. + +Available field tags: +- ti: Search in title +- abs: Search in abstract +- au: Search for author +- all: Search in all fields (default) + +Boolean operators: +- AND: Both terms must appear +- OR: Either term can appear +- NOT: Exclude terms +- (): Group terms +- "": Exact phrase match + +Examples: + +1. Natural query: "Recent papers about transformer models by Vaswani" +ti:"transformer model" AND au:Vaswani AND year:[2017 TO 2024] + +2. Natural query: "Deep learning for computer vision, excluding surveys" +ti:(deep learning AND "computer vision") NOT (ti:survey OR ti:review) + +3. Natural query: "Attention mechanism in language models" +ti:(attention OR "attention mechanism") AND abs:"language model" + +4. Natural query: "GANs or generative adversarial networks for image generation" +(ti:GAN OR ti:"generative adversarial network") AND abs:"image generation" + +Please analyze the query and respond ONLY with XML tags: +Provide the optimized search query using appropriate operators and tags + +Note: +- Use quotes for exact phrases +- Combine multiple conditions with boolean operators +- Consider both title and abstract for important concepts +- Include author names when relevant +- Use parentheses for complex logical groupings""" + +# Sort parameters prompt +ARXIV_SORT_PROMPT = """Determine optimal sorting parameters for the research query. + +Query: {query} + +Task: Select the most appropriate sorting parameters to help users find the most relevant papers. + +Available sorting options: + +1. Sort by: + - relevance: Best match to query terms (default) + - lastUpdatedDate: Most recently updated papers + - submittedDate: Most recently submitted papers + +2. Sort order: + - descending: Newest/Most relevant first (default) + - ascending: Oldest/Least relevant first + +3. Result limit: + - Minimum: 10 papers + - Maximum: 50 papers + - Recommended: 20-30 papers for most queries + +Examples: + +1. Query: "Latest developments in transformer models" +submittedDate +descending +30 + +2. Query: "Foundational papers about neural networks" +relevance +descending +20 + +3. Query: "Evolution of deep learning since 2012" +submittedDate +ascending +50 + +Please analyze the query and respond ONLY with XML tags: +Choose: relevance, lastUpdatedDate, or submittedDate +Choose: ascending or descending +Suggest number between 10-50 + +Note: +- Choose relevance for specific technical queries +- Use lastUpdatedDate for tracking paper revisions +- Use submittedDate for following recent developments +- Consider query context when setting the limit""" + +# System prompts for each task +ARXIV_TYPE_SYSTEM_PROMPT = """You are an expert at analyzing academic queries. +Your task is to determine whether the query is better suited for keyword search or category-based search. +Consider the query's specificity, scope, and intended search area when making your decision. +Always respond in English regardless of the input language.""" + +ARXIV_QUERY_SYSTEM_PROMPT = """You are an expert at crafting arXiv search queries. +Your task is to optimize natural language queries using boolean operators and field tags. +Focus on creating precise, targeted queries that will return the most relevant results. +Always generate English search terms regardless of the input language.""" + +ARXIV_CATEGORIES_SYSTEM_PROMPT = """You are an expert at arXiv category classification. +Your task is to select the most relevant categories for the given research query. +Consider both primary and related interdisciplinary categories, while maintaining focus on the main research area. +Always respond in English regardless of the input language.""" + +ARXIV_SORT_SYSTEM_PROMPT = """You are an expert at optimizing search results. +Your task is to determine the best sorting parameters based on the query context. +Consider the user's likely intent and temporal aspects of the research topic. +Always respond in English regardless of the input language.""" + +# 添加新的搜索提示词 +ARXIV_SEARCH_PROMPT = """Analyze and optimize the research query for arXiv search. + +Query: {query} + +Task: Transform the natural language query into an optimized arXiv search query. + +Available search options: +1. Basic search with field tags: + - ti: Search in title + - abs: Search in abstract + - au: Search for author + Example: "ti:transformer AND abs:attention" + +2. Category-based search: + - Use specific arXiv categories + Example: "cat:cs.AI AND neural networks" + +3. Date range: + - Specify date range using submittedDate + Example: "deep learning AND submittedDate:[20200101 TO 20231231]" + +Examples: + +1. Query: "Recent papers about transformer models by Vaswani" + +ti:"transformer model" AND au:Vaswani AND submittedDate:[20170101 TO 99991231] +cs.CL, cs.AI, cs.LG +submittedDate +descending +30 + + +2. Query: "Latest developments in computer vision" + +cat:cs.CV AND submittedDate:[20220101 TO 99991231] +cs.CV, cs.AI, cs.LG +submittedDate +descending +25 + + +Please analyze the query and respond with XML tags containing search criteria.""" + +ARXIV_SEARCH_SYSTEM_PROMPT = """You are an expert at crafting arXiv search queries. +Your task is to analyze research queries and transform them into optimized arXiv search criteria. +Consider query intent, relevant categories, and temporal aspects when creating the search parameters. +Always generate English search terms and respond in English regardless of the input language.""" + +# Categories selection prompt +ARXIV_CATEGORIES_PROMPT = """Select the most relevant arXiv categories for the research query. + +Query: {query} + +Task: Choose 2-4 most relevant categories that best match the research topic. + +Available Categories: + +Computer Science (cs): +- cs.AI: Artificial Intelligence (neural networks, machine learning, NLP) +- cs.CL: Computation and Language (NLP, machine translation) +- cs.CV: Computer Vision and Pattern Recognition +- cs.LG: Machine Learning (deep learning, reinforcement learning) +- cs.NE: Neural and Evolutionary Computing +- cs.RO: Robotics +- cs.IR: Information Retrieval +- cs.SE: Software Engineering +- cs.DB: Databases +- cs.DC: Distributed Computing +- cs.CY: Computers and Society +- cs.HC: Human-Computer Interaction + +Mathematics (math): +- math.OC: Optimization and Control +- math.PR: Probability +- math.ST: Statistics +- math.NA: Numerical Analysis +- math.DS: Dynamical Systems + +Statistics (stat): +- stat.ML: Machine Learning +- stat.ME: Methodology +- stat.TH: Theory +- stat.AP: Applications + +Physics (physics): +- physics.comp-ph: Computational Physics +- physics.data-an: Data Analysis +- physics.soc-ph: Physics and Society + +Electrical Engineering (eess): +- eess.SP: Signal Processing +- eess.AS: Audio and Speech Processing +- eess.IV: Image and Video Processing +- eess.SY: Systems and Control + +Examples: + +1. Query: "Deep learning for computer vision" +cs.CV, cs.LG, stat.ML + +2. Query: "Natural language processing with transformers" +cs.CL, cs.AI, cs.LG + +3. Query: "Reinforcement learning for robotics" +cs.RO, cs.AI, cs.LG + +4. Query: "Statistical methods in machine learning" +stat.ML, cs.LG, math.ST + +Please analyze the query and respond ONLY with XML tags: +List 2-4 most relevant categories, comma-separated + +Note: +- Choose primary categories first, then add related ones +- Limit to 2-4 most relevant categories +- Order by relevance (most relevant first) +- Use comma and space between categories (e.g., "cs.AI, cs.LG")""" + +# 在文件末尾添加新的 prompt +ARXIV_LATEST_PROMPT = """Determine if the query is requesting latest papers from arXiv. + +Query: {query} + +Task: Analyze if the query is specifically asking for recent/latest papers from arXiv. + +IMPORTANT RULE: +- The query MUST explicitly mention "arXiv" or "arxiv" to be considered a latest arXiv papers request +- Queries only asking for recent/latest papers WITHOUT mentioning arXiv should return false + +Indicators for latest papers request: +1. MUST HAVE keywords about arXiv: + - "arxiv" + - "arXiv" + AND + +2. Keywords about recency: + - "latest" + - "recent" + - "new" + - "newest" + - "just published" + - "this week/month" + +Examples: + +1. Latest papers request (Valid): +Query: "Show me the latest AI papers on arXiv" +true + +2. Latest papers request (Valid): +Query: "What are the recent papers about transformers on arxiv" +true + +3. Not a latest papers request (Invalid - no mention of arXiv): +Query: "Show me the latest papers about BERT" +false + +4. Not a latest papers request (Invalid - no recency): +Query: "Find papers on arxiv about transformers" +false + +Please analyze the query and respond ONLY with XML tags: +true/false + +Note: The response should be true ONLY if both conditions are met: +1. Query explicitly mentions arXiv/arxiv +2. Query asks for recent/latest papers""" + +ARXIV_LATEST_SYSTEM_PROMPT = """You are an expert at analyzing academic queries. +Your task is to determine if the query is specifically requesting latest/recent papers from arXiv. +Remember: The query MUST explicitly mention arXiv to be considered valid, even if it asks for recent papers. +Always respond in English regardless of the input language.""" diff --git a/crazy_functions/review_fns/prompts/crossref_prompts.py b/crazy_functions/review_fns/prompts/crossref_prompts.py new file mode 100644 index 00000000..47525a91 --- /dev/null +++ b/crazy_functions/review_fns/prompts/crossref_prompts.py @@ -0,0 +1,55 @@ +# Crossref query optimization prompt +CROSSREF_QUERY_PROMPT = """Analyze and optimize the query for Crossref search. + +Query: {query} + +Task: Transform the natural language query into an optimized Crossref search query. +Always generate English search terms regardless of the input language. + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements. Focus only on the core research topic for the search query. + +Available search fields and filters: +1. Basic fields: + - title: Search in title + - abstract: Search in abstract + - author: Search for author names + - container-title: Search in journal/conference name + - publisher: Search by publisher name + - type: Filter by work type (journal-article, book-chapter, etc.) + - year: Filter by publication year + +2. Boolean operators: + - AND: Both terms must appear + - OR: Either term can appear + - NOT: Exclude terms + - "": Exact phrase match + +3. Special filters: + - is-referenced-by-count: Filter by citation count + - from-pub-date: Filter by publication date + - has-abstract: Filter papers with abstracts + +Examples: + +1. Query: "Machine learning in healthcare after 2020" +title:"machine learning" AND title:healthcare AND from-pub-date:2020 + +2. Query: "Papers by Geoffrey Hinton about deep learning" +author:"Hinton, Geoffrey" AND (title:"deep learning" OR abstract:"deep learning") + +3. Query: "Most cited papers about transformers in Nature" +title:transformer AND container-title:Nature AND is-referenced-by-count:[100 TO *] + +4. Query: "Recent BERT applications in medical domain" +title:BERT AND abstract:medical AND from-pub-date:2020 AND type:journal-article + +Please analyze the query and respond ONLY with XML tags: +Provide the optimized Crossref search query using appropriate fields and operators""" + +# System prompt +CROSSREF_QUERY_SYSTEM_PROMPT = """You are an expert at crafting Crossref search queries. +Your task is to optimize natural language queries for Crossref's API. +Focus on creating precise queries that will return relevant results. +Always generate English search terms regardless of the input language. +Consider using field-specific search terms and appropriate filters to improve search accuracy.""" \ No newline at end of file diff --git a/crazy_functions/review_fns/prompts/paper_prompts.py b/crazy_functions/review_fns/prompts/paper_prompts.py new file mode 100644 index 00000000..7ddf6858 --- /dev/null +++ b/crazy_functions/review_fns/prompts/paper_prompts.py @@ -0,0 +1,47 @@ +# 新建文件，添加论文识别提示 +PAPER_IDENTIFY_PROMPT = """Analyze the query to identify paper details. + +Query: {query} + +Task: Extract paper identification information from the query. +Always generate English search terms regardless of the input language. + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements. Focus only on identifying paper details. + +Possible paper identifiers: +1. arXiv ID (e.g., 2103.14030, arXiv:2103.14030) +2. DOI (e.g., 10.1234/xxx.xxx) +3. Paper title (e.g., "Attention is All You Need") + +Examples: + +1. Query with arXiv ID: +Query: "Analyze paper 2103.14030" + +arxiv +2103.14030 + + + +2. Query with DOI: +Query: "Review the paper with DOI 10.1234/xxx.xxx" + +doi +10.1234/xxx.xxx + + + +3. Query with paper title: +Query: "Analyze 'Attention is All You Need' paper" + +title + +Attention is All You Need + + +Please analyze the query and respond ONLY with XML tags containing paper information.""" + +PAPER_IDENTIFY_SYSTEM_PROMPT = """You are an expert at identifying academic paper references. +Your task is to extract paper identification information from queries. +Look for arXiv IDs, DOIs, and paper titles.""" \ No newline at end of file diff --git a/crazy_functions/review_fns/prompts/pubmed_prompts.py b/crazy_functions/review_fns/prompts/pubmed_prompts.py new file mode 100644 index 00000000..6c9a3e8e --- /dev/null +++ b/crazy_functions/review_fns/prompts/pubmed_prompts.py @@ -0,0 +1,108 @@ +# PubMed search type prompt +PUBMED_TYPE_PROMPT = """Analyze the research query and determine the appropriate PubMed search type. + +Query: {query} + +Available search types: +1. basic: General keyword search for medical/biomedical topics +2. author: Search by author name +3. journal: Search within specific journals +4. none: Query not related to medical/biomedical research + +Examples: + +1. Query: "COVID-19 treatment outcomes" +basic + +2. Query: "Papers by Anthony Fauci" +author + +3. Query: "Recent papers in Nature about CRISPR" +journal + +4. Query: "Deep learning for computer vision" +none + +5. Query: "Transformer architecture for NLP" +none + +Please analyze the query and respond ONLY with XML tags: +Choose: basic, author, journal, or none""" + +# PubMed query optimization prompt +PUBMED_QUERY_PROMPT = """Optimize the following query for PubMed search. + +Query: {query} + +Task: Transform the natural language query into an optimized PubMed search query. +Requirements: +- Always generate English search terms regardless of input language +- Translate any non-English terms to English before creating the query +- Never include non-English characters in the final query + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements. Focus only on the core medical/biomedical topic for the search query. + +Available field tags: +- [Title] - Search in title +- [Author] - Search for author +- [Journal] - Search in journal name +- [MeSH Terms] - Search using MeSH terms + +Boolean operators: +- AND +- OR +- NOT + +Examples: + +1. Query: "COVID-19 treatment in elderly patients" +COVID-19[Title] AND treatment[Title/Abstract] AND elderly[Title/Abstract] + +2. Query: "Cancer immunotherapy review articles" +cancer immunotherapy[Title/Abstract] AND review[Publication Type] + +Please analyze the query and respond ONLY with XML tags: +Provide the optimized PubMed search query""" + +# PubMed sort parameters prompt +PUBMED_SORT_PROMPT = """Determine optimal sorting parameters for PubMed results. + +Query: {query} + +Task: Select the most appropriate sorting method and result limit. + +Available sort options: +- relevance: Best match to query +- date: Most recent first +- journal: Sort by journal name + +Examples: + +1. Query: "Latest developments in gene therapy" +date +30 + +2. Query: "Classic papers about DNA structure" +relevance +20 + +Please analyze the query and respond ONLY with XML tags: +Choose: relevance, date, or journal +Suggest number between 10-50""" + +# System prompts +PUBMED_TYPE_SYSTEM_PROMPT = """You are an expert at analyzing medical and scientific queries. +Your task is to determine the most appropriate PubMed search type. +Consider the query's focus and intended search scope. +Always respond in English regardless of the input language.""" + +PUBMED_QUERY_SYSTEM_PROMPT = """You are an expert at crafting PubMed search queries. +Your task is to optimize natural language queries using PubMed's search syntax. +Focus on creating precise, targeted queries that will return relevant medical literature. +Always generate English search terms regardless of the input language.""" + +PUBMED_SORT_SYSTEM_PROMPT = """You are an expert at optimizing PubMed search results. +Your task is to determine the best sorting parameters based on the query context. +Consider the balance between relevance and recency. +Always respond in English regardless of the input language.""" \ No newline at end of file diff --git a/crazy_functions/review_fns/prompts/semantic_prompts.py b/crazy_functions/review_fns/prompts/semantic_prompts.py new file mode 100644 index 00000000..9138df3d --- /dev/null +++ b/crazy_functions/review_fns/prompts/semantic_prompts.py @@ -0,0 +1,276 @@ +# Search type prompt +SEMANTIC_TYPE_PROMPT = """Determine the most appropriate search type for Semantic Scholar. + +Query: {query} + +Task: Analyze the research query and select the most appropriate search type for Semantic Scholar API. + +Available search types: + +1. paper: General paper search + - Use for broad topic searches + - Looking for specific papers + - Keyword-based searches + Example: "transformer models in NLP" + +2. author: Author-based search + - Finding works by specific researchers + - Author profile analysis + Example: "papers by Yoshua Bengio" + +3. paper_details: Specific paper lookup + - Getting details about a known paper + - Finding specific versions or citations + Example: "Attention is All You Need paper details" + +4. citations: Citation analysis + - Finding papers that cite a specific work + - Impact analysis + Example: "papers citing BERT" + +5. references: Reference analysis + - Finding papers cited by a specific work + - Background research + Example: "references in GPT-3 paper" + +6. recommendations: Paper recommendations + - Finding similar papers + - Research direction exploration + Example: "papers similar to Transformer" + +Examples: + +1. Query: "Latest papers about deep learning" +paper + +2. Query: "Works by Geoffrey Hinton since 2020" +author + +3. Query: "Papers citing the original Transformer paper" +citations + +Please analyze the query and respond ONLY with XML tags: +Choose the most appropriate search type from the list above""" + +# Query optimization prompt +SEMANTIC_QUERY_PROMPT = """Optimize the following query for Semantic Scholar search. + +Query: {query} + +Task: Transform the natural language query into an optimized search query for maximum relevance. +Always generate English search terms regardless of the input language. + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements. Focus only on the core research topic for the search query. + +Query optimization guidelines: + +1. Use quotes for exact phrases + - Ensures exact matching + - Reduces irrelevant results + Example: "\"attention mechanism\"" vs attention mechanism + +2. Include key technical terms + - Use specific technical terminology + - Include common variations + Example: "transformer architecture" neural networks + +3. Author names (if relevant) + - Include full names when known + - Consider common name variations + Example: "Geoffrey Hinton" OR "G. E. Hinton" + +Examples: + +1. Natural query: "Recent advances in transformer models" +"transformer model" "neural architecture" deep learning + +2. Natural query: "BERT applications in text classification" +"BERT" "text classification" "language model" application + +3. Natural query: "Deep learning for computer vision by Kaiming He" +"deep learning" "computer vision" author:"Kaiming He" + +Please analyze the query and respond ONLY with XML tags: +Provide the optimized search query + +Note: +- Balance between specificity and coverage +- Include important technical terms +- Use quotes for key phrases +- Consider synonyms and related terms""" + +# Fields selection prompt +SEMANTIC_FIELDS_PROMPT = """Select relevant fields to retrieve from Semantic Scholar. + +Query: {query} + +Task: Determine which paper fields should be retrieved based on the research needs. + +Available fields: + +Core fields: +- title: Paper title (always included) +- abstract: Full paper abstract +- authors: Author information +- year: Publication year +- venue: Publication venue + +Citation fields: +- citations: Papers citing this work +- references: Papers cited by this work + +Additional fields: +- embedding: Paper vector embedding +- tldr: AI-generated summary +- venue: Publication venue/journal +- url: Paper URL + +Examples: + +1. Query: "Latest developments in NLP" +title, abstract, authors, year, venue, citations + +2. Query: "Most influential papers in deep learning" +title, abstract, authors, year, citations, references + +3. Query: "Survey of transformer architectures" +title, abstract, authors, year, tldr, references + +Please analyze the query and respond ONLY with XML tags: +List relevant fields, comma-separated + +Note: +- Choose fields based on the query's purpose +- Include citation data for impact analysis +- Consider tldr for quick paper screening +- Balance completeness with API efficiency""" + +# Sort parameters prompt +SEMANTIC_SORT_PROMPT = """Determine optimal sorting parameters for the query. + +Query: {query} + +Task: Select the most appropriate sorting method and result limit for the search. +Always generate English search terms regardless of the input language. + +Sorting options: + +1. relevance (default) + - Best match to query terms + - Recommended for specific technical searches + Example: "specific algorithm implementations" + +2. citations + - Sort by citation count + - Best for finding influential papers + Example: "most important papers in deep learning" + +3. year + - Sort by publication date + - Best for following recent developments + Example: "latest advances in NLP" + +Examples: + +1. Query: "Recent breakthroughs in AI" +year +30 + +2. Query: "Most influential papers about GANs" +citations +20 + +3. Query: "Specific papers about BERT fine-tuning" +relevance +25 + +Please analyze the query and respond ONLY with XML tags: +Choose: relevance, citations, or year +Suggest number between 10-50 + +Note: +- Consider the query's temporal aspects +- Balance between comprehensive coverage and information overload +- Use citation sorting for impact analysis +- Use year sorting for tracking developments""" + +# System prompts for each task +SEMANTIC_TYPE_SYSTEM_PROMPT = """You are an expert at analyzing academic queries. +Your task is to determine the most appropriate type of search on Semantic Scholar. +Consider the query's intent, scope, and specific research needs. +Always respond in English regardless of the input language.""" + +SEMANTIC_QUERY_SYSTEM_PROMPT = """You are an expert at crafting Semantic Scholar search queries. +Your task is to optimize natural language queries for maximum relevance. +Focus on creating precise queries that leverage the platform's search capabilities. +Always generate English search terms regardless of the input language.""" + +SEMANTIC_FIELDS_SYSTEM_PROMPT = """You are an expert at Semantic Scholar data fields. +Your task is to select the most relevant fields based on the research context. +Consider both essential and supplementary information needs. +Always respond in English regardless of the input language.""" + +SEMANTIC_SORT_SYSTEM_PROMPT = """You are an expert at optimizing search results. +Your task is to determine the best sorting parameters based on the query context. +Consider the balance between relevance, impact, and recency. +Always respond in English regardless of the input language.""" + +# 添加新的综合搜索提示词 +SEMANTIC_SEARCH_PROMPT = """Analyze and optimize the research query for Semantic Scholar search. + +Query: {query} + +Task: Transform the natural language query into optimized search criteria for Semantic Scholar. + +IMPORTANT: Ignore any requirements about journal ranking (CAS, JCR, IF index), +or output format requirements when generating the search terms. These requirements +should be considered only for post-search filtering, not as part of the core query. + +Available search options: +1. Paper search: + - Title and abstract search + - Author search + - Field-specific search + Example: "transformer architecture neural networks" + +2. Field tags: + - title: Search in title + - abstract: Search in abstract + - authors: Search by author names + - venue: Search by publication venue + Example: "title:transformer authors:\"Vaswani\"" + +3. Advanced options: + - Year range filtering + - Citation count filtering + - Venue filtering + Example: "deep learning year>2020 venue:\"NeurIPS\"" + +Examples: + +1. Query: "Recent transformer papers by Vaswani with high impact" + +title:transformer authors:"Vaswani" year>2017 +paper +title,abstract,authors,year,citations,venue +citations +30 + + +2. Query: "Most cited papers about BERT in top conferences" + +title:BERT venue:"ACL|EMNLP|NAACL" +paper +title,abstract,authors,year,citations,venue,references +citations +25 + + +Please analyze the query and respond with XML tags containing complete search criteria.""" + +SEMANTIC_SEARCH_SYSTEM_PROMPT = """You are an expert at crafting Semantic Scholar search queries. +Your task is to analyze research queries and transform them into optimized search criteria. +Consider query intent, field relevance, and citation impact when creating the search parameters. +Focus on producing precise and comprehensive search criteria that will yield the most relevant results. +Always generate English search terms and respond in English regardless of the input language.""" diff --git a/crazy_functions/review_fns/query_analyzer.py b/crazy_functions/review_fns/query_analyzer.py new file mode 100644 index 00000000..507b4b87 --- /dev/null +++ b/crazy_functions/review_fns/query_analyzer.py @@ -0,0 +1,493 @@ +from typing import Dict, List +from dataclasses import dataclass +from textwrap import dedent +from datetime import datetime +import re + + +@dataclass +class SearchCriteria: + """搜索条件""" + query_type: str # 查询类型: review/recommend/qa/paper + main_topic: str # 主题 + sub_topics: List[str] # 子主题列表 + start_year: int # 起始年份 + end_year: int # 结束年份 + arxiv_params: Dict # arXiv搜索参数 + semantic_params: Dict # Semantic Scholar搜索参数 + pubmed_params: Dict # 新增: PubMed搜索参数 + crossref_params: Dict # 添加 Crossref 参数 + adsabs_params: Dict # 添加 ADS 参数 + paper_id: str = "" # 论文ID (arxiv ID 或 DOI) + paper_title: str = "" # 论文标题 + paper_source: str = "" # 论文来源 (arxiv/doi/title) + original_query: str = "" # 新增: 原始查询字符串 + + +class QueryAnalyzer: + """查询分析器""" + + # 响应索引常量 + BASIC_QUERY_INDEX = 0 + PAPER_IDENTIFY_INDEX = 1 + ARXIV_QUERY_INDEX = 2 + ARXIV_CATEGORIES_INDEX = 3 + ARXIV_LATEST_INDEX = 4 + ARXIV_SORT_INDEX = 5 + SEMANTIC_QUERY_INDEX = 6 + SEMANTIC_FIELDS_INDEX = 7 + PUBMED_TYPE_INDEX = 8 + PUBMED_QUERY_INDEX = 9 + CROSSREF_QUERY_INDEX = 10 + ADSABS_QUERY_INDEX = 11 + + def __init__(self): + self.current_year = datetime.now().year + self.valid_types = { + "review": ["review", "literature review", "survey"], + "recommend": ["recommend", "recommendation", "suggest", "similar"], + "qa": ["qa", "question", "answer", "explain", "what", "how", "why"], + "paper": ["paper", "analyze", "analysis"] + } + + def analyze_query(self, query: str, chatbot: List, llm_kwargs: Dict): + """分析查询意图""" + from crazy_functions.crazy_utils import \ + request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency as request_gpt + from crazy_functions.review_fns.prompts.arxiv_prompts import ( + ARXIV_QUERY_PROMPT, ARXIV_CATEGORIES_PROMPT, ARXIV_LATEST_PROMPT, + ARXIV_SORT_PROMPT, ARXIV_QUERY_SYSTEM_PROMPT, ARXIV_CATEGORIES_SYSTEM_PROMPT, ARXIV_SORT_SYSTEM_PROMPT, + ARXIV_LATEST_SYSTEM_PROMPT + ) + from crazy_functions.review_fns.prompts.semantic_prompts import ( + SEMANTIC_QUERY_PROMPT, SEMANTIC_FIELDS_PROMPT, + SEMANTIC_QUERY_SYSTEM_PROMPT, SEMANTIC_FIELDS_SYSTEM_PROMPT + ) + from .prompts.paper_prompts import PAPER_IDENTIFY_PROMPT, PAPER_IDENTIFY_SYSTEM_PROMPT + from .prompts.pubmed_prompts import ( + PUBMED_TYPE_PROMPT, PUBMED_QUERY_PROMPT, PUBMED_SORT_PROMPT, + PUBMED_TYPE_SYSTEM_PROMPT, PUBMED_QUERY_SYSTEM_PROMPT, PUBMED_SORT_SYSTEM_PROMPT + ) + from .prompts.crossref_prompts import ( + CROSSREF_QUERY_PROMPT, + CROSSREF_QUERY_SYSTEM_PROMPT + ) + from .prompts.adsabs_prompts import ADSABS_QUERY_PROMPT, ADSABS_QUERY_SYSTEM_PROMPT + + # 1. 基本查询分析 + type_prompt = dedent(f"""Please analyze this academic query and respond STRICTLY in the following XML format: + + Query: {query} + + Instructions: + 1. Your response must use XML tags exactly as shown below + 2. Do not add any text outside the tags + 3. Choose query type from: review/recommend/qa/paper + - review: for literature review or survey requests + - recommend: for paper recommendation requests + - qa: for general questions about research topics + - paper: ONLY for queries about a SPECIFIC paper (with paper ID, DOI, or exact title) + 4. Identify main topic and subtopics + 5. Specify year range if mentioned + + Required format: + ANSWER HERE + ANSWER HERE + SUBTOPIC1, SUBTOPIC2, ... + START_YEAR-END_YEAR + + Example responses: + + 1. Literature Review Request: + Query: "Review recent developments in transformer models for NLP from 2020 to 2023" + review + transformer models in natural language processing + architecture improvements, pre-training methods, fine-tuning techniques + 2020-2023 + + 2. Paper Recommendation Request: + Query: "Suggest papers about reinforcement learning in robotics since 2018" + recommend + reinforcement learning in robotics + robot control, policy learning, sim-to-real transfer + 2018-2023""" + ) + + try: + # 构建提示数组 + prompts = [ + type_prompt, + PAPER_IDENTIFY_PROMPT.format(query=query), + ARXIV_QUERY_PROMPT.format(query=query), + ARXIV_CATEGORIES_PROMPT.format(query=query), + ARXIV_LATEST_PROMPT.format(query=query), + ARXIV_SORT_PROMPT.format(query=query), + SEMANTIC_QUERY_PROMPT.format(query=query), + SEMANTIC_FIELDS_PROMPT.format(query=query), + PUBMED_TYPE_PROMPT.format(query=query), + PUBMED_QUERY_PROMPT.format(query=query), + CROSSREF_QUERY_PROMPT.format(query=query), + ADSABS_QUERY_PROMPT.format(query=query) + ] + + show_messages = [ + "Analyzing query type...", + "Identifying paper details...", + "Determining arXiv search type...", + "Selecting arXiv categories...", + "Checking if latest papers requested...", + "Determining arXiv sort parameters...", + "Optimizing Semantic Scholar query...", + "Selecting Semantic Scholar fields...", + "Determining PubMed search type...", + "Optimizing PubMed query...", + "Optimizing Crossref query...", + "Optimizing ADS query..." + ] + + sys_prompts = [ + "You are an expert at analyzing academic queries.", + PAPER_IDENTIFY_SYSTEM_PROMPT, + ARXIV_QUERY_SYSTEM_PROMPT, + ARXIV_CATEGORIES_SYSTEM_PROMPT, + ARXIV_LATEST_SYSTEM_PROMPT, + ARXIV_SORT_SYSTEM_PROMPT, + SEMANTIC_QUERY_SYSTEM_PROMPT, + SEMANTIC_FIELDS_SYSTEM_PROMPT, + PUBMED_TYPE_SYSTEM_PROMPT, + PUBMED_QUERY_SYSTEM_PROMPT, + CROSSREF_QUERY_SYSTEM_PROMPT, + ADSABS_QUERY_SYSTEM_PROMPT + ] + new_llm_kwargs = llm_kwargs.copy() + # new_llm_kwargs['llm_model'] = 'deepseek-chat' # deepseek-ai/DeepSeek-V2.5 + + # 使用同步方式调用LLM + responses = yield from request_gpt( + inputs_array=prompts, + inputs_show_user_array=show_messages, + llm_kwargs=new_llm_kwargs, + chatbot=chatbot, + history_array=[[] for _ in prompts], + sys_prompt_array=sys_prompts, + max_workers=5 + ) + + # 从收集的响应中提取我们需要的内容 + extracted_responses = [] + for i in range(len(prompts)): + if (i * 2 + 1) < len(responses): + response = responses[i * 2 + 1] + if response is None: + raise Exception(f"Response {i} is None") + if not isinstance(response, str): + try: + response = str(response) + except: + raise Exception(f"Cannot convert response {i} to string") + extracted_responses.append(response) + else: + raise Exception(f"未收到第 {i + 1} 个响应") + + # 解析基本信息 + query_type = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "query_type") + if not query_type: + print( + f"Debug - Failed to extract query_type. Response was: {extracted_responses[self.BASIC_QUERY_INDEX]}") + raise Exception("无法提取query_type标签内容") + query_type = query_type.lower() + + main_topic = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "main_topic") + if not main_topic: + print(f"Debug - Failed to extract main_topic. Using query as fallback.") + main_topic = query + + query_type = self._normalize_query_type(query_type, query) + + # 解析arXiv参数 + try: + arxiv_params = { + "query": self._extract_tag(extracted_responses[self.ARXIV_QUERY_INDEX], "query"), + "categories": [cat.strip() for cat in + self._extract_tag(extracted_responses[self.ARXIV_CATEGORIES_INDEX], + "categories").split(",")], + "sort_by": self._extract_tag(extracted_responses[self.ARXIV_SORT_INDEX], "sort_by"), + "sort_order": self._extract_tag(extracted_responses[self.ARXIV_SORT_INDEX], "sort_order"), + "limit": 20 + } + + # 安全地解析limit值 + limit_str = self._extract_tag(extracted_responses[self.ARXIV_SORT_INDEX], "limit") + if limit_str and limit_str.isdigit(): + arxiv_params["limit"] = int(limit_str) + + except Exception as e: + print(f"Warning: Error parsing arXiv parameters: {str(e)}") + arxiv_params = { + "query": "", + "categories": [], + "sort_by": "relevance", + "sort_order": "descending", + "limit": 0 + } + + # 解析Semantic Scholar参数 + try: + semantic_params = { + "query": self._extract_tag(extracted_responses[self.SEMANTIC_QUERY_INDEX], "query"), + "fields": [field.strip() for field in + self._extract_tag(extracted_responses[self.SEMANTIC_FIELDS_INDEX], "fields").split(",")], + "sort_by": "relevance", + "limit": 20 + } + except Exception as e: + print(f"Warning: Error parsing Semantic Scholar parameters: {str(e)}") + semantic_params = { + "query": query, + "fields": ["title", "abstract", "authors", "year"], + "sort_by": "relevance", + "limit": 20 + } + + # 解析PubMed参数 + try: + # 首先检查是否需要PubMed搜索 + pubmed_search_type = self._extract_tag(extracted_responses[self.PUBMED_TYPE_INDEX], "search_type") + + if pubmed_search_type == "none": + # 不需要PubMed搜索，使用空参数 + pubmed_params = { + "search_type": "none", + "query": "", + "sort_by": "relevance", + "limit": 0 + } + else: + # 需要PubMed搜索，解析完整参数 + pubmed_params = { + "search_type": pubmed_search_type, + "query": self._extract_tag(extracted_responses[self.PUBMED_QUERY_INDEX], "query"), + "sort_by": "relevance", + "limit": 200 + } + except Exception as e: + print(f"Warning: Error parsing PubMed parameters: {str(e)}") + pubmed_params = { + "search_type": "none", + "query": "", + "sort_by": "relevance", + "limit": 0 + } + + # 解析Crossref参数 + try: + crossref_query = self._extract_tag(extracted_responses[self.CROSSREF_QUERY_INDEX], "query") + + if not crossref_query: + crossref_params = { + "search_type": "none", + "query": "", + "sort_by": "relevance", + "limit": 0 + } + else: + crossref_params = { + "search_type": "basic", + "query": crossref_query, + "sort_by": "relevance", + "limit": 20 + } + except Exception as e: + print(f"Warning: Error parsing Crossref parameters: {str(e)}") + crossref_params = { + "search_type": "none", + "query": "", + "sort_by": "relevance", + "limit": 0 + } + + # 解析ADS参数 + try: + adsabs_query = self._extract_tag(extracted_responses[self.ADSABS_QUERY_INDEX], "query") + + if not adsabs_query: + adsabs_params = { + "search_type": "none", + "query": "", + "sort_by": "relevance", + "limit": 0 + } + else: + adsabs_params = { + "search_type": "basic", + "query": adsabs_query, + "sort_by": "relevance", + "limit": 20 + } + except Exception as e: + print(f"Warning: Error parsing ADS parameters: {str(e)}") + adsabs_params = { + "search_type": "none", + "query": "", + "sort_by": "relevance", + "limit": 0 + } + + print(f"Debug - Extracted information:") + print(f"Query type: {query_type}") + print(f"Main topic: {main_topic}") + print(f"arXiv params: {arxiv_params}") + print(f"Semantic params: {semantic_params}") + print(f"PubMed params: {pubmed_params}") + print(f"Crossref params: {crossref_params}") + print(f"ADS params: {adsabs_params}") + + # 提取子主题 + sub_topics = [] + if "sub_topics" in query.lower(): + sub_topics_text = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "sub_topics") + if sub_topics_text: + sub_topics = [topic.strip() for topic in sub_topics_text.split(",")] + + # 提取年份范围 + start_year = self.current_year - 5 # 默认最近5年 + end_year = self.current_year + year_range = self._extract_tag(extracted_responses[self.BASIC_QUERY_INDEX], "year_range") + if year_range: + try: + years = year_range.split("-") + if len(years) == 2: + start_year = int(years[0].strip()) + end_year = int(years[1].strip()) + except: + pass + + # 提取 latest request 判断 + is_latest_request = self._extract_tag(extracted_responses[self.ARXIV_LATEST_INDEX], + "is_latest_request").lower() == "true" + + # 如果是最新论文请求，将查询类型改为 "latest" + if is_latest_request: + query_type = "latest" + + # 提取论文标识信息 + paper_source = self._extract_tag(extracted_responses[self.PAPER_IDENTIFY_INDEX], "paper_source") + paper_id = self._extract_tag(extracted_responses[self.PAPER_IDENTIFY_INDEX], "paper_id") + paper_title = self._extract_tag(extracted_responses[self.PAPER_IDENTIFY_INDEX], "paper_title") + if start_year > end_year: + start_year, end_year = end_year, start_year + # 更新返回的 SearchCriteria + return SearchCriteria( + query_type=query_type, + main_topic=main_topic, + sub_topics=sub_topics, + start_year=start_year, + end_year=end_year, + arxiv_params=arxiv_params, + semantic_params=semantic_params, + pubmed_params=pubmed_params, + crossref_params=crossref_params, + paper_id=paper_id, + paper_title=paper_title, + paper_source=paper_source, + original_query=query, + adsabs_params=adsabs_params + ) + + except Exception as e: + raise Exception(f"Failed to analyze query: {str(e)}") + + def _normalize_query_type(self, query_type: str, query: str) -> str: + """规范化查询类型""" + if query_type in ["review", "recommend", "qa", "paper"]: + return query_type + + query_lower = query.lower() + for type_name, keywords in self.valid_types.items(): + for keyword in keywords: + if keyword in query_lower: + return type_name + + query_type_lower = query_type.lower() + for type_name, keywords in self.valid_types.items(): + for keyword in keywords: + if keyword in query_type_lower: + return type_name + + return "qa" # 默认返回qa类型 + + def _extract_tag(self, text: str, tag: str) -> str: + """提取标记内容""" + if not text: + return "" + + # 1. 标准XML格式（处理多行和特殊字符） + pattern = f"<{tag}>(.*?)" + match = re.search(pattern, text, re.DOTALL | re.IGNORECASE) + if match: + content = match.group(1).strip() + if content: + return content + + # 2. 处理特定标签的复杂内容 + if tag == "categories": + # 处理arXiv类别 + patterns = [ + # 标准格式：cs.CL, cs.AI, cs.LG + r"\s*((?:(?:cs|stat|math|physics|q-bio|q-fin|nlin|astro-ph|cond-mat|gr-qc|hep-[a-z]+|math-ph|nucl-[a-z]+|quant-ph)\.[A-Z]+(?:\s*,\s*)?)+)\s*", + # 简单列表格式：cs.CL, cs.AI, cs.LG + r"(?:^|\s)((?:(?:cs|stat|math|physics|q-bio|q-fin|nlin|astro-ph|cond-mat|gr-qc|hep-[a-z]+|math-ph|nucl-[a-z]+|quant-ph)\.[A-Z]+(?:\s*,\s*)?)+)(?:\s|$)", + # 单个类别格式：cs.AI + r"(?:^|\s)((?:cs|stat|math|physics|q-bio|q-fin|nlin|astro-ph|cond-mat|gr-qc|hep-[a-z]+|math-ph|nucl-[a-z]+|quant-ph)\.[A-Z]+)(?:\s|$)" + ] + + elif tag == "query": + # 处理搜索查询 + patterns = [ + # 完整的查询格式：complex query + r"\s*((?:(?:ti|abs|au|cat):[^\n]*?|(?:AND|OR|NOT|\(|\)|\d{4}|year:\d{4}|[\"'][^\"']*[\"']|\s+))+)\s*", + # 简单的关键词列表：keyword1, keyword2 + r"(?:^|\s)((?:\"[^\"]*\"|'[^']*'|[^\s,]+)(?:\s*,\s*(?:\"[^\"]*\"|'[^']*'|[^\s,]+))*)", + # 字段搜索格式：field:value + r"((?:ti|abs|au|cat):\s*(?:\"[^\"]*\"|'[^']*'|[^\s]+))" + ] + + elif tag == "fields": + # 处理字段列表 + patterns = [ + # 标准格式：field1, field2 + r"\s*([\w\s,]+)\s*", + # 简单列表格式：field1, field2 + r"(?:^|\s)([\w]+(?:\s*,\s*[\w]+)*)", + ] + + elif tag == "sort_by": + # 处理排序字段 + patterns = [ + # 标准格式：value + r"\s*(relevance|date|citations|submittedDate|year)\s*", + # 简单值格式：relevance + r"(?:^|\s)(relevance|date|citations|submittedDate|year)(?:\s|$)" + ] + + else: + # 通用模式 + patterns = [ + f"<{tag}>\s*([\s\S]*?)\s*", # 标准XML格式 + f"<{tag}>([\s\S]*?)(?:|$)", # 未闭合的标签 + f"[{tag}]([\s\S]*?)[/{tag}]", # 方括号格式 + f"{tag}:\s*(.*?)(?=\n\w|$)", # 冒号格式 + f"<{tag}>\s*(.*?)(?=<|$)" # 部分闭合 + ] + + # 3. 尝试所有模式 + for pattern in patterns: + match = re.search(pattern, text, re.IGNORECASE | re.DOTALL) + if match: + content = match.group(1).strip() + if content: # 确保提取的内容不为空 + return content + + # 4. 如果所有模式都失败，返回空字符串 + return "" + diff --git a/crazy_functions/review_fns/query_processor.py b/crazy_functions/review_fns/query_processor.py new file mode 100644 index 00000000..f6b7e124 --- /dev/null +++ b/crazy_functions/review_fns/query_processor.py @@ -0,0 +1,64 @@ +from typing import List, Dict, Any +from .query_analyzer import QueryAnalyzer, SearchCriteria +from .data_sources.arxiv_source import ArxivSource +from .data_sources.semantic_source import SemanticScholarSource +from .handlers.review_handler import 文献综述功能 +from .handlers.recommend_handler import 论文推荐功能 +from .handlers.qa_handler import 学术问答功能 +from .handlers.paper_handler import 单篇论文分析功能 + +class QueryProcessor: + """查询处理器""" + + def __init__(self): + self.analyzer = QueryAnalyzer() + self.arxiv = ArxivSource() + self.semantic = SemanticScholarSource() + + # 初始化各种处理器 + self.handlers = { + "review": 文献综述功能(self.arxiv, self.semantic), + "recommend": 论文推荐功能(self.arxiv, self.semantic), + "qa": 学术问答功能(self.arxiv, self.semantic), + "paper": 单篇论文分析功能(self.arxiv, self.semantic) + } + + async def process_query( + self, + query: str, + chatbot: List[List[str]], + history: List[List[str]], + system_prompt: str, + llm_kwargs: Dict[str, Any], + plugin_kwargs: Dict[str, Any], + ) -> List[List[str]]: + """处理用户查询""" + + # 设置默认的插件参数 + default_plugin_kwargs = { + 'max_papers': 20, # 最大论文数量 + 'min_year': 2015, # 最早年份 + 'search_multiplier': 3, # 检索倍数 + } + # 更新插件参数 + plugin_kwargs.update({k: v for k, v in default_plugin_kwargs.items() if k not in plugin_kwargs}) + + # 1. 分析查询意图 + criteria = self.analyzer.analyze_query(query, chatbot, llm_kwargs) + + # 2. 根据查询类型选择处理器 + handler = self.handlers.get(criteria.query_type) + if not handler: + handler = self.handlers["qa"] # 默认使用QA处理器 + + # 3. 处理查询 + response = await handler.handle( + criteria, + chatbot, + history, + system_prompt, + llm_kwargs, + plugin_kwargs + ) + + return response \ No newline at end of file diff --git a/request_llms/embed_models/bge_llm.py b/request_llms/embed_models/bge_llm.py new file mode 100644 index 00000000..8e254e70 --- /dev/null +++ b/request_llms/embed_models/bge_llm.py @@ -0,0 +1,109 @@ +import re +import requests +from loguru import logger +from typing import List, Dict +from urllib3.util import Retry +from requests.adapters import HTTPAdapter +from textwrap import dedent +from request_llms.bridge_all import predict_no_ui_long_connection + +class BGELLMRanker: + """使用LLM进行论文相关性判断的类""" + def __init__(self, llm_kwargs): + self.llm_kwargs = llm_kwargs + + def is_paper_relevant(self, query: str, paper_text: str) -> bool: + """判断论文是否与查询相关""" + prompt = dedent(f""" + Evaluate if this academic paper contains information that directly addresses the user's query. + + Query: {query} + + Paper Content: + {paper_text} + + Evaluation Criteria: + 1. The paper must contain core information that directly answers the query + 2. The paper's main research focus must be highly relevant to the query + 3. Papers that only mention query-related content in abstract should be excluded + 4. Papers with superficial or general discussions should be excluded + 5. For queries about "recent" or "latest" advances, paper should be from last 3 years + + Instructions: + - Carefully evaluate against ALL criteria above + - Return true ONLY if paper meets ALL criteria + - If any criteria is not met or unclear, return false + - Be strict but not overly restrictive + + Output Rules: + - Must ONLY respond with true or false + - true = paper contains relevant information to answer the query + - false = paper does not contain sufficient relevant information + + Do not include any explanation or additional text.""" + ) + response = predict_no_ui_long_connection( + inputs=prompt, + history=[], + llm_kwargs=self.llm_kwargs, + sys_prompt="You are an expert at determining paper relevance to queries. Respond only with true or false." + ) + # 提取decision标签中的内容 + match = re.search(r'(.*?)', response, re.IGNORECASE) + if match: + decision = match.group(1).lower() + return decision == "true" + else: + return False + + def batch_check_relevance(self, query: str, paper_texts: List[str], show_progress: bool = True) -> List[bool]: + """批量检查论文相关性 + + Args: + query: 用户查询 + paper_texts: 论文文本列表 + show_progress: 是否显示进度条 + + Returns: + List[bool]: 相关性判断结果列表 + """ + from concurrent.futures import ThreadPoolExecutor, as_completed + from tqdm import tqdm + + results = [False] * len(paper_texts) + + # 减少并发线程数以避免连接池耗尽 + max_workers = min(20, len(paper_texts)) # 限制最大线程数 + + with ThreadPoolExecutor(max_workers=max_workers) as executor: + future_to_idx = { + executor.submit(self.is_paper_relevant, query, text): i + for i, text in enumerate(paper_texts) + } + iterator = as_completed(future_to_idx) + if show_progress: + iterator = tqdm(iterator, total=len(paper_texts), desc="判断论文相关性") + for future in iterator: + idx = future_to_idx[future] + try: + results[idx] = future.result() + except Exception as e: + logger.exception(f"处理论文 {idx} 时出错: {str(e)}") + results[idx] = False + return results + +def main(): + # 测试代码 + ranker = BGELLMRanker() + + query = "Recent advances in transformer models" + paper_text = """ + Title: Attention Is All You Need + Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks that include an encoder and a decoder. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely... + """ + + is_relevant = ranker.is_paper_relevant(query, paper_text) + print(f"Paper relevant: {is_relevant}") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/request_llms/embed_models/bridge_all_embed.py b/request_llms/embed_models/bridge_all_embed.py index e3ea8a6f..95dc243a 100644 --- a/request_llms/embed_models/bridge_all_embed.py +++ b/request_llms/embed_models/bridge_all_embed.py @@ -8,13 +8,10 @@ API_URL_REDIRECT, AZURE_ENDPOINT, AZURE_ENGINE = get_conf("API_URL_REDIRECT", "A openai_endpoint = "https://api.openai.com/v1/chat/completions" if not AZURE_ENDPOINT.endswith('/'): AZURE_ENDPOINT += '/' azure_endpoint = AZURE_ENDPOINT + f'openai/deployments/{AZURE_ENGINE}/chat/completions?api-version=2023-05-15' - - if openai_endpoint in API_URL_REDIRECT: openai_endpoint = API_URL_REDIRECT[openai_endpoint] - openai_embed_endpoint = openai_endpoint.replace("chat/completions", "embeddings") -from .openai_embed import OpenAiEmbeddingModel +from request_llms.embed_models.openai_embed import OpenAiEmbeddingModel embed_model_info = { # text-embedding-3-small Increased performance over 2nd generation ada embedding model | 1,536 diff --git a/requirements.txt b/requirements.txt index 266c81c2..0b033305 100644 --- a/requirements.txt +++ b/requirements.txt @@ -23,6 +23,7 @@ mdtex2html dashscope pyautogen colorama +docx2pdf Markdown pygments edge-tts>=7.0.0 diff --git a/tests/test_academic_conversation.py b/tests/test_academic_conversation.py new file mode 100644 index 00000000..9656a5b5 --- /dev/null +++ b/tests/test_academic_conversation.py @@ -0,0 +1,64 @@ +""" +对项目中的各个插件进行测试。运行方法：直接运行 python tests/test_plugins.py +""" + +import init_test +import os, sys + + +if __name__ == "__main__": + from test_utils import plugin_test + plugin_test(plugin='crazy_functions.Academic_Conversation->学术对话', main_input="搜索最新使用GAIA Benchmark的论文") + + # plugin_test(plugin='crazy_functions.Internet_GPT->连接网络回答问题', main_input="谁是应急食品？") + + # plugin_test(plugin='crazy_functions.函数动态生成->函数动态生成', main_input='交换图像的蓝色通道和红色通道', advanced_arg={"file_path_arg": "./build/ants.jpg"}) + + # plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2307.07522") + + # plugin_test(plugin='crazy_functions.PDF_Translate->批量翻译PDF文档', main_input='build/pdf/t1.pdf') + + # plugin_test( + # plugin="crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF", + # main_input="G:/SEAFILE_LOCAL/50503047/我的资料库/学位/paperlatex/aaai/Fu_8368_with_appendix", + # ) + + # plugin_test(plugin='crazy_functions.虚空终端->虚空终端', main_input='修改api-key为sk-jhoejriotherjep') + + # plugin_test(plugin='crazy_functions.批量翻译PDF文档_NOUGAT->批量翻译PDF文档', main_input='crazy_functions/test_project/pdf_and_word/aaai.pdf') + + # plugin_test(plugin='crazy_functions.虚空终端->虚空终端', main_input='调用插件，对C:/Users/fuqingxu/Desktop/旧文件/gpt/chatgpt_academic/crazy_functions/latex_fns中的python文件进行解析') + + # plugin_test(plugin='crazy_functions.命令行助手->命令行助手', main_input='查看当前的docker容器列表') + + # plugin_test(plugin='crazy_functions.SourceCode_Analyse->解析一个Python项目', main_input="crazy_functions/test_project/python/dqn") + + # plugin_test(plugin='crazy_functions.SourceCode_Analyse->解析一个C项目', main_input="crazy_functions/test_project/cpp/cppipc") + + # plugin_test(plugin='crazy_functions.Latex_Project_Polish->Latex英文润色', main_input="crazy_functions/test_project/latex/attention") + + # plugin_test(plugin='crazy_functions.Markdown_Translate->Markdown中译英', main_input="README.md") + + # plugin_test(plugin='crazy_functions.PDF_Translate->批量翻译PDF文档', main_input='crazy_functions/test_project/pdf_and_word/aaai.pdf') + + # plugin_test(plugin='crazy_functions.谷歌检索小助手->谷歌检索小助手', main_input="https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=auto+reinforcement+learning&btnG=") + + # plugin_test(plugin='crazy_functions.总结word文档->总结word文档', main_input="crazy_functions/test_project/pdf_and_word") + + # plugin_test(plugin='crazy_functions.下载arxiv论文翻译摘要->下载arxiv论文并翻译摘要', main_input="1812.10695") + + # plugin_test(plugin='crazy_functions.解析JupyterNotebook->解析ipynb文件', main_input="crazy_functions/test_samples") + + # plugin_test(plugin='crazy_functions.数学动画生成manim->动画生成', main_input="A ball split into 2, and then split into 4, and finally split into 8.") + + # for lang in ["English", "French", "Japanese", "Korean", "Russian", "Italian", "German", "Portuguese", "Arabic"]: + # plugin_test(plugin='crazy_functions.Markdown_Translate->Markdown翻译指定语言', main_input="README.md", advanced_arg={"advanced_arg": lang}) + + # plugin_test(plugin='crazy_functions.知识库文件注入->知识库文件注入', main_input="./") + + # plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="What is the installation method？") + + # plugin_test(plugin='crazy_functions.知识库文件注入->读取知识库作答', main_input="远程云服务器部署？") + + # plugin_test(plugin='crazy_functions.Latex_Function->Latex翻译中文并重新编译PDF', main_input="2210.03629") +