镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 14:36:48 +00:00
345 行
12 KiB
Python
345 行
12 KiB
Python
from typing import List, Dict, Any, Optional, Tuple
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from .base_handler import BaseHandler
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from crazy_functions.review_fns.query_analyzer import SearchCriteria
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import asyncio
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from crazy_functions.crazy_utils import request_gpt_model_multi_threads_with_very_awesome_ui_and_high_efficiency as request_gpt
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class 单篇论文分析功能(BaseHandler):
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"""论文分析处理器"""
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def __init__(self, arxiv, semantic, llm_kwargs=None):
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super().__init__(arxiv, semantic, llm_kwargs)
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async def handle(
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self,
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criteria: SearchCriteria,
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chatbot: List[List[str]],
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history: List[List[str]],
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system_prompt: str,
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llm_kwargs: Dict[str, Any],
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plugin_kwargs: Dict[str, Any],
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) -> str:
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"""处理论文分析请求，返回最终的prompt"""
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# 1. 获取论文详情
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paper = await self._get_paper_details(criteria)
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if not paper:
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return self._generate_apology_prompt(criteria)
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# 保存为ranked_papers以便统一接口
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self.ranked_papers = [paper]
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# 2. 构建最终的prompt
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current_time = self._get_current_time()
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# 获取论文信息
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title = getattr(paper, "title", "Unknown Title")
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authors = getattr(paper, "authors", [])
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year = getattr(paper, "year", "Unknown Year")
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abstract = getattr(paper, "abstract", "No abstract available")
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citations = getattr(paper, "citations", "N/A")
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# 添加论文ID信息
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paper_id = ""
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if criteria.paper_source == "arxiv":
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paper_id = f"arXiv ID: {criteria.paper_id}\n"
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elif criteria.paper_source == "doi":
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paper_id = f"DOI: {criteria.paper_id}\n"
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# 格式化作者列表
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authors_str = ', '.join(authors) if isinstance(authors, list) else authors
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final_prompt = f"""Current time: {current_time}
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Please provide a comprehensive analysis of the following paper:
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{paper_id}Title: {title}
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Authors: {authors_str}
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Year: {year}
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Citations: {citations}
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Publication Venue: {paper.venue_name} ({paper.venue_type})
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{f"Publisher: {paper.venue_info.get('publisher')}" if paper.venue_info.get('publisher') else ""}
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{f"Journal Reference: {paper.venue_info.get('journal_ref')}" if paper.venue_info.get('journal_ref') else ""}
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Abstract: {abstract}
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Please provide:
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1. Publication Context
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- Publication venue analysis and impact factor (if available)
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- Paper type (journal article, conference paper, preprint)
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- Publication timeline and peer review status
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- Publisher reputation and venue prestige
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2. Research Context
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- Field positioning and significance
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- Historical context and prior work
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- Related research streams
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- Cross-venue impact analysis
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3. Technical Analysis
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- Detailed methodology review
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- Implementation details
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- Experimental setup and results
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- Technical innovations
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4. Impact Analysis
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- Citation patterns and influence
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- Cross-venue recognition
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- Industry vs. academic impact
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- Practical applications
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5. Critical Review
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- Methodological rigor assessment
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- Result reliability and reproducibility
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- Venue-appropriate evaluation standards
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- Limitations and potential improvements
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IMPORTANT:
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- Strictly use ONLY the information provided above about the paper
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- Do not make ANY assumptions or inferences beyond the given data
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- If certain information is not provided, explicitly state that it is unknown
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- For any unclear or missing details, acknowledge the limitation rather than speculating
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- When discussing methodology or results, only describe what is explicitly stated in the abstract
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- Never fabricate or assume any details about:
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* Publication venues or status
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* Implementation details not mentioned
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* Results or findings not stated
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* Impact or influence not supported by the citation count
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* Authors' affiliations or backgrounds
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* Future work or implications not mentioned
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- You can find the paper's download options in the 📥 PDF Downloads section
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- Available download formats include arXiv PDF, DOI links, and source URLs
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Format your response in markdown with clear sections.
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Language requirement:
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- If the query explicitly specifies a language, use that language
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- Otherwise, match the language of the original user query
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"""
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return final_prompt
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async def _get_paper_details(self, criteria: SearchCriteria):
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"""获取论文详情"""
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try:
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if criteria.paper_source == "arxiv":
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# 使用 arxiv ID 搜索
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papers = await self.arxiv.search_by_id(criteria.paper_id)
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return papers[0] if papers else None
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elif criteria.paper_source == "doi":
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# 尝试从所有来源获取
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paper = await self.semantic.get_paper_by_doi(criteria.paper_id)
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if not paper:
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# 如果Semantic Scholar没有找到，尝试PubMed
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papers = await self.pubmed.search(
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f"{criteria.paper_id}[doi]",
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limit=1
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)
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if papers:
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return papers[0]
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return paper
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elif criteria.paper_source == "title":
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# 使用_search_all_sources搜索
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search_params = {
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'max_papers': 1,
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'min_year': 1900, # 不限制年份
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'search_multiplier': 1
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}
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# 设置搜索参数
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criteria.arxiv_params = {
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"search_type": "basic",
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"query": f'ti:"{criteria.paper_title}"',
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"limit": 1
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}
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criteria.semantic_params = {
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"query": criteria.paper_title,
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"limit": 1
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}
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criteria.pubmed_params = {
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"search_type": "basic",
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"query": f'"{criteria.paper_title}"[Title]',
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"limit": 1
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}
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papers = await self._search_all_sources(criteria, search_params)
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return papers[0] if papers else None
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# 如果都没有找到，尝试使用 main_topic 作为标题搜索
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if not criteria.paper_title and not criteria.paper_id:
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search_params = {
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'max_papers': 1,
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'min_year': 1900,
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'search_multiplier': 1
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}
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# 设置搜索参数
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criteria.arxiv_params = {
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"search_type": "basic",
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"query": f'ti:"{criteria.main_topic}"',
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"limit": 1
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}
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criteria.semantic_params = {
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"query": criteria.main_topic,
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"limit": 1
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}
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criteria.pubmed_params = {
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"search_type": "basic",
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"query": f'"{criteria.main_topic}"[Title]',
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"limit": 1
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}
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papers = await self._search_all_sources(criteria, search_params)
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return papers[0] if papers else None
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return None
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except Exception as e:
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print(f"获取论文详情时出错: {str(e)}")
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return None
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async def _get_citation_context(self, paper: Dict, plugin_kwargs: Dict) -> Tuple[List, List]:
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"""获取引用上下文"""
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search_params = self._get_search_params(plugin_kwargs)
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# 使用论文标题构建搜索参数
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title_query = f'ti:"{getattr(paper, "title", "")}"'
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arxiv_params = {
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"query": title_query,
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"limit": search_params['max_papers'],
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"search_type": "basic",
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"sort_by": "relevance",
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"sort_order": "descending"
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}
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semantic_params = {
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"query": getattr(paper, "title", ""),
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"limit": search_params['max_papers']
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}
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citations, references = await asyncio.gather(
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self._search_semantic(
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semantic_params,
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limit_multiplier=search_params['search_multiplier'],
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min_year=search_params['min_year']
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),
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self._search_arxiv(
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arxiv_params,
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limit_multiplier=search_params['search_multiplier'],
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min_year=search_params['min_year']
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)
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)
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return citations, references
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async def _generate_analysis(
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self,
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paper: Dict,
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citations: List,
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references: List,
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chatbot: List[List[str]],
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history: List[List[str]],
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system_prompt: str,
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llm_kwargs: Dict[str, Any]
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) -> List[List[str]]:
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"""生成论文分析"""
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# 构建提示
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analysis_prompt = f"""Please provide a comprehensive analysis of the following paper:
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Paper details:
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{self._format_paper(paper)}
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Key references (papers cited by this paper):
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{self._format_papers(references)}
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Important citations (papers that cite this paper):
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{self._format_papers(citations)}
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Please provide:
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1. Paper Overview
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- Main research question/objective
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- Key methodology/approach
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- Main findings/contributions
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2. Technical Analysis
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- Detailed methodology review
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- Technical innovations
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- Implementation details
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- Experimental setup and results
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3. Impact Analysis
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- Significance in the field
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- Influence on subsequent research (based on citing papers)
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- Relationship to prior work (based on cited papers)
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- Practical applications
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4. Critical Review
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- Strengths and limitations
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- Potential improvements
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- Open questions and future directions
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- Alternative approaches
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5. Related Research Context
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- How it builds on previous work
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- How it has influenced subsequent research
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- Comparison with alternative approaches
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Format your response in markdown with clear sections."""
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# 并行生成概述和技术分析
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for response_chunk in request_gpt(
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inputs_array=[
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analysis_prompt,
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self._get_technical_prompt(paper)
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],
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inputs_show_user_array=[
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"Generating paper analysis...",
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"Analyzing technical details..."
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],
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llm_kwargs=llm_kwargs,
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chatbot=chatbot,
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history_array=[history, []],
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sys_prompt_array=[
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system_prompt,
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"You are an expert at analyzing technical details in research papers."
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]
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):
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pass # 等待生成完成
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# 获取最后的两个回答
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if chatbot and len(chatbot[-2:]) == 2:
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analysis = chatbot[-2][1]
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technical = chatbot[-1][1]
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full_analysis = f"""# Paper Analysis: {paper.title}
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## General Analysis
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{analysis}
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## Technical Deep Dive
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{technical}
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"""
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chatbot.append(["Here is the paper analysis:", full_analysis])
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else:
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chatbot.append(["Here is the paper analysis:", "Failed to generate analysis."])
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return chatbot
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def _get_technical_prompt(self, paper: Dict) -> str:
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"""生成技术分析提示"""
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return f"""Please provide a detailed technical analysis of the following paper:
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{self._format_paper(paper)}
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Focus on:
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1. Mathematical formulations and their implications
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2. Algorithm design and complexity analysis
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3. Architecture details and design choices
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4. Implementation challenges and solutions
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5. Performance analysis and bottlenecks
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6. Technical limitations and potential improvements
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Format your response in markdown, focusing purely on technical aspects."""
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