镜像自地址
https://github.com/binary-husky/gpt_academic.git
已同步 2025-12-06 06:26:47 +00:00
Master 4.0 (#2210)
* stage academic conversation * stage document conversation * fix buggy gradio version * file dynamic load * merge more academic plugins * accelerate nltk * feat: 为predict函数添加文件和URL读取功能 - 添加URL检测和网页内容提取功能，支持自动提取网页文本 - 添加文件路径识别和文件内容读取功能，支持private_upload路径格式 - 集成WebTextExtractor处理网页内容提取 - 集成TextContentLoader处理本地文件读取 - 支持文件路径与问题组合的智能处理 * back * block unstable --------- Co-authored-by: XiaoBoAI <liuboyin2019@ia.ac.cn>
这个提交包含在：
binary-husky
@@ -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:
|
||||
<query_type>ANSWER HERE</query_type>
|
||||
<main_topic>ANSWER HERE</main_topic>
|
||||
<sub_topics>SUBTOPIC1, SUBTOPIC2, ...</sub_topics>
|
||||
<year_range>START_YEAR-END_YEAR</year_range>
|
||||
|
||||
Example responses:
|
||||
|
||||
1. Literature Review Request:
|
||||
Query: "Review recent developments in transformer models for NLP from 2020 to 2023"
|
||||
<query_type>review</query_type>
|
||||
<main_topic>transformer models in natural language processing</main_topic>
|
||||
<sub_topics>architecture improvements, pre-training methods, fine-tuning techniques</sub_topics>
|
||||
<year_range>2020-2023</year_range>
|
||||
|
||||
2. Paper Recommendation Request:
|
||||
Query: "Suggest papers about reinforcement learning in robotics since 2018"
|
||||
<query_type>recommend</query_type>
|
||||
<main_topic>reinforcement learning in robotics</main_topic>
|
||||
<sub_topics>robot control, policy learning, sim-to-real transfer</sub_topics>
|
||||
<year_range>2018-2023</year_range>"""
|
||||
)
|
||||
|
||||
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}>(.*?)</{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：<categories>cs.CL, cs.AI, cs.LG</categories>
|
||||
r"<categories>\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*</categories>",
|
||||
# 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：<query>complex query</query>
|
||||
r"<query>\s*((?:(?:ti|abs|au|cat):[^\n]*?|(?:AND|OR|NOT|\(|\)|\d{4}|year:\d{4}|[\"'][^\"']*[\"']|\s+))+)\s*</query>",
|
||||
# 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：<fields>field1, field2</fields>
|
||||
r"<fields>\s*([\w\s,]+)\s*</fields>",
|
||||
# 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：<sort_by>value</sort_by>
|
||||
r"<sort_by>\s*(relevance|date|citations|submittedDate|year)\s*</sort_by>",
|
||||
# 简单值格式：relevance
|
||||
r"(?:^|\s)(relevance|date|citations|submittedDate|year)(?:\s|$)"
|
||||
]
|
||||
|
||||
else:
|
||||
# 通用模式
|
||||
patterns = [
|
||||
f"<{tag}>\s*([\s\S]*?)\s*</{tag}>", # 标准XML格式
|
||||
f"<{tag}>([\s\S]*?)(?:</{tag}>|$)", # 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 ""
|
||||
|
||||
在新工单中引用
屏蔽一个用户