"""
智能新闻检索工具
提供模糊搜索、链接查询、历史相关新闻检索等高级搜索功能。
"""
import re
from collections import Counter
from datetime import datetime, timedelta
from difflib import SequenceMatcher
from typing import Dict, List, Optional, Tuple, Union
from ..services.data_service import DataService
from ..utils.validators import validate_keyword, validate_limit, validate_threshold, normalize_date_range
from ..utils.errors import MCPError, InvalidParameterError, DataNotFoundError
class SearchTools:
"""智能新闻检索工具类"""
def __init__(self, project_root: str = None):
"""
初始化智能检索工具
Args:
project_root: 项目根目录
"""
self.data_service = DataService(project_root)
def search_news_unified(
self,
query: str,
search_mode: str = "keyword",
date_range: Optional[Union[Dict[str, str], str]] = None,
platforms: Optional[List[str]] = None,
limit: int = 50,
sort_by: str = "relevance",
threshold: float = 0.6,
include_url: bool = False,
include_rss: bool = False,
rss_limit: int = 20
) -> Dict:
"""
统一新闻搜索工具 - 整合多种搜索模式，支持同时搜索热榜和RSS
Args:
query: 查询内容（必需）- 关键词、内容片段或实体名称
search_mode: 搜索模式，可选值：
- "keyword": 精确关键词匹配（默认）
- "fuzzy": 模糊内容匹配（使用相似度算法）
- "entity": 实体名称搜索（自动按权重排序）
date_range: 日期范围（可选）
- **格式**: {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}
- **示例**: {"start": "2025-01-01", "end": "2025-01-07"}
- **默认**: 不指定时默认查询今天
- **注意**: start和end可以相同（表示单日查询）
platforms: 平台过滤列表，如 ['zhihu', 'weibo']
limit: 热榜返回条数限制，默认50
sort_by: 排序方式，可选值：
- "relevance": 按相关度排序（默认）
- "weight": 按新闻权重排序
- "date": 按日期排序
threshold: 相似度阈值（仅fuzzy模式有效），0-1之间，默认0.6
include_url: 是否包含URL链接，默认False（节省token）
include_rss: 是否同时搜索RSS数据，默认False
rss_limit: RSS返回条数限制，默认20
Returns:
搜索结果字典，包含匹配的新闻列表（热榜和RSS分开展示）
Examples:
- search_news_unified(query="人工智能", search_mode="keyword")
- search_news_unified(query="特斯拉降价", search_mode="fuzzy", threshold=0.4)
- search_news_unified(query="马斯克", search_mode="entity", limit=20)
- search_news_unified(query="AI", include_rss=True) # 同时搜索热榜和RSS
- search_news_unified(query="iPhone 16", date_range={"start": "2025-01-01", "end": "2025-01-07"})
"""
try:
# 参数验证
query = validate_keyword(query)
if search_mode not in ["keyword", "fuzzy", "entity"]:
raise InvalidParameterError(
f"无效的搜索模式: {search_mode}",
suggestion="支持的模式: keyword, fuzzy, entity"
)
if sort_by not in ["relevance", "weight", "date"]:
raise InvalidParameterError(
f"无效的排序方式: {sort_by}",
suggestion="支持的排序: relevance, weight, date"
)
limit = validate_limit(limit, default=50)
threshold = validate_threshold(threshold, default=0.6, min_value=0.0, max_value=1.0)
# 处理日期范围
if date_range:
from ..utils.validators import validate_date_range
date_range_tuple = validate_date_range(date_range)
start_date, end_date = date_range_tuple
else:
# 不指定日期时，使用最新可用数据日期（而非 datetime.now()）
earliest, latest = self.data_service.get_available_date_range()
if latest is None:
# 没有任何可用数据
return {
"success": False,
"error": {
"code": "NO_DATA_AVAILABLE",
"message": "output 目录下没有可用的新闻数据",
"suggestion": "请先运行爬虫生成数据，或检查 output 目录"
}
}
# 使用最新可用日期
start_date = end_date = latest
# 收集所有匹配的新闻
all_matches = []
current_date = start_date
while current_date <= end_date:
try:
all_titles, id_to_name, timestamps = self.data_service.parser.read_all_titles_for_date(
date=current_date,
platform_ids=platforms
)
# 根据搜索模式执行不同的搜索逻辑
if search_mode == "keyword":
matches = self._search_by_keyword_mode(
query, all_titles, id_to_name, current_date, include_url
)
elif search_mode == "fuzzy":
matches = self._search_by_fuzzy_mode(
query, all_titles, id_to_name, current_date, threshold, include_url
)
else: # entity
matches = self._search_by_entity_mode(
query, all_titles, id_to_name, current_date, include_url
)
all_matches.extend(matches)
except DataNotFoundError:
# 该日期没有数据，继续下一天
pass
current_date += timedelta(days=1)
if not all_matches:
# 获取可用日期范围用于错误提示
earliest, latest = self.data_service.get_available_date_range()
# 判断时间范围描述
if start_date.date() == datetime.now().date() and start_date == end_date:
time_desc = "今天"
elif start_date == end_date:
time_desc = start_date.strftime("%Y-%m-%d")
else:
time_desc = f"{start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}"
# 构建错误消息
if earliest and latest:
available_desc = f"{earliest.strftime('%Y-%m-%d')} 至 {latest.strftime('%Y-%m-%d')}"
message = f"未找到匹配的新闻（查询范围: {time_desc}，可用数据: {available_desc}）"
else:
message = f"未找到匹配的新闻（{time_desc}）"
result = {
"success": True,
"results": [],
"total": 0,
"query": query,
"search_mode": search_mode,
"time_range": time_desc,
"message": message
}
return result
# 统一排序逻辑
if sort_by == "relevance":
all_matches.sort(key=lambda x: x.get("similarity_score", 1.0), reverse=True)
elif sort_by == "weight":
from .analytics import calculate_news_weight
all_matches.sort(key=lambda x: calculate_news_weight(x), reverse=True)
elif sort_by == "date":
all_matches.sort(key=lambda x: x.get("date", ""), reverse=True)
# 限制返回数量
results = all_matches[:limit]
# 构建时间范围描述（正确判断是否为今天）
if start_date.date() == datetime.now().date() and start_date == end_date:
time_range_desc = "今天"
elif start_date == end_date:
time_range_desc = start_date.strftime("%Y-%m-%d")
else:
time_range_desc = f"{start_date.strftime('%Y-%m-%d')} 至 {end_date.strftime('%Y-%m-%d')}"
result = {
"success": True,
"summary": {
"description": f"新闻搜索结果（{search_mode}模式）",
"total_found": len(all_matches),
"returned": len(results),
"requested_limit": limit,
"search_mode": search_mode,
"query": query,
"platforms": platforms or "所有平台",
"time_range": time_range_desc,
"sort_by": sort_by
},
"data": results
}
if search_mode == "fuzzy":
result["summary"]["threshold"] = threshold
if len(all_matches) < limit:
result["note"] = f"模糊搜索模式下，相似度阈值 {threshold} 仅匹配到 {len(all_matches)} 条结果"
# 如果启用 RSS 搜索，同时搜索 RSS 数据
if include_rss:
rss_results = self._search_rss_by_keyword(
query=query,
start_date=start_date,
end_date=end_date,
limit=rss_limit,
include_url=include_url
)
result["rss"] = rss_results["items"]
result["rss_total"] = rss_results["total"]
result["summary"]["include_rss"] = True
result["summary"]["rss_found"] = rss_results["total"]
result["summary"]["rss_returned"] = len(rss_results["items"])
return result
except MCPError as e:
return {
"success": False,
"error": e.to_dict()
}
except Exception as e:
return {
"success": False,
"error": {
"code": "INTERNAL_ERROR",
"message": str(e)
}
}
def _search_by_keyword_mode(
self,
query: str,
all_titles: Dict,
id_to_name: Dict,
current_date: datetime,
include_url: bool
) -> List[Dict]:
"""
关键词搜索模式（精确匹配）
Args:
query: 搜索关键词
all_titles: 所有标题字典
id_to_name: 平台ID到名称映射
current_date: 当前日期
Returns:
匹配的新闻列表
"""
matches = []
query_lower = query.lower()
for platform_id, titles in all_titles.items():
platform_name = id_to_name.get(platform_id, platform_id)
for title, info in titles.items():
# 精确包含判断
if query_lower in title.lower():
news_item = {
"title": title,
"platform": platform_id,
"platform_name": platform_name,
"date": current_date.strftime("%Y-%m-%d"),
"similarity_score": 1.0, # 精确匹配，相似度为1
"ranks": info.get("ranks", []),
"count": len(info.get("ranks", [])),
"rank": info["ranks"][0] if info["ranks"] else 999
}
# 条件性添加 URL 字段
if include_url:
news_item["url"] = info.get("url", "")
news_item["mobileUrl"] = info.get("mobileUrl", "")
matches.append(news_item)
return matches
def _search_by_fuzzy_mode(
self,
query: str,
all_titles: Dict,
id_to_name: Dict,
current_date: datetime,
threshold: float,
include_url: bool
) -> List[Dict]:
"""
模糊搜索模式（使用相似度算法）
Args:
query: 搜索内容
all_titles: 所有标题字典
id_to_name: 平台ID到名称映射
current_date: 当前日期
threshold: 相似度阈值
Returns:
匹配的新闻列表
"""
matches = []
for platform_id, titles in all_titles.items():
platform_name = id_to_name.get(platform_id, platform_id)
for title, info in titles.items():
# 模糊匹配
is_match, similarity = self._fuzzy_match(query, title, threshold)
if is_match:
news_item = {
"title": title,
"platform": platform_id,
"platform_name": platform_name,
"date": current_date.strftime("%Y-%m-%d"),
"similarity_score": round(similarity, 4),
"ranks": info.get("ranks", []),
"count": len(info.get("ranks", [])),
"rank": info["ranks"][0] if info["ranks"] else 999
}
# 条件性添加 URL 字段
if include_url:
news_item["url"] = info.get("url", "")
news_item["mobileUrl"] = info.get("mobileUrl", "")
matches.append(news_item)
return matches
def _search_by_entity_mode(
self,
query: str,
all_titles: Dict,
id_to_name: Dict,
current_date: datetime,
include_url: bool
) -> List[Dict]:
"""
实体搜索模式（自动按权重排序）
Args:
query: 实体名称
all_titles: 所有标题字典
id_to_name: 平台ID到名称映射
current_date: 当前日期
Returns:
匹配的新闻列表
"""
matches = []
for platform_id, titles in all_titles.items():
platform_name = id_to_name.get(platform_id, platform_id)
for title, info in titles.items():
# 实体搜索：精确包含实体名称
if query in title:
news_item = {
"title": title,
"platform": platform_id,
"platform_name": platform_name,
"date": current_date.strftime("%Y-%m-%d"),
"similarity_score": 1.0,
"ranks": info.get("ranks", []),
"count": len(info.get("ranks", [])),
"rank": info["ranks"][0] if info["ranks"] else 999
}
# 条件性添加 URL 字段
if include_url:
news_item["url"] = info.get("url", "")
news_item["mobileUrl"] = info.get("mobileUrl", "")
matches.append(news_item)
return matches
def _calculate_similarity(self, text1: str, text2: str) -> float:
"""
计算两个文本的相似度
Args:
text1: 文本1
text2: 文本2
Returns:
相似度分数 (0-1之间)
"""
# 使用 difflib.SequenceMatcher 计算序列相似度
return SequenceMatcher(None, text1.lower(), text2.lower()).ratio()
def _fuzzy_match(self, query: str, text: str, threshold: float = 0.3) -> Tuple[bool, float]:
"""
模糊匹配函数
Args:
query: 查询文本
text: 待匹配文本
threshold: 匹配阈值
Returns:
(是否匹配, 相似度分数)
"""
# 直接包含判断
if query.lower() in text.lower():
return True, 1.0
# 计算整体相似度
similarity = self._calculate_similarity(query, text)
if similarity >= threshold:
return True, similarity
# 分词后的部分匹配
query_words = set(self._extract_keywords(query))
text_words = set(self._extract_keywords(text))
if not query_words or not text_words:
return False, 0.0
# 计算关键词重合度
common_words = query_words & text_words
keyword_overlap = len(common_words) / len(query_words)
if keyword_overlap >= 0.5: # 50%的关键词重合
return True, keyword_overlap
return False, similarity
def _extract_keywords(self, text: str, min_length: int = 2) -> List[str]:
"""
从文本中提取关键词
Args:
text: 输入文本
min_length: 最小词长
Returns:
关键词列表
"""
# 移除URL和特殊字符
text = re.sub(r'http[s]?://\S+', '', text)
text = re.sub(r'\[.*?\]', '', text) # 移除方括号内容
# 使用正则表达式分词（中文和英文）
words = re.findall(r'[\w]+', text)
# 过滤短词
keywords = [word for word in words if word and len(word) >= min_length]
return keywords
def _calculate_keyword_overlap(self, keywords1: List[str], keywords2: List[str]) -> float:
"""
计算两个关键词列表的重合度
Args:
keywords1: 关键词列表1
keywords2: 关键词列表2
Returns:
重合度分数 (0-1之间)
"""
if not keywords1 or not keywords2:
return 0.0
set1 = set(keywords1)
set2 = set(keywords2)
# Jaccard 相似度
intersection = len(set1 & set2)
union = len(set1 | set2)
if union == 0:
return 0.0
return intersection / union
def _jaccard_similarity(self, list1: List[str], list2: List[str]) -> float:
"""
计算两个列表的 Jaccard 相似度
Args:
list1: 列表1
list2: 列表2
Returns:
Jaccard 相似度 (0-1之间)
"""
if not list1 or not list2:
return 0.0
set1 = set(list1)
set2 = set(list2)
intersection = len(set1 & set2)
union = len(set1 | set2)
if union == 0:
return 0.0
return intersection / union
def search_related_news_history(
self,
reference_title: str,
time_preset: str = "yesterday",
start_date: Optional[datetime] = None,
end_date: Optional[datetime] = None,
threshold: float = 0.4,
limit: int = 50,
include_url: bool = False
) -> Dict:
"""
在历史数据中搜索与给定新闻相关的新闻
Args:
reference_title: 参考新闻标题或内容
time_preset: 时间范围预设值，可选：
- "yesterday": 昨天
- "last_week": 上周 (7天)
- "last_month": 上个月 (30天)
- "custom": 自定义日期范围（需要提供 start_date 和 end_date）
start_date: 自定义开始日期（仅当 time_preset="custom" 时有效）
end_date: 自定义结束日期（仅当 time_preset="custom" 时有效）
threshold: 相似度阈值 (0-1之间)，默认0.4
limit: 返回条数限制，默认50
include_url: 是否包含URL链接，默认False（节省token）
Returns:
搜索结果字典，包含相关新闻列表
Example:
>>> tools = SearchTools()
>>> result = tools.search_related_news_history(
... reference_title="人工智能技术突破",
... time_preset="last_week",
... threshold=0.4,
... limit=50
... )
>>> for news in result['results']:
... print(f"{news['date']}: {news['title']} (相似度: {news['similarity_score']})")
"""
try:
# 参数验证
reference_title = validate_keyword(reference_title)
threshold = validate_threshold(threshold, default=0.4, min_value=0.0, max_value=1.0)
limit = validate_limit(limit, default=50)
# 确定查询日期范围
today = datetime.now()
if time_preset == "yesterday":
search_start = today - timedelta(days=1)
search_end = today - timedelta(days=1)
elif time_preset == "last_week":
search_start = today - timedelta(days=7)
search_end = today - timedelta(days=1)
elif time_preset == "last_month":
search_start = today - timedelta(days=30)
search_end = today - timedelta(days=1)
elif time_preset == "custom":
if not start_date or not end_date:
raise InvalidParameterError(
"自定义时间范围需要提供 start_date 和 end_date",
suggestion="请提供 start_date 和 end_date 参数"
)
search_start = start_date
search_end = end_date
else:
raise InvalidParameterError(
f"不支持的时间范围: {time_preset}",
suggestion="请使用 'yesterday', 'last_week', 'last_month' 或 'custom'"
)
# 提取参考文本的关键词
reference_keywords = self._extract_keywords(reference_title)
if not reference_keywords:
raise InvalidParameterError(
"无法从参考文本中提取关键词",
suggestion="请提供更详细的文本内容"
)
# 收集所有相关新闻
all_related_news = []
current_date = search_start
while current_date <= search_end:
try:
# 读取该日期的数据
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date(current_date)
# 搜索相关新闻
for platform_id, titles in all_titles.items():
platform_name = id_to_name.get(platform_id, platform_id)
for title, info in titles.items():
# 计算标题相似度
title_similarity = self._calculate_similarity(reference_title, title)
# 提取标题关键词
title_keywords = self._extract_keywords(title)
# 计算关键词重合度
keyword_overlap = self._calculate_keyword_overlap(
reference_keywords,
title_keywords
)
# 综合相似度 (70% 关键词重合 + 30% 文本相似度)
combined_score = keyword_overlap * 0.7 + title_similarity * 0.3
if combined_score >= threshold:
news_item = {
"title": title,
"platform": platform_id,
"platform_name": platform_name,
"date": current_date.strftime("%Y-%m-%d"),
"similarity_score": round(combined_score, 4),
"keyword_overlap": round(keyword_overlap, 4),
"text_similarity": round(title_similarity, 4),
"common_keywords": list(set(reference_keywords) & set(title_keywords)),
"rank": info["ranks"][0] if info["ranks"] else 0
}
# 条件性添加 URL 字段
if include_url:
news_item["url"] = info.get("url", "")
news_item["mobileUrl"] = info.get("mobileUrl", "")
all_related_news.append(news_item)
except DataNotFoundError:
# 该日期没有数据，继续下一天
pass
except Exception as e:
# 记录错误但继续处理其他日期
print(f"Warning: 处理日期 {current_date.strftime('%Y-%m-%d')} 时出错: {e}")
# 移动到下一天
current_date += timedelta(days=1)
if not all_related_news:
return {
"success": True,
"results": [],
"total": 0,
"query": reference_title,
"time_preset": time_preset,
"date_range": {
"start": search_start.strftime("%Y-%m-%d"),
"end": search_end.strftime("%Y-%m-%d")
},
"message": "未找到相关新闻"
}
# 按相似度排序
all_related_news.sort(key=lambda x: x["similarity_score"], reverse=True)
# 限制返回数量
results = all_related_news[:limit]
# 统计信息
platform_distribution = Counter([news["platform"] for news in all_related_news])
date_distribution = Counter([news["date"] for news in all_related_news])
result = {
"success": True,
"summary": {
"description": "历史相关新闻搜索结果",
"total_found": len(all_related_news),
"returned": len(results),
"requested_limit": limit,
"threshold": threshold,
"reference_title": reference_title,
"reference_keywords": reference_keywords,
"time_preset": time_preset,
"date_range": {
"start": search_start.strftime("%Y-%m-%d"),
"end": search_end.strftime("%Y-%m-%d")
}
},
"data": results,
"statistics": {
"platform_distribution": dict(platform_distribution),
"date_distribution": dict(date_distribution),
"avg_similarity": round(
sum([news["similarity_score"] for news in all_related_news]) / len(all_related_news),
4
) if all_related_news else 0.0
}
}
if len(all_related_news) < limit:
result["note"] = f"相关性阈值 {threshold} 下仅找到 {len(all_related_news)} 条相关新闻"
return result
except MCPError as e:
return {
"success": False,
"error": e.to_dict()
}
except Exception as e:
return {
"success": False,
"error": {
"code": "INTERNAL_ERROR",
"message": str(e)
}
}
def find_related_news_unified(
self,
reference_title: str,
date_range: Optional[Union[Dict[str, str], str]] = None,
threshold: float = 0.5,
limit: int = 50,
include_url: bool = False
) -> Dict:
"""
统一的相关新闻查找工具 - 整合相似新闻和历史相关搜索
Args:
reference_title: 参考新闻标题
date_range: 日期范围（可选）
- 不指定: 只查询今天的数据
- {"start": "YYYY-MM-DD", "end": "YYYY-MM-DD"}: 查询指定日期范围
- "today": 今天
- "yesterday": 昨天
- "last_week": 最近7天
- "last_month": 最近30天
threshold: 相似度阈值，0-1之间，默认0.5
limit: 返回条数限制，默认50
include_url: 是否包含URL链接，默认False
Returns:
相关新闻列表，按相似度排序
"""
try:
# 参数验证
reference_title = validate_keyword(reference_title)
threshold = validate_threshold(threshold, default=0.5, min_value=0.0, max_value=1.0)
limit = validate_limit(limit, default=50)
# 确定日期范围
today = datetime.now()
# 规范化 date_range（处理 JSON 字符串序列化问题）
date_range = normalize_date_range(date_range)
if date_range is None or date_range == "today":
# 只查询今天
search_dates = [today]
elif isinstance(date_range, str):
# 预设时间范围
if date_range == "yesterday":
search_dates = [today - timedelta(days=1)]
elif date_range == "last_week":
search_dates = [today - timedelta(days=i) for i in range(7)]
elif date_range == "last_month":
search_dates = [today - timedelta(days=i) for i in range(30)]
else:
# 单日字符串格式
try:
single_date = datetime.strptime(date_range, "%Y-%m-%d")
search_dates = [single_date]
except ValueError:
search_dates = [today]
elif isinstance(date_range, dict):
# 日期范围对象
start_str = date_range.get("start")
end_str = date_range.get("end")
if start_str and end_str:
start_date = datetime.strptime(start_str, "%Y-%m-%d")
end_date = datetime.strptime(end_str, "%Y-%m-%d")
search_dates = []
current = start_date
while current <= end_date:
search_dates.append(current)
current += timedelta(days=1)
else:
search_dates = [today]
else:
search_dates = [today]
# 提取参考标题的关键词
reference_keywords = self._extract_keywords(reference_title)
# 收集所有相关新闻
all_related_news = []
for search_date in search_dates:
try:
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date(search_date)
for platform_id, titles in all_titles.items():
platform_name = id_to_name.get(platform_id, platform_id)
for title, info in titles.items():
if title == reference_title:
continue
# 计算相似度（使用混合算法）
text_similarity = self._calculate_similarity(reference_title, title)
# 如果有关键词，也计算关键词重合度
if reference_keywords:
title_keywords = self._extract_keywords(title)
keyword_similarity = self._jaccard_similarity(reference_keywords, title_keywords)
# 混合相似度：70% 文本 + 30% 关键词
similarity = 0.7 * text_similarity + 0.3 * keyword_similarity
else:
similarity = text_similarity
if similarity >= threshold:
news_item = {
"title": title,
"platform": platform_id,
"platform_name": platform_name,
"date": search_date.strftime("%Y-%m-%d"),
"similarity": round(similarity, 3),
"rank": info["ranks"][0] if info["ranks"] else 0
}
if include_url:
news_item["url"] = info.get("url", "")
all_related_news.append(news_item)
except Exception:
# 某天数据读取失败，跳过
continue
# 按相似度排序
all_related_news.sort(key=lambda x: x["similarity"], reverse=True)
# 限制数量
results = all_related_news[:limit]
# 统计信息
from collections import Counter
platform_dist = Counter([n["platform_name"] for n in all_related_news])
date_dist = Counter([n["date"] for n in all_related_news])
return {
"success": True,
"summary": {
"description": "相关新闻搜索结果",
"total_found": len(all_related_news),
"returned": len(results),
"reference_title": reference_title,
"threshold": threshold,
"date_range": {
"start": min(search_dates).strftime("%Y-%m-%d"),
"end": max(search_dates).strftime("%Y-%m-%d")
} if search_dates else None
},
"data": results,
"statistics": {
"platform_distribution": dict(platform_dist),
"date_distribution": dict(date_dist)
}
}
except MCPError as e:
return {"success": False, "error": e.to_dict()}
except Exception as e:
return {"success": False, "error": {"code": "INTERNAL_ERROR", "message": str(e)}}
def _search_rss_by_keyword(
self,
query: str,
start_date: datetime,
end_date: datetime,
limit: int = 20,
include_url: bool = False
) -> Dict:
"""
在 RSS 数据中搜索关键词
Args:
query: 搜索关键词
start_date: 开始日期
end_date: 结束日期
limit: 返回条数限制
include_url: 是否包含 URL
Returns:
RSS 搜索结果字典
"""
all_rss_matches = []
query_lower = query.lower()
current_date = start_date
while current_date <= end_date:
try:
# 读取该日期的 RSS 数据
all_titles, id_to_name, _ = self.data_service.parser.read_all_titles_for_date(
date=current_date,
platform_ids=None,
db_type="rss"
)
for feed_id, items in all_titles.items():
feed_name = id_to_name.get(feed_id, feed_id)
for title, info in items.items():
# 关键词匹配（标题或摘要）
title_match = query_lower in title.lower()
summary = info.get("summary", "")
summary_match = query_lower in summary.lower() if summary else False
if title_match or summary_match:
rss_item = {
"title": title,
"feed_id": feed_id,
"feed_name": feed_name,
"date": current_date.strftime("%Y-%m-%d"),
"published_at": info.get("published_at", ""),
"author": info.get("author", ""),
"match_in": "title" if title_match else "summary"
}
if include_url:
rss_item["url"] = info.get("url", "")
all_rss_matches.append(rss_item)
except DataNotFoundError:
# 该日期没有 RSS 数据，继续下一天
pass
except Exception:
# 其他错误，跳过
pass
current_date += timedelta(days=1)
# 按发布时间排序（最新的在前）
all_rss_matches.sort(key=lambda x: x.get("published_at", ""), reverse=True)
return {
"items": all_rss_matches[:limit],
"total": len(all_rss_matches)
}
