生成日报、周报
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"""
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报告生成器基类
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提供数据源接口、AI处理接口等扩展能力
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"""
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from abc import ABC, abstractmethod
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from typing import List, Dict, Any, Optional
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from datetime import datetime, timedelta
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import os
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import sys
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from loguru import logger
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# 添加父目录到路径
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current_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(os.path.dirname(current_dir))
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if parent_dir not in sys.path:
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sys.path.insert(0, parent_dir)
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from utils.mysql_agent import MySQLAgent
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from config import Config
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class DataSource(ABC):
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"""数据源接口基类，用于后续扩展其他数据源"""
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@abstractmethod
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def fetch_data(self, start_time: datetime, end_time: datetime) -> List[Dict[str, Any]]:
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"""
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获取指定时间范围内的数据
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Args:
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start_time: 开始时间
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end_time: 结束时间
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Returns:
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数据列表，每条数据应包含：标题、链接、摘要、发布时间等字段
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"""
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pass
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@abstractmethod
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def get_source_name(self) -> str:
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"""获取数据源名称"""
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pass
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class RSSDataSource(DataSource):
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"""RSS数据源实现"""
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def __init__(self, db_agent: MySQLAgent, table_name: str = "collector_rss_subscriptions"):
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self.db_agent = db_agent
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self.table_name = table_name
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self.logger = logger.bind(module="RSSDataSource")
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def fetch_data(self, start_time: datetime, end_time: datetime) -> List[Dict[str, Any]]:
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"""从数据库获取RSS数据"""
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try:
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sql = f"""
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SELECT
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`文章标题` as title,
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`文章链接` as link,
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`文章摘要` as summary,
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`发布时间` as publish_time,
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`来源URL` as source_url,
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`创建时间` as create_time
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FROM `{self.table_name}`
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WHERE `发布时间` >= %s AND `发布时间` < %s
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ORDER BY `发布时间` DESC
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"""
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params = (
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start_time.strftime('%Y-%m-%d %H:%M:%S'),
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end_time.strftime('%Y-%m-%d %H:%M:%S')
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)
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df = self.db_agent.query_to_df(sql, params=params, is_print=False)
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if df.empty:
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self.logger.info(f"时间范围 {start_time} 到 {end_time} 内没有RSS数据")
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return []
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# 转换为字典列表
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data_list = df.to_dict('records')
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self.logger.info(f"获取到 {len(data_list)} 条RSS数据")
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return data_list
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except Exception as e:
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self.logger.error(f"获取RSS数据失败: {str(e)}", exc_info=True)
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return []
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def get_source_name(self) -> str:
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return "RSS订阅"
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class AIAnalysisDataSource(DataSource):
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"""AI分析结果数据源实现 - 从ai_processor_rss_analysis表获取已筛选的相关内容"""
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def __init__(self, db_agent: MySQLAgent, table_name: str = "ai_processor_rss_analysis"):
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self.db_agent = db_agent
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self.table_name = table_name
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self.logger = logger.bind(module="AIAnalysisDataSource")
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def fetch_data(self, start_time: datetime, end_time: datetime) -> List[Dict[str, Any]]:
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"""从AI分析结果表获取相关数据（是否相关=1）"""
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try:
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sql = f"""
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SELECT
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`文章标题` as title,
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`文章链接` as link,
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`文章摘要` as summary,
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`发布时间` as publish_time,
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`来源URL` as source_url,
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`分类` as category,
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`标签` as tags,
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`相关度评分` as relevance_score,
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`分析说明` as analysis_note,
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`处理时间` as process_time
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FROM `{self.table_name}`
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WHERE `发布时间` >= %s AND `发布时间` < %s
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AND `是否相关` = 1
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ORDER BY `发布时间` DESC, `相关度评分` DESC
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"""
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params = (
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start_time.strftime('%Y-%m-%d %H:%M:%S'),
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end_time.strftime('%Y-%m-%d %H:%M:%S')
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)
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df = self.db_agent.query_to_df(sql, params=params, is_print=False)
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if df.empty:
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self.logger.info(f"时间范围 {start_time} 到 {end_time} 内没有相关数据（是否相关=1）")
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return []
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# 转换为字典列表
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data_list = df.to_dict('records')
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self.logger.info(f"获取到 {len(data_list)} 条相关数据（是否相关=1）")
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return data_list
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except Exception as e:
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self.logger.error(f"获取AI分析数据失败: {str(e)}", exc_info=True)
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return []
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def get_source_name(self) -> str:
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return "AI分析结果"
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class AIProcessor:
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"""AI处理器，用于筛选和分析内容"""
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def __init__(self, api_key: str = None, model: str = None):
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from openai import OpenAI
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self.base_url = 'https://qianfan.baidubce.com/v2'
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self.api_key = api_key or Config.BAIDU_AI_CONFIG.get('api_key')
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self.model = model or Config.BAIDU_AI_CONFIG.get('model', 'ernie-x1-turbo-32k')
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self.client = OpenAI(
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base_url=self.base_url,
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api_key=self.api_key
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)
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self.logger = logger.bind(module="AIProcessor")
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def filter_automotive_content(self, articles: List[Dict[str, Any]]) -> List[Dict[str, Any]]:
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"""
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筛选与汽车后市场相关的内容
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Args:
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articles: 文章列表
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Returns:
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筛选后的文章列表（包含AI标记信息）
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"""
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if not articles:
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return []
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self.logger.info(f"开始AI筛选 {len(articles)} 篇文章")
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# 批量处理，避免API限流
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batch_size = 10
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filtered_articles = []
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for i in range(0, len(articles), batch_size):
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batch = articles[i:i + batch_size]
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try:
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# 构建批量分析的prompt
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articles_text = ""
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for idx, article in enumerate(batch):
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articles_text += f"\n[{idx + i}] 标题: {article.get('title', '')}\n"
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articles_text += f"摘要: {article.get('summary', '')}\n"
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prompt = f"""请分析以下新闻文章，判断哪些与汽车后市场相关。
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汽车后市场的定义：汽车销售以后，围绕汽车使用过程中的各种服务，包括：
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- 汽车维修保养
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- 汽车配件
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- 汽车改装
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- 汽车美容
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- 汽车用品
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- 汽车金融
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- 汽车保险
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- 二手车交易
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- 汽车租赁
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- 汽车检测
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- 汽车报废回收
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- 汽车相关法律法规和政策
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文章列表：
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{articles_text}
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请按以下JSON格式返回结果：
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{{
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"related_articles": [
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{{
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"index": 文章的序号（从0开始）,
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"is_related": true/false,
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"reason": "判断理由",
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"category": "所属类别（如：维修保养、配件、政策等）"
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}}
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]
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}}
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只返回JSON，不要其他文字说明。"""
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response = self.client.chat.completions.create(
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model=self.model,
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messages=[{
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"role": "user",
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"content": prompt
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}]
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)
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result_text = response.choices[0].message.content.strip()
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# 尝试解析JSON（去除可能的markdown代码块标记）
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import json
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import re
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# 提取JSON部分（尝试多种方式）
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result_json = None
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# 方式1：查找markdown代码块中的JSON
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json_match = re.search(r'```(?:json)?\s*(\{.*?\})\s*```', result_text, re.DOTALL)
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if json_match:
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try:
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result_json = json.loads(json_match.group(1))
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except:
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pass
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# 方式2：直接查找JSON对象
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if result_json is None:
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json_match = re.search(r'\{.*\}', result_text, re.DOTALL)
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if json_match:
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try:
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result_json = json.loads(json_match.group())
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except:
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pass
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# 方式3：尝试直接解析
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if result_json is None:
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try:
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result_json = json.loads(result_text)
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except:
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self.logger.warning(f"无法解析AI返回的JSON: {result_text[:200]}")
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result_json = {'related_articles': []}
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# 处理结果
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for item in result_json.get('related_articles', []):
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idx = item.get('index', -1)
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if 0 <= idx < len(batch):
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article = batch[idx]
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if item.get('is_related', False):
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article['ai_marked'] = True
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article['ai_category'] = item.get('category', '其他')
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article['ai_reason'] = item.get('reason', '')
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filtered_articles.append(article)
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# 避免API限流
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import time
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if i + batch_size < len(articles):
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time.sleep(1.5)
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except Exception as e:
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self.logger.error(f"AI筛选批处理失败: {str(e)}", exc_info=True)
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# 如果AI处理失败，保留所有文章但标记为未筛选
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for article in batch:
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article['ai_marked'] = False
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article['ai_error'] = str(e)
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self.logger.info(f"AI筛选完成，找到 {len(filtered_articles)} 篇相关文章")
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return filtered_articles
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def generate_news_summary(self, articles: List[Dict[str, Any]]) -> str:
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"""
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生成新闻摘要
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Args:
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articles: 筛选后的文章列表
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Returns:
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Markdown格式的新闻摘要
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"""
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if not articles:
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return "## 相关新闻\n\n暂无相关新闻。\n"
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articles_text = ""
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for idx, article in enumerate(articles, 1):
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category = article.get('ai_category', '其他')
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reason = article.get('ai_reason', '')
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articles_text += f"\n### {idx}. {article.get('title', '无标题')}\n"
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articles_text += f"- **类别**: {category}\n"
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articles_text += f"- **摘要**: {article.get('summary', '无摘要')}\n"
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articles_text += f"- **链接**: [{article.get('link', '')}]({article.get('link', '')})\n"
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articles_text += f"- **发布时间**: {article.get('publish_time', '')}\n"
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if reason:
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articles_text += f"- **相关性说明**: {reason}\n"
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articles_text += "\n"
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return f"## 汽车后市场相关新闻\n\n共找到 {len(articles)} 篇相关新闻：\n\n{articles_text}"
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class BaseReporter:
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"""报告生成器基类"""
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def __init__(self, data_sources: List[DataSource] = None):
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self.data_sources = data_sources or []
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self.ai_processor = AIProcessor()
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self.logger = logger.bind(module="BaseReporter")
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def add_data_source(self, data_source: DataSource):
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"""添加数据源"""
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self.data_sources.append(data_source)
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self.logger.info(f"添加数据源: {data_source.get_source_name()}")
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def collect_data(self, start_time: datetime, end_time: datetime) -> List[Dict[str, Any]]:
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"""从所有数据源收集数据"""
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all_data = []
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for source in self.data_sources:
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try:
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data = source.fetch_data(start_time, end_time)
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# 标记数据来源
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for item in data:
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item['data_source'] = source.get_source_name()
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all_data.extend(data)
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except Exception as e:
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self.logger.error(f"从 {source.get_source_name()} 收集数据失败: {str(e)}")
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# 按发布时间排序
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all_data.sort(key=lambda x: x.get('publish_time', ''), reverse=True)
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return all_data
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def generate_report_content(self, articles: List[Dict[str, Any]], report_type: str = "日报") -> str:
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"""
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生成报告内容（Markdown格式）
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Args:
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articles: 文章列表（已从AI分析结果表筛选，是否相关=1）
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report_type: 报告类型（"日报"或"周报"），用于无数据时的提示
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"""
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# 数据已经是从AI分析结果表筛选过的（是否相关=1），直接使用
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related_articles = articles
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# 生成统计信息
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related_count = len(related_articles)
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# 如果没有相关数据，返回提示信息
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if related_count == 0:
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if report_type == "日报":
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message = "昨日无汽车后市场相关的新闻"
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else:
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message = "上周无汽车后市场相关的新闻"
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return f"""
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## 数据统计
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- **相关文章数**: 0
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## 相关新闻
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{message}
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"""
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# 生成新闻摘要
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news_summary = self._generate_news_summary_from_analysis(related_articles)
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stats = f"""
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## 数据统计
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- **相关文章数**: {related_count}
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"""
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return stats + news_summary
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def _generate_news_summary_from_analysis(self, articles: List[Dict[str, Any]]) -> str:
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"""
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从AI分析结果生成新闻摘要（使用数据库中已有的分类和分析说明）
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Args:
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articles: 文章列表（包含category、tags、analysis_note等字段）
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Returns:
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Markdown格式的新闻摘要
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"""
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if not articles:
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return "## 相关新闻\n\n暂无相关新闻。\n"
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articles_text = ""
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for idx, article in enumerate(articles, 1):
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category = article.get('category', '其他')
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tags = article.get('tags', '')
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analysis_note = article.get('analysis_note', '')
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relevance_score = article.get('relevance_score', '')
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articles_text += f"\n### {idx}. {article.get('title', '无标题')}\n"
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articles_text += f"- **分类**: {category}\n"
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if tags:
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articles_text += f"- **标签**: {tags}\n"
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articles_text += f"- **摘要**: {article.get('summary', '无摘要')}\n"
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articles_text += f"- **链接**: [{article.get('link', '')}]({article.get('link', '')})\n"
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articles_text += f"- **发布时间**: {article.get('publish_time', '')}\n"
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if relevance_score:
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articles_text += f"- **相关度评分**: {relevance_score}\n"
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if analysis_note:
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articles_text += f"- **分析说明**: {analysis_note}\n"
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articles_text += "\n"
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return f"## 汽车后市场相关新闻\n\n共找到 {len(articles)} 篇相关新闻：\n\n{articles_text}"
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def generate_html_report(self, markdown_content: str, template_path: str = None) -> str:
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"""生成HTML报告"""
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||||
# 使用相对导入避免循环依赖
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||||
from .html_template import HTMLTemplateManager
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template_manager = HTMLTemplateManager()
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||||
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||||
if template_path and os.path.exists(template_path):
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# 使用外部模板
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html_content = template_manager.render_external_template(template_path, markdown_content)
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else:
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# 使用内置模板
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html_content = template_manager.render_builtin_template(markdown_content)
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return html_content
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|
||||
def save_report(self, html_content: str, output_path: str):
|
||||
"""保存HTML报告到文件"""
|
||||
os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else '.', exist_ok=True)
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
f.write(html_content)
|
||||
|
||||
self.logger.info(f"HTML报告已保存到: {output_path}")
|
||||
|
||||
def save_markdown_report(self, markdown_content: str, output_path: str):
|
||||
"""保存Markdown报告到文件"""
|
||||
os.makedirs(os.path.dirname(output_path) if os.path.dirname(output_path) else '.', exist_ok=True)
|
||||
|
||||
with open(output_path, 'w', encoding='utf-8') as f:
|
||||
f.write(markdown_content)
|
||||
|
||||
self.logger.info(f"Markdown报告已保存到: {output_path}")
|
||||
|
||||
Reference in New Issue
Block a user