mirror of
https://github.com/stardrophere/InsightRadar.git
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stardrophere
264 lines
9.1 KiB
Python
264 lines
9.1 KiB
Python
# app/services/fetcher_service.py
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import os
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import hashlib
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from datetime import timedelta
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import httpx
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import json
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import numpy as np
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from dotenv import load_dotenv
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from sklearn.metrics.pairwise import cosine_similarity
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from sentence_transformers import SentenceTransformer
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from app.database import SessionLocal
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from app.models.models import (
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InfoSource, TrendingEvent, NewsArticle, DataSyncTask, TaskStatus,
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HeadlineRevision, RankingLog, SourceType, utcnow, UnifiedEvent
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)
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# 加载环境变量
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load_dotenv()
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hf_token = os.getenv("HF_TOKEN")
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SIMILARITY_THRESHOLD = float(os.getenv("SIMILARITY_THRESHOLD", 0.72))
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API_BASE_URL = os.getenv("API_BASE_URL", "https://newsnow.busiyi.world/api/s")
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EMBEDDING_MODEL_PATH = os.getenv("EMBEDDING_MODEL_PATH", "")
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print("正在加载 BAAI/bge-m3 向量模型...")
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# 全局单例
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embedder_model = SentenceTransformer(EMBEDDING_MODEL_PATH, local_files_only=True, device="cuda")
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print("模型加载完成。")
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def generate_md5(text: str) -> str:
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"""生成32位MD5哈希值作为全局唯一指纹"""
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return hashlib.md5(text.encode('utf-8')).hexdigest()
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def generate_embedding_json(text: str) -> str:
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"""辅助函数：调用大模型生成向量，并序列化为 JSON 字符串"""
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raw_vec = embedder_model.encode([text], normalize_embeddings=True, show_progress_bar=False)[0]
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truncated_vec = [round(float(x), 5) for x in raw_vec]
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return json.dumps(truncated_vec, separators=(',', ':'))
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def match_or_create_unified_event(db, title: str, embedding_json: str) -> int:
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"""
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辅助函数：大事件聚类中枢。
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拿着新计算的向量去数据库里碰，碰到了就返回老 ID，碰不到就建新的。
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"""
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# 提取刚算出来的向量
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new_vec = np.array(json.loads(embedding_json))
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# 只取最近 3 天的活跃大事件进行比对
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three_days_ago = utcnow() - timedelta(days=3)
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recent_events = db.query(UnifiedEvent).filter(
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UnifiedEvent.created_at >= three_days_ago
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).order_by(UnifiedEvent.created_at.desc()).limit(200).all()
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if recent_events:
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valid_events = [ev for ev in recent_events if ev.center_embedding]
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if valid_events:
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event_vectors = [json.loads(ev.center_embedding) for ev in valid_events]
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# 批量矩阵计算相似度
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sim_scores = cosine_similarity([new_vec], event_vectors)[0]
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max_idx = np.argmax(sim_scores)
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if sim_scores[max_idx] >= SIMILARITY_THRESHOLD:
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matched_event = valid_events[max_idx]
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matched_event.hot_score += 1
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return matched_event.id
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# 没匹配到，创建一个新的统一大事件
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new_unified = UnifiedEvent(
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unified_title=title,
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center_embedding=embedding_json,
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hot_score=1 # 初始热度
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)
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db.add(new_unified)
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db.flush() # 获取自增的主键 ID
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return new_unified.id
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def process_hot_trend_item(db, source, item, index: int, external_id: str):
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"""
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处理【热搜/短新闻】的业务逻辑，现已加入 AI 聚类功能
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"""
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title = item.get("title")
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item_url = item.get("url", "")
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existing_event = db.query(TrendingEvent).filter(
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TrendingEvent.source_id == source.id,
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TrendingEvent.external_id == external_id
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).first()
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event_to_log = None
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# 核心逻辑：查重后再决定是否调用模型
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if existing_event:
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# 场景 A1：老熟人
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if existing_event.current_headline != title:
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# 标题被暗改，此时需要重新算一次 Embedding
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new_embedding_json = generate_embedding_json(title)
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revision = HeadlineRevision(
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event_id=existing_event.id,
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previous_headline=existing_event.current_headline,
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revised_headline=title
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)
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db.add(revision)
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existing_event.current_headline = title
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existing_event.title_embedding = new_embedding_json # 更新为新标题的语义向量
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# 注：这里不改变它所属的 unified_event_id，因为大体还是同一件事
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existing_event.current_ranking = index
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existing_event.event_url = item_url
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event_to_log = existing_event
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else:
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# 场景 A2：这是一条彻底的全新热搜
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# 1. 计算向量
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new_embedding_json = generate_embedding_json(title)
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# 2. 扔进聚类中枢找归宿
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matched_event_id = match_or_create_unified_event(db, title, new_embedding_json)
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# 3. 落库
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new_event = TrendingEvent(
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source_id=source.id,
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external_id=external_id,
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current_headline=title,
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event_url=item_url,
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current_ranking=index,
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title_embedding=new_embedding_json, # 存入向量
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unified_event_id=matched_event_id # 挂载到大事件下
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)
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db.add(new_event)
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db.flush()
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event_to_log = new_event
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# 强制记录排名轨迹
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rank_log = RankingLog(
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event_id=event_to_log.id,
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ranking_position=index
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)
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db.add(rank_log)
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def process_rss_feed_item(db, source, item, external_id: str):
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"""
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处理【长文章/传统订阅】分支的核心业务逻辑 (写入 NewsArticle 表)
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"""
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title = item.get("title")
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item_url = item.get("url", "")
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existing_article = db.query(NewsArticle).filter(
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NewsArticle.source_id == source.id,
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NewsArticle.external_id == external_id
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).first()
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if existing_article:
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# 文章若存在，仅更新基础字段
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existing_article.article_title = title
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existing_article.article_url = item_url
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else:
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# 全新文章入库
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new_article = NewsArticle(
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source_id=source.id,
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external_id=external_id,
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article_title=title,
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article_url=item_url,
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)
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db.add(new_article)
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def process_source_data(db, source, items: list) -> int:
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"""
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数据清洗与路由分发层：
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遍历 API 返回的 items，生成唯一指纹，并路由到不同的处理模块。
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返回成功处理的条目数量。
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"""
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saved_count = 0
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platform_id = source.home_url
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for index, item in enumerate(items, 1):
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title = item.get("title")
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if not title:
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continue
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item_url = item.get("url", "")
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# ID 兜底策略：接口ID -> URL -> Title
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raw_id = item.get("id") or item_url or title
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external_id = generate_md5(f"{platform_id}_{raw_id}")
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# 核心路由分流
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if source.source_type in (SourceType.HOT_TREND, SourceType.API):
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process_hot_trend_item(db, source, item, index, external_id)
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elif source.source_type == SourceType.RSS_FEED:
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process_rss_feed_item(db, source, item, external_id)
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saved_count += 1
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return saved_count
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async def fetch_and_save_trending_data():
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"""
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调度层：负责网络请求、数据库事务管理和异常监控隔离。
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"""
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print(f"[{utcnow()}] 开始执行定时抓取任务...")
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with SessionLocal() as db:
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# 获取启用的信息源
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sources = db.query(InfoSource).filter(InfoSource.is_enabled == True).all()
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if not sources:
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print("没有找到启用的信息源，任务结束。")
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return
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# 伪装请求头，规避反爬
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custom_headers = {
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"User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/145.0.0.0 Safari/537.36",
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"Accept": "application/json, text/plain, */*",
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"Referer": "https://newsnow.busiyi.world/",
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"Origin": "https://newsnow.busiyi.world"
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}
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async with httpx.AsyncClient(timeout=15.0, headers=custom_headers) as client:
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for source in sources:
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platform_id = source.home_url
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if not platform_id:
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continue
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url = f"{API_BASE_URL}?id={platform_id}&latest"
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# 初始化监控日志
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task_log = DataSyncTask(source_id=source.id, items_fetched=0)
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try:
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# 发起网络请求
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response = await client.get(url)
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response.raise_for_status()
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data_json = response.json()
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items = data_json.get("items", [])
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# 调用数据处理层
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saved_count = process_source_data(db, source, items)
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# 业务事务成功提交
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task_log.items_fetched = saved_count
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task_log.task_status = TaskStatus.SUCCESS
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db.add(task_log)
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db.commit()
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print(f"[{source.source_name}] ({source.source_type}) 成功抓取并更新了 {saved_count} 条数据")
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except Exception as e:
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# 异常拦截与错误隔离
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db.rollback()
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task_log.task_status = TaskStatus.ERROR
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task_log.error_trace = str(e)
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db.add(task_log)
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db.commit()
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print(f"[{source.source_name}] 抓取失败: {e}")
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