mirror of
https://github.com/stardrophere/InsightRadar.git
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348 lines
13 KiB
Python
348 lines
13 KiB
Python
# app/services/fetcher_service.py
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"""
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抓取服务：从外部 API 拉取热搜/RSS 数据，做查重、向量聚类、入库
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热搜分支：语义聚类到 UnifiedEvent；RSS 分支：写入 NewsArticle
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"""
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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("正在加载模型...")
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# 全局单例
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embedder_model = SentenceTransformer(EMBEDDING_MODEL_PATH, local_files_only=True)
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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 作为 external_id，用于跨平台去重"""
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return hashlib.md5(text.encode('utf-8')).hexdigest()
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def generate_embeddings_batch(texts: list[str]) -> dict:
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"""批量生成向量，返回 {text: (embedding_json, numpy_array)}"""
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if not texts:
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return {}
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unique_texts = list(set(texts))
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raw_vecs = embedder_model.encode(unique_texts, normalize_embeddings=True, show_progress_bar=False)
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result = {}
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for text, raw_vec in zip(unique_texts, raw_vecs):
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truncated_vec = [round(float(x), 5) for x in raw_vec]
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emb_json = json.dumps(truncated_vec, separators=(',', ':'))
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result[text] = (emb_json, raw_vec)
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return result
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class UnifiedEventClusterer:
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def __init__(self, db):
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self.db = db
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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(300).all()
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self.event_vectors = []
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self.event_ids = []
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for ev in recent_events:
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if ev.center_embedding:
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self.event_vectors.append(np.array(json.loads(ev.center_embedding)))
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self.event_ids.append(ev.id)
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def match_or_create(self, title: str, embedding_json: str, new_vec: np.ndarray) -> int:
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"""语义相似则归入已有事件并累加热度，否则创建新 UnifiedEvent"""
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if self.event_vectors:
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# 批量矩阵计算相似度
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sim_scores = cosine_similarity([new_vec], self.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_id = self.event_ids[max_idx]
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# 更新热度
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matched_event = self.db.query(UnifiedEvent).get(matched_event_id)
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if matched_event:
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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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self.db.add(new_unified)
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self.db.flush() # 获取自增的主键 ID
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# 更新缓存
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self.event_vectors.append(new_vec)
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self.event_ids.append(new_unified.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, existing_event, embeddings_dict: dict, clusterer: UnifiedEventClusterer):
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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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event_to_log = None
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# 查重：已存在则可能只需更新标题/排名；不存在则需聚类并新建
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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, _ = embeddings_dict[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, new_vec = embeddings_dict[title]
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# 2. 扔进聚类中枢找归宿
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matched_event_id = clusterer.match_or_create(title, new_embedding_json, new_vec)
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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, existing_article):
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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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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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采用批量查重和批量向量计算优化性能，避免数据库锁死。
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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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# 1. 批量计算外部 ID 并聚合要计算的文本
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valid_items = []
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external_ids = []
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for item in items:
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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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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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valid_items.append((item, external_id))
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external_ids.append(external_id)
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if not valid_items:
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return 0
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# 批量查重：按 external_id 判断是更新还是新增
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existing_events_dict = {}
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existing_articles_dict = {}
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if source.source_type in (SourceType.HOT_TREND, SourceType.API):
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existing_events = db.query(TrendingEvent).filter(
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TrendingEvent.source_id == source.id,
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TrendingEvent.external_id.in_(external_ids)
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).all()
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existing_events_dict = {ev.external_id: ev for ev in existing_events}
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elif source.source_type == SourceType.RSS_FEED:
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existing_articles = db.query(NewsArticle).filter(
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NewsArticle.source_id == source.id,
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NewsArticle.external_id.in_(external_ids)
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).all()
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existing_articles_dict = {art.external_id: art for art in existing_articles}
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# 仅对需要算向量的标题做批量 embedding，避免重复计算
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texts_to_embed = []
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if source.source_type in (SourceType.HOT_TREND, SourceType.API):
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for item, external_id in valid_items:
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title = item.get("title")
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existing_event = existing_events_dict.get(external_id)
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if existing_event:
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if existing_event.current_headline != title:
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texts_to_embed.append(title)
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else:
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texts_to_embed.append(title)
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# 4. 批量执行大模型推理
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embeddings_dict = generate_embeddings_batch(texts_to_embed)
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# 初始化聚类器（只在热搜模式下需要，且只初始化一次）
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clusterer = None
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if source.source_type in (SourceType.HOT_TREND, SourceType.API):
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clusterer = UnifiedEventClusterer(db)
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# 按来源类型分流：热搜/API → TrendingEvent + 聚类；RSS → NewsArticle
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for index, (item, external_id) in enumerate(valid_items, 1):
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if source.source_type in (SourceType.HOT_TREND, SourceType.API):
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existing_event = existing_events_dict.get(external_id)
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process_hot_trend_item(
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db, source, item, index, external_id,
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existing_event, embeddings_dict, clusterer
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)
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elif source.source_type == SourceType.RSS_FEED:
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existing_article = existing_articles_dict.get(external_id)
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process_rss_feed_item(db, source, item, external_id, existing_article)
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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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# 获取启用的信息源 - 这个只读操作用一个短连接
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with SessionLocal() as db:
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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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# 我们把 source 的信息提前提取出来，避免在异步中长期持有 session
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source_configs = [
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{
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"id": s.id,
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"home_url": s.home_url,
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"source_name": s.source_name,
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"source_type": s.source_type
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}
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for s in sources
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]
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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 s_config in source_configs:
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platform_id = s_config["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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try:
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# 1. 网络请求（可能耗时较长，不要包在 db session 里）
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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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# 2. 数据库事务操作（尽量短，单独使用 session）
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with SessionLocal() as db:
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# 重新从短 session 中获取 source 实例，以免 detached
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source = db.query(InfoSource).get(s_config["id"])
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if not source:
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continue
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task_log = DataSyncTask(source_id=source.id, items_fetched=0)
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try:
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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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db.rollback()
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raise e # 抛出给外层捕获记录日志
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except Exception as e:
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# 异常拦截与错误隔离，另起一个超短事务记录日志
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with SessionLocal() as log_db:
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try:
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new_task_log = DataSyncTask(source_id=s_config["id"], items_fetched=0)
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new_task_log.task_status = TaskStatus.ERROR
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new_task_log.error_trace = str(e)
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log_db.add(new_task_log)
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log_db.commit()
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print(f"[{s_config['source_name']}] 抓取失败: {e}")
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except Exception as inner_e:
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log_db.rollback()
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print(f"[{s_config['source_name']}] 抓取失败，且日志写入失败: {e}, {inner_e}")
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