207 lines
8.2 KiB
Python
207 lines
8.2 KiB
Python
# service.py
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from sqlalchemy import select, insert, update, delete, and_
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from .database import db_instance
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from .utils import normalize_url
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class CrawlerService:
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def __init__(self):
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self.db = db_instance
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def register_task(self, url: str):
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"""完全使用库 API 实现的注册"""
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clean_url = normalize_url(url)
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with self.db.engine.begin() as conn:
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# 使用 select() API
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query = select(self.db.tasks.c.id).where(self.db.tasks.c.root_url == clean_url)
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existing = conn.execute(query).fetchone()
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if existing:
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return {"task_id": existing[0], "is_new_task": False}
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# 使用 insert() API
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stmt = insert(self.db.tasks).values(root_url=clean_url).returning(self.db.tasks.c.id)
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new_task = conn.execute(stmt).fetchone()
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return {"task_id": new_task[0], "is_new_task": True}
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def add_urls(self, task_id: int, urls_obj: dict):
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"""通用 API 实现的批量添加（含详细返回）"""
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success_urls, skipped_urls, failed_urls = [], [], []
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# 从 urls_obj 中提取 urls 列表
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urls = urls_obj.get("urls", [])
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with self.db.engine.begin() as conn:
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for url in urls:
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clean_url = normalize_url(url)
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try:
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# 检查队列中是否已存在该 URL (通用写法)
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check_q = select(self.db.queue).where(
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and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == clean_url)
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)
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if conn.execute(check_q).fetchone():
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skipped_urls.append(clean_url)
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continue
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# 插入新 URL
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conn.execute(insert(self.db.queue).values(
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task_id=task_id, url=clean_url, status='pending'
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))
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success_urls.append(clean_url)
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except Exception:
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failed_urls.append(clean_url)
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return {"success_urls": success_urls, "skipped_urls": skipped_urls, "failed_urls": failed_urls}
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def get_pending_urls(self, task_id: int, limit: int):
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"""原子锁定 API 实现"""
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with self.db.engine.begin() as conn:
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query = select(self.db.queue.c.url).where(
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and_(self.db.queue.c.task_id == task_id, self.db.queue.c.status == 'pending')
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).limit(limit)
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urls = [r[0] for r in conn.execute(query).fetchall()]
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if urls:
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upd = update(self.db.queue).where(
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and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url.in_(urls))
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).values(status='processing')
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conn.execute(upd)
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return {"urls": urls}
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def save_results(self, task_id: int, results: list):
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"""
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保存同一 URL 的多个切片。
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返回：该 URL 下切片的详细处理统计及页面更新状态。
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"""
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if not results:
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return {"msg": "No data provided"}
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# 1. 基础信息提取 (假设 results 里的 source_url 都是一致的)
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first_item = results[0] if isinstance(results[0], dict) else results[0].__dict__
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target_url = normalize_url(first_item.get('source_url'))
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# 结果统计容器
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inserted_chunks = []
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updated_chunks = []
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failed_chunks = []
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is_page_update = False
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with self.db.engine.begin() as conn:
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# 2. 判断该 URL 是否已经有切片存在 (以此判定是否为“页面更新”)
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check_page_stmt = select(self.db.chunks.c.id).where(
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and_(self.db.chunks.c.task_id == task_id, self.db.chunks.c.source_url == target_url)
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).limit(1)
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if conn.execute(check_page_stmt).fetchone():
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is_page_update = True
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# 3. 逐个处理切片
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for res in results:
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data = res if isinstance(res, dict) else res.__dict__
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c_idx = data.get('chunk_index')
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try:
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# 检查具体某个 index 的切片是否存在
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find_chunk_stmt = select(self.db.chunks.c.id).where(
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and_(
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self.db.chunks.c.task_id == task_id,
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self.db.chunks.c.source_url == target_url,
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self.db.chunks.c.chunk_index == c_idx
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)
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)
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existing_chunk = conn.execute(find_chunk_stmt).fetchone()
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if existing_chunk:
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# 覆盖更新现有切片
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upd_stmt = update(self.db.chunks).where(
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self.db.chunks.c.id == existing_chunk[0]
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).values(
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title=data.get('title'),
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content=data.get('content'),
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embedding=data.get('embedding')
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)
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conn.execute(upd_stmt)
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updated_chunks.append(c_idx)
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else:
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# 插入新切片
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ins_stmt = insert(self.db.chunks).values(
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task_id=task_id,
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source_url=target_url,
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chunk_index=c_idx,
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title=data.get('title'),
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content=data.get('content'),
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embedding=data.get('embedding')
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)
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conn.execute(ins_stmt)
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inserted_chunks.append(c_idx)
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except Exception as e:
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print(f"Chunk {c_idx} failed: {e}")
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failed_chunks.append(c_idx)
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# 4. 最终更新队列状态
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conn.execute(
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update(self.db.queue).where(
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and_(self.db.queue.c.task_id == task_id, self.db.queue.c.url == target_url)
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).values(status='completed')
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)
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return {
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"source_url": target_url,
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"is_page_update": is_page_update, # 标志：此页面此前是否有过内容
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"detail": {
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"inserted_chunk_indexes": inserted_chunks,
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"updated_chunk_indexes": updated_chunks,
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"failed_chunk_indexes": failed_chunks
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},
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"counts": {
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"inserted": len(inserted_chunks),
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"updated": len(updated_chunks),
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"failed": len(failed_chunks)
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}
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}
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def search_knowledge(self, query_embedding: list, task_id: int = None, limit: int = 5):
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"""
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高性能向量搜索方法
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:param query_embedding: 问题的向量
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:param task_id: 可选的任务ID，不传则搜全表
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:param limit: 返回结果数量
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"""
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with self.db.engine.connect() as conn:
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# 1. 选择需要的字段
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# 我们同时返回 task_id，方便在全库搜索时知道来源哪个任务
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stmt = select(
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self.db.chunks.c.task_id,
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self.db.chunks.c.source_url,
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self.db.chunks.c.title,
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self.db.chunks.c.content,
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self.db.chunks.c.chunk_index
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)
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# 2. 动态添加过滤条件
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if task_id is not None:
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stmt = stmt.where(self.db.chunks.c.task_id == task_id)
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# 3. 按余弦距离排序（1 - 余弦相似度）
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# 距离越小，相似度越高
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stmt = stmt.order_by(
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self.db.chunks.c.embedding.cosine_distance(query_embedding)
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).limit(limit)
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# 4. 执行并解析结果
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rows = conn.execute(stmt).fetchall()
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results = []
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for r in rows:
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results.append({
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"task_id": r[0],
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"source_url": r[1],
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"title": r[2],
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"content": r[3],
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"chunk_index": r[4]
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})
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return results
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crawler_service = CrawlerService() |