201 lines
7.7 KiB
Python
201 lines
7.7 KiB
Python
import dashscope
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from http import HTTPStatus
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from firecrawl import FirecrawlApp
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from langchain_text_splitters import RecursiveCharacterTextSplitter
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from ..config import settings
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from .crawler_sql_service import crawler_sql_service
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# 初始化配置
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dashscope.api_key = settings.DASHSCOPE_API_KEY
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class AutomatedCrawler:
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def __init__(self):
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self.firecrawl = FirecrawlApp(api_key=settings.FIRECRAWL_API_KEY)
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self.splitter = RecursiveCharacterTextSplitter(
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chunk_size=500,
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chunk_overlap=100,
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separators=["\n\n", "\n", "。", "！", "？", " ", ""]
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)
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def _get_embedding(self, text: str):
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"""内部方法：调用 Dashscope 生成向量"""
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# 注意：此方法是内部辅助，出错返回 None，由调用方处理状态
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embedding = None
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try:
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resp = dashscope.TextEmbedding.call(
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model=dashscope.TextEmbedding.Models.text_embedding_v3, # 确认你的模型版本
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input=text,
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dimension=1536
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)
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if resp.status_code == HTTPStatus.OK:
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embedding = resp.output['embeddings'][0]['embedding']
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else:
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print(f"Embedding API Error: {resp}")
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except Exception as e:
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print(f"Embedding Exception: {e}")
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return embedding
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def map_and_ingest(self, start_url: str):
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"""
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V2 步骤1: 地图式扫描并入库
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"""
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print(f"[WorkFlow] Start mapping: {start_url}")
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result = {}
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try:
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# 1. 在数据库注册任务
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task_info = crawler_sql_service.register_task(start_url)
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task_id = task_info['task_id']
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is_new_task = task_info['is_new_task']
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# 2. 调用 Firecrawl Map
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if is_new_task:
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map_result = self.firecrawl.map(start_url)
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urls = []
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# 兼容 firecrawl sdk 不同版本的返回结构
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# 如果 map_result 是对象且有 links 属性
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if hasattr(map_result, 'links'):
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for link in map_result.links:
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# 假设 link 是对象或字典，视具体 SDK 版本而定
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# 如果 link 是字符串直接 append
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if isinstance(link, str):
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urls.append(link)
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else:
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urls.append(getattr(link, 'url', str(link)))
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# 如果是字典
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elif isinstance(map_result, dict):
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urls = map_result.get('links', [])
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print(f"[WorkFlow] Found {len(urls)} links")
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# 3. 批量入库
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res = {"msg": "No urls found to add"}
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if urls:
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res = crawler_sql_service.add_urls(task_id, urls)
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result = {
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"msg": "Task successfully mapped and URLs added",
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"task_id": task_id,
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"is_new_task": is_new_task,
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"url_count": len(urls),
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"map_detail": res
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}
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else:
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result = {
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"msg": "Task already exists, skipped mapping",
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"task_id": task_id,
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"is_new_task": False,
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"url_count": 0,
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"map_detail": {}
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}
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except Exception as e:
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print(f"[WorkFlow] Map Error: {e}")
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# 向上抛出异常，由 main.py 捕获并返回错误 Response
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raise e
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return result
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def process_task_queue(self, task_id: int, limit: int = 10):
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"""
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V2 步骤2: 消费队列 -> 抓取 -> 切片 -> 向量化 -> 存储
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"""
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processed_count = 0
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total_chunks_saved = 0
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result = {}
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# 1. 获取待处理 URL
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pending = crawler_sql_service.get_pending_urls(task_id, limit)
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urls = pending['urls']
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if not urls:
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result = {"msg": "Queue is empty, no processing needed", "processed_count": 0}
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else:
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for url in urls:
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try:
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print(f"[WorkFlow] Processing: {url}")
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# 2. 单页抓取
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scrape_res = self.firecrawl.scrape(
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url,
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params={'formats': ['markdown'], 'onlyMainContent': True}
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)
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# 兼容 SDK 返回类型 (对象或字典)
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content = ""
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metadata = {}
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if isinstance(scrape_res, dict):
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content = scrape_res.get('markdown', '')
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metadata = scrape_res.get('metadata', {})
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else:
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content = getattr(scrape_res, 'markdown', '')
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metadata = getattr(scrape_res, 'metadata', {})
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if not metadata and hasattr(scrape_res, 'metadata_dict'):
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metadata = scrape_res.metadata_dict
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title = metadata.get('title', url)
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if not content:
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print(f"[WorkFlow] Skip empty content: {url}")
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continue
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# 3. 切片
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chunks = self.splitter.split_text(content)
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results_to_save = []
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# 4. 向量化
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for idx, chunk_text in enumerate(chunks):
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vector = self._get_embedding(chunk_text)
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if vector:
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results_to_save.append({
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"source_url": url,
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"chunk_index": idx,
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"title": title,
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"content": chunk_text,
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"embedding": vector
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})
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# 5. 保存
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if results_to_save:
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save_res = crawler_sql_service.save_results(task_id, results_to_save)
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processed_count += 1
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total_chunks_saved += save_res['counts']['inserted'] + save_res['counts']['updated']
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except Exception as e:
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print(f"[WorkFlow] Error processing {url}: {e}")
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# 此处不抛出异常，以免打断整个批次的循环
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# 实际生产建议在这里调用 service 将 url 标记为 failed
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result = {
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"msg": f"Batch processing complete. URLs processed: {processed_count}",
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"processed_urls": processed_count,
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"total_chunks_saved": total_chunks_saved
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}
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return result
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def search_with_embedding(self, query_text: str, task_id: int = None, limit: int = 5):
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"""
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V2 搜索: 输入文本 -> 自动转向量 -> 搜索数据库
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"""
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result = {}
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# 1. 获取向量
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vector = self._get_embedding(query_text)
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if not vector:
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result = {
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"msg": "Failed to generate embedding for query",
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"results": []
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}
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else:
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# 2. 执行搜索
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# search_knowledge 现在已经返回带 msg 的字典了
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result = crawler_sql_service.search_knowledge(vector, task_id, limit)
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return result
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# 单例模式
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workflow = AutomatedCrawler() |