mcp调试完成
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@@ -1,41 +1,64 @@
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import concurrent.futures
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import threading
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import logging
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from typing import Dict, Any, List, Optional, Union
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from firecrawl import FirecrawlApp
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from backend.core.config import settings
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from backend.services.data_service import data_service
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from backend.services.llm_service import llm_service
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from backend.utils.text_process import text_processor
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import logging
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# 获取当前模块的专用 Logger
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# __name__ 会自动识别为 "backend.services.crawler_service" 这样的路径
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logger = logging.getLogger(__name__)
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class CrawlerService:
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"""
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爬虫业务服务层 (Crawler Service)
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职责：
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1. 协调外部 API (Firecrawl) 和内部服务 (DataService, LLMService)。
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2. 管理多线程爬取任务及其状态。
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3. 提供统一的搜索入口 (混合检索 + Rerank)。
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"""
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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.max_workers = 5
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self.max_workers = 5 # 线程池最大并发数
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# [新增] 内存状态追踪
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# 结构: { task_id: { url: "status_desc" } }
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self._active_workers = {}
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# 内存状态追踪: { task_id: set([url1, url2]) }
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self._active_workers: Dict[int, set] = {}
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self._lock = threading.Lock()
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def _track_start(self, task_id, url):
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"""开始追踪某个URL"""
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def _track_start(self, task_id: int, url: str):
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"""[Internal] 标记某个URL开始处理"""
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with self._lock:
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if task_id not in self._active_workers:
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self._active_workers[task_id] = set()
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self._active_workers[task_id].add(url)
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def _track_end(self, task_id, url):
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"""结束追踪某个URL"""
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def _track_end(self, task_id: int, url: str):
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"""[Internal] 标记某个URL处理结束"""
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with self._lock:
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if task_id in self._active_workers:
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self._active_workers[task_id].discard(url)
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def get_task_status(self, task_id: int):
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def get_task_status(self, task_id: int) -> Optional[Dict[str, Any]]:
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"""
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[综合监控] 获取全量状态 = 数据库统计 + 实时线程列表
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获取任务的实时综合状态。
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Args:
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task_id (int): 任务 ID
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Returns:
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dict: 包含数据库统计和实时线程信息的字典。如果任务不存在返回 None。
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结构示例:
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{
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"root_url": "https://example.com",
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"stats": {"pending": 10, "processing": 2, "completed": 5, "failed": 0},
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"active_threads": ["https://example.com/page1"],
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"active_thread_count": 1
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}
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"""
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# 1. 获取数据库层面的统计 (宏观)
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db_data = data_service.get_task_monitor_data(task_id)
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@@ -45,19 +68,33 @@ class CrawlerService:
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# 2. 获取内存层面的活跃线程 (微观)
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with self._lock:
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active_urls = list(self._active_workers.get(task_id, []))
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# 输出情况
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# 日志输出当前状态
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logger.info(f"Task {task_id} active threads: {active_urls}")
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logger.info(f"Task {task_id} stats: {db_data['db_stats']}") # 打印数据库统计信息
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logger.info(f"Task {task_id} stats: {db_data['db_stats']}")
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return {
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"root_url": db_data["root_url"],
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"stats": db_data["db_stats"], # Pending, Completed, Failed 等
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"active_threads": active_urls, # 当前 CPU/网络 正在处理的 URL
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"stats": db_data["db_stats"],
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"active_threads": active_urls,
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"active_thread_count": len(active_urls)
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}
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def map_site(self, start_url: str):
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"""阶段1：站点地图扫描"""
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def map_site(self, start_url: str) -> Dict[str, Any]:
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"""
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第一阶段：站点地图扫描 (Map)
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Args:
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start_url (str): 目标网站的根 URL
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Returns:
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dict: 包含任务 ID 和发现链接数的字典。
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{
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"task_id": 123,
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"count": 50,
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"is_new": True
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}
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"""
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logger.info(f"Mapping: {start_url}")
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try:
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task_res = data_service.register_task(start_url)
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@@ -75,7 +112,9 @@ class CrawlerService:
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# 新任务执行 Map
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try:
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map_res = self.firecrawl.map(start_url)
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# 兼容不同版本的 SDK 返回结构
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found_links = map_res.get('links', []) if isinstance(map_res, dict) else getattr(map_res, 'links', [])
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for link in found_links:
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u = link if isinstance(link, str) else getattr(link, 'url', str(link))
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urls_to_add.append(u)
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@@ -96,7 +135,7 @@ class CrawlerService:
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raise e
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def _process_single_url(self, task_id: int, url: str):
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"""[Worker] 单个 URL 处理线程"""
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"""[Internal Worker] 单个 URL 处理线程逻辑"""
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# 1. 内存标记：开始
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self._track_start(task_id, url)
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logger.info(f"[THREAD START] {url}")
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@@ -107,6 +146,7 @@ class CrawlerService:
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url, formats=['markdown'], only_main_content=True
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)
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# 兼容性提取
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raw_md = getattr(scrape_res, 'markdown', '') if not isinstance(scrape_res, dict) else scrape_res.get('markdown', '')
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metadata = getattr(scrape_res, 'metadata', {}) if not isinstance(scrape_res, dict) else scrape_res.get('metadata', {})
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title = getattr(metadata, 'title', url) if not isinstance(metadata, dict) else metadata.get('title', url)
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@@ -124,6 +164,7 @@ class CrawlerService:
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headers = chunk['metadata']
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path = " > ".join(headers.values())
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emb_input = f"{title}\n{path}\n{chunk['content']}"
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vector = llm_service.get_embedding(emb_input)
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if vector:
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chunks_data.append({
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@@ -145,34 +186,72 @@ class CrawlerService:
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# 5. 内存标记：结束 (无论成功失败都要移除)
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self._track_end(task_id, url)
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def process_queue_concurrent(self, task_id: int, batch_size: int = 10):
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"""阶段2：多线程并发处理"""
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def process_queue_concurrent(self, task_id: int, batch_size: int = 10) -> Dict[str, Any]:
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"""
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第二阶段：多线程并发处理 (Process)
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Args:
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task_id (int): 任务 ID
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batch_size (int): 本次批次处理的 URL 数量（会分配给线程池并发执行）
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Returns:
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dict: 处理结果概览
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{
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"msg": "Batch completed",
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"count": 10
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}
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"""
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urls = data_service.get_pending_urls(task_id, limit=batch_size)
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if not urls: return {"msg": "No pending urls"}
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if not urls: return {"msg": "No pending urls", "count": 0}
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logger.info(f"Batch started: {len(urls)} urls")
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with concurrent.futures.ThreadPoolExecutor(max_workers=self.max_workers) as executor:
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# 提交任务到线程池
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futures = {executor.submit(self._process_single_url, task_id, url): url for url in urls}
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# 等待完成
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# 等待完成 (阻塞直到所有线程结束)
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concurrent.futures.wait(futures)
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return {"msg": "Batch completed", "count": len(urls)}
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def search(self, query: str, task_id, return_num: int):
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"""阶段3：搜索"""
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def search(self, query: str, task_id: Optional[int], return_num: int) -> Dict[str, Any]:
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"""
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第三阶段：智能搜索 (Search)
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流程：用户问题 -> Embedding -> 数据库混合检索(粗排) -> Rerank模型(精排) -> 结果
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Args:
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query (str): 用户问题
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task_id (Optional[int]): 指定搜索的任务 ID，None 为全库搜索
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return_num (int): 最终返回给用户的条数 (Top K)
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Returns:
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dict: 搜索结果列表
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{
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"results": [
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{"content": "...", "score": 0.98, "meta_info": {...}},
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...
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]
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}
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"""
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# 1. 生成向量
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vector = llm_service.get_embedding(query)
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if not vector: return {"msg": "Embedding failed", "results": []}
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# 2. 数据库粗排 (召回 10 倍数量或至少 50 条)
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coarse_limit = min(return_num * 10, 100)
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coarse_limit = max(coarse_limit, 50)
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coarse_res = data_service.search(query, vector, task_id, coarse_limit)
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coarse_res = data_service.search(
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query_text=query,
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query_vector=vector,
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task_id=task_id,
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candidates_num=coarse_limit
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)
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candidates = coarse_res.get('results', [])
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if not candidates: return {"results": []}
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# 3. LLM 精排 (Rerank)
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final_res = llm_service.rerank(query, candidates, return_num)
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return {"results": final_res}
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