refactor(core): reorganize project structure and remove legacy code
Major reorganization of the project structure: - Moved legacy synchronous crawler code to legacy folder - Removed deprecated CLI and docs manager - Consolidated version manager into utils.py - Added CrawlerHub to __init__.py exports - Fixed type hints in async_webcrawler.py - Fixed minor bugs in chunking and crawler strategies BREAKING CHANGE: Removed synchronous WebCrawler, CLI, and docs management functionality. Users should migrate to AsyncWebCrawler.
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UncleCode
294
crawl4ai/legacy/web_crawler.py
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294
crawl4ai/legacy/web_crawler.py
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import os, time
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os.environ["TOKENIZERS_PARALLELISM"] = "false"
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from pathlib import Path
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from .models import UrlModel, CrawlResult
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from .database import init_db, get_cached_url, cache_url
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from .utils import *
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from .chunking_strategy import *
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from .extraction_strategy import *
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from .crawler_strategy import *
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from typing import List
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from concurrent.futures import ThreadPoolExecutor
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from .content_scraping_strategy import WebScrapingStrategy
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from .config import *
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import warnings
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import json
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warnings.filterwarnings(
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"ignore",
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message='Field "model_name" has conflict with protected namespace "model_".',
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)
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class WebCrawler:
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def __init__(
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self,
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crawler_strategy: CrawlerStrategy = None,
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always_by_pass_cache: bool = False,
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verbose: bool = False,
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):
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self.crawler_strategy = crawler_strategy or LocalSeleniumCrawlerStrategy(
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verbose=verbose
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)
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self.always_by_pass_cache = always_by_pass_cache
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self.crawl4ai_folder = os.path.join(
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os.getenv("CRAWL4_AI_BASE_DIRECTORY", Path.home()), ".crawl4ai"
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)
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os.makedirs(self.crawl4ai_folder, exist_ok=True)
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os.makedirs(f"{self.crawl4ai_folder}/cache", exist_ok=True)
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init_db()
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self.ready = False
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def warmup(self):
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print("[LOG] 🌤️ Warming up the WebCrawler")
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self.run(
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url="https://google.com/",
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word_count_threshold=5,
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extraction_strategy=NoExtractionStrategy(),
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bypass_cache=False,
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verbose=False,
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)
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self.ready = True
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print("[LOG] 🌞 WebCrawler is ready to crawl")
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def fetch_page(
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self,
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url_model: UrlModel,
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provider: str = DEFAULT_PROVIDER,
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api_token: str = None,
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extract_blocks_flag: bool = True,
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word_count_threshold=MIN_WORD_THRESHOLD,
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css_selector: str = None,
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screenshot: bool = False,
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use_cached_html: bool = False,
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extraction_strategy: ExtractionStrategy = None,
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chunking_strategy: ChunkingStrategy = RegexChunking(),
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**kwargs,
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) -> CrawlResult:
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return self.run(
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url_model.url,
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word_count_threshold,
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extraction_strategy or NoExtractionStrategy(),
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chunking_strategy,
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bypass_cache=url_model.forced,
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css_selector=css_selector,
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screenshot=screenshot,
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**kwargs,
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)
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pass
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def fetch_pages(
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self,
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url_models: List[UrlModel],
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provider: str = DEFAULT_PROVIDER,
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api_token: str = None,
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extract_blocks_flag: bool = True,
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word_count_threshold=MIN_WORD_THRESHOLD,
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use_cached_html: bool = False,
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css_selector: str = None,
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screenshot: bool = False,
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extraction_strategy: ExtractionStrategy = None,
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chunking_strategy: ChunkingStrategy = RegexChunking(),
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**kwargs,
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) -> List[CrawlResult]:
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extraction_strategy = extraction_strategy or NoExtractionStrategy()
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def fetch_page_wrapper(url_model, *args, **kwargs):
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return self.fetch_page(url_model, *args, **kwargs)
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with ThreadPoolExecutor() as executor:
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results = list(
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executor.map(
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fetch_page_wrapper,
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url_models,
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[provider] * len(url_models),
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[api_token] * len(url_models),
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[extract_blocks_flag] * len(url_models),
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[word_count_threshold] * len(url_models),
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[css_selector] * len(url_models),
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[screenshot] * len(url_models),
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[use_cached_html] * len(url_models),
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[extraction_strategy] * len(url_models),
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[chunking_strategy] * len(url_models),
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*[kwargs] * len(url_models),
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)
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)
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return results
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def run(
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self,
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url: str,
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word_count_threshold=MIN_WORD_THRESHOLD,
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extraction_strategy: ExtractionStrategy = None,
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chunking_strategy: ChunkingStrategy = RegexChunking(),
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bypass_cache: bool = False,
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css_selector: str = None,
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screenshot: bool = False,
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user_agent: str = None,
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verbose=True,
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**kwargs,
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) -> CrawlResult:
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try:
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extraction_strategy = extraction_strategy or NoExtractionStrategy()
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extraction_strategy.verbose = verbose
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if not isinstance(extraction_strategy, ExtractionStrategy):
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raise ValueError("Unsupported extraction strategy")
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if not isinstance(chunking_strategy, ChunkingStrategy):
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raise ValueError("Unsupported chunking strategy")
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word_count_threshold = max(word_count_threshold, MIN_WORD_THRESHOLD)
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cached = None
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screenshot_data = None
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extracted_content = None
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if not bypass_cache and not self.always_by_pass_cache:
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cached = get_cached_url(url)
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if kwargs.get("warmup", True) and not self.ready:
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return None
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if cached:
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html = sanitize_input_encode(cached[1])
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extracted_content = sanitize_input_encode(cached[4])
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if screenshot:
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screenshot_data = cached[9]
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if not screenshot_data:
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cached = None
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if not cached or not html:
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if user_agent:
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self.crawler_strategy.update_user_agent(user_agent)
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t1 = time.time()
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html = sanitize_input_encode(self.crawler_strategy.crawl(url, **kwargs))
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t2 = time.time()
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if verbose:
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print(
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f"[LOG] 🚀 Crawling done for {url}, success: {bool(html)}, time taken: {t2 - t1:.2f} seconds"
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)
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if screenshot:
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screenshot_data = self.crawler_strategy.take_screenshot()
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crawl_result = self.process_html(
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url,
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html,
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extracted_content,
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word_count_threshold,
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extraction_strategy,
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chunking_strategy,
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css_selector,
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screenshot_data,
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verbose,
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bool(cached),
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**kwargs,
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)
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crawl_result.success = bool(html)
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return crawl_result
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except Exception as e:
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if not hasattr(e, "msg"):
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e.msg = str(e)
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print(f"[ERROR] 🚫 Failed to crawl {url}, error: {e.msg}")
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return CrawlResult(url=url, html="", success=False, error_message=e.msg)
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def process_html(
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self,
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url: str,
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html: str,
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extracted_content: str,
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word_count_threshold: int,
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extraction_strategy: ExtractionStrategy,
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chunking_strategy: ChunkingStrategy,
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css_selector: str,
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screenshot: bool,
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verbose: bool,
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is_cached: bool,
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**kwargs,
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) -> CrawlResult:
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t = time.time()
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# Extract content from HTML
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try:
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t1 = time.time()
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scrapping_strategy = WebScrapingStrategy()
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extra_params = {
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k: v
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for k, v in kwargs.items()
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if k not in ["only_text", "image_description_min_word_threshold"]
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}
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result = scrapping_strategy.scrap(
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url,
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html,
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word_count_threshold=word_count_threshold,
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css_selector=css_selector,
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only_text=kwargs.get("only_text", False),
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image_description_min_word_threshold=kwargs.get(
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"image_description_min_word_threshold",
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IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD,
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),
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**extra_params,
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)
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# result = get_content_of_website_optimized(url, html, word_count_threshold, css_selector=css_selector, only_text=kwargs.get("only_text", False))
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if verbose:
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print(
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f"[LOG] 🚀 Content extracted for {url}, success: True, time taken: {time.time() - t1:.2f} seconds"
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)
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if result is None:
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raise ValueError(f"Failed to extract content from the website: {url}")
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except InvalidCSSSelectorError as e:
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raise ValueError(str(e))
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cleaned_html = sanitize_input_encode(result.get("cleaned_html", ""))
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markdown = sanitize_input_encode(result.get("markdown", ""))
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media = result.get("media", [])
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links = result.get("links", [])
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metadata = result.get("metadata", {})
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if extracted_content is None:
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if verbose:
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print(
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f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}"
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)
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sections = chunking_strategy.chunk(markdown)
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extracted_content = extraction_strategy.run(url, sections)
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extracted_content = json.dumps(
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extracted_content, indent=4, default=str, ensure_ascii=False
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)
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if verbose:
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print(
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f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t:.2f} seconds."
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)
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screenshot = None if not screenshot else screenshot
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if not is_cached:
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cache_url(
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url,
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html,
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cleaned_html,
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markdown,
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extracted_content,
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True,
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json.dumps(media),
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json.dumps(links),
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json.dumps(metadata),
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screenshot=screenshot,
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)
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return CrawlResult(
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url=url,
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html=html,
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cleaned_html=format_html(cleaned_html),
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markdown=markdown,
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media=media,
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links=links,
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metadata=metadata,
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screenshot=screenshot,
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extracted_content=extracted_content,
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success=True,
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error_message="",
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)
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