- Test all methods
- Update index.hml - Update Readme - Resolve some bugs
This commit is contained in:
unclecode
@@ -2,7 +2,7 @@ import os, time
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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 .database import init_db, get_cached_url, cache_url, DB_PATH
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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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@@ -10,6 +10,7 @@ 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 .config import *
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# from .model_loader import load_bert_base_uncased, load_bge_small_en_v1_5, load_spacy_model
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class WebCrawler:
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@@ -36,11 +37,11 @@ class WebCrawler:
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def warmup(self):
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print("[LOG] 🌤️ Warming up the WebCrawler")
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single_url = UrlModel(url='https://crawl4ai.uccode.io/', forced=False)
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result = self.run(
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single_url,
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url='https://crawl4ai.uccode.io/',
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word_count_threshold=5,
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extraction_strategy= CosinegStrategy(),
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extraction_strategy= CosineStrategy(),
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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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@@ -60,10 +61,11 @@ class WebCrawler:
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**kwargs,
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) -> CrawlResult:
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return self.run(
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url_model,
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url_model.url,
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word_count_threshold,
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extraction_strategy,
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chunking_strategy,
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bypass_cache=url_model.forced,
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**kwargs,
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)
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pass
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@@ -71,77 +73,85 @@ class WebCrawler:
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def run(
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self,
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url_model: UrlModel,
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url: str,
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word_count_threshold=MIN_WORD_THRESHOLD,
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extraction_strategy: ExtractionStrategy = NoExtractionStrategy(),
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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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verbose=True,
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**kwargs,
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) -> CrawlResult:
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# Check if extraction strategy is an instance of ExtractionStrategy if not raise an error
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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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# make sure word_count_threshold is not lesser than MIN_WORD_THRESHOLD
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if word_count_threshold < MIN_WORD_THRESHOLD:
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word_count_threshold = MIN_WORD_THRESHOLD
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# Check cache first
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cached = get_cached_url(self.db_path, str(url_model.url))
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if cached and not url_model.forced:
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return CrawlResult(
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**{
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"url": cached[0],
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"html": cached[1],
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"cleaned_html": cached[2],
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"markdown": cached[3],
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"parsed_json": cached[4],
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"success": cached[5],
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"error_message": "",
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}
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)
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if not bypass_cache:
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cached = get_cached_url(url)
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if cached:
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return CrawlResult(
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**{
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"url": cached[0],
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"html": cached[1],
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"cleaned_html": cached[2],
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"markdown": cached[3],
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"parsed_json": cached[4],
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"success": cached[5],
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"error_message": "",
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}
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)
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# Initialize WebDriver for crawling
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t = time.time()
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try:
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html = self.crawler_strategy.crawl(str(url_model.url))
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success = True
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error_message = ""
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except Exception as e:
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html = ""
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success = False
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error_message = str(e)
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html = self.crawler_strategy.crawl(url)
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success = True
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error_message = ""
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# Extract content from HTML
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result = get_content_of_website(html, word_count_threshold)
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try:
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result = get_content_of_website(html, word_count_threshold, css_selector=css_selector)
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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 = result.get("cleaned_html", html)
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markdown = result.get("markdown", "")
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# Print a profession LOG style message, show time taken and say crawling is done
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if verbose:
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print(
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f"[LOG] 🚀 Crawling done for {url_model.url}, success: {success}, time taken: {time.time() - t} seconds"
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f"[LOG] 🚀 Crawling done for {url}, success: {success}, time taken: {time.time() - t} seconds"
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)
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parsed_json = []
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if verbose:
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print(f"[LOG] 🔥 Extracting semantic blocks for {url_model.url}")
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print(f"[LOG] 🔥 Extracting semantic blocks for {url}, Strategy: {extraction_strategy.name}")
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t = time.time()
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# Split markdown into sections
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sections = chunking_strategy.chunk(markdown)
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# sections = merge_chunks_based_on_token_threshold(sections, CHUNK_TOKEN_THRESHOLD)
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parsed_json = extraction_strategy.run(
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str(url_model.url), sections,
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url, sections,
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)
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parsed_json = json.dumps(parsed_json)
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if verbose:
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print(
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f"[LOG] 🚀 Extraction done for {url_model.url}, time taken: {time.time() - t} seconds."
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f"[LOG] 🚀 Extraction done for {url}, time taken: {time.time() - t} seconds."
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)
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# Cache the result
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cleaned_html = beautify_html(cleaned_html)
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cache_url(
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self.db_path,
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str(url_model.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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@@ -150,7 +160,7 @@ class WebCrawler:
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)
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return CrawlResult(
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url=str(url_model.url),
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url=url,
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html=html,
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cleaned_html=cleaned_html,
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markdown=markdown,
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