refactor(models): rename final_url to redirected_url for consistency
Renames the final_url field to redirected_url across all components to maintain consistent terminology throughout the codebase. This change affects: - AsyncCrawlResponse model - AsyncPlaywrightCrawlerStrategy - Documentation and examples No functional changes, purely naming consistency improvement.
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UncleCode
@@ -1,266 +1,138 @@
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# Crawl4AI 0.4.3b1 is Here: Faster, Smarter, and Ready for Real-World Crawling!
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# Crawl4AI 0.4.3: Major Performance Boost & LLM Integration
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Hey, Crawl4AI enthusiasts! We're thrilled to announce the release of **Crawl4AI 0.4.3b1**, packed with powerful new features and enhancements that take web crawling to a whole new level of efficiency and intelligence. This release is all about giving you more control, better performance, and deeper insights into your crawled data.
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We're excited to announce Crawl4AI 0.4.3, focusing on three key areas: Speed & Efficiency, LLM Integration, and Core Platform Improvements. This release significantly improves crawling performance while adding powerful new LLM-powered features.
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Let's dive into what's new!
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## ⚡ Speed & Efficiency Improvements
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## 🚀 Major Feature Highlights
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### 1. LLM-Powered Schema Generation: Zero to Structured Data in Seconds!
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Tired of manually crafting CSS or XPath selectors? We've got you covered! Crawl4AI now features a revolutionary **schema generator** that uses the power of Large Language Models (LLMs) to automatically create extraction schemas for you.
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**How it Works:**
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1. **Provide HTML**: Feed in a sample HTML snippet that contains the type of data you want to extract (e.g., product listings, article sections).
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2. **Describe Your Needs (Optional)**: You can provide a natural language query like "extract all product names and prices" to guide the schema creation.
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3. **Choose Your LLM**: Use either **OpenAI** (GPT-4o recommended) for top-tier accuracy or **Ollama** for a local, open-source option.
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4. **Get Your Schema**: The tool outputs a ready-to-use JSON schema that works seamlessly with `JsonCssExtractionStrategy` or `JsonXPathExtractionStrategy`.
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**Why You'll Love It:**
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- **No More Tedious Selector Writing**: Let the LLM analyze the HTML and create the selectors for you!
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- **One-Time Cost**: Schema generation uses LLM, but once you have your schema, subsequent extractions are fast and LLM-free.
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- **Handles Complex Structures**: The LLM can understand nested elements, lists, and variations in layout—far beyond what simple CSS selectors can achieve.
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- **Learn by Example**: The generated schemas are a fantastic way to learn best practices for writing your own schemas.
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**Example:**
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### 1. Memory-Adaptive Dispatcher System
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The new dispatcher system provides intelligent resource management and real-time monitoring:
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```python
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from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
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# Sample HTML snippet (imagine this is part of a product listing page)
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html = """
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<div class="product">
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<h2 class="name">Awesome Gadget</h2>
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<span class="price">$99.99</span>
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</div>
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"""
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# Generate schema using OpenAI
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schema = JsonCssExtractionStrategy.generate_schema(
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html,
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llm_provider="openai/gpt-4o",
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api_token="YOUR_API_TOKEN"
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)
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# Or use Ollama for a local, open-source option
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# schema = JsonCssExtractionStrategy.generate_schema(
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# html,
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# llm_provider="ollama/llama3"
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# )
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print(json.dumps(schema, indent=2))
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```
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**Output (Schema):**
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```json
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{
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"name": null,
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"baseSelector": "div.product",
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"fields": [
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{
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"name": "name",
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"selector": "h2.name",
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"type": "text"
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},
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{
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"name": "price",
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"selector": "span.price",
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"type": "text"
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}
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]
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}
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```
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You can now **save** this schema and use it for all your extractions on pages with the same structure. No more LLM costs, just **fast, reliable** data extraction!
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### 2. Robots.txt Compliance: Crawl Responsibly
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Crawl4AI now respects website rules! With the new `check_robots_txt=True` option in `CrawlerRunConfig`, the crawler automatically fetches, parses, and obeys each site's `robots.txt` file.
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**Key Features**:
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- **Efficient Caching**: Stores parsed `robots.txt` files locally for 7 days to avoid re-fetching.
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- **Automatic Integration**: Works seamlessly with both `arun()` and `arun_many()`.
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- **Clear Status Codes**: Returns a 403 status code if a URL is disallowed.
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- **Customizable**: Adjust the cache directory and TTL if needed.
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**Example**:
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```python
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from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, CacheMode
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from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, DisplayMode
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from crawl4ai.async_dispatcher import MemoryAdaptiveDispatcher, CrawlerMonitor
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async def main():
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config = CrawlerRunConfig(
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cache_mode=CacheMode.ENABLED,
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check_robots_txt=True
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)
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun("https://example.com/private-page", config=config)
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if result.status_code == 403:
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print("Access denied by robots.txt")
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if __name__ == "__main__":
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asyncio.run(main())
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```
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### 3. Proxy Support in `CrawlerRunConfig`
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Need more control over your proxy settings? Now you can configure proxies directly within `CrawlerRunConfig` for each crawl:
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```python
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from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
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async def main():
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config = CrawlerRunConfig(
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proxy_config={
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"server": "http://your-proxy.com:8080",
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"username": "your_username", # Optional
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"password": "your_password" # Optional
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}
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)
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun("https://example.com", config=config)
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```
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This allows for dynamic proxy assignment per URL or even per request.
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### 4. LLM-Powered Markdown Filtering (Beta)
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We're introducing an experimental **`LLMContentFilter`**! This filter, when used with the `DefaultMarkdownGenerator`, can produce highly focused markdown output by using an LLM to analyze content relevance.
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**How it Works:**
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1. You provide an **instruction** (e.g., "extract only the key technical details").
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2. The LLM analyzes each section of the page based on your instruction.
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3. Only the most relevant content is included in the final `fit_markdown`.
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**Example**:
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```python
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from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
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from crawl4ai.content_filter_strategy import LLMContentFilter
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from crawl4ai.markdown_generation_strategy import DefaultMarkdownGenerator
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async def main():
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llm_filter = LLMContentFilter(
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provider="openai/gpt-4o",
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api_token="YOUR_API_TOKEN", # Or use "ollama/llama3" with no token
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instruction="Extract the core educational content about Python classes."
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)
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config = CrawlerRunConfig(
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markdown_generator=DefaultMarkdownGenerator(content_filter=llm_filter)
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)
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async with AsyncWebCrawler() as crawler:
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result = await crawler.arun(
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"https://docs.python.org/3/tutorial/classes.html",
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config=config
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urls = ["https://example1.com", "https://example2.com"] * 50
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# Configure memory-aware dispatch
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dispatcher = MemoryAdaptiveDispatcher(
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memory_threshold_percent=80.0, # Auto-throttle at 80% memory
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check_interval=0.5, # Check every 0.5 seconds
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max_session_permit=20, # Max concurrent sessions
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monitor=CrawlerMonitor( # Real-time monitoring
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display_mode=DisplayMode.DETAILED
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)
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)
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async with AsyncWebCrawler() as crawler:
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results = await dispatcher.run_urls(
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urls=urls,
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crawler=crawler,
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config=CrawlerRunConfig()
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)
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print(result.markdown_v2.fit_markdown)
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if __name__ == "__main__":
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asyncio.run(main())
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```
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**Note**: This is a beta feature. We're actively working on improving its accuracy and performance.
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### 5. Streamlined `arun_many()` with Dispatchers
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We've simplified concurrent crawling! `arun_many()` now intelligently handles multiple URLs, either returning a **list** of results or an **async generator** for streaming.
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**Basic Usage (Batch)**:
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### 2. Streaming Support
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Process crawled URLs in real-time instead of waiting for all results:
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```python
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results = await crawler.arun_many(
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urls=["https://site1.com", "https://site2.com"],
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config=CrawlerRunConfig()
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)
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config = CrawlerRunConfig(stream=True)
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for res in results:
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print(res.url, "crawled successfully:", res.success)
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async with AsyncWebCrawler() as crawler:
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async for result in await crawler.arun_many(urls, config=config):
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print(f"Got result for {result.url}")
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# Process each result immediately
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```
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**Streaming Mode**:
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### 3. LXML-Based Scraping
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New LXML scraping strategy offering up to 20x faster parsing:
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```python
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async for result in await crawler.arun_many(
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urls=["https://site1.com", "https://site2.com"],
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config=CrawlerRunConfig(stream=True)
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):
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print("Just finished:", result.url)
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# Process each result immediately
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```
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**Advanced:** You can now customize how `arun_many` handles concurrency by passing a **dispatcher**. See [Advanced Multi-URL Crawling](../advanced/multi-url-crawling.md) for details.
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### 6. Enhanced Browser Context Management
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We've improved how Crawl4AI manages browser contexts for better resource utilization and session handling.
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- **`shared_data` in `CrawlerRunConfig`**: Pass data between hooks using the `shared_data` dictionary.
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- **Context Reuse**: The crawler now intelligently reuses browser contexts based on configuration, reducing overhead.
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### 7. Faster Scraping with `LXMLWebScrapingStrategy`
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Introducing a new, optional **`LXMLWebScrapingStrategy`** that can be **10-20x faster** than the default BeautifulSoup approach for large, complex pages.
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**How to Use**:
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```python
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from crawl4ai import LXMLWebScrapingStrategy
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config = CrawlerRunConfig(
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scraping_strategy=LXMLWebScrapingStrategy() # Add this line
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scraping_strategy=LXMLWebScrapingStrategy(),
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cache_mode=CacheMode.ENABLED
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)
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```
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**When to Use**:
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- If profiling shows a bottleneck in `WebScrapingStrategy`.
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- For very large HTML documents where parsing speed matters.
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## 🤖 LLM Integration
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**Caveats**:
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- It might not handle malformed HTML as gracefully as BeautifulSoup.
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- We're still gathering data, so report any issues!
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### 1. LLM-Powered Markdown Generation
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Smart content filtering and organization using LLMs:
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---
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```python
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config = CrawlerRunConfig(
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markdown_generator=DefaultMarkdownGenerator(
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content_filter=LLMContentFilter(
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provider="openai/gpt-4o",
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instruction="Extract technical documentation and code examples"
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)
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)
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)
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```
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## Try the Feature Demo Script!
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### 2. Automatic Schema Generation
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Generate extraction schemas instantly using LLMs instead of manual CSS/XPath writing:
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We've prepared a Python script demonstrating these new features. You can find it at:
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```python
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schema = JsonCssExtractionStrategy.generate_schema(
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html_content,
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schema_type="CSS",
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query="Extract product name, price, and description"
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)
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```
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[**`features_demo.py`**](https://github.com/unclecode/crawl4ai/blob/main/docs/examples/0_4_3b1_feature_demo.py)
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## 🔧 Core Improvements
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**To run the demo:**
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### 1. Proxy Support & Rotation
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Integrated proxy support with automatic rotation and verification:
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1. Make sure you have Crawl4AI installed (`pip install crawl4ai`).
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2. Copy the `features_demo.py` script to your local environment.
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3. Set your OpenAI API key as an environment variable (if using OpenAI models):
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```bash
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export OPENAI_API_KEY="your_api_key"
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```
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4. Run the script:
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```bash
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python features_demo.py
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```
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```python
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config = CrawlerRunConfig(
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proxy_config={
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"server": "http://proxy:8080",
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"username": "user",
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"password": "pass"
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}
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)
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```
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The script will execute various crawl scenarios, showcasing the new features and printing results to your console.
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### 2. Robots.txt Compliance
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Built-in robots.txt support with SQLite caching:
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## Conclusion
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```python
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config = CrawlerRunConfig(check_robots_txt=True)
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result = await crawler.arun(url, config=config)
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if result.status_code == 403:
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print("Access blocked by robots.txt")
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```
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Crawl4AI version 0.4.3b1 is a major step forward in flexibility, performance, and ease of use. With automatic schema generation, robots.txt handling, advanced content filtering, and streamlined multi-URL crawling, you can build powerful, efficient, and responsible web scrapers.
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### 3. URL Redirection Tracking
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Track final URLs after redirects:
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We encourage you to try out these new capabilities, explore the updated documentation, and share your feedback! Your input is invaluable as we continue to improve Crawl4AI.
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```python
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result = await crawler.arun(url)
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print(f"Initial URL: {url}")
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print(f"Final URL: {result.redirected_url}")
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```
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**Stay Connected:**
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## Performance Impact
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- **Star** us on [GitHub](https://github.com/unclecode/crawl4ai) to show your support!
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- **Follow** [@unclecode](https://twitter.com/unclecode) on Twitter for updates and tips.
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- **Join** our community on Discord (link coming soon) to discuss your projects and get help.
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- Memory usage reduced by up to 40% with adaptive dispatcher
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- Parsing speed increased up to 20x with LXML strategy
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- Streaming reduces memory footprint for large crawls by ~60%
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Happy crawling!
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## Getting Started
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```bash
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pip install -U crawl4ai
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```
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For complete examples, check our [demo repository](https://github.com/unclecode/crawl4ai/examples).
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## Stay Connected
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- Star us on [GitHub](https://github.com/unclecode/crawl4ai)
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- Follow [@unclecode](https://twitter.com/unclecode)
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- Join our [Discord](https://discord.gg/crawl4ai)
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Happy crawling! 🕷️
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Reference in New Issue
Block a user