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Merge branch '2025-JUN-1' into next-MAY

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ntohidi
2025-07-09 09:41:03 +02:00
parent 026e96a2df 9332326457
commit 0ebce590f8
15 changed files with 664 additions and 46 deletions
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6
crawl4ai/async_configs.py
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@@ -926,6 +926,8 @@ class CrawlerRunConfig():
Default: False.
scroll_delay (float): Delay in seconds between scroll steps if scan_full_page is True.
Default: 0.2.
max_scroll_steps (Optional[int]): Maximum number of scroll steps to perform during full page scan.
If None, scrolls until the entire page is loaded. Default: None.
process_iframes (bool): If True, attempts to process and inline iframe content.
Default: False.
remove_overlay_elements (bool): If True, remove overlays/popups before extracting HTML.
@@ -1066,6 +1068,7 @@ class CrawlerRunConfig():
ignore_body_visibility: bool = True,
scan_full_page: bool = False,
scroll_delay: float = 0.2,
max_scroll_steps: Optional[int] = None,
process_iframes: bool = False,
remove_overlay_elements: bool = False,
simulate_user: bool = False,
@@ -1170,6 +1173,7 @@ class CrawlerRunConfig():
self.ignore_body_visibility = ignore_body_visibility
self.scan_full_page = scan_full_page
self.scroll_delay = scroll_delay
self.max_scroll_steps = max_scroll_steps
self.process_iframes = process_iframes
self.remove_overlay_elements = remove_overlay_elements
self.simulate_user = simulate_user
@@ -1387,6 +1391,7 @@ class CrawlerRunConfig():
ignore_body_visibility=kwargs.get("ignore_body_visibility", True),
scan_full_page=kwargs.get("scan_full_page", False),
scroll_delay=kwargs.get("scroll_delay", 0.2),
max_scroll_steps=kwargs.get("max_scroll_steps"),
process_iframes=kwargs.get("process_iframes", False),
remove_overlay_elements=kwargs.get("remove_overlay_elements", False),
simulate_user=kwargs.get("simulate_user", False),
@@ -1499,6 +1504,7 @@ class CrawlerRunConfig():
"ignore_body_visibility": self.ignore_body_visibility,
"scan_full_page": self.scan_full_page,
"scroll_delay": self.scroll_delay,
"max_scroll_steps": self.max_scroll_steps,
"process_iframes": self.process_iframes,
"remove_overlay_elements": self.remove_overlay_elements,
"simulate_user": self.simulate_user,

52
crawl4ai/async_crawler_strategy.py
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@@ -469,9 +469,15 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
console_messages=captured_console,
)
elif url.startswith("raw:") or url.startswith("raw://"):
#####
# Since both "raw:" and "raw://" start with "raw:", the first condition is always true for both, so "raw://" will be sliced as "//...", which is incorrect.
# Fix: Check for "raw://" first, then "raw:"
# Also, the prefix "raw://" is actually 6 characters long, not 7, so it should be sliced accordingly: url[6:]
#####
elif url.startswith("raw://") or url.startswith("raw:"):
# Process raw HTML content
raw_html = url[4:] if url[:4] == "raw:" else url[7:]
# raw_html = url[4:] if url[:4] == "raw:" else url[7:]
raw_html = url[6:] if url.startswith("raw://") else url[4:]
html = raw_html
if config.screenshot:
screenshot_data = await self._generate_screenshot_from_html(html)
@@ -930,7 +936,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# Handle full page scanning
if config.scan_full_page:
await self._handle_full_page_scan(page, config.scroll_delay)
# await self._handle_full_page_scan(page, config.scroll_delay)
await self._handle_full_page_scan(page, config.scroll_delay, config.max_scroll_steps)
# Handle virtual scroll if configured
if config.virtual_scroll_config:
@@ -1122,7 +1129,8 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# Close the page
await page.close()
async def _handle_full_page_scan(self, page: Page, scroll_delay: float = 0.1):
# async def _handle_full_page_scan(self, page: Page, scroll_delay: float = 0.1):
async def _handle_full_page_scan(self, page: Page, scroll_delay: float = 0.1, max_scroll_steps: Optional[int] = None):
"""
Helper method to handle full page scanning.
@@ -1137,6 +1145,7 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
Args:
page (Page): The Playwright page object
scroll_delay (float): The delay between page scrolls
max_scroll_steps (Optional[int]): Maximum number of scroll steps to perform. If None, scrolls until end.
"""
try:
@@ -1161,9 +1170,21 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
dimensions = await self.get_page_dimensions(page)
total_height = dimensions["height"]
scroll_step_count = 0
while current_position < total_height:
####
# NEW FEATURE: Check if we've reached the maximum allowed scroll steps
# This prevents infinite scrolling on very long pages or infinite scroll scenarios
# If max_scroll_steps is None, this check is skipped (unlimited scrolling - original behavior)
####
if max_scroll_steps is not None and scroll_step_count >= max_scroll_steps:
break
current_position = min(current_position + viewport_height, total_height)
await self.safe_scroll(page, 0, current_position, delay=scroll_delay)
# Increment the step counter for max_scroll_steps tracking
scroll_step_count += 1
# await page.evaluate(f"window.scrollTo(0, {current_position})")
# await asyncio.sleep(scroll_delay)
@@ -1804,12 +1825,31 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
# then wait for the new page to load before continuing
result = None
try:
# OLD VERSION:
# result = await page.evaluate(
# f"""
# (async () => {{
# try {{
# const script_result = {script};
# return {{ success: true, result: script_result }};
# }} catch (err) {{
# return {{ success: false, error: err.toString(), stack: err.stack }};
# }}
# }})();
# """
# )
# """ NEW VERSION:
# When {script} contains statements (e.g., const link = …; link.click();),
# this forms invalid JavaScript, causing Playwright execution error: SyntaxError: Unexpected token 'const'.
# """
result = await page.evaluate(
f"""
(async () => {{
try {{
const script_result = {script};
return {{ success: true, result: script_result }};
return await (async () => {{
{script}
}})();
}} catch (err) {{
return {{ success: false, error: err.toString(), stack: err.stack }};
}}

28
crawl4ai/deep_crawling/filters.py
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@@ -227,10 +227,21 @@ class URLPatternFilter(URLFilter):
# Prefix check (/foo/*)
if self._simple_prefixes:
path = url.split("?")[0]
if any(path.startswith(p) for p in self._simple_prefixes):
result = True
self._update_stats(result)
return not result if self._reverse else result
# if any(path.startswith(p) for p in self._simple_prefixes):
# result = True
# self._update_stats(result)
# return not result if self._reverse else result
####
# Modified the prefix matching logic to ensure path boundary checking:
# - Check if the matched prefix is followed by a path separator (`/`), query parameter (`?`), fragment (`#`), or is at the end of the path
# - This ensures `/api/` only matches complete path segments, not substrings like `/apiv2/`
####
for prefix in self._simple_prefixes:
if path.startswith(prefix):
if len(path) == len(prefix) or path[len(prefix)] in ['/', '?', '#']:
result = True
self._update_stats(result)
return not result if self._reverse else result
# Complex patterns
if self._path_patterns:
@@ -337,6 +348,15 @@ class ContentTypeFilter(URLFilter):
"sqlite": "application/vnd.sqlite3",
# Placeholder
"unknown": "application/octet-stream", # Fallback for unknown file types
# php
"php": "application/x-httpd-php",
"php3": "application/x-httpd-php",
"php4": "application/x-httpd-php",
"php5": "application/x-httpd-php",
"php7": "application/x-httpd-php",
"phtml": "application/x-httpd-php",
"phps": "application/x-httpd-php-source",
}
@staticmethod

2
deploy/docker/c4ai-doc-context.md
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@@ -332,7 +332,7 @@ The `clone()` method:
### Key fields to note
1. **`provider`**:
- Which LLM provoder to use.
- Which LLM provider to use.
- Possible values are `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)*
2. **`api_token`**:

72
docs/examples/llm_extraction_openai_pricing.py
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@@ -1,43 +1,55 @@
from crawl4ai import LLMConfig
from crawl4ai import AsyncWebCrawler, LLMExtractionStrategy
import asyncio
import os
import json
from pydantic import BaseModel, Field
url = "https://openai.com/api/pricing/"
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LLMConfig, BrowserConfig, CacheMode
from crawl4ai.extraction_strategy import LLMExtractionStrategy
from typing import Dict
import os
class OpenAIModelFee(BaseModel):
model_name: str = Field(..., description="Name of the OpenAI model.")
input_fee: str = Field(..., description="Fee for input token for the OpenAI model.")
output_fee: str = Field(
..., description="Fee for output token for the OpenAI model."
output_fee: str = Field(..., description="Fee for output token for the OpenAI model.")
async def extract_structured_data_using_llm(provider: str, api_token: str = None, extra_headers: Dict[str, str] = None):
print(f"\n--- Extracting Structured Data with {provider} ---")
if api_token is None and provider != "ollama":
print(f"API token is required for {provider}. Skipping this example.")
return
browser_config = BrowserConfig(headless=True)
extra_args = {"temperature": 0, "top_p": 0.9, "max_tokens": 2000}
if extra_headers:
extra_args["extra_headers"] = extra_headers
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
word_count_threshold=1,
page_timeout=80000,
extraction_strategy=LLMExtractionStrategy(
llm_config=LLMConfig(provider=provider, api_token=api_token),
schema=OpenAIModelFee.model_json_schema(),
extraction_type="schema",
instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens.
Do not miss any models in the entire content.""",
extra_args=extra_args,
),
)
async def main():
# Use AsyncWebCrawler
async with AsyncWebCrawler() as crawler:
async with AsyncWebCrawler(config=browser_config) as crawler:
result = await crawler.arun(
url=url,
word_count_threshold=1,
extraction_strategy=LLMExtractionStrategy(
# provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'),
llm_config=LLMConfig(provider="groq/llama-3.1-70b-versatile", api_token=os.getenv("GROQ_API_KEY")),
schema=OpenAIModelFee.model_json_schema(),
extraction_type="schema",
instruction="From the crawled content, extract all mentioned model names along with their "
"fees for input and output tokens. Make sure not to miss anything in the entire content. "
"One extracted model JSON format should look like this: "
'{ "model_name": "GPT-4", "input_fee": "US$10.00 / 1M tokens", "output_fee": "US$30.00 / 1M tokens" }',
),
url="https://openai.com/api/pricing/",
config=crawler_config
)
print("Success:", result.success)
model_fees = json.loads(result.extracted_content)
print(len(model_fees))
with open(".data/data.json", "w", encoding="utf-8") as f:
f.write(result.extracted_content)
print(result.extracted_content)
asyncio.run(main())
if __name__ == "__main__":
asyncio.run(
extract_structured_data_using_llm(
provider="openai/gpt-4o", api_token=os.getenv("OPENAI_API_KEY")
)
)

201
docs/md_v2/advanced/pdf-parsing.md Normal file
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@@ -0,0 +1,201 @@
# PDF Processing Strategies
Crawl4AI provides specialized strategies for handling and extracting content from PDF files. These strategies allow you to seamlessly integrate PDF processing into your crawling workflows, whether the PDFs are hosted online or stored locally.
## `PDFCrawlerStrategy`
### Overview
`PDFCrawlerStrategy` is an implementation of `AsyncCrawlerStrategy` designed specifically for PDF documents. Instead of interpreting the input URL as an HTML webpage, this strategy treats it as a pointer to a PDF file. It doesn't perform deep crawling or HTML parsing itself but rather prepares the PDF source for a dedicated PDF scraping strategy. Its primary role is to identify the PDF source (web URL or local file) and pass it along the processing pipeline in a way that `AsyncWebCrawler` can handle.
### When to Use
Use `PDFCrawlerStrategy` when you need to:
- Process PDF files using the `AsyncWebCrawler`.
- Handle PDFs from both web URLs (e.g., `https://example.com/document.pdf`) and local file paths (e.g., `file:///path/to/your/document.pdf`).
- Integrate PDF content extraction into a unified `CrawlResult` object, allowing consistent handling of PDF data alongside web page data.
### Key Methods and Their Behavior
- **`__init__(self, logger: AsyncLogger = None)`**:
- Initializes the strategy.
- `logger`: An optional `AsyncLogger` instance (from `crawl4ai.async_logger`) for logging purposes.
- **`async crawl(self, url: str, **kwargs) -> AsyncCrawlResponse`**:
- This method is called by the `AsyncWebCrawler` during the `arun` process.
- It takes the `url` (which should point to a PDF) and creates a minimal `AsyncCrawlResponse`.
- The `html` attribute of this response is typically empty or a placeholder, as the actual PDF content processing is deferred to the `PDFContentScrapingStrategy` (or a similar PDF-aware scraping strategy).
- It sets `response_headers` to indicate "application/pdf" and `status_code` to 200.
- **`async close(self)`**:
- A method for cleaning up any resources used by the strategy. For `PDFCrawlerStrategy`, this is usually minimal.
- **`async __aenter__(self)` / `async __aexit__(self, exc_type, exc_val, exc_tb)`**:
- Enables asynchronous context management for the strategy, allowing it to be used with `async with`.
### Example Usage
```python
import asyncio
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.processors.pdf import PDFCrawlerStrategy, PDFContentScrapingStrategy
async def main():
# Initialize the PDF crawler strategy
pdf_crawler_strategy = PDFCrawlerStrategy()
# PDFCrawlerStrategy is typically used in conjunction with PDFContentScrapingStrategy
# The scraping strategy handles the actual PDF content extraction
pdf_scraping_strategy = PDFContentScrapingStrategy()
run_config = CrawlerRunConfig(scraping_strategy=pdf_scraping_strategy)
async with AsyncWebCrawler(crawler_strategy=pdf_crawler_strategy) as crawler:
# Example with a remote PDF URL
pdf_url = "https://arxiv.org/pdf/2310.06825.pdf" # A public PDF from arXiv
print(f"Attempting to process PDF: {pdf_url}")
result = await crawler.arun(url=pdf_url, config=run_config)
if result.success:
print(f"Successfully processed PDF: {result.url}")
print(f"Metadata Title: {result.metadata.get('title', 'N/A')}")
# Further processing of result.markdown, result.media, etc.
# would be done here, based on what PDFContentScrapingStrategy extracts.
if result.markdown and hasattr(result.markdown, 'raw_markdown'):
print(f"Extracted text (first 200 chars): {result.markdown.raw_markdown[:200]}...")
else:
print("No markdown (text) content extracted.")
else:
print(f"Failed to process PDF: {result.error_message}")
if __name__ == "__main__":
asyncio.run(main())
```
### Pros and Cons
**Pros:**
- Enables `AsyncWebCrawler` to handle PDF sources directly using familiar `arun` calls.
- Provides a consistent interface for specifying PDF sources (URLs or local paths).
- Abstracts the source handling, allowing a separate scraping strategy to focus on PDF content parsing.
**Cons:**
- Does not perform any PDF data extraction itself; it strictly relies on a compatible scraping strategy (like `PDFContentScrapingStrategy`) to process the PDF.
- Has limited utility on its own; most of its value comes from being paired with a PDF-specific content scraping strategy.
---
## `PDFContentScrapingStrategy`
### Overview
`PDFContentScrapingStrategy` is an implementation of `ContentScrapingStrategy` designed to extract text, metadata, and optionally images from PDF documents. It is intended to be used in conjunction with a crawler strategy that can provide it with a PDF source, such as `PDFCrawlerStrategy`. This strategy uses the `NaivePDFProcessorStrategy` internally to perform the low-level PDF parsing.
### When to Use
Use `PDFContentScrapingStrategy` when your `AsyncWebCrawler` (often configured with `PDFCrawlerStrategy`) needs to:
- Extract textual content page by page from a PDF document.
- Retrieve standard metadata embedded within the PDF (e.g., title, author, subject, creation date, page count).
- Optionally, extract images contained within the PDF pages. These images can be saved to a local directory or made available for further processing.
- Produce a `ScrapingResult` that can be converted into a `CrawlResult`, making PDF content accessible in a manner similar to HTML web content (e.g., text in `result.markdown`, metadata in `result.metadata`).
### Key Configuration Attributes
When initializing `PDFContentScrapingStrategy`, you can configure its behavior using the following attributes:
- **`extract_images: bool = False`**: If `True`, the strategy will attempt to extract images from the PDF.
- **`save_images_locally: bool = False`**: If `True` (and `extract_images` is also `True`), extracted images will be saved to disk in the `image_save_dir`. If `False`, image data might be available in another form (e.g., base64, depending on the underlying processor) but not saved as separate files by this strategy.
- **`image_save_dir: str = None`**: Specifies the directory where extracted images should be saved if `save_images_locally` is `True`. If `None`, a default or temporary directory might be used.
- **`batch_size: int = 4`**: Defines how many PDF pages are processed in a single batch. This can be useful for managing memory when dealing with very large PDF documents.
- **`logger: AsyncLogger = None`**: An optional `AsyncLogger` instance for logging.
### Key Methods and Their Behavior
- **`__init__(self, save_images_locally: bool = False, extract_images: bool = False, image_save_dir: str = None, batch_size: int = 4, logger: AsyncLogger = None)`**:
- Initializes the strategy with configurations for image handling, batch processing, and logging. It sets up an internal `NaivePDFProcessorStrategy` instance which performs the actual PDF parsing.
- **`scrap(self, url: str, html: str, **params) -> ScrapingResult`**:
- This is the primary synchronous method called by the crawler (via `ascrap`) to process the PDF.
- `url`: The path or URL to the PDF file (provided by `PDFCrawlerStrategy` or similar).
- `html`: Typically an empty string when used with `PDFCrawlerStrategy`, as the content is a PDF, not HTML.
- It first ensures the PDF is accessible locally (downloads it to a temporary file if `url` is remote).
- It then uses its internal PDF processor to extract text, metadata, and images (if configured).
- The extracted information is compiled into a `ScrapingResult` object:
- `cleaned_html`: Contains an HTML-like representation of the PDF, where each page's content is often wrapped in a `<div>` with page number information.
- `media`: A dictionary where `media["images"]` will contain information about extracted images if `extract_images` was `True`.
- `links`: A dictionary where `links["urls"]` can contain URLs found within the PDF content.
- `metadata`: A dictionary holding PDF metadata (e.g., title, author, num_pages).
- **`async ascrap(self, url: str, html: str, **kwargs) -> ScrapingResult`**:
- The asynchronous version of `scrap`. Under the hood, it typically runs the synchronous `scrap` method in a separate thread using `asyncio.to_thread` to avoid blocking the event loop.
- **`_get_pdf_path(self, url: str) -> str`**:
- A private helper method to manage PDF file access. If the `url` is remote (http/https), it downloads the PDF to a temporary local file and returns its path. If `url` indicates a local file (`file://` or a direct path), it resolves and returns the local path.
### Example Usage
```python
import asyncio
from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
from crawl4ai.processors.pdf import PDFCrawlerStrategy, PDFContentScrapingStrategy
import os # For creating image directory
async def main():
# Define the directory for saving extracted images
image_output_dir = "./my_pdf_images"
os.makedirs(image_output_dir, exist_ok=True)
# Configure the PDF content scraping strategy
# Enable image extraction and specify where to save them
pdf_scraping_cfg = PDFContentScrapingStrategy(
extract_images=True,
save_images_locally=True,
image_save_dir=image_output_dir,
batch_size=2 # Process 2 pages at a time for demonstration
)
# The PDFCrawlerStrategy is needed to tell AsyncWebCrawler how to "crawl" a PDF
pdf_crawler_cfg = PDFCrawlerStrategy()
# Configure the overall crawl run
run_cfg = CrawlerRunConfig(
scraping_strategy=pdf_scraping_cfg # Use our PDF scraping strategy
)
# Initialize the crawler with the PDF-specific crawler strategy
async with AsyncWebCrawler(crawler_strategy=pdf_crawler_cfg) as crawler:
pdf_url = "https://arxiv.org/pdf/2310.06825.pdf" # Example PDF
print(f"Starting PDF processing for: {pdf_url}")
result = await crawler.arun(url=pdf_url, config=run_cfg)
if result.success:
print("\n--- PDF Processing Successful ---")
print(f"Processed URL: {result.url}")
print("\n--- Metadata ---")
for key, value in result.metadata.items():
print(f" {key.replace('_', ' ').title()}: {value}")
if result.markdown and hasattr(result.markdown, 'raw_markdown'):
print(f"\n--- Extracted Text (Markdown Snippet) ---")
print(result.markdown.raw_markdown[:500].strip() + "...")
else:
print("\nNo text (markdown) content extracted.")
if result.media and result.media.get("images"):
print(f"\n--- Image Extraction ---")
print(f"Extracted {len(result.media['images'])} image(s).")
for i, img_info in enumerate(result.media["images"][:2]): # Show info for first 2 images
print(f" Image {i+1}:")
print(f" Page: {img_info.get('page')}")
print(f" Format: {img_info.get('format', 'N/A')}")
if img_info.get('path'):
print(f" Saved at: {img_info.get('path')}")
else:
print("\nNo images were extracted (or extract_images was False).")
else:
print(f"\n--- PDF Processing Failed ---")
print(f"Error: {result.error_message}")
if __name__ == "__main__":
asyncio.run(main())
```
### Pros and Cons
**Pros:**
- Provides a comprehensive way to extract text, metadata, and (optionally) images from PDF documents.
- Handles both remote PDFs (via URL) and local PDF files.
- Configurable image extraction allows saving images to disk or accessing their data.
- Integrates smoothly with the `CrawlResult` object structure, making PDF-derived data accessible in a way consistent with web-scraped data.
- The `batch_size` parameter can help in managing memory consumption when processing large or numerous PDF pages.
**Cons:**
- Extraction quality and performance can vary significantly depending on the PDF's complexity, encoding, and whether it's image-based (scanned) or text-based.
- Image extraction can be resource-intensive (both CPU and disk space if `save_images_locally` is true).
- Relies on `NaivePDFProcessorStrategy` internally, which might have limitations with very complex layouts, encrypted PDFs, or forms compared to more sophisticated PDF parsing libraries. Scanned PDFs will not yield text unless an OCR step is performed (which is not part of this strategy by default).
- Link extraction from PDFs can be basic and depends on how hyperlinks are embedded in the document.

2
docs/md_v2/api/parameters.md
View File

@@ -298,7 +298,7 @@ LLMConfig is useful to pass LLM provider config to strategies and functions that
## 3.1 Parameters
| **Parameter** | **Type / Default** | **What It Does** |
|-----------------------|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
| **`provider`** | `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)* | Which LLM provoder to use.
| **`provider`** | `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)* | Which LLM provider to use.
| **`api_token`** |1.Optional. When not provided explicitly, api_token will be read from environment variables based on provider. For example: If a gemini model is passed as provider then,`"GEMINI_API_KEY"` will be read from environment variables <br/> 2. API token of LLM provider <br/> eg: `api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"` <br/> 3. Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"` | API token to use for the given provider
| **`base_url`** |Optional. Custom API endpoint | If your provider has a custom endpoint

2
docs/md_v2/core/browser-crawler-config.md
View File

@@ -252,7 +252,7 @@ The `clone()` method:
### Key fields to note
1. **`provider`**:
- Which LLM provoder to use.
- Which LLM provider to use.
- Possible values are `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)*
2. **`api_token`**:

10
docs/md_v2/extraction/llm-strategies.md
View File

@@ -218,7 +218,7 @@ import json
import asyncio
from typing import List
from pydantic import BaseModel, Field
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LLMConfig
from crawl4ai import LLMExtractionStrategy
class Entity(BaseModel):
@@ -238,8 +238,8 @@ class KnowledgeGraph(BaseModel):
async def main():
# LLM extraction strategy
llm_strat = LLMExtractionStrategy(
llmConfig = LlmConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
schema=KnowledgeGraph.schema_json(),
llmConfig = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
schema=KnowledgeGraph.model_json_schema(),
extraction_type="schema",
instruction="Extract entities and relationships from the content. Return valid JSON.",
chunk_token_threshold=1400,
@@ -258,6 +258,10 @@ async def main():
url = "https://www.nbcnews.com/business"
result = await crawler.arun(url=url, config=crawl_config)
print("--- LLM RAW RESPONSE ---")
print(result.extracted_content)
print("--- END LLM RAW RESPONSE ---")
if result.success:
with open("kb_result.json", "w", encoding="utf-8") as f:
f.write(result.extracted_content)

1
mkdocs.yml
View File

@@ -48,6 +48,7 @@ nav:
- "Identity Based Crawling": "advanced/identity-based-crawling.md"
- "SSL Certificate": "advanced/ssl-certificate.md"
- "Network & Console Capture": "advanced/network-console-capture.md"
- "PDF Parsing": "advanced/pdf-parsing.md"
- Extraction:
- "LLM-Free Strategies": "extraction/no-llm-strategies.md"
- "LLM Strategies": "extraction/llm-strategies.md"

75
tests/deep_crwaling/test_filter.py Normal file
View File

@@ -0,0 +1,75 @@
# // File: tests/deep_crawling/test_filters.py
import pytest
from urllib.parse import urlparse
from crawl4ai import ContentTypeFilter, URLFilter
# Minimal URLFilter base class stub if not already importable directly for tests
# In a real scenario, this would be imported from the library
if not hasattr(URLFilter, '_update_stats'): # Check if it's a basic stub
class URLFilter: # Basic stub for testing if needed
def __init__(self, name=None): self.name = name
def apply(self, url: str) -> bool: raise NotImplementedError
def _update_stats(self, passed: bool): pass # Mock implementation
# Assume ContentTypeFilter is structured as discussed. If its definition is not fully
# available for direct import in the test environment, a more elaborate stub or direct
# instantiation of the real class (if possible) would be needed.
# For this example, we assume ContentTypeFilter can be imported and used.
class TestContentTypeFilter:
@pytest.mark.parametrize(
"url, allowed_types, expected",
[
# Existing tests (examples)
("http://example.com/page.html", ["text/html"], True),
("http://example.com/page.json", ["application/json"], True),
("http://example.com/image.png", ["text/html"], False),
("http://example.com/document.pdf", ["application/pdf"], True),
("http://example.com/page", ["text/html"], True), # No extension, allowed
("http://example.com/page", ["text/html"], False), # No extension, disallowed
("http://example.com/page.unknown", ["text/html"], False), # Unknown extension
# Tests for PHP extensions
("http://example.com/index.php", ["application/x-httpd-php"], True),
("http://example.com/script.php3", ["application/x-httpd-php"], True),
("http://example.com/legacy.php4", ["application/x-httpd-php"], True),
("http://example.com/main.php5", ["application/x-httpd-php"], True),
("http://example.com/api.php7", ["application/x-httpd-php"], True),
("http://example.com/index.phtml", ["application/x-httpd-php"], True),
("http://example.com/source.phps", ["application/x-httpd-php-source"], True),
# Test rejection of PHP extensions
("http://example.com/index.php", ["text/html"], False),
("http://example.com/script.php3", ["text/plain"], False),
("http://example.com/source.phps", ["application/x-httpd-php"], False), # Mismatch MIME
("http://example.com/source.php", ["application/x-httpd-php-source"], False), # Mismatch MIME for .php
# Test case-insensitivity of extensions in URL
("http://example.com/PAGE.HTML", ["text/html"], True),
("http://example.com/INDEX.PHP", ["application/x-httpd-php"], True),
("http://example.com/SOURCE.PHPS", ["application/x-httpd-php-source"], True),
# Test case-insensitivity of allowed_types
("http://example.com/index.php", ["APPLICATION/X-HTTPD-PHP"], True),
],
)
def test_apply(self, url, allowed_types, expected):
content_filter = ContentTypeFilter(
allowed_types=allowed_types
)
assert content_filter.apply(url) == expected
@pytest.mark.parametrize(
"url, expected_extension",
[
("http://example.com/file.html", "html"),
("http://example.com/file.tar.gz", "gz"),
("http://example.com/path/", ""),
("http://example.com/nodot", ""),
("http://example.com/.config", "config"), # hidden file with extension
("http://example.com/path/to/archive.BIG.zip", "zip"), # Case test
]
)
def test_extract_extension(self, url, expected_extension):
# Test the static method directly
assert ContentTypeFilter._extract_extension(url) == expected_extension

25
tests/general/test_async_crawler_strategy.py
View File

@@ -15,6 +15,24 @@ CRAWL4AI_HOME_DIR = Path(os.path.expanduser("~")).joinpath(".crawl4ai")
if not CRAWL4AI_HOME_DIR.joinpath("profiles", "test_profile").exists():
CRAWL4AI_HOME_DIR.joinpath("profiles", "test_profile").mkdir(parents=True)
@pytest.fixture
def basic_html():
return """
<html lang="en">
<head>
<title>Basic HTML</title>
</head>
<body>
<h1>Main Heading</h1>
<main>
<div class="container">
<p>Basic HTML document for testing purposes.</p>
</div>
</main>
</body>
</html>
"""
# Test Config Files
@pytest.fixture
def basic_browser_config():
@@ -325,6 +343,13 @@ async def test_stealth_mode(crawler_strategy):
)
assert response.status_code == 200
@pytest.mark.asyncio
@pytest.mark.parametrize("prefix", ("raw:", "raw://"))
async def test_raw_urls(crawler_strategy, basic_html, prefix):
url = f"{prefix}{basic_html}"
response = await crawler_strategy.crawl(url, CrawlerRunConfig())
assert response.html == basic_html
# Error Handling Tests
@pytest.mark.asyncio
async def test_invalid_url():

34
tests/general/test_download_file.py Normal file
View File

@@ -0,0 +1,34 @@
import asyncio
from crawl4ai import CrawlerRunConfig, AsyncWebCrawler, BrowserConfig
from pathlib import Path
import os
async def test_basic_download():
# Custom folder (otherwise defaults to ~/.crawl4ai/downloads)
downloads_path = os.path.join(Path.home(), ".crawl4ai", "downloads")
os.makedirs(downloads_path, exist_ok=True)
browser_config = BrowserConfig(
accept_downloads=True,
downloads_path=downloads_path
)
async with AsyncWebCrawler(config=browser_config) as crawler:
run_config = CrawlerRunConfig(
js_code="""
const link = document.querySelector('a[href$=".exe"]');
if (link) { link.click(); }
""",
delay_before_return_html=5
)
result = await crawler.arun("https://www.python.org/downloads/", config=run_config)
if result.downloaded_files:
print("Downloaded files:")
for file_path in result.downloaded_files:
print("•", file_path)
else:
print("No files downloaded.")
if __name__ == "__main__":
asyncio.run(test_basic_download())

115
tests/general/test_max_scroll.py Normal file
View File

@@ -0,0 +1,115 @@
"""
Sample script to test the max_scroll_steps parameter implementation
"""
import asyncio
import os
import sys
# Get the grandparent directory
grandparent_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(grandparent_dir)
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
from crawl4ai import AsyncWebCrawler
from crawl4ai.async_configs import CrawlerRunConfig
async def test_max_scroll_steps():
"""
Test the max_scroll_steps parameter with different configurations
"""
print("🚀 Testing max_scroll_steps parameter implementation")
print("=" * 60)
async with AsyncWebCrawler(verbose=True) as crawler:
# Test 1: Without max_scroll_steps (unlimited scrolling)
print("\\n📋 Test 1: Unlimited scrolling (max_scroll_steps=None)")
config1 = CrawlerRunConfig(
scan_full_page=True,
scroll_delay=0.1,
max_scroll_steps=None, # Default behavior
verbose=True
)
print(f"Config: scan_full_page={config1.scan_full_page}, max_scroll_steps={config1.max_scroll_steps}")
try:
result1 = await crawler.arun(
url="https://example.com", # Simple page for testing
config=config1
)
print(f"✅ Test 1 Success: Crawled {len(result1.markdown)} characters")
except Exception as e:
print(f"❌ Test 1 Failed: {e}")
# Test 2: With limited scroll steps
print("\\n📋 Test 2: Limited scrolling (max_scroll_steps=3)")
config2 = CrawlerRunConfig(
scan_full_page=True,
scroll_delay=0.1,
max_scroll_steps=3, # Limit to 3 scroll steps
verbose=True
)
print(f"Config: scan_full_page={config2.scan_full_page}, max_scroll_steps={config2.max_scroll_steps}")
try:
result2 = await crawler.arun(
url="https://techcrunch.com/", # Another test page
config=config2
)
print(f"✅ Test 2 Success: Crawled {len(result2.markdown)} characters")
except Exception as e:
print(f"❌ Test 2 Failed: {e}")
# Test 3: Test serialization/deserialization
print("\\n📋 Test 3: Configuration serialization test")
config3 = CrawlerRunConfig(
scan_full_page=True,
max_scroll_steps=5,
scroll_delay=0.2
)
# Test to_dict
config_dict = config3.to_dict()
print(f"Serialized max_scroll_steps: {config_dict.get('max_scroll_steps')}")
# Test from_kwargs
config4 = CrawlerRunConfig.from_kwargs({
'scan_full_page': True,
'max_scroll_steps': 7,
'scroll_delay': 0.3
})
print(f"Deserialized max_scroll_steps: {config4.max_scroll_steps}")
print("✅ Test 3 Success: Serialization works correctly")
# Test 4: Edge case - max_scroll_steps = 0
print("\\n📋 Test 4: Edge case (max_scroll_steps=0)")
config5 = CrawlerRunConfig(
scan_full_page=True,
max_scroll_steps=0, # Should not scroll at all
verbose=True
)
try:
result5 = await crawler.arun(
url="https://techcrunch.com/",
config=config5
)
print(f"✅ Test 4 Success: No scrolling performed, crawled {len(result5.markdown)} characters")
except Exception as e:
print(f"❌ Test 4 Failed: {e}")
print("\\n" + "=" * 60)
print("🎉 All tests completed!")
print("\\nThe max_scroll_steps parameter is working correctly:")
print("- None: Unlimited scrolling (default behavior)")
print("- Positive integer: Limits scroll steps to that number")
print("- 0: No scrolling performed")
print("- Properly serializes/deserializes in config")
if __name__ == "__main__":
print("Starting max_scroll_steps test...")
asyncio.run(test_max_scroll_steps())

85
tests/general/test_url_pattern.py Normal file
View File

@@ -0,0 +1,85 @@
import sys
import os
# Get the grandparent directory
grandparent_dir = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
sys.path.append(grandparent_dir)
__location__ = os.path.realpath(os.path.join(os.getcwd(), os.path.dirname(__file__)))
import asyncio
from crawl4ai.deep_crawling.filters import URLPatternFilter
def test_prefix_boundary_matching():
"""Test that prefix patterns respect path boundaries"""
print("=== Testing URLPatternFilter Prefix Boundary Fix ===")
filter_obj = URLPatternFilter(patterns=['https://langchain-ai.github.io/langgraph/*'])
test_cases = [
('https://langchain-ai.github.io/langgraph/', True),
('https://langchain-ai.github.io/langgraph/concepts/', True),
('https://langchain-ai.github.io/langgraph/tutorials/', True),
('https://langchain-ai.github.io/langgraph?param=1', True),
('https://langchain-ai.github.io/langgraph#section', True),
('https://langchain-ai.github.io/langgraphjs/', False),
('https://langchain-ai.github.io/langgraphjs/concepts/', False),
('https://other-site.com/langgraph/', False),
]
all_passed = True
for url, expected in test_cases:
result = filter_obj.apply(url)
status = "PASS" if result == expected else "FAIL"
if result != expected:
all_passed = False
print(f"{status:4} | Expected: {expected:5} | Got: {result:5} | {url}")
return all_passed
def test_edge_cases():
"""Test edge cases for path boundary matching"""
print("\n=== Testing Edge Cases ===")
test_patterns = [
('/api/*', [
('/api/', True),
('/api/v1', True),
('/api?param=1', True),
('/apiv2/', False),
('/api_old/', False),
]),
('*/docs/*', [
('example.com/docs/', True),
('example.com/docs/guide', True),
('example.com/documentation/', False),
('example.com/docs_old/', False),
]),
]
all_passed = True
for pattern, test_cases in test_patterns:
print(f"\nPattern: {pattern}")
filter_obj = URLPatternFilter(patterns=[pattern])
for url, expected in test_cases:
result = filter_obj.apply(url)
status = "PASS" if result == expected else "FAIL"
if result != expected:
all_passed = False
print(f" {status:4} | Expected: {expected:5} | Got: {result:5} | {url}")
return all_passed
if __name__ == "__main__":
test1_passed = test_prefix_boundary_matching()
test2_passed = test_edge_cases()
if test1_passed and test2_passed:
print("\n✅ All tests passed!")
sys.exit(0)
else:
print("\n❌ Some tests failed!")
sys.exit(1)