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docs-llm-s
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bugfix/aru
| Author | SHA1 | Date | |
|---|---|---|---|
|
c003cb6e4f |
@@ -1047,14 +1047,28 @@ class AsyncPlaywrightCrawlerStrategy(AsyncCrawlerStrategy):
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raise e
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finally:
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# If no session_id is given we should close the page
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# Clean up page after crawl completes
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# For managed CDP browsers, close pages that are not part of a session to prevent memory leaks
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all_contexts = page.context.browser.contexts
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total_pages = sum(len(context.pages) for context in all_contexts)
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total_pages = sum(len(context.pages) for context in all_contexts)
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should_close_page = False
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if config.session_id:
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# Session pages are kept alive for reuse
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pass
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elif total_pages <= 1 and (self.browser_config.use_managed_browser or self.browser_config.headless):
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elif self.browser_config.use_managed_browser:
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# For managed browsers (CDP), close non-session pages to prevent tab accumulation
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# This is especially important for arun_many() with multiple concurrent crawls
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should_close_page = True
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elif total_pages <= 1 and self.browser_config.headless:
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# Keep the last page in headless mode to avoid closing the browser
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pass
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else:
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# For non-managed browsers, close the page
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should_close_page = True
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if should_close_page:
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# Detach listeners before closing to prevent potential errors during close
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if config.capture_network_requests:
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page.remove_listener("request", handle_request_capture)
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@@ -617,17 +617,7 @@ class AsyncWebCrawler:
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else config.chunking_strategy
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)
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sections = chunking.chunk(content)
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# extracted_content = config.extraction_strategy.run(url, sections)
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# Use async version if available for better parallelism
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if hasattr(config.extraction_strategy, 'arun'):
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extracted_content = await config.extraction_strategy.arun(url, sections)
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else:
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# Fallback to sync version run in thread pool to avoid blocking
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extracted_content = await asyncio.to_thread(
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config.extraction_strategy.run, url, sections
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)
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extracted_content = config.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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@@ -369,9 +369,6 @@ class ManagedBrowser:
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]
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if self.headless:
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flags.append("--headless=new")
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# Add viewport flag if specified in config
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if self.browser_config.viewport_height and self.browser_config.viewport_width:
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flags.append(f"--window-size={self.browser_config.viewport_width},{self.browser_config.viewport_height}")
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# merge common launch flags
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flags.extend(self.build_browser_flags(self.browser_config))
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elif self.browser_type == "firefox":
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@@ -1038,34 +1035,20 @@ class BrowserManager:
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self.sessions[crawlerRunConfig.session_id] = (context, page, time.time())
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return page, context
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# If using a managed browser, just grab the shared default_context
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# If using a managed browser, reuse the default context and create new pages
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if self.config.use_managed_browser:
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context = self.default_context
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if self.config.storage_state:
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context = await self.create_browser_context(crawlerRunConfig)
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ctx = self.default_context # default context, one window only
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# Clone runtime state from storage to the shared context
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ctx = self.default_context
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ctx = await clone_runtime_state(context, ctx, crawlerRunConfig, self.config)
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# Avoid concurrent new_page on shared persistent context
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# See GH-1198: context.pages can be empty under races
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async with self._page_lock:
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page = await ctx.new_page()
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await self._apply_stealth_to_page(page)
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else:
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context = self.default_context
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pages = context.pages
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page = next((p for p in pages if p.url == crawlerRunConfig.url), None)
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if not page:
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if pages:
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page = pages[0]
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else:
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# Double-check under lock to avoid TOCTOU and ensure only
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# one task calls new_page when pages=[] concurrently
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async with self._page_lock:
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pages = context.pages
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if pages:
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page = pages[0]
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else:
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page = await context.new_page()
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await self._apply_stealth_to_page(page)
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# Always create a new page for concurrent safety
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# The page-level isolation prevents race conditions while sharing the same context
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async with self._page_lock:
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page = await context.new_page()
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await self._apply_stealth_to_page(page)
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else:
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# Otherwise, check if we have an existing context for this config
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config_signature = self._make_config_signature(crawlerRunConfig)
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@@ -94,20 +94,6 @@ class ExtractionStrategy(ABC):
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extracted_content.extend(future.result())
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return extracted_content
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async def arun(self, url: str, sections: List[str], *q, **kwargs) -> List[Dict[str, Any]]:
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"""
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Async version: Process sections of text in parallel using asyncio.
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Default implementation runs the sync version in a thread pool.
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Subclasses can override this for true async processing.
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:param url: The URL of the webpage.
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:param sections: List of sections (strings) to process.
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:return: A list of processed JSON blocks.
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"""
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import asyncio
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return await asyncio.to_thread(self.run, url, sections, *q, **kwargs)
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class NoExtractionStrategy(ExtractionStrategy):
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"""
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@@ -794,177 +780,6 @@ class LLMExtractionStrategy(ExtractionStrategy):
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return extracted_content
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async def aextract(self, url: str, ix: int, html: str) -> List[Dict[str, Any]]:
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"""
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Async version: Extract meaningful blocks or chunks from the given HTML using an LLM.
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How it works:
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1. Construct a prompt with variables.
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2. Make an async request to the LLM using the prompt.
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3. Parse the response and extract blocks or chunks.
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Args:
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url: The URL of the webpage.
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ix: Index of the block.
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html: The HTML content of the webpage.
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Returns:
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A list of extracted blocks or chunks.
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"""
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from .utils import aperform_completion_with_backoff
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if self.verbose:
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print(f"[LOG] Call LLM for {url} - block index: {ix}")
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variable_values = {
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"URL": url,
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"HTML": escape_json_string(sanitize_html(html)),
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}
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prompt_with_variables = PROMPT_EXTRACT_BLOCKS
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if self.instruction:
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variable_values["REQUEST"] = self.instruction
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prompt_with_variables = PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION
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if self.extract_type == "schema" and self.schema:
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variable_values["SCHEMA"] = json.dumps(self.schema, indent=2)
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prompt_with_variables = PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION
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if self.extract_type == "schema" and not self.schema:
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prompt_with_variables = PROMPT_EXTRACT_INFERRED_SCHEMA
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for variable in variable_values:
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prompt_with_variables = prompt_with_variables.replace(
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"{" + variable + "}", variable_values[variable]
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)
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try:
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response = await aperform_completion_with_backoff(
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self.llm_config.provider,
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prompt_with_variables,
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self.llm_config.api_token,
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base_url=self.llm_config.base_url,
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json_response=self.force_json_response,
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extra_args=self.extra_args,
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)
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# Track usage
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usage = TokenUsage(
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completion_tokens=response.usage.completion_tokens,
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prompt_tokens=response.usage.prompt_tokens,
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total_tokens=response.usage.total_tokens,
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completion_tokens_details=response.usage.completion_tokens_details.__dict__
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if response.usage.completion_tokens_details
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else {},
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prompt_tokens_details=response.usage.prompt_tokens_details.__dict__
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if response.usage.prompt_tokens_details
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else {},
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)
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self.usages.append(usage)
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# Update totals
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self.total_usage.completion_tokens += usage.completion_tokens
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self.total_usage.prompt_tokens += usage.prompt_tokens
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self.total_usage.total_tokens += usage.total_tokens
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try:
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content = response.choices[0].message.content
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blocks = None
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if self.force_json_response:
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blocks = json.loads(content)
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if isinstance(blocks, dict):
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if len(blocks) == 1 and isinstance(list(blocks.values())[0], list):
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blocks = list(blocks.values())[0]
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else:
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blocks = [blocks]
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elif isinstance(blocks, list):
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blocks = blocks
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else:
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blocks = extract_xml_data(["blocks"], content)["blocks"]
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blocks = json.loads(blocks)
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for block in blocks:
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block["error"] = False
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except Exception:
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parsed, unparsed = split_and_parse_json_objects(
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response.choices[0].message.content
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)
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blocks = parsed
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if unparsed:
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blocks.append(
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{"index": 0, "error": True, "tags": ["error"], "content": unparsed}
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)
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if self.verbose:
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print(
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"[LOG] Extracted",
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len(blocks),
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"blocks from URL:",
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url,
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"block index:",
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ix,
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)
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return blocks
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except Exception as e:
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if self.verbose:
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print(f"[LOG] Error in LLM extraction: {e}")
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return [
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{
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"index": ix,
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"error": True,
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"tags": ["error"],
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"content": str(e),
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}
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]
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async def arun(self, url: str, sections: List[str]) -> List[Dict[str, Any]]:
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"""
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Async version: Process sections with true parallelism using asyncio.gather.
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Args:
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url: The URL of the webpage.
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sections: List of sections (strings) to process.
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Returns:
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A list of extracted blocks or chunks.
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"""
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import asyncio
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merged_sections = self._merge(
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sections,
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self.chunk_token_threshold,
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overlap=int(self.chunk_token_threshold * self.overlap_rate),
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)
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extracted_content = []
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# Create tasks for all sections to run in parallel
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tasks = [
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self.aextract(url, ix, sanitize_input_encode(section))
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for ix, section in enumerate(merged_sections)
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]
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# Execute all tasks concurrently
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results = await asyncio.gather(*tasks, return_exceptions=True)
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# Process results
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for result in results:
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if isinstance(result, Exception):
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if self.verbose:
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print(f"Error in async extraction: {result}")
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extracted_content.append(
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{
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"index": 0,
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"error": True,
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"tags": ["error"],
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"content": str(result),
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}
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)
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else:
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extracted_content.extend(result)
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return extracted_content
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def show_usage(self) -> None:
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"""Print a detailed token usage report showing total and per-request usage."""
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print("\n=== Token Usage Summary ===")
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@@ -1825,82 +1825,6 @@ def perform_completion_with_backoff(
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# ]
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async def aperform_completion_with_backoff(
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provider,
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prompt_with_variables,
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api_token,
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json_response=False,
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base_url=None,
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**kwargs,
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):
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"""
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Async version: Perform an API completion request with exponential backoff.
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How it works:
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1. Sends an async completion request to the API.
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2. Retries on rate-limit errors with exponential delays (async).
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3. Returns the API response or an error after all retries.
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Args:
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provider (str): The name of the API provider.
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prompt_with_variables (str): The input prompt for the completion request.
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api_token (str): The API token for authentication.
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json_response (bool): Whether to request a JSON response. Defaults to False.
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base_url (Optional[str]): The base URL for the API. Defaults to None.
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**kwargs: Additional arguments for the API request.
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Returns:
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dict: The API response or an error message after all retries.
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"""
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from litellm import acompletion
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from litellm.exceptions import RateLimitError
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import asyncio
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max_attempts = 3
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base_delay = 2 # Base delay in seconds, you can adjust this based on your needs
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extra_args = {"temperature": 0.01, "api_key": api_token, "base_url": base_url}
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if json_response:
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extra_args["response_format"] = {"type": "json_object"}
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if kwargs.get("extra_args"):
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extra_args.update(kwargs["extra_args"])
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for attempt in range(max_attempts):
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try:
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response = await acompletion(
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model=provider,
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messages=[{"role": "user", "content": prompt_with_variables}],
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**extra_args,
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)
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return response # Return the successful response
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except RateLimitError as e:
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print("Rate limit error:", str(e))
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if attempt == max_attempts - 1:
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# Last attempt failed, raise the error.
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raise
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# Check if we have exhausted our max attempts
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if attempt < max_attempts - 1:
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# Calculate the delay and wait
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delay = base_delay * (2**attempt) # Exponential backoff formula
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print(f"Waiting for {delay} seconds before retrying...")
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await asyncio.sleep(delay)
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else:
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# Return an error response after exhausting all retries
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return [
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{
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"index": 0,
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"tags": ["error"],
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"content": ["Rate limit error. Please try again later."],
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}
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]
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except Exception as e:
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raise e # Raise any other exceptions immediately
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def extract_blocks(url, html, provider=DEFAULT_PROVIDER, api_token=None, base_url=None):
|
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"""
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Extract content blocks from website HTML using an AI provider.
|
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|
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594
docs/md_v2/advanced/cdp-browser-crawling.md
Normal file
594
docs/md_v2/advanced/cdp-browser-crawling.md
Normal file
@@ -0,0 +1,594 @@
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# CDP Browser Crawling
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> **New in v0.7.6**: Efficient concurrent crawling with managed CDP (Chrome DevTools Protocol) browsers. Connect to a running browser instance and perform multiple crawls without spawning new windows.
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## 1. Overview
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When working with CDP browsers, you can connect to an existing browser instance instead of launching a new one for each crawl. This is particularly useful for:
|
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|
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- **Development**: Keep your browser open with DevTools for debugging
|
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- **Persistent Sessions**: Maintain authentication across multiple crawls
|
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- **Resource Efficiency**: Reuse a single browser instance for multiple operations
|
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- **Concurrent Crawling**: Run multiple crawls simultaneously with proper isolation
|
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|
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**Key Benefits:**
|
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|
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- ✅ Single browser window with multiple tabs (no window clutter)
|
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- ✅ Shared state (cookies, localStorage) across crawls
|
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- ✅ Concurrent safety with automatic page isolation
|
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- ✅ Automatic cleanup to prevent memory leaks
|
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- ✅ Works seamlessly with `arun_many()` for parallel crawling
|
||||
|
||||
---
|
||||
|
||||
## 2. Quick Start
|
||||
|
||||
### 2.1 Starting a CDP Browser
|
||||
|
||||
Use the Crawl4AI CLI to start a managed CDP browser:
|
||||
|
||||
```bash
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||||
# Start CDP browser on default port (9222)
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crwl cdp
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|
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# Start on custom port
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crwl cdp -d 9223
|
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|
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# Start in headless mode
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crwl cdp --headless
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```
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|
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The browser will stay running until you press 'q' or close the terminal.
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||||
|
||||
### 2.2 Basic CDP Connection
|
||||
|
||||
```python
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import asyncio
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from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
|
||||
|
||||
async def main():
|
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# Configure CDP connection
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||||
browser_cfg = BrowserConfig(
|
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browser_type="chromium",
|
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cdp_url="http://localhost:9222",
|
||||
verbose=True
|
||||
)
|
||||
|
||||
# Crawl a single URL
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
result = await crawler.arun(
|
||||
url="https://example.com",
|
||||
config=CrawlerRunConfig()
|
||||
)
|
||||
print(f"Success: {result.success}")
|
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print(f"Content length: {len(result.markdown)}")
|
||||
|
||||
if __name__ == "__main__":
|
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asyncio.run(main())
|
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```
|
||||
|
||||
---
|
||||
|
||||
## 3. Concurrent Crawling with arun_many()
|
||||
|
||||
The real power of CDP crawling shines with `arun_many()`. The browser manager automatically handles:
|
||||
|
||||
- **Page Isolation**: Each crawl gets its own tab
|
||||
- **Context Sharing**: All tabs share cookies and localStorage
|
||||
- **Concurrent Safety**: Proper locking prevents race conditions
|
||||
- **Auto Cleanup**: Tabs are closed after crawling (except sessions)
|
||||
|
||||
### 3.1 Basic Concurrent Crawling
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
|
||||
|
||||
async def crawl_multiple_urls():
|
||||
# URLs to crawl
|
||||
urls = [
|
||||
"https://example.com",
|
||||
"https://httpbin.org/html",
|
||||
"https://www.python.org",
|
||||
]
|
||||
|
||||
# Configure CDP browser
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium",
|
||||
cdp_url="http://localhost:9222",
|
||||
verbose=False
|
||||
)
|
||||
|
||||
# Configure crawler (bypass cache for fresh data)
|
||||
crawler_cfg = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS
|
||||
)
|
||||
|
||||
# Crawl all URLs concurrently
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = await crawler.arun_many(
|
||||
urls=urls,
|
||||
config=crawler_cfg
|
||||
)
|
||||
|
||||
# Process results
|
||||
for result in results:
|
||||
print(f"\nURL: {result.url}")
|
||||
if result.success:
|
||||
print(f"✓ Success | Content length: {len(result.markdown)}")
|
||||
else:
|
||||
print(f"✗ Failed: {result.error_message}")
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(crawl_multiple_urls())
|
||||
```
|
||||
|
||||
### 3.2 With Session Management
|
||||
|
||||
Use sessions to maintain authentication and state across individual crawls:
|
||||
|
||||
```python
|
||||
async def crawl_with_sessions():
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium",
|
||||
cdp_url="http://localhost:9222"
|
||||
)
|
||||
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
# First crawl: Login page
|
||||
login_result = await crawler.arun(
|
||||
url="https://example.com/login",
|
||||
config=CrawlerRunConfig(
|
||||
session_id="my-session", # Session persists
|
||||
js_code="document.querySelector('#login').click();"
|
||||
)
|
||||
)
|
||||
|
||||
# Second crawl: Reuse authenticated session
|
||||
dashboard_result = await crawler.arun(
|
||||
url="https://example.com/dashboard",
|
||||
config=CrawlerRunConfig(
|
||||
session_id="my-session" # Same session, cookies preserved
|
||||
)
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 4. How It Works
|
||||
|
||||
### 4.1 Browser Context Reuse
|
||||
|
||||
When using CDP browsers, Crawl4AI:
|
||||
|
||||
1. **Connects** to the existing browser via CDP URL
|
||||
2. **Reuses** the default browser context (single window)
|
||||
3. **Creates** new pages (tabs) for each crawl
|
||||
4. **Locks** page creation to prevent concurrent races
|
||||
5. **Cleans up** pages after crawling (unless it's a session)
|
||||
|
||||
```python
|
||||
# Internal behavior (simplified)
|
||||
if self.config.use_managed_browser:
|
||||
context = self.default_context # Shared context
|
||||
|
||||
# Thread-safe page creation
|
||||
async with self._page_lock:
|
||||
page = await context.new_page() # New tab per crawl
|
||||
|
||||
# After crawl completes
|
||||
if not config.session_id:
|
||||
await page.close() # Auto cleanup
|
||||
```
|
||||
|
||||
### 4.2 Page Lifecycle
|
||||
|
||||
```mermaid
|
||||
graph TD
|
||||
A[Start Crawl] --> B{Has session_id?}
|
||||
B -->|Yes| C[Reuse existing page]
|
||||
B -->|No| D[Create new page/tab]
|
||||
D --> E[Navigate & Extract]
|
||||
C --> E
|
||||
E --> F{Is session?}
|
||||
F -->|Yes| G[Keep page open]
|
||||
F -->|No| H[Close page]
|
||||
H --> I[End]
|
||||
G --> I
|
||||
```
|
||||
|
||||
### 4.3 State Sharing
|
||||
|
||||
All pages in the same context share:
|
||||
|
||||
- 🍪 **Cookies**: Authentication tokens, preferences
|
||||
- 💾 **localStorage**: Client-side data storage
|
||||
- 🔐 **sessionStorage**: Per-tab session data
|
||||
- 🌐 **Network cache**: Shared HTTP cache
|
||||
|
||||
This makes it perfect for crawling authenticated sites or maintaining state across multiple pages.
|
||||
|
||||
---
|
||||
|
||||
## 5. Configuration Options
|
||||
|
||||
### 5.1 BrowserConfig for CDP
|
||||
|
||||
```python
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium", # Must be "chromium" for CDP
|
||||
cdp_url="http://localhost:9222", # CDP endpoint URL
|
||||
verbose=True, # Log browser operations
|
||||
|
||||
# Optional: Override headers for all requests
|
||||
headers={
|
||||
"Accept-Language": "en-US,en;q=0.9",
|
||||
},
|
||||
|
||||
# Optional: Set user agent
|
||||
user_agent="Mozilla/5.0 ...",
|
||||
|
||||
# Optional: Enable stealth mode (requires dedicated browser)
|
||||
# enable_stealth=False, # Not compatible with CDP
|
||||
)
|
||||
```
|
||||
|
||||
### 5.2 CrawlerRunConfig Options
|
||||
|
||||
```python
|
||||
crawler_cfg = CrawlerRunConfig(
|
||||
# Session management
|
||||
session_id="my-session", # Persist page across calls
|
||||
|
||||
# Caching
|
||||
cache_mode=CacheMode.BYPASS, # Fresh data every time
|
||||
|
||||
# Browser location (affects timezone, locale)
|
||||
locale="en-US",
|
||||
timezone_id="America/New_York",
|
||||
geolocation={
|
||||
"latitude": 40.7128,
|
||||
"longitude": -74.0060
|
||||
},
|
||||
|
||||
# Proxy (per-crawl override)
|
||||
proxy_config={
|
||||
"server": "http://proxy.example.com:8080",
|
||||
"username": "user",
|
||||
"password": "pass"
|
||||
}
|
||||
)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 6. Advanced Patterns
|
||||
|
||||
### 6.1 Streaming Results
|
||||
|
||||
Process URLs as they complete instead of waiting for all:
|
||||
|
||||
```python
|
||||
async def stream_crawl_results():
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium",
|
||||
cdp_url="http://localhost:9222"
|
||||
)
|
||||
|
||||
urls = ["https://example.com" for _ in range(100)]
|
||||
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
# Stream results as they complete
|
||||
async for result in crawler.arun_many(
|
||||
urls=urls,
|
||||
config=CrawlerRunConfig(stream=True)
|
||||
):
|
||||
if result.success:
|
||||
print(f"✓ {result.url}: {len(result.markdown)} chars")
|
||||
# Process immediately instead of waiting for all
|
||||
await save_to_database(result)
|
||||
```
|
||||
|
||||
### 6.2 Custom Concurrency Control
|
||||
|
||||
```python
|
||||
from crawl4ai import CrawlerRunConfig
|
||||
|
||||
# Limit concurrent crawls to 3
|
||||
crawler_cfg = CrawlerRunConfig(
|
||||
semaphore_count=3, # Max 3 concurrent requests
|
||||
mean_delay=0.5, # Average 0.5s delay between requests
|
||||
max_range=1.0, # +/- 1s random delay
|
||||
)
|
||||
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = await crawler.arun_many(urls, config=crawler_cfg)
|
||||
```
|
||||
|
||||
### 6.3 Multi-Config Crawling
|
||||
|
||||
Different configurations for different URL groups:
|
||||
|
||||
```python
|
||||
from crawl4ai import CrawlerRunConfig
|
||||
|
||||
# Fast crawl for static pages
|
||||
fast_config = CrawlerRunConfig(
|
||||
wait_until="domcontentloaded",
|
||||
page_timeout=30000
|
||||
)
|
||||
|
||||
# Slow crawl for dynamic pages
|
||||
slow_config = CrawlerRunConfig(
|
||||
wait_until="networkidle",
|
||||
page_timeout=60000,
|
||||
js_code="window.scrollTo(0, document.body.scrollHeight);"
|
||||
)
|
||||
|
||||
configs = [fast_config, slow_config, fast_config]
|
||||
urls = ["https://static.com", "https://dynamic.com", "https://static2.com"]
|
||||
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = await crawler.arun_many(urls, configs=configs)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 7. Best Practices
|
||||
|
||||
### 7.1 Resource Management
|
||||
|
||||
✅ **DO:**
|
||||
```python
|
||||
# Use context manager for automatic cleanup
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = await crawler.arun_many(urls)
|
||||
# Browser connection closed automatically
|
||||
```
|
||||
|
||||
❌ **DON'T:**
|
||||
```python
|
||||
# Manual management risks resource leaks
|
||||
crawler = AsyncWebCrawler(config=browser_cfg)
|
||||
await crawler.start()
|
||||
results = await crawler.arun_many(urls)
|
||||
# Forgot to call crawler.close()!
|
||||
```
|
||||
|
||||
### 7.2 Session Management
|
||||
|
||||
✅ **DO:**
|
||||
```python
|
||||
# Use sessions for related crawls
|
||||
config = CrawlerRunConfig(session_id="user-flow")
|
||||
await crawler.arun(login_url, config=config)
|
||||
await crawler.arun(dashboard_url, config=config)
|
||||
await crawler.kill_session("user-flow") # Clean up when done
|
||||
```
|
||||
|
||||
❌ **DON'T:**
|
||||
```python
|
||||
# Creating new session IDs unnecessarily
|
||||
for i in range(100):
|
||||
config = CrawlerRunConfig(session_id=f"session-{i}")
|
||||
await crawler.arun(url, config=config)
|
||||
# 100 unclosed sessions accumulate!
|
||||
```
|
||||
|
||||
### 7.3 Error Handling
|
||||
|
||||
```python
|
||||
async def robust_crawl(urls):
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium",
|
||||
cdp_url="http://localhost:9222"
|
||||
)
|
||||
|
||||
try:
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = await crawler.arun_many(urls)
|
||||
|
||||
# Separate successes and failures
|
||||
successes = [r for r in results if r.success]
|
||||
failures = [r for r in results if not r.success]
|
||||
|
||||
print(f"✓ {len(successes)} succeeded")
|
||||
print(f"✗ {len(failures)} failed")
|
||||
|
||||
# Retry failures with different config
|
||||
if failures:
|
||||
retry_urls = [r.url for r in failures]
|
||||
retry_config = CrawlerRunConfig(
|
||||
page_timeout=120000, # Longer timeout
|
||||
wait_until="networkidle"
|
||||
)
|
||||
retry_results = await crawler.arun_many(
|
||||
retry_urls,
|
||||
config=retry_config
|
||||
)
|
||||
|
||||
return successes + retry_results
|
||||
|
||||
except Exception as e:
|
||||
print(f"Fatal error: {e}")
|
||||
return []
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 8. Troubleshooting
|
||||
|
||||
### 8.1 Connection Issues
|
||||
|
||||
**Problem**: `Cannot connect to CDP browser`
|
||||
|
||||
```python
|
||||
# Check CDP browser is running
|
||||
$ lsof -i :9222
|
||||
# Should show: Chromium PID USER FD TYPE ...
|
||||
|
||||
# Or start it if not running
|
||||
$ crwl cdp
|
||||
```
|
||||
|
||||
**Problem**: `ERR_ABORTED` errors in concurrent crawls
|
||||
|
||||
✅ **Fixed in v0.7.6**: This issue has been resolved. Pages are now properly isolated with locking.
|
||||
|
||||
### 8.2 Performance Issues
|
||||
|
||||
**Problem**: Too many open tabs
|
||||
|
||||
```python
|
||||
# Ensure you're not using session_id for everything
|
||||
config = CrawlerRunConfig() # No session_id
|
||||
await crawler.arun_many(urls, config=config)
|
||||
# Pages auto-close after crawling
|
||||
```
|
||||
|
||||
**Problem**: Memory leaks
|
||||
|
||||
```python
|
||||
# Always use context manager
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
# Crawling code here
|
||||
pass
|
||||
# Automatic cleanup on exit
|
||||
```
|
||||
|
||||
### 8.3 State Issues
|
||||
|
||||
**Problem**: Cookies not persisting
|
||||
|
||||
```python
|
||||
# Use the same context (automatic with CDP)
|
||||
browser_cfg = BrowserConfig(cdp_url="http://localhost:9222")
|
||||
# All crawls share cookies automatically
|
||||
```
|
||||
|
||||
**Problem**: Need isolated state
|
||||
|
||||
```python
|
||||
# Use different CDP endpoints or non-CDP browsers
|
||||
browser_cfg_1 = BrowserConfig(cdp_url="http://localhost:9222")
|
||||
browser_cfg_2 = BrowserConfig(cdp_url="http://localhost:9223")
|
||||
# Completely isolated browsers
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 9. Comparison: CDP vs Regular Browsers
|
||||
|
||||
| Feature | CDP Browser | Regular Browser |
|
||||
|---------|-------------|-----------------|
|
||||
| **Window Management** | ✅ Single window, multiple tabs | ❌ New window per context |
|
||||
| **Startup Time** | ✅ Instant (already running) | ⏱️ ~2-3s per launch |
|
||||
| **State Sharing** | ✅ Shared cookies/localStorage | ⚠️ Isolated by default |
|
||||
| **Concurrent Safety** | ✅ Automatic locking | ✅ Separate processes |
|
||||
| **Memory Usage** | ✅ Lower (shared browser) | ⚠️ Higher (multiple processes) |
|
||||
| **Session Persistence** | ✅ Native support | ✅ Via session_id |
|
||||
| **Stealth Mode** | ❌ Not compatible | ✅ Full support |
|
||||
| **Best For** | Development, authenticated crawls | Production, isolated crawls |
|
||||
|
||||
---
|
||||
|
||||
## 10. Real-World Examples
|
||||
|
||||
### 10.1 E-commerce Product Scraping
|
||||
|
||||
```python
|
||||
async def scrape_products():
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium",
|
||||
cdp_url="http://localhost:9222"
|
||||
)
|
||||
|
||||
# Get product URLs from category page
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
category_result = await crawler.arun(
|
||||
url="https://shop.example.com/category",
|
||||
config=CrawlerRunConfig(
|
||||
css_selector=".product-link"
|
||||
)
|
||||
)
|
||||
|
||||
# Extract product URLs
|
||||
product_urls = extract_urls(category_result.links)
|
||||
|
||||
# Crawl all products concurrently
|
||||
product_results = await crawler.arun_many(
|
||||
urls=product_urls,
|
||||
config=CrawlerRunConfig(
|
||||
css_selector=".product-details",
|
||||
semaphore_count=5 # Polite crawling
|
||||
)
|
||||
)
|
||||
|
||||
return [extract_product_data(r) for r in product_results]
|
||||
```
|
||||
|
||||
### 10.2 News Article Monitoring
|
||||
|
||||
```python
|
||||
import asyncio
|
||||
from datetime import datetime
|
||||
|
||||
async def monitor_news_sites():
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="chromium",
|
||||
cdp_url="http://localhost:9222"
|
||||
)
|
||||
|
||||
news_sites = [
|
||||
"https://news.site1.com",
|
||||
"https://news.site2.com",
|
||||
"https://news.site3.com"
|
||||
]
|
||||
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
while True:
|
||||
print(f"\n[{datetime.now()}] Checking for updates...")
|
||||
|
||||
results = await crawler.arun_many(
|
||||
urls=news_sites,
|
||||
config=CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS, # Always fresh
|
||||
css_selector=".article-headline"
|
||||
)
|
||||
)
|
||||
|
||||
for result in results:
|
||||
if result.success:
|
||||
headlines = extract_headlines(result)
|
||||
for headline in headlines:
|
||||
if is_new(headline):
|
||||
notify_user(headline)
|
||||
|
||||
# Check every 5 minutes
|
||||
await asyncio.sleep(300)
|
||||
```
|
||||
|
||||
---
|
||||
|
||||
## 11. Summary
|
||||
|
||||
CDP browser crawling offers:
|
||||
|
||||
- 🚀 **Performance**: Faster startup, lower resource usage
|
||||
- 🔄 **State Management**: Shared cookies and authentication
|
||||
- 🎯 **Concurrent Safety**: Automatic page isolation and cleanup
|
||||
- 💻 **Developer Friendly**: Visual debugging with DevTools
|
||||
|
||||
**When to use CDP:**
|
||||
- Development and debugging
|
||||
- Authenticated crawling (login required)
|
||||
- Sequential crawls needing state
|
||||
- Resource-constrained environments
|
||||
|
||||
**When to use regular browsers:**
|
||||
- Production deployments
|
||||
- Maximum isolation required
|
||||
- Stealth mode needed
|
||||
- Distributed/cloud crawling
|
||||
|
||||
For most use cases, **CDP browsers provide the best balance** of performance, convenience, and safety.
|
||||
@@ -20,10 +20,10 @@ In some cases, you need to extract **complex or unstructured** information from
|
||||
|
||||
## 2. Provider-Agnostic via LiteLLM
|
||||
|
||||
You can use LLMConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LLMConfig [here](/api/parameters).
|
||||
You can use LlmConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LlmConfig [here](/api/parameters).
|
||||
|
||||
```python
|
||||
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
|
||||
llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
|
||||
```
|
||||
|
||||
Crawl4AI uses a “provider string” (e.g., `"openai/gpt-4o"`, `"ollama/llama2.0"`, `"aws/titan"`) to identify your LLM. **Any** model that LiteLLM supports is fair game. You just provide:
|
||||
@@ -58,7 +58,7 @@ For structured data, `"schema"` is recommended. You provide `schema=YourPydantic
|
||||
|
||||
Below is an overview of important LLM extraction parameters. All are typically set inside `LLMExtractionStrategy(...)`. You then put that strategy in your `CrawlerRunConfig(..., extraction_strategy=...)`.
|
||||
|
||||
1. **`llm_config`** (LLMConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.
|
||||
1. **`llmConfig`** (LlmConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.
|
||||
2. **`schema`** (dict): A JSON schema describing the fields you want. Usually generated by `YourModel.model_json_schema()`.
|
||||
3. **`extraction_type`** (str): `"schema"` or `"block"`.
|
||||
4. **`instruction`** (str): Prompt text telling the LLM what you want extracted. E.g., “Extract these fields as a JSON array.”
|
||||
@@ -112,7 +112,7 @@ async def main():
|
||||
# 1. Define the LLM extraction strategy
|
||||
llm_strategy = LLMExtractionStrategy(
|
||||
llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv('OPENAI_API_KEY')),
|
||||
schema=Product.model_json_schema(), # Or use model_json_schema()
|
||||
schema=Product.schema_json(), # Or use model_json_schema()
|
||||
extraction_type="schema",
|
||||
instruction="Extract all product objects with 'name' and 'price' from the content.",
|
||||
chunk_token_threshold=1000,
|
||||
@@ -238,7 +238,7 @@ class KnowledgeGraph(BaseModel):
|
||||
async def main():
|
||||
# LLM extraction strategy
|
||||
llm_strat = LLMExtractionStrategy(
|
||||
llm_config = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
|
||||
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.",
|
||||
|
||||
63
tests/test_arun_many_cdp.py
Normal file
63
tests/test_arun_many_cdp.py
Normal file
@@ -0,0 +1,63 @@
|
||||
"""
|
||||
Test for arun_many with managed CDP browser to ensure each crawl gets its own tab.
|
||||
"""
|
||||
import pytest
|
||||
import asyncio
|
||||
from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_arun_many_with_cdp():
|
||||
"""Test arun_many opens a new tab for each url with managed CDP browser."""
|
||||
# NOTE: Requires a running CDP browser at localhost:9222
|
||||
# Can be started with: crwl cdp -d 9222
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="cdp",
|
||||
cdp_url="http://localhost:9222",
|
||||
verbose=False,
|
||||
)
|
||||
urls = [
|
||||
"https://example.com",
|
||||
"https://httpbin.org/html",
|
||||
"https://www.python.org",
|
||||
]
|
||||
crawler_cfg = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
)
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = await crawler.arun_many(urls=urls, config=crawler_cfg)
|
||||
# All results should be successful and distinct
|
||||
assert len(results) == 3
|
||||
for result in results:
|
||||
assert result.success, f"Crawl failed: {result.url} - {result.error_message}"
|
||||
assert result.markdown is not None
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_arun_many_with_cdp_sequential():
|
||||
"""Test arun_many sequentially to isolate issues."""
|
||||
browser_cfg = BrowserConfig(
|
||||
browser_type="cdp",
|
||||
cdp_url="http://localhost:9222",
|
||||
verbose=True,
|
||||
)
|
||||
urls = [
|
||||
"https://example.com",
|
||||
"https://httpbin.org/html",
|
||||
"https://www.python.org",
|
||||
]
|
||||
crawler_cfg = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
)
|
||||
async with AsyncWebCrawler(config=browser_cfg) as crawler:
|
||||
results = []
|
||||
for url in urls:
|
||||
result = await crawler.arun(url=url, config=crawler_cfg)
|
||||
results.append(result)
|
||||
assert result.success, f"Crawl failed: {result.url} - {result.error_message}"
|
||||
assert result.markdown is not None
|
||||
assert len(results) == 3
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(test_arun_many_with_cdp())
|
||||
@@ -1,220 +0,0 @@
|
||||
"""
|
||||
Final verification test for Issue #1055 fix
|
||||
|
||||
This test demonstrates that LLM extraction now runs in parallel
|
||||
when using arun_many with multiple URLs.
|
||||
"""
|
||||
|
||||
import os
|
||||
import sys
|
||||
import time
|
||||
import asyncio
|
||||
|
||||
grandparent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
|
||||
sys.path.append(grandparent_dir)
|
||||
|
||||
from crawl4ai import (
|
||||
AsyncWebCrawler,
|
||||
BrowserConfig,
|
||||
CrawlerRunConfig,
|
||||
CacheMode,
|
||||
LLMExtractionStrategy,
|
||||
LLMConfig,
|
||||
)
|
||||
|
||||
from pydantic import BaseModel
|
||||
|
||||
|
||||
class SimpleData(BaseModel):
|
||||
title: str
|
||||
summary: str
|
||||
|
||||
|
||||
def print_section(title):
|
||||
print("\n" + "=" * 80)
|
||||
print(title)
|
||||
print("=" * 80 + "\n")
|
||||
|
||||
|
||||
async def test_without_llm():
|
||||
"""Baseline: Test crawling without LLM extraction"""
|
||||
print_section("TEST 1: Crawling WITHOUT LLM Extraction")
|
||||
|
||||
config = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
)
|
||||
|
||||
browser_config = BrowserConfig(headless=True, verbose=False)
|
||||
|
||||
urls = [
|
||||
"https://www.example.com",
|
||||
"https://www.iana.org",
|
||||
"https://www.wikipedia.org",
|
||||
]
|
||||
|
||||
print(f"Crawling {len(urls)} URLs without LLM extraction...")
|
||||
print("Expected: Fast and parallel\n")
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
async with AsyncWebCrawler(config=browser_config) as crawler:
|
||||
results = await crawler.arun_many(urls=urls, config=config)
|
||||
|
||||
duration = time.time() - start_time
|
||||
|
||||
print(f"\n✅ Completed in {duration:.2f}s")
|
||||
print(f" Successful: {sum(1 for r in results if r.success)}/{len(urls)}")
|
||||
print(f" Average: {duration/len(urls):.2f}s per URL")
|
||||
|
||||
return duration
|
||||
|
||||
|
||||
async def test_with_llm_before_fix():
|
||||
"""Demonstrate the problem: Sequential execution with LLM"""
|
||||
print_section("TEST 2: What Issue #1055 Reported (LLM Sequential Behavior)")
|
||||
|
||||
print("The issue reported that with LLM extraction, URLs would crawl")
|
||||
print("one after another instead of in parallel.")
|
||||
print("\nWithout our fix, this would show:")
|
||||
print(" - URL 1 fetches → extracts → completes")
|
||||
print(" - URL 2 fetches → extracts → completes")
|
||||
print(" - URL 3 fetches → extracts → completes")
|
||||
print("\nTotal time would be approximately sum of all individual times.")
|
||||
|
||||
|
||||
async def test_with_llm_after_fix():
|
||||
"""Demonstrate the fix: Parallel execution with LLM"""
|
||||
print_section("TEST 3: After Fix - LLM Extraction in Parallel")
|
||||
|
||||
config = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
extraction_strategy=LLMExtractionStrategy(
|
||||
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
|
||||
schema=SimpleData.model_json_schema(),
|
||||
extraction_type="schema",
|
||||
instruction="Extract title and summary",
|
||||
)
|
||||
)
|
||||
|
||||
browser_config = BrowserConfig(headless=True, verbose=False)
|
||||
|
||||
urls = [
|
||||
"https://www.example.com",
|
||||
"https://www.iana.org",
|
||||
"https://www.wikipedia.org",
|
||||
]
|
||||
|
||||
print(f"Crawling {len(urls)} URLs WITH LLM extraction...")
|
||||
print("Expected: Parallel execution with our fix\n")
|
||||
|
||||
completion_times = {}
|
||||
start_time = time.time()
|
||||
|
||||
async with AsyncWebCrawler(config=browser_config) as crawler:
|
||||
results = await crawler.arun_many(urls=urls, config=config)
|
||||
for result in results:
|
||||
elapsed = time.time() - start_time
|
||||
completion_times[result.url] = elapsed
|
||||
print(f" [{elapsed:5.2f}s] ✓ {result.url[:50]}")
|
||||
|
||||
duration = time.time() - start_time
|
||||
|
||||
print(f"\n✅ Total time: {duration:.2f}s")
|
||||
print(f" Successful: {sum(1 for url in urls if url in completion_times)}/{len(urls)}")
|
||||
|
||||
# Analyze parallelism
|
||||
times = list(completion_times.values())
|
||||
if len(times) >= 2:
|
||||
# If parallel, completion times should be staggered, not evenly spaced
|
||||
time_diffs = [times[i+1] - times[i] for i in range(len(times)-1)]
|
||||
avg_diff = sum(time_diffs) / len(time_diffs)
|
||||
|
||||
print(f"\nParallelism Analysis:")
|
||||
print(f" Completion time differences: {[f'{d:.2f}s' for d in time_diffs]}")
|
||||
print(f" Average difference: {avg_diff:.2f}s")
|
||||
|
||||
# In parallel mode, some tasks complete close together
|
||||
# In sequential mode, they're evenly spaced (avg ~2-3s apart)
|
||||
if avg_diff < duration / len(urls):
|
||||
print(f" ✅ PARALLEL: Tasks completed with overlapping execution")
|
||||
else:
|
||||
print(f" ⚠️ SEQUENTIAL: Tasks completed one after another")
|
||||
|
||||
return duration
|
||||
|
||||
|
||||
async def test_multiple_arun_calls():
|
||||
"""Test multiple individual arun() calls in parallel"""
|
||||
print_section("TEST 4: Multiple arun() Calls with asyncio.gather")
|
||||
|
||||
config = CrawlerRunConfig(
|
||||
cache_mode=CacheMode.BYPASS,
|
||||
extraction_strategy=LLMExtractionStrategy(
|
||||
llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
|
||||
schema=SimpleData.model_json_schema(),
|
||||
extraction_type="schema",
|
||||
instruction="Extract title and summary",
|
||||
)
|
||||
)
|
||||
|
||||
browser_config = BrowserConfig(headless=True, verbose=False)
|
||||
|
||||
urls = [
|
||||
"https://www.example.com",
|
||||
"https://www.iana.org",
|
||||
"https://www.wikipedia.org",
|
||||
]
|
||||
|
||||
print(f"Running {len(urls)} arun() calls with asyncio.gather()...")
|
||||
print("Expected: True parallel execution\n")
|
||||
|
||||
start_time = time.time()
|
||||
|
||||
async with AsyncWebCrawler(config=browser_config) as crawler:
|
||||
tasks = [crawler.arun(url, config=config) for url in urls]
|
||||
results = await asyncio.gather(*tasks)
|
||||
|
||||
duration = time.time() - start_time
|
||||
|
||||
print(f"\n✅ Completed in {duration:.2f}s")
|
||||
print(f" Successful: {sum(1 for r in results if r.success)}/{len(urls)}")
|
||||
print(f" This proves the async LLM extraction works correctly")
|
||||
|
||||
return duration
|
||||
|
||||
|
||||
async def main():
|
||||
print("\n" + "🚀" * 40)
|
||||
print("ISSUE #1055 FIX VERIFICATION")
|
||||
print("Testing: Sequential → Parallel LLM Extraction")
|
||||
print("🚀" * 40)
|
||||
|
||||
# Run tests
|
||||
await test_without_llm()
|
||||
|
||||
await test_with_llm_before_fix()
|
||||
|
||||
time_with_llm = await test_with_llm_after_fix()
|
||||
|
||||
time_gather = await test_multiple_arun_calls()
|
||||
|
||||
# Final summary
|
||||
print_section("FINAL VERDICT")
|
||||
|
||||
print("✅ Fix Verified!")
|
||||
print("\nWhat changed:")
|
||||
print(" • Created aperform_completion_with_backoff() using litellm.acompletion")
|
||||
print(" • Added arun() method to ExtractionStrategy base class")
|
||||
print(" • Implemented parallel arun() in LLMExtractionStrategy")
|
||||
print(" • Updated AsyncWebCrawler to use arun() when available")
|
||||
print("\nResult:")
|
||||
print(" • LLM extraction now runs in parallel across multiple URLs")
|
||||
print(" • Backward compatible - existing strategies still work")
|
||||
print(" • No breaking changes to the API")
|
||||
print("\n✨ Issue #1055 is RESOLVED!")
|
||||
|
||||
print("\n" + "=" * 80 + "\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
Reference in New Issue
Block a user