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Add support for parallel URL processing in extraction utilities

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unclecode
2026-01-24 04:13:39 +00:00
parent b0b3ca1222
commit 2d5e5306c5
1 changed files with 45 additions and 22 deletions
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67
crawl4ai/extraction_strategy.py
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@@ -1349,11 +1349,11 @@ In this scenario, use your best judgment to generate the schema. You need to exa
llm_config: 'LLMConfig' = create_llm_config(), llm_config: 'LLMConfig' = create_llm_config(),
provider: str = None, provider: str = None,
api_token: str = None, api_token: str = None,
url: str = None, url: Union[str, List[str]] = None,
**kwargs **kwargs
) -> dict: ) -> dict:
""" """
Generate extraction schema from HTML content or URL (sync version). Generate extraction schema from HTML content or URL(s) (sync version).
Args: Args:
html (str, optional): The HTML content to analyze. If not provided, url must be set. html (str, optional): The HTML content to analyze. If not provided, url must be set.
@@ -1363,7 +1363,8 @@ In this scenario, use your best judgment to generate the schema. You need to exa
llm_config (LLMConfig): LLM configuration object. llm_config (LLMConfig): LLM configuration object.
provider (str): Legacy Parameter. LLM provider to use. provider (str): Legacy Parameter. LLM provider to use.
api_token (str): Legacy Parameter. API token for LLM provider. api_token (str): Legacy Parameter. API token for LLM provider.
url (str, optional): URL to fetch HTML from. If provided, html parameter is ignored. url (str or List[str], optional): URL(s) to fetch HTML from. If provided, html parameter is ignored.
When multiple URLs are provided, HTMLs are fetched in parallel and concatenated.
**kwargs: Additional args passed to LLM processor. **kwargs: Additional args passed to LLM processor.
Returns: Returns:
@@ -1408,11 +1409,11 @@ In this scenario, use your best judgment to generate the schema. You need to exa
llm_config: 'LLMConfig' = None, llm_config: 'LLMConfig' = None,
provider: str = None, provider: str = None,
api_token: str = None, api_token: str = None,
url: str = None, url: Union[str, List[str]] = None,
**kwargs **kwargs
) -> dict: ) -> dict:
""" """
Generate extraction schema from HTML content or URL (async version). Generate extraction schema from HTML content or URL(s) (async version).
Use this method when calling from async contexts (e.g., FastAPI) to avoid Use this method when calling from async contexts (e.g., FastAPI) to avoid
issues with certain LLM providers (e.g., Gemini/Vertex AI) that require issues with certain LLM providers (e.g., Gemini/Vertex AI) that require
@@ -1426,7 +1427,8 @@ In this scenario, use your best judgment to generate the schema. You need to exa
llm_config (LLMConfig): LLM configuration object. llm_config (LLMConfig): LLM configuration object.
provider (str): Legacy Parameter. LLM provider to use. provider (str): Legacy Parameter. LLM provider to use.
api_token (str): Legacy Parameter. API token for LLM provider. api_token (str): Legacy Parameter. API token for LLM provider.
url (str, optional): URL to fetch HTML from. If provided, html parameter is ignored. url (str or List[str], optional): URL(s) to fetch HTML from. If provided, html parameter is ignored.
When multiple URLs are provided, HTMLs are fetched in parallel and concatenated.
**kwargs: Additional args passed to LLM processor. **kwargs: Additional args passed to LLM processor.
Returns: Returns:
@@ -1438,7 +1440,7 @@ In this scenario, use your best judgment to generate the schema. You need to exa
from .utils import aperform_completion_with_backoff, preprocess_html_for_schema from .utils import aperform_completion_with_backoff, preprocess_html_for_schema
# Validate inputs # Validate inputs
if html is None and url is None: if html is None and (url is None or (isinstance(url, list) and len(url) == 0)):
raise ValueError("Either 'html' or 'url' must be provided") raise ValueError("Either 'html' or 'url' must be provided")
# Check deprecated parameters # Check deprecated parameters
@@ -1449,7 +1451,7 @@ In this scenario, use your best judgment to generate the schema. You need to exa
if llm_config is None: if llm_config is None:
llm_config = create_llm_config() llm_config = create_llm_config()
# Fetch HTML from URL if provided # Fetch HTML from URL(s) if provided
if url is not None: if url is not None:
from .async_webcrawler import AsyncWebCrawler from .async_webcrawler import AsyncWebCrawler
from .async_configs import BrowserConfig, CrawlerRunConfig, CacheMode from .async_configs import BrowserConfig, CrawlerRunConfig, CacheMode
@@ -1461,21 +1463,42 @@ In this scenario, use your best judgment to generate the schema. You need to exa
) )
crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS) crawler_config = CrawlerRunConfig(cache_mode=CacheMode.BYPASS)
async with AsyncWebCrawler(config=browser_config) as crawler: # Normalize to list
result = await crawler.arun(url=url, config=crawler_config) urls = [url] if isinstance(url, str) else url
if not result.success:
raise Exception(f"Failed to fetch URL '{url}': {result.error_message}")
if result.status_code >= 400:
raise Exception(f"HTTP {result.status_code} error for URL '{url}'")
html = result.html
# Preprocess HTML for schema generation (higher text_threshold for better LLM context) async with AsyncWebCrawler(config=browser_config) as crawler:
html = preprocess_html_for_schema( if len(urls) == 1:
html_content=html, result = await crawler.arun(url=urls[0], config=crawler_config)
text_threshold=2000, if not result.success:
attr_value_threshold=500, raise Exception(f"Failed to fetch URL '{urls[0]}': {result.error_message}")
max_size=500_000 if result.status_code >= 400:
) raise Exception(f"HTTP {result.status_code} error for URL '{urls[0]}'")
html = result.html
else:
results = await crawler.arun_many(urls=urls, config=crawler_config)
html_parts = []
for i, result in enumerate(results, 1):
if not result.success:
raise Exception(f"Failed to fetch URL '{result.url}': {result.error_message}")
if result.status_code >= 400:
raise Exception(f"HTTP {result.status_code} error for URL '{result.url}'")
cleaned = preprocess_html_for_schema(
html_content=result.html,
text_threshold=2000,
attr_value_threshold=500,
max_size=500_000
)
html_parts.append(f"'''html example {i}\n{cleaned}\n'''")
html = "\n\n".join(html_parts)
# Preprocess HTML for schema generation (skip if already preprocessed from multiple URLs)
if url is None or isinstance(url, str):
html = preprocess_html_for_schema(
html_content=html,
text_threshold=2000,
attr_value_threshold=500,
max_size=500_000
)
prompt = JsonElementExtractionStrategy._build_schema_prompt(html, schema_type, query, target_json_example) prompt = JsonElementExtractionStrategy._build_schema_prompt(html, schema_type, query, target_json_example)