This commit resolves issue #1055 where LLM extraction was blocking async
execution, causing URLs to be processed sequentially instead of in parallel. Changes: - Added aperform_completion_with_backoff() using litellm.acompletion for async LLM calls - Implemented arun() method in ExtractionStrategy base class with thread pool fallback - Created async arun() and aextract() methods in LLMExtractionStrategy using asyncio.gather - Updated AsyncWebCrawler.arun() to detect and use arun() when available - Added comprehensive test suite to verify parallel execution Impact: - LLM extraction now runs truly in parallel across multiple URLs - Significant performance improvement for multi-URL crawls with LLM strategies - Backward compatible - existing extraction strategies continue to work - No breaking changes to public API Technical details: - Uses litellm.acompletion for non-blocking LLM calls - Leverages asyncio.gather for concurrent chunk processing - Maintains backward compatibility via asyncio.to_thread fallback - Works seamlessly with MemoryAdaptiveDispatcher and other dispatchers
This commit is contained in:
ntohidi
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tests/test_llm_extraction_parallel_issue_1055.py
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tests/test_llm_extraction_parallel_issue_1055.py
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"""
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Final verification test for Issue #1055 fix
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This test demonstrates that LLM extraction now runs in parallel
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when using arun_many with multiple URLs.
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"""
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import os
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import sys
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import time
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import asyncio
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grandparent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
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sys.path.append(grandparent_dir)
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from crawl4ai import (
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AsyncWebCrawler,
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BrowserConfig,
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CrawlerRunConfig,
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CacheMode,
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LLMExtractionStrategy,
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LLMConfig,
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)
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from pydantic import BaseModel
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class SimpleData(BaseModel):
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title: str
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summary: str
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def print_section(title):
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print("\n" + "=" * 80)
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print(title)
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print("=" * 80 + "\n")
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async def test_without_llm():
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"""Baseline: Test crawling without LLM extraction"""
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print_section("TEST 1: Crawling WITHOUT LLM Extraction")
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config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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)
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browser_config = BrowserConfig(headless=True, verbose=False)
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urls = [
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"https://www.example.com",
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"https://www.iana.org",
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"https://www.wikipedia.org",
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]
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print(f"Crawling {len(urls)} URLs without LLM extraction...")
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print("Expected: Fast and parallel\n")
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start_time = time.time()
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async with AsyncWebCrawler(config=browser_config) as crawler:
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results = await crawler.arun_many(urls=urls, config=config)
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duration = time.time() - start_time
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print(f"\n✅ Completed in {duration:.2f}s")
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print(f" Successful: {sum(1 for r in results if r.success)}/{len(urls)}")
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print(f" Average: {duration/len(urls):.2f}s per URL")
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return duration
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async def test_with_llm_before_fix():
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"""Demonstrate the problem: Sequential execution with LLM"""
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print_section("TEST 2: What Issue #1055 Reported (LLM Sequential Behavior)")
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print("The issue reported that with LLM extraction, URLs would crawl")
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print("one after another instead of in parallel.")
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print("\nWithout our fix, this would show:")
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print(" - URL 1 fetches → extracts → completes")
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print(" - URL 2 fetches → extracts → completes")
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print(" - URL 3 fetches → extracts → completes")
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print("\nTotal time would be approximately sum of all individual times.")
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async def test_with_llm_after_fix():
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"""Demonstrate the fix: Parallel execution with LLM"""
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print_section("TEST 3: After Fix - LLM Extraction in Parallel")
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config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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extraction_strategy=LLMExtractionStrategy(
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llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
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schema=SimpleData.model_json_schema(),
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extraction_type="schema",
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instruction="Extract title and summary",
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)
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)
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browser_config = BrowserConfig(headless=True, verbose=False)
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urls = [
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"https://www.example.com",
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"https://www.iana.org",
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"https://www.wikipedia.org",
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]
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print(f"Crawling {len(urls)} URLs WITH LLM extraction...")
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print("Expected: Parallel execution with our fix\n")
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completion_times = {}
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start_time = time.time()
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async with AsyncWebCrawler(config=browser_config) as crawler:
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results = await crawler.arun_many(urls=urls, config=config)
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for result in results:
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elapsed = time.time() - start_time
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completion_times[result.url] = elapsed
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print(f" [{elapsed:5.2f}s] ✓ {result.url[:50]}")
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duration = time.time() - start_time
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print(f"\n✅ Total time: {duration:.2f}s")
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print(f" Successful: {sum(1 for url in urls if url in completion_times)}/{len(urls)}")
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# Analyze parallelism
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times = list(completion_times.values())
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if len(times) >= 2:
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# If parallel, completion times should be staggered, not evenly spaced
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time_diffs = [times[i+1] - times[i] for i in range(len(times)-1)]
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avg_diff = sum(time_diffs) / len(time_diffs)
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print(f"\nParallelism Analysis:")
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print(f" Completion time differences: {[f'{d:.2f}s' for d in time_diffs]}")
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print(f" Average difference: {avg_diff:.2f}s")
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# In parallel mode, some tasks complete close together
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# In sequential mode, they're evenly spaced (avg ~2-3s apart)
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if avg_diff < duration / len(urls):
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print(f" ✅ PARALLEL: Tasks completed with overlapping execution")
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else:
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print(f" ⚠️ SEQUENTIAL: Tasks completed one after another")
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return duration
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async def test_multiple_arun_calls():
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"""Test multiple individual arun() calls in parallel"""
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print_section("TEST 4: Multiple arun() Calls with asyncio.gather")
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config = CrawlerRunConfig(
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cache_mode=CacheMode.BYPASS,
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extraction_strategy=LLMExtractionStrategy(
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llm_config=LLMConfig(provider="openai/gpt-4o-mini"),
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schema=SimpleData.model_json_schema(),
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extraction_type="schema",
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instruction="Extract title and summary",
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)
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)
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browser_config = BrowserConfig(headless=True, verbose=False)
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urls = [
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"https://www.example.com",
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"https://www.iana.org",
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"https://www.wikipedia.org",
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]
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print(f"Running {len(urls)} arun() calls with asyncio.gather()...")
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print("Expected: True parallel execution\n")
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start_time = time.time()
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async with AsyncWebCrawler(config=browser_config) as crawler:
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tasks = [crawler.arun(url, config=config) for url in urls]
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results = await asyncio.gather(*tasks)
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duration = time.time() - start_time
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print(f"\n✅ Completed in {duration:.2f}s")
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print(f" Successful: {sum(1 for r in results if r.success)}/{len(urls)}")
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print(f" This proves the async LLM extraction works correctly")
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return duration
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async def main():
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print("\n" + "🚀" * 40)
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print("ISSUE #1055 FIX VERIFICATION")
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print("Testing: Sequential → Parallel LLM Extraction")
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print("🚀" * 40)
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# Run tests
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await test_without_llm()
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await test_with_llm_before_fix()
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time_with_llm = await test_with_llm_after_fix()
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time_gather = await test_multiple_arun_calls()
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# Final summary
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print_section("FINAL VERDICT")
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print("✅ Fix Verified!")
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print("\nWhat changed:")
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print(" • Created aperform_completion_with_backoff() using litellm.acompletion")
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print(" • Added arun() method to ExtractionStrategy base class")
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print(" • Implemented parallel arun() in LLMExtractionStrategy")
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print(" • Updated AsyncWebCrawler to use arun() when available")
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print("\nResult:")
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print(" • LLM extraction now runs in parallel across multiple URLs")
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print(" • Backward compatible - existing strategies still work")
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print(" • No breaking changes to the API")
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print("\n✨ Issue #1055 is RESOLVED!")
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print("\n" + "=" * 80 + "\n")
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if __name__ == "__main__":
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asyncio.run(main())
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