fix: allow custom LLM providers for adaptive crawler embedding config. ref: #1291

- Change embedding_llm_config from Dict to Union[LLMConfig, Dict] for type safety
  - Add backward-compatible conversion property _embedding_llm_config_dict
  - Replace all hardcoded OpenAI embedding configs with configurable options
  - Fix LLMConfig object attribute access in query expansion logic
  - Add comprehensive example demonstrating multiple provider configurations
  - Update documentation with both LLMConfig object and dictionary usage patterns

  Users can now specify any LLM provider for query expansion in embedding strategy:
  - New: embedding_llm_config=LLMConfig(provider='anthropic/claude-3', api_token='key')
  - Old: embedding_llm_config={'provider': 'openai/gpt-4', 'api_token': 'key'} (still works)

crawl4ai/adaptive_crawler.py

@@ -19,7 +19,7 @@ import re
 from pathlib import Path
 
 from crawl4ai.async_webcrawler import AsyncWebCrawler
-from crawl4ai.async_configs import CrawlerRunConfig, LinkPreviewConfig
+from crawl4ai.async_configs import CrawlerRunConfig, LinkPreviewConfig, LLMConfig
 from crawl4ai.models import Link, CrawlResult
 import numpy as np
 
@@ -178,7 +178,7 @@ class AdaptiveConfig:
     
     # Embedding strategy parameters
     embedding_model: str = "sentence-transformers/all-MiniLM-L6-v2"
-    embedding_llm_config: Optional[Dict] = None  # Separate config for embeddings
+    embedding_llm_config: Optional[Union[LLMConfig, Dict]] = None  # Separate config for embeddings
     n_query_variations: int = 10
     coverage_threshold: float = 0.85
     alpha_shape_alpha: float = 0.5
@@ -250,6 +250,30 @@ class AdaptiveConfig:
         assert 0 <= self.embedding_quality_max_confidence <= 1, "embedding_quality_max_confidence must be between 0 and 1"
         assert self.embedding_quality_scale_factor > 0, "embedding_quality_scale_factor must be positive"
         assert 0 <= self.embedding_min_confidence_threshold <= 1, "embedding_min_confidence_threshold must be between 0 and 1"
+    
+    @property
+    def _embedding_llm_config_dict(self) -> Optional[Dict]:
+        """Convert LLMConfig to dict format for backward compatibility."""
+        if self.embedding_llm_config is None:
+            return None
+        
+        if isinstance(self.embedding_llm_config, dict):
+            # Already a dict - return as-is for backward compatibility
+            return self.embedding_llm_config
+        
+        # Convert LLMConfig object to dict format
+        return {
+            'provider': self.embedding_llm_config.provider,
+            'api_token': self.embedding_llm_config.api_token,
+            'base_url': getattr(self.embedding_llm_config, 'base_url', None),
+            'temperature': getattr(self.embedding_llm_config, 'temperature', None),
+            'max_tokens': getattr(self.embedding_llm_config, 'max_tokens', None),
+            'top_p': getattr(self.embedding_llm_config, 'top_p', None),
+            'frequency_penalty': getattr(self.embedding_llm_config, 'frequency_penalty', None),
+            'presence_penalty': getattr(self.embedding_llm_config, 'presence_penalty', None),
+            'stop': getattr(self.embedding_llm_config, 'stop', None),
+            'n': getattr(self.embedding_llm_config, 'n', None),
+        }
 
 
 class CrawlStrategy(ABC):
@@ -593,7 +617,7 @@ class StatisticalStrategy(CrawlStrategy):
 class EmbeddingStrategy(CrawlStrategy):
     """Embedding-based adaptive crawling using semantic space coverage"""
     
-    def __init__(self, embedding_model: str = None, llm_config: Dict = None):
+    def __init__(self, embedding_model: str = None, llm_config: Union[LLMConfig, Dict] = None):
         self.embedding_model = embedding_model or "sentence-transformers/all-MiniLM-L6-v2"
         self.llm_config = llm_config
         self._embedding_cache = {}
@@ -605,14 +629,24 @@ class EmbeddingStrategy(CrawlStrategy):
         self._kb_embeddings_hash = None  # Track KB changes
         self._validation_embeddings_cache = None  # Cache validation query embeddings
         self._kb_similarity_threshold = 0.95  # Threshold for deduplication
+    
+    def _get_embedding_llm_config_dict(self) -> Dict:
+        """Get embedding LLM config as dict with fallback to default."""
+        if hasattr(self, 'config') and self.config:
+            config_dict = self.config._embedding_llm_config_dict
+            if config_dict:
+                return config_dict
+        
+        # Fallback to default if no config provided
+        return {
+            'provider': 'openai/text-embedding-3-small',
+            'api_token': os.getenv('OPENAI_API_KEY')
+        }
         
     async def _get_embeddings(self, texts: List[str]) -> Any:
         """Get embeddings using configured method"""
         from .utils import get_text_embeddings
-        embedding_llm_config = {
-            'provider': 'openai/text-embedding-3-small',
-            'api_token': os.getenv('OPENAI_API_KEY')
-        }
+        embedding_llm_config = self._get_embedding_llm_config_dict()
         return await get_text_embeddings(
             texts, 
             embedding_llm_config,
@@ -679,8 +713,20 @@ class EmbeddingStrategy(CrawlStrategy):
         Return as a JSON array of strings."""
         
         # Use the LLM for query generation
-        provider = self.llm_config.get('provider', 'openai/gpt-4o-mini') if self.llm_config else 'openai/gpt-4o-mini'
-        api_token = self.llm_config.get('api_token') if self.llm_config else None
+        # Convert LLMConfig to dict if needed
+        llm_config_dict = None
+        if self.llm_config:
+            if isinstance(self.llm_config, dict):
+                llm_config_dict = self.llm_config
+            else:
+                # Convert LLMConfig object to dict
+                llm_config_dict = {
+                    'provider': self.llm_config.provider,
+                    'api_token': self.llm_config.api_token
+                }
+        
+        provider = llm_config_dict.get('provider', 'openai/gpt-4o-mini') if llm_config_dict else 'openai/gpt-4o-mini'
+        api_token = llm_config_dict.get('api_token') if llm_config_dict else None
         
         # response = perform_completion_with_backoff(
         #     provider=provider,
@@ -843,10 +889,7 @@ class EmbeddingStrategy(CrawlStrategy):
         
         # Batch embed only uncached links
         if texts_to_embed:
-            embedding_llm_config = {
-                'provider': 'openai/text-embedding-3-small',
-                'api_token': os.getenv('OPENAI_API_KEY')
-            }
+            embedding_llm_config = self._get_embedding_llm_config_dict()
             new_embeddings = await get_text_embeddings(texts_to_embed, embedding_llm_config, self.embedding_model)
 
             # Cache the new embeddings
@@ -1184,10 +1227,7 @@ class EmbeddingStrategy(CrawlStrategy):
             return
             
         # Get embeddings for new texts
-        embedding_llm_config = {
-            'provider': 'openai/text-embedding-3-small',
-            'api_token': os.getenv('OPENAI_API_KEY')
-        }        
+        embedding_llm_config = self._get_embedding_llm_config_dict()      
         new_embeddings = await get_text_embeddings(new_texts, embedding_llm_config, self.embedding_model)
 
         # Deduplicate embeddings before adding to KB
@@ -1256,10 +1296,12 @@ class AdaptiveCrawler:
         if strategy_name == "statistical":
             return StatisticalStrategy()
         elif strategy_name == "embedding":
-            return EmbeddingStrategy(
+            strategy = EmbeddingStrategy(
                 embedding_model=self.config.embedding_model,
                 llm_config=self.config.embedding_llm_config
             )
+            strategy.config = self.config  # Pass config to strategy
+            return strategy
         else:
             raise ValueError(f"Unknown strategy: {strategy_name}")
     

docs/examples/adaptive_crawling/llm_config_example.py

@@ -0,0 +1,154 @@
+import asyncio
+import os
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler, AdaptiveConfig, LLMConfig
+
+
+async def test_configuration(name: str, config: AdaptiveConfig, url: str, query: str):
+    """Test a specific configuration"""
+    print(f"\n{'='*60}")
+    print(f"Configuration: {name}")
+    print(f"{'='*60}")
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        result = await adaptive.digest(start_url=url, query=query)
+        
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        print(f"\n{'='*50}")
+        print(f"Pages crawled: {len(result.crawled_urls)}")
+        print(f"Final confidence: {adaptive.confidence:.1%}")
+        print(f"Stopped reason: {result.metrics.get('stopped_reason', 'max_pages')}")
+        
+        if result.metrics.get('is_irrelevant', False):
+            print("⚠️  Query detected as irrelevant!")
+        
+        return result
+
+
+async def llm_embedding():
+    """Demonstrate various embedding configurations"""
+    
+    print("EMBEDDING STRATEGY CONFIGURATION EXAMPLES")
+    print("=" * 60)
+    
+    # Base URL and query for testing
+    test_url = "https://docs.python.org/3/library/asyncio.html"
+    
+    openai_llm_config = LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token=os.getenv('OPENAI_API_KEY'),
+        temperature=0.7,
+        max_tokens=2000
+    )
+    config_openai = AdaptiveConfig(
+        strategy="embedding",
+        max_pages=10,
+        
+        # Use OpenAI embeddings
+        embedding_llm_config=openai_llm_config,
+        # embedding_llm_config={
+        #     'provider': 'openai/text-embedding-3-small',
+        #     'api_token': os.getenv('OPENAI_API_KEY')
+        # },
+        
+        # OpenAI embeddings are high quality, can be stricter
+        embedding_k_exp=4.0,
+        n_query_variations=12
+    )
+    
+    await test_configuration(
+        "OpenAI Embeddings",
+        config_openai,
+        test_url,
+        # "event-driven architecture patterns"
+        "async await context managers coroutines"
+    )
+    return
+    
+    
+
+async def basic_adaptive_crawling():
+    """Basic adaptive crawling example"""
+    
+    # Initialize the crawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Create an adaptive crawler with default settings (statistical strategy)
+        adaptive = AdaptiveCrawler(crawler)
+        
+        # Note: You can also use embedding strategy for semantic understanding:
+        # from crawl4ai import AdaptiveConfig
+        # config = AdaptiveConfig(strategy="embedding")
+        # adaptive = AdaptiveCrawler(crawler, config)
+        
+        # Start adaptive crawling
+        print("Starting adaptive crawl for Python async programming information...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/library/asyncio.html",
+            query="async await context managers coroutines"
+        )
+        
+        # Display crawl statistics
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        # Show final confidence
+        print(f"\n{'='*50}")
+        print(f"Final Confidence: {adaptive.confidence:.2%}")
+        print(f"Total Pages Crawled: {len(result.crawled_urls)}")
+        print(f"Knowledge Base Size: {len(adaptive.state.knowledge_base)} documents")
+        
+        
+        if adaptive.confidence >= 0.8:
+            print("✓ High confidence - can answer detailed questions about async Python")
+        elif adaptive.confidence >= 0.6:
+            print("~ Moderate confidence - can answer basic questions") 
+        else:
+            print("✗ Low confidence - need more information")
+
+
+
+if __name__ == "__main__":
+    asyncio.run(llm_embedding())
+    # asyncio.run(basic_adaptive_crawling())

docs/md_v2/core/adaptive-crawling.md

@@ -108,7 +108,19 @@ config = AdaptiveConfig(
     embedding_min_confidence_threshold=0.1  # Stop if completely irrelevant
 )
 
-# With custom embedding provider (e.g., OpenAI)
+# With custom LLM provider for query expansion (recommended)
+from crawl4ai import LLMConfig
+
+config = AdaptiveConfig(
+    strategy="embedding",
+    embedding_llm_config=LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token='your-api-key',
+        temperature=0.7
+    )
+)
+
+# Alternative: Dictionary format (backward compatible)
 config = AdaptiveConfig(
     strategy="embedding",
     embedding_llm_config={

tests/adaptive/test_llm_embedding.py

@@ -0,0 +1,154 @@
+import asyncio
+import os
+from crawl4ai import AsyncWebCrawler, AdaptiveCrawler, AdaptiveConfig, LLMConfig
+
+
+async def test_configuration(name: str, config: AdaptiveConfig, url: str, query: str):
+    """Test a specific configuration"""
+    print(f"\n{'='*60}")
+    print(f"Configuration: {name}")
+    print(f"{'='*60}")
+    
+    async with AsyncWebCrawler(verbose=False) as crawler:
+        adaptive = AdaptiveCrawler(crawler, config)
+        result = await adaptive.digest(start_url=url, query=query)
+        
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        print(f"\n{'='*50}")
+        print(f"Pages crawled: {len(result.crawled_urls)}")
+        print(f"Final confidence: {adaptive.confidence:.1%}")
+        print(f"Stopped reason: {result.metrics.get('stopped_reason', 'max_pages')}")
+        
+        if result.metrics.get('is_irrelevant', False):
+            print("⚠️  Query detected as irrelevant!")
+        
+        return result
+
+
+async def llm_embedding():
+    """Demonstrate various embedding configurations"""
+    
+    print("EMBEDDING STRATEGY CONFIGURATION EXAMPLES")
+    print("=" * 60)
+    
+    # Base URL and query for testing
+    test_url = "https://docs.python.org/3/library/asyncio.html"
+    
+    openai_llm_config = LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token=os.getenv('OPENAI_API_KEY'),
+        temperature=0.7,
+        max_tokens=2000
+    )
+    config_openai = AdaptiveConfig(
+        strategy="embedding",
+        max_pages=10,
+        
+        # Use OpenAI embeddings
+        embedding_llm_config=openai_llm_config,
+        # embedding_llm_config={
+        #     'provider': 'openai/text-embedding-3-small',
+        #     'api_token': os.getenv('OPENAI_API_KEY')
+        # },
+        
+        # OpenAI embeddings are high quality, can be stricter
+        embedding_k_exp=4.0,
+        n_query_variations=12
+    )
+    
+    await test_configuration(
+        "OpenAI Embeddings",
+        config_openai,
+        test_url,
+        # "event-driven architecture patterns"
+        "async await context managers coroutines"
+    )
+    return
+    
+    
+
+async def basic_adaptive_crawling():
+    """Basic adaptive crawling example"""
+    
+    # Initialize the crawler
+    async with AsyncWebCrawler(verbose=True) as crawler:
+        # Create an adaptive crawler with default settings (statistical strategy)
+        adaptive = AdaptiveCrawler(crawler)
+        
+        # Note: You can also use embedding strategy for semantic understanding:
+        # from crawl4ai import AdaptiveConfig
+        # config = AdaptiveConfig(strategy="embedding")
+        # adaptive = AdaptiveCrawler(crawler, config)
+        
+        # Start adaptive crawling
+        print("Starting adaptive crawl for Python async programming information...")
+        result = await adaptive.digest(
+            start_url="https://docs.python.org/3/library/asyncio.html",
+            query="async await context managers coroutines"
+        )
+        
+        # Display crawl statistics
+        print("\n" + "="*50)
+        print("CRAWL STATISTICS")
+        print("="*50)
+        adaptive.print_stats(detailed=False)
+        
+        # Get the most relevant content found
+        print("\n" + "="*50)
+        print("MOST RELEVANT PAGES")
+        print("="*50)
+        
+        relevant_pages = adaptive.get_relevant_content(top_k=5)
+        for i, page in enumerate(relevant_pages, 1):
+            print(f"\n{i}. {page['url']}")
+            print(f"   Relevance Score: {page['score']:.2%}")
+            
+            # Show a snippet of the content
+            content = page['content'] or ""
+            if content:
+                snippet = content[:200].replace('\n', ' ')
+                if len(content) > 200:
+                    snippet += "..."
+                print(f"   Preview: {snippet}")
+        
+        # Show final confidence
+        print(f"\n{'='*50}")
+        print(f"Final Confidence: {adaptive.confidence:.2%}")
+        print(f"Total Pages Crawled: {len(result.crawled_urls)}")
+        print(f"Knowledge Base Size: {len(adaptive.state.knowledge_base)} documents")
+        
+        
+        if adaptive.confidence >= 0.8:
+            print("✓ High confidence - can answer detailed questions about async Python")
+        elif adaptive.confidence >= 0.6:
+            print("~ Moderate confidence - can answer basic questions") 
+        else:
+            print("✗ Low confidence - need more information")
+
+
+
+if __name__ == "__main__":
+    asyncio.run(llm_embedding())
+    # asyncio.run(basic_adaptive_crawling())