feat(crawl4ai): Implement adaptive crawling feature

This commit introduces the adaptive crawling feature to the crawl4ai project. The adaptive crawling feature intelligently determines when sufficient information has been gathered during a crawl, improving efficiency and reducing unnecessary resource usage.

The changes include the addition of new files related to the adaptive crawler, modifications to the existing files, and updates to the documentation. The new files include the main adaptive crawler script, utility functions, and various configuration and strategy scripts. The existing files that were modified include the project's initialization file and utility functions. The documentation has been updated to include detailed explanations and examples of the adaptive crawling feature.

The adaptive crawling feature will significantly enhance the capabilities of the crawl4ai project, providing users with a more efficient and intelligent web crawling tool.

Significant modifications:
- Added adaptive_crawler.py and related scripts
- Modified __init__.py and utils.py
- Updated documentation with details about the adaptive crawling feature
- Added tests for the new feature

BREAKING CHANGE: This is a significant feature addition that may affect the overall behavior of the crawl4ai project. Users are advised to review the updated documentation to understand how to use the new feature.

Refs: #123, #456

crawl4ai/utils.py

@@ -32,7 +32,6 @@ import hashlib
 
 from urllib.robotparser import RobotFileParser
 import aiohttp
-from urllib.parse import urlparse, urlunparse
 from functools import lru_cache
 
 from packaging import version
@@ -43,6 +42,14 @@ from itertools import chain
 from collections import deque
 from typing import  Generator, Iterable
 
+import numpy as np
+
+from urllib.parse import (
+    urljoin, urlparse, urlunparse,
+    parse_qsl, urlencode, quote, unquote
+)
+
+
 def chunk_documents(
     documents: Iterable[str],
     chunk_token_threshold: int,
@@ -2071,6 +2078,92 @@ def normalize_url(href, base_url):
     return normalized
 
 
+def normalize_url(
+    href: str,
+    base_url: str,
+    *,
+    drop_query_tracking=True,
+    sort_query=True,
+    keep_fragment=False,
+    extra_drop_params=None
+):
+    """
+    Extended URL normalizer
+
+    Parameters
+    ----------
+    href : str
+        The raw link extracted from a page.
+    base_url : str
+        The page’s canonical URL (used to resolve relative links).
+    drop_query_tracking : bool (default True)
+        Remove common tracking query parameters.
+    sort_query : bool (default True)
+        Alphabetically sort query keys for deterministic output.
+    keep_fragment : bool (default False)
+        Preserve the hash fragment (#section) if you need in-page links.
+    extra_drop_params : Iterable[str] | None
+        Additional query keys to strip (case-insensitive).
+
+    Returns
+    -------
+    str | None
+        A clean, canonical URL or None if href is empty/None.
+    """
+    if not href:
+        return None
+
+    # Resolve relative paths first
+    full_url = urljoin(base_url, href.strip())
+
+    # Parse once, edit parts, then rebuild
+    parsed = urlparse(full_url)
+
+    # ── netloc ──
+    netloc = parsed.netloc.lower()
+
+    # ── path ──
+    # Strip duplicate slashes and trailing “/” (except root)
+    path = quote(unquote(parsed.path))
+    if path.endswith('/') and path != '/':
+        path = path.rstrip('/')
+
+    # ── query ──
+    query = parsed.query
+    if query:
+        # explode, mutate, then rebuild
+        params = [(k.lower(), v) for k, v in parse_qsl(query, keep_blank_values=True)]
+
+        if drop_query_tracking:
+            default_tracking = {
+                'utm_source', 'utm_medium', 'utm_campaign', 'utm_term',
+                'utm_content', 'gclid', 'fbclid', 'ref', 'ref_src'
+            }
+            if extra_drop_params:
+                default_tracking |= {p.lower() for p in extra_drop_params}
+            params = [(k, v) for k, v in params if k not in default_tracking]
+
+        if sort_query:
+            params.sort(key=lambda kv: kv[0])
+
+        query = urlencode(params, doseq=True) if params else ''
+
+    # ── fragment ──
+    fragment = parsed.fragment if keep_fragment else ''
+
+    # Re-assemble
+    normalized = urlunparse((
+        parsed.scheme,
+        netloc,
+        path,
+        parsed.params,
+        query,
+        fragment
+    ))
+
+    return normalized
+
+
 def normalize_url_for_deep_crawl(href, base_url):
     """Normalize URLs to ensure consistent format"""
     from urllib.parse import urljoin, urlparse, urlunparse, parse_qs, urlencode
@@ -3148,3 +3241,108 @@ def calculate_total_score(
     
     return max(0.0, min(total, 10.0))
 
+
+# Embedding utilities
+async def get_text_embeddings(
+    texts: List[str], 
+    llm_config: Optional[Dict] = None,
+    model_name: str = "sentence-transformers/all-MiniLM-L6-v2",
+    batch_size: int = 32
+) -> np.ndarray:
+    """
+    Compute embeddings for a list of texts using specified model.
+    
+    Args:
+        texts: List of texts to embed
+        llm_config: Optional LLM configuration for API-based embeddings
+        model_name: Model name (used when llm_config is None)
+        batch_size: Batch size for processing
+        
+    Returns:
+        numpy array of embeddings
+    """
+    import numpy as np
+    
+    if not texts:
+        return np.array([])
+    
+    # If LLMConfig provided, use litellm for embeddings
+    if llm_config is not None:
+        from litellm import aembedding
+        
+        # Get embedding model from config or use default
+        embedding_model = llm_config.get('provider', 'text-embedding-3-small')
+        api_base = llm_config.get('base_url', llm_config.get('api_base'))
+        
+        # Prepare kwargs
+        kwargs = {
+            'model': embedding_model,
+            'input': texts,
+            'api_key': llm_config.get('api_token', llm_config.get('api_key'))
+        }
+        
+        if api_base:
+            kwargs['api_base'] = api_base
+            
+        # Handle OpenAI-compatible endpoints
+        if api_base and 'openai/' not in embedding_model:
+            kwargs['model'] = f"openai/{embedding_model}"
+        
+        # Get embeddings
+        response = await aembedding(**kwargs)
+        
+        # Extract embeddings from response
+        embeddings = []
+        for item in response.data:
+            embeddings.append(item['embedding'])
+            
+        return np.array(embeddings)
+    
+    # Default: use sentence-transformers
+    else:
+        # Lazy load to avoid importing heavy libraries unless needed
+        from sentence_transformers import SentenceTransformer
+        
+        # Cache the model in function attribute to avoid reloading
+        if not hasattr(get_text_embeddings, '_models'):
+            get_text_embeddings._models = {}
+        
+        if model_name not in get_text_embeddings._models:
+            get_text_embeddings._models[model_name] = SentenceTransformer(model_name)
+        
+        encoder = get_text_embeddings._models[model_name]
+        
+        # Batch encode for efficiency
+        embeddings = encoder.encode(
+            texts,
+            batch_size=batch_size,
+            show_progress_bar=False,
+            convert_to_numpy=True
+        )
+        
+        return embeddings
+
+
+def get_text_embeddings_sync(
+    texts: List[str],
+    llm_config: Optional[Dict] = None,
+    model_name: str = "sentence-transformers/all-MiniLM-L6-v2",
+    batch_size: int = 32
+) -> np.ndarray:
+    """Synchronous wrapper for get_text_embeddings"""
+    import numpy as np
+    return asyncio.run(get_text_embeddings(texts, llm_config, model_name, batch_size))
+
+
+def cosine_similarity(vec1: np.ndarray, vec2: np.ndarray) -> float:
+    """Calculate cosine similarity between two vectors"""
+    import numpy as np
+    dot_product = np.dot(vec1, vec2)
+    norm_product = np.linalg.norm(vec1) * np.linalg.norm(vec2)
+    return float(dot_product / norm_product) if norm_product != 0 else 0.0
+
+
+def cosine_distance(vec1: np.ndarray, vec2: np.ndarray) -> float:
+    """Calculate cosine distance (1 - similarity) between two vectors"""
+    return 1 - cosine_similarity(vec1, vec2)
+