Advanced Adaptive Strategies

Overview

While the default adaptive crawling configuration works well for most use cases, understanding the underlying strategies and scoring mechanisms allows you to fine-tune the crawler for specific domains and requirements.

The Three-Layer Scoring System

1. Coverage Score

Coverage measures how comprehensively your knowledge base covers the query terms and related concepts.

Mathematical Foundation

Coverage(K, Q) = Σ(t ∈ Q) score(t, K) / |Q|

where score(t, K) = doc_coverage(t) × (1 + freq_boost(t))

Components

• Document Coverage: Percentage of documents containing the term
• Frequency Boost: Logarithmic bonus for term frequency
• Query Decomposition: Handles multi-word queries intelligently

Tuning Coverage

# For technical documentation with specific terminology
config = AdaptiveConfig(
    confidence_threshold=0.85,  # Require high coverage
    top_k_links=5              # Cast wider net
)

# For general topics with synonyms
config = AdaptiveConfig(
    confidence_threshold=0.6,   # Lower threshold
    top_k_links=2              # More focused
)

2. Consistency Score

Consistency evaluates whether the information across pages is coherent and non-contradictory.

How It Works

1. Extracts key statements from each document
2. Compares statements across documents
3. Measures agreement vs. contradiction
4. Returns normalized score (0-1)

Practical Impact

• High consistency (>0.8): Information is reliable and coherent
• Medium consistency (0.5-0.8): Some variation, but generally aligned
• Low consistency (<0.5): Conflicting information, need more sources

3. Saturation Score

Saturation detects when new pages stop providing novel information.

Detection Algorithm

# Tracks new unique terms per page
new_terms_page_1 = 50
new_terms_page_2 = 30  # 60% of first
new_terms_page_3 = 15  # 50% of second
new_terms_page_4 = 5   # 33% of third
# Saturation detected: rapidly diminishing returns

Configuration

config = AdaptiveConfig(
    min_gain_threshold=0.1  # Stop if <10% new information
)

Link Ranking Algorithm

Expected Information Gain

Each uncrawled link is scored based on:

ExpectedGain(link) = Relevance × Novelty × Authority

1. Relevance Scoring

Uses BM25 algorithm on link preview text:

relevance = BM25(link.preview_text, query)

Factors: - Term frequency in preview - Inverse document frequency - Preview length normalization

2. Novelty Estimation

Measures how different the link appears from already-crawled content:

novelty = 1 - max_similarity(preview, knowledge_base)

Prevents crawling duplicate or highly similar pages.

3. Authority Calculation

URL structure and domain analysis:

authority = f(domain_rank, url_depth, url_structure)

Factors: - Domain reputation - URL depth (fewer slashes = higher authority) - Clean URL structure

Custom Link Scoring

class CustomLinkScorer:
    def score(self, link: Link, query: str, state: CrawlState) -> float:
        # Prioritize specific URL patterns
        if "/api/reference/" in link.href:
            return 2.0  # Double the score

        # Deprioritize certain sections
        if "/archive/" in link.href:
            return 0.1  # Reduce score by 90%

        # Default scoring
        return 1.0

# Use with adaptive crawler
adaptive = AdaptiveCrawler(
    crawler,
    config=config,
    link_scorer=CustomLinkScorer()
)

Domain-Specific Configurations

Technical Documentation

tech_doc_config = AdaptiveConfig(
    confidence_threshold=0.85,
    max_pages=30,
    top_k_links=3,
    min_gain_threshold=0.05  # Keep crawling for small gains
)

Rationale: - High threshold ensures comprehensive coverage - Lower gain threshold captures edge cases - Moderate link following for depth

News & Articles

news_config = AdaptiveConfig(
    confidence_threshold=0.6,
    max_pages=10,
    top_k_links=5,
    min_gain_threshold=0.15  # Stop quickly on repetition
)

Rationale: - Lower threshold (articles often repeat information) - Higher gain threshold (avoid duplicate stories) - More links per page (explore different perspectives)

E-commerce

ecommerce_config = AdaptiveConfig(
    confidence_threshold=0.7,
    max_pages=20,
    top_k_links=2,
    min_gain_threshold=0.1
)

Rationale: - Balanced threshold for product variations - Focused link following (avoid infinite products) - Standard gain threshold

Research & Academic

research_config = AdaptiveConfig(
    confidence_threshold=0.9,
    max_pages=50,
    top_k_links=4,
    min_gain_threshold=0.02  # Very low - capture citations
)

Rationale: - Very high threshold for completeness - Many pages allowed for thorough research - Very low gain threshold to capture references

Performance Optimization

Memory Management

# For large crawls, use streaming
config = AdaptiveConfig(
    max_pages=100,
    save_state=True,
    state_path="large_crawl.json"
)

# Periodically clean state
if len(state.knowledge_base) > 1000:
    # Keep only most relevant
    state.knowledge_base = get_top_relevant(state.knowledge_base, 500)

Parallel Processing

# Use multiple start points
start_urls = [
    "https://docs.example.com/intro",
    "https://docs.example.com/api",
    "https://docs.example.com/guides"
]

# Crawl in parallel
tasks = [
    adaptive.digest(url, query)
    for url in start_urls
]
results = await asyncio.gather(*tasks)

Caching Strategy

# Enable caching for repeated crawls
async with AsyncWebCrawler(
    config=BrowserConfig(
        cache_mode=CacheMode.ENABLED
    )
) as crawler:
    adaptive = AdaptiveCrawler(crawler, config)

Debugging & Analysis

Enable Verbose Logging

import logging

logging.basicConfig(level=logging.DEBUG)
adaptive = AdaptiveCrawler(crawler, config, verbose=True)

Analyze Crawl Patterns

# After crawling
state = await adaptive.digest(start_url, query)

# Analyze link selection
print("Link selection order:")
for i, url in enumerate(state.crawl_order):
    print(f"{i+1}. {url}")

# Analyze term discovery
print("\nTerm discovery rate:")
for i, new_terms in enumerate(state.new_terms_history):
    print(f"Page {i+1}: {new_terms} new terms")

# Analyze score progression
print("\nScore progression:")
print(f"Coverage: {state.metrics['coverage_history']}")
print(f"Saturation: {state.metrics['saturation_history']}")

Export for Analysis

# Export detailed metrics
import json

metrics = {
    "query": query,
    "total_pages": len(state.crawled_urls),
    "confidence": adaptive.confidence,
    "coverage_stats": adaptive.coverage_stats,
    "crawl_order": state.crawl_order,
    "term_frequencies": dict(state.term_frequencies),
    "new_terms_history": state.new_terms_history
}

with open("crawl_analysis.json", "w") as f:
    json.dump(metrics, f, indent=2)

Custom Strategies

Implementing a Custom Strategy

from crawl4ai.adaptive_crawler import BaseStrategy

class DomainSpecificStrategy(BaseStrategy):
    def calculate_coverage(self, state: CrawlState) -> float:
        # Custom coverage calculation
        # e.g., weight certain terms more heavily
        pass

    def calculate_consistency(self, state: CrawlState) -> float:
        # Custom consistency logic
        # e.g., domain-specific validation
        pass

    def rank_links(self, links: List[Link], state: CrawlState) -> List[Link]:
        # Custom link ranking
        # e.g., prioritize specific URL patterns
        pass

# Use custom strategy
adaptive = AdaptiveCrawler(
    crawler,
    config=config,
    strategy=DomainSpecificStrategy()
)

Combining Strategies

class HybridStrategy(BaseStrategy):
    def __init__(self):
        self.strategies = [
            TechnicalDocStrategy(),
            SemanticSimilarityStrategy(),
            URLPatternStrategy()
        ]

    def calculate_confidence(self, state: CrawlState) -> float:
        # Weighted combination of strategies
        scores = [s.calculate_confidence(state) for s in self.strategies]
        weights = [0.5, 0.3, 0.2]
        return sum(s * w for s, w in zip(scores, weights))

Best Practices

1. Start Conservative

Begin with default settings and adjust based on results:

# Start with defaults
result = await adaptive.digest(url, query)

# Analyze and adjust
if adaptive.confidence < 0.7:
    config.max_pages += 10
    config.confidence_threshold -= 0.1

2. Monitor Resource Usage

import psutil

# Check memory before large crawls
memory_percent = psutil.virtual_memory().percent
if memory_percent > 80:
    config.max_pages = min(config.max_pages, 20)

3. Use Domain Knowledge

# For API documentation
if "api" in start_url:
    config.top_k_links = 2  # APIs have clear structure

# For blogs
if "blog" in start_url:
    config.min_gain_threshold = 0.2  # Avoid similar posts

4. Validate Results

# Always validate the knowledge base
relevant_content = adaptive.get_relevant_content(top_k=10)

# Check coverage
query_terms = set(query.lower().split())
covered_terms = set()

for doc in relevant_content:
    content_lower = doc['content'].lower()
    for term in query_terms:
        if term in content_lower:
            covered_terms.add(term)

coverage_ratio = len(covered_terms) / len(query_terms)
print(f"Query term coverage: {coverage_ratio:.0%}")

Next Steps

• Explore Custom Strategy Implementation
• Learn about Knowledge Base Management
• See Performance Benchmarks