201843a204
- Implemented `test_adapter_verification.py` to verify correct usage of browser adapters. - Created `test_all_features.py` for a comprehensive suite covering URL seeding, adaptive crawling, browser adapters, proxy rotation, and dispatchers. - Developed `test_anti_bot_strategy.py` to validate the functionality of various anti-bot strategies. - Added `test_antibot_simple.py` for simple testing of anti-bot strategies using async web crawling. - Introduced `test_bot_detection.py` to assess adapter performance against bot detection mechanisms. - Compiled `test_final_summary.py` to provide a detailed summary of all tests and their results.
306 lines
10 KiB
Python
306 lines
10 KiB
Python
import dns.resolver
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import logging
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import yaml
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import os
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from datetime import datetime
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from enum import Enum
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from pathlib import Path
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from fastapi import Request
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from typing import Dict, Optional
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# Import dispatchers from crawl4ai
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from crawl4ai.async_dispatcher import (
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BaseDispatcher,
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MemoryAdaptiveDispatcher,
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SemaphoreDispatcher,
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)
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class TaskStatus(str, Enum):
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PROCESSING = "processing"
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FAILED = "failed"
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COMPLETED = "completed"
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class FilterType(str, Enum):
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RAW = "raw"
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FIT = "fit"
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BM25 = "bm25"
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LLM = "llm"
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# ============================================================================
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# Dispatcher Configuration and Factory
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# ============================================================================
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# Default dispatcher configurations (hardcoded, no env variables)
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DISPATCHER_DEFAULTS = {
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"memory_adaptive": {
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"memory_threshold_percent": 70.0,
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"critical_threshold_percent": 85.0,
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"recovery_threshold_percent": 65.0,
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"check_interval": 1.0,
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"max_session_permit": 20,
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"fairness_timeout": 600.0,
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"memory_wait_timeout": 600.0,
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},
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"semaphore": {
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"semaphore_count": 5,
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"max_session_permit": 10,
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}
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}
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DEFAULT_DISPATCHER_TYPE = "memory_adaptive"
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def create_dispatcher(dispatcher_type: str) -> BaseDispatcher:
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"""
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Factory function to create dispatcher instances.
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Args:
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dispatcher_type: Type of dispatcher to create ("memory_adaptive" or "semaphore")
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Returns:
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BaseDispatcher instance
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Raises:
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ValueError: If dispatcher type is unknown
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"""
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dispatcher_type = dispatcher_type.lower()
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if dispatcher_type == "memory_adaptive":
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config = DISPATCHER_DEFAULTS["memory_adaptive"]
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return MemoryAdaptiveDispatcher(
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memory_threshold_percent=config["memory_threshold_percent"],
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critical_threshold_percent=config["critical_threshold_percent"],
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recovery_threshold_percent=config["recovery_threshold_percent"],
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check_interval=config["check_interval"],
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max_session_permit=config["max_session_permit"],
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fairness_timeout=config["fairness_timeout"],
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memory_wait_timeout=config["memory_wait_timeout"],
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)
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elif dispatcher_type == "semaphore":
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config = DISPATCHER_DEFAULTS["semaphore"]
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return SemaphoreDispatcher(
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semaphore_count=config["semaphore_count"],
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max_session_permit=config["max_session_permit"],
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)
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else:
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raise ValueError(f"Unknown dispatcher type: {dispatcher_type}")
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def get_dispatcher_config(dispatcher_type: str) -> Dict:
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"""
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Get configuration for a dispatcher type.
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Args:
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dispatcher_type: Type of dispatcher ("memory_adaptive" or "semaphore")
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Returns:
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Dictionary containing dispatcher configuration
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Raises:
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ValueError: If dispatcher type is unknown
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"""
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dispatcher_type = dispatcher_type.lower()
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if dispatcher_type not in DISPATCHER_DEFAULTS:
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raise ValueError(f"Unknown dispatcher type: {dispatcher_type}")
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return DISPATCHER_DEFAULTS[dispatcher_type].copy()
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def get_available_dispatchers() -> Dict[str, Dict]:
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"""
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Get information about all available dispatchers.
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Returns:
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Dictionary mapping dispatcher types to their metadata
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"""
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return {
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"memory_adaptive": {
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"name": "Memory Adaptive Dispatcher",
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"description": "Dynamically adjusts concurrency based on system memory usage. "
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"Monitors memory pressure and adapts crawl sessions accordingly.",
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"config": DISPATCHER_DEFAULTS["memory_adaptive"],
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"features": [
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"Dynamic concurrency adjustment",
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"Memory pressure monitoring",
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"Automatic task requeuing under high memory",
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"Fairness timeout for long-waiting URLs"
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]
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},
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"semaphore": {
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"name": "Semaphore Dispatcher",
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"description": "Fixed concurrency limit using semaphore-based control. "
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"Simple and predictable for controlled crawling.",
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"config": DISPATCHER_DEFAULTS["semaphore"],
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"features": [
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"Fixed concurrency limit",
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"Simple semaphore-based control",
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"Predictable resource usage"
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]
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}
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}
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# ============================================================================
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# End Dispatcher Configuration
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# ============================================================================
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def load_config() -> Dict:
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"""Load and return application configuration with environment variable overrides."""
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config_path = Path(__file__).parent / "config.yml"
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with open(config_path, "r") as config_file:
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config = yaml.safe_load(config_file)
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# Override LLM provider from environment if set
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llm_provider = os.environ.get("LLM_PROVIDER")
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if llm_provider:
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config["llm"]["provider"] = llm_provider
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logging.info(f"LLM provider overridden from environment: {llm_provider}")
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# Also support direct API key from environment if the provider-specific key isn't set
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llm_api_key = os.environ.get("LLM_API_KEY")
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if llm_api_key and "api_key" not in config["llm"]:
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config["llm"]["api_key"] = llm_api_key
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logging.info("LLM API key loaded from LLM_API_KEY environment variable")
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return config
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def setup_logging(config: Dict) -> None:
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"""Configure application logging."""
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logging.basicConfig(
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level=config["logging"]["level"],
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format=config["logging"]["format"]
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)
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def get_base_url(request: Request) -> str:
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"""Get base URL including scheme and host."""
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return f"{request.url.scheme}://{request.url.netloc}"
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def is_task_id(value: str) -> bool:
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"""Check if the value matches task ID pattern."""
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return value.startswith("llm_") and "_" in value
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def datetime_handler(obj: any) -> Optional[str]:
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"""Handle datetime serialization for JSON."""
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if hasattr(obj, 'isoformat'):
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return obj.isoformat()
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raise TypeError(f"Object of type {type(obj)} is not JSON serializable")
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def should_cleanup_task(created_at: str, ttl_seconds: int = 3600) -> bool:
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"""Check if task should be cleaned up based on creation time."""
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created = datetime.fromisoformat(created_at)
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return (datetime.now() - created).total_seconds() > ttl_seconds
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def decode_redis_hash(hash_data: Dict[bytes, bytes]) -> Dict[str, str]:
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"""Decode Redis hash data from bytes to strings."""
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return {k.decode('utf-8'): v.decode('utf-8') for k, v in hash_data.items()}
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def get_llm_api_key(config: Dict, provider: Optional[str] = None) -> Optional[str]:
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"""Get the appropriate API key based on the LLM provider.
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Args:
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config: The application configuration dictionary
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provider: Optional provider override (e.g., "openai/gpt-4")
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Returns:
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The API key if directly configured, otherwise None to let litellm handle it
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"""
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# Check if direct API key is configured (for backward compatibility)
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if "api_key" in config["llm"]:
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return config["llm"]["api_key"]
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# Return None - litellm will automatically find the right environment variable
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return None
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def validate_llm_provider(config: Dict, provider: Optional[str] = None) -> tuple[bool, str]:
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"""Validate that the LLM provider has an associated API key.
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Args:
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config: The application configuration dictionary
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provider: Optional provider override (e.g., "openai/gpt-4")
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Returns:
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Tuple of (is_valid, error_message)
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"""
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# If a direct API key is configured, validation passes
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if "api_key" in config["llm"]:
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return True, ""
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# Otherwise, trust that litellm will find the appropriate environment variable
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# We can't easily validate this without reimplementing litellm's logic
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return True, ""
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def get_llm_temperature(config: Dict, provider: Optional[str] = None) -> Optional[float]:
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"""Get temperature setting based on the LLM provider.
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Priority order:
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1. Provider-specific environment variable (e.g., OPENAI_TEMPERATURE)
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2. Global LLM_TEMPERATURE environment variable
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3. None (to use litellm/provider defaults)
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Args:
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config: The application configuration dictionary
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provider: Optional provider override (e.g., "openai/gpt-4")
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Returns:
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The temperature setting if configured, otherwise None
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"""
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# Check provider-specific temperature first
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if provider:
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provider_name = provider.split('/')[0].upper()
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provider_temp = os.environ.get(f"{provider_name}_TEMPERATURE")
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if provider_temp:
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try:
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return float(provider_temp)
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except ValueError:
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logging.warning(f"Invalid temperature value for {provider_name}: {provider_temp}")
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# Check global LLM_TEMPERATURE
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global_temp = os.environ.get("LLM_TEMPERATURE")
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if global_temp:
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try:
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return float(global_temp)
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except ValueError:
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logging.warning(f"Invalid global temperature value: {global_temp}")
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# Return None to use litellm/provider defaults
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return None
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def get_llm_base_url(config: Dict, provider: Optional[str] = None) -> Optional[str]:
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"""Get base URL setting based on the LLM provider.
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Priority order:
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1. Provider-specific environment variable (e.g., OPENAI_BASE_URL)
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2. Global LLM_BASE_URL environment variable
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3. None (to use default endpoints)
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Args:
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config: The application configuration dictionary
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provider: Optional provider override (e.g., "openai/gpt-4")
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Returns:
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The base URL if configured, otherwise None
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"""
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# Check provider-specific base URL first
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if provider:
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provider_name = provider.split('/')[0].upper()
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provider_url = os.environ.get(f"{provider_name}_BASE_URL")
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if provider_url:
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return provider_url
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# Check global LLM_BASE_URL
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return os.environ.get("LLM_BASE_URL")
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def verify_email_domain(email: str) -> bool:
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try:
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domain = email.split('@')[1]
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# Try to resolve MX records for the domain.
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records = dns.resolver.resolve(domain, 'MX')
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return True if records else False
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except Exception as e:
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return False |