Files
crawl4ai/tests/docker/test_docker.py
UncleCode 53ac3ec0b4 feat(docker): add Docker service integration and config serialization
Add Docker service integration with FastAPI server and client implementation.
Implement serialization utilities for BrowserConfig and CrawlerRunConfig to support
Docker service communication. Clean up imports and improve error handling.

- Add Crawl4aiDockerClient class
- Implement config serialization/deserialization
- Add FastAPI server with streaming support
- Add health check endpoint
- Clean up imports and type hints
2025-01-31 18:00:16 +08:00

174 lines
5.5 KiB
Python

import requests
import time
import httpx
import asyncio
from typing import Dict, Any
from crawl4ai import (
BrowserConfig, CrawlerRunConfig, DefaultMarkdownGenerator,
PruningContentFilter, JsonCssExtractionStrategy, LLMContentFilter, CacheMode
)
from crawl4ai.docker_client import Crawl4aiDockerClient
class Crawl4AiTester:
def __init__(self, base_url: str = "http://localhost:11235"):
self.base_url = base_url
def submit_and_wait(
self, request_data: Dict[str, Any], timeout: int = 300
) -> Dict[str, Any]:
# Submit crawl job
response = requests.post(f"{self.base_url}/crawl", json=request_data)
task_id = response.json()["task_id"]
print(f"Task ID: {task_id}")
# Poll for result
start_time = time.time()
while True:
if time.time() - start_time > timeout:
raise TimeoutError(
f"Task {task_id} did not complete within {timeout} seconds"
)
result = requests.get(f"{self.base_url}/task/{task_id}")
status = result.json()
if status["status"] == "failed":
print("Task failed:", status.get("error"))
raise Exception(f"Task failed: {status.get('error')}")
if status["status"] == "completed":
return status
time.sleep(2)
async def test_direct_api():
"""Test direct API endpoints without using the client SDK"""
print("\n=== Testing Direct API Calls ===")
# Test 1: Basic crawl with content filtering
browser_config = BrowserConfig(
headless=True,
viewport_width=1200,
viewport_height=800
)
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(
threshold=0.48,
threshold_type="fixed",
min_word_threshold=0
),
options={"ignore_links": True}
)
)
request_data = {
"urls": ["https://example.com"],
"browser_config": browser_config.dump(),
"crawler_config": crawler_config.dump()
}
# Make direct API call
async with httpx.AsyncClient() as client:
response = await client.post(
"http://localhost:8000/crawl",
json=request_data,
timeout=300
)
assert response.status_code == 200
result = response.json()
print("Basic crawl result:", result["success"])
# Test 2: Structured extraction with JSON CSS
schema = {
"baseSelector": "article.post",
"fields": [
{"name": "title", "selector": "h1", "type": "text"},
{"name": "content", "selector": ".content", "type": "html"}
]
}
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
extraction_strategy=JsonCssExtractionStrategy(schema=schema)
)
request_data["crawler_config"] = crawler_config.dump()
async with httpx.AsyncClient() as client:
response = await client.post(
"http://localhost:8000/crawl",
json=request_data
)
assert response.status_code == 200
result = response.json()
print("Structured extraction result:", result["success"])
# Test 3: Get schema
# async with httpx.AsyncClient() as client:
# response = await client.get("http://localhost:8000/schema")
# assert response.status_code == 200
# schemas = response.json()
# print("Retrieved schemas for:", list(schemas.keys()))
async def test_with_client():
"""Test using the Crawl4AI Docker client SDK"""
print("\n=== Testing Client SDK ===")
async with Crawl4aiDockerClient(verbose=True) as client:
# Test 1: Basic crawl
browser_config = BrowserConfig(headless=True)
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=PruningContentFilter(
threshold=0.48,
threshold_type="fixed"
)
)
)
result = await client.crawl(
urls=["https://example.com"],
browser_config=browser_config,
crawler_config=crawler_config
)
print("Client SDK basic crawl:", result.success)
# Test 2: LLM extraction with streaming
crawler_config = CrawlerRunConfig(
cache_mode=CacheMode.BYPASS,
markdown_generator=DefaultMarkdownGenerator(
content_filter=LLMContentFilter(
provider="openai/gpt-40",
instruction="Extract key technical concepts"
)
),
stream=True
)
async for result in await client.crawl(
urls=["https://example.com"],
browser_config=browser_config,
crawler_config=crawler_config
):
print(f"Streaming result for: {result.url}")
# # Test 3: Get schema
# schemas = await client.get_schema()
# print("Retrieved client schemas for:", list(schemas.keys()))
async def main():
"""Run all tests"""
# Test direct API
print("Testing direct API calls...")
# await test_direct_api()
# Test client SDK
print("\nTesting client SDK...")
await test_with_client()
if __name__ == "__main__":
asyncio.run(main())