diff --git a/docs/examples/docker_example.py b/docs/examples/docker_example.py
new file mode 100644
index 00000000..c22acd55
--- /dev/null
+++ b/docs/examples/docker_example.py
@@ -0,0 +1,300 @@
+import requests
+import json
+import time
+import sys
+import base64
+import os
+from typing import Dict, Any
+
+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)
+
+def test_docker_deployment(version="basic"):
+    tester = Crawl4AiTester()
+    print(f"Testing Crawl4AI Docker {version} version")
+    
+    # Health check with timeout and retry
+    max_retries = 5
+    for i in range(max_retries):
+        try:
+            health = requests.get(f"{tester.base_url}/health", timeout=10)
+            print("Health check:", health.json())
+            break
+        except requests.exceptions.RequestException as e:
+            if i == max_retries - 1:
+                print(f"Failed to connect after {max_retries} attempts")
+                sys.exit(1)
+            print(f"Waiting for service to start (attempt {i+1}/{max_retries})...")
+            time.sleep(5)
+    
+    # Test cases based on version
+    test_basic_crawl(tester)
+    
+    # if version in ["full", "transformer"]:
+    #     test_cosine_extraction(tester)
+
+    # test_js_execution(tester)
+    # test_css_selector(tester)
+    # test_structured_extraction(tester)
+    # test_llm_extraction(tester)
+    # test_llm_with_ollama(tester)
+    # test_screenshot(tester)
+    
+
+def test_basic_crawl(tester: Crawl4AiTester):
+    print("\n=== Testing Basic Crawl ===")
+    request = {
+        "urls": "https://www.nbcnews.com/business",
+        "priority": 10
+    }
+    
+    result = tester.submit_and_wait(request)
+    print(f"Basic crawl result length: {len(result['result']['markdown'])}")
+    assert result["result"]["success"]
+    assert len(result["result"]["markdown"]) > 0
+
+def test_js_execution(tester: Crawl4AiTester):
+    print("\n=== Testing JS Execution ===")
+    request = {
+        "urls": "https://www.nbcnews.com/business",
+        "priority": 8,
+        "js_code": [
+            "const loadMoreButton = Array.from(document.querySelectorAll('button')).find(button => button.textContent.includes('Load More')); loadMoreButton && loadMoreButton.click();"
+        ],
+        "wait_for": "article.tease-card:nth-child(10)",
+        "crawler_params": {
+            "headless": True
+        }
+    }
+    
+    result = tester.submit_and_wait(request)
+    print(f"JS execution result length: {len(result['result']['markdown'])}")
+    assert result["result"]["success"]
+
+def test_css_selector(tester: Crawl4AiTester):
+    print("\n=== Testing CSS Selector ===")
+    request = {
+        "urls": "https://www.nbcnews.com/business",
+        "priority": 7,
+        "css_selector": ".wide-tease-item__description",
+        "crawler_params": {
+            "headless": True
+        },
+        "extra": {"word_count_threshold": 10}
+        
+    }
+    
+    result = tester.submit_and_wait(request)
+    print(f"CSS selector result length: {len(result['result']['markdown'])}")
+    assert result["result"]["success"]
+
+def test_structured_extraction(tester: Crawl4AiTester):
+    print("\n=== Testing Structured Extraction ===")
+    schema = {
+        "name": "Coinbase Crypto Prices",
+        "baseSelector": ".cds-tableRow-t45thuk",
+        "fields": [
+            {
+                "name": "crypto",
+                "selector": "td:nth-child(1) h2",
+                "type": "text",
+            },
+            {
+                "name": "symbol",
+                "selector": "td:nth-child(1) p",
+                "type": "text",
+            },
+            {
+                "name": "price",
+                "selector": "td:nth-child(2)",
+                "type": "text",
+            }
+        ],
+    }
+    
+    request = {
+        "urls": "https://www.coinbase.com/explore",
+        "priority": 9,
+        "extraction_config": {
+            "type": "json_css",
+            "params": {
+                "schema": schema
+            }
+        }
+    }
+    
+    result = tester.submit_and_wait(request)
+    extracted = json.loads(result["result"]["extracted_content"])
+    print(f"Extracted {len(extracted)} items")
+    print("Sample item:", json.dumps(extracted[0], indent=2))
+    assert result["result"]["success"]
+    assert len(extracted) > 0
+
+def test_llm_extraction(tester: Crawl4AiTester):
+    print("\n=== Testing LLM Extraction ===")
+    schema = {
+        "type": "object",
+        "properties": {
+            "model_name": {
+                "type": "string",
+                "description": "Name of the OpenAI model."
+            },
+            "input_fee": {
+                "type": "string",
+                "description": "Fee for input token for the OpenAI model."
+            },
+            "output_fee": {
+                "type": "string",
+                "description": "Fee for output token for the OpenAI model."
+            }
+        },
+        "required": ["model_name", "input_fee", "output_fee"]
+    }
+    
+    request = {
+        "urls": "https://openai.com/api/pricing",
+        "priority": 8,
+        "extraction_config": {
+            "type": "llm",
+            "params": {
+                "provider": "openai/gpt-4o-mini",
+                "api_token": os.getenv("OPENAI_API_KEY"),
+                "schema": schema,
+                "extraction_type": "schema",
+                "instruction": """From the crawled content, extract all mentioned model names along with their fees for input and output tokens."""
+            }
+        },
+        "crawler_params": {"word_count_threshold": 1}
+    }
+    
+    try:
+        result = tester.submit_and_wait(request)
+        extracted = json.loads(result["result"]["extracted_content"])
+        print(f"Extracted {len(extracted)} model pricing entries")
+        print("Sample entry:", json.dumps(extracted[0], indent=2))
+        assert result["result"]["success"]
+    except Exception as e:
+        print(f"LLM extraction test failed (might be due to missing API key): {str(e)}")
+
+def test_llm_with_ollama(tester: Crawl4AiTester):
+    print("\n=== Testing LLM with Ollama ===")
+    schema = {
+        "type": "object",
+        "properties": {
+            "article_title": {
+                "type": "string",
+                "description": "The main title of the news article"
+            },
+            "summary": {
+                "type": "string",
+                "description": "A brief summary of the article content"
+            },
+            "main_topics": {
+                "type": "array",
+                "items": {"type": "string"},
+                "description": "Main topics or themes discussed in the article"
+            }
+        }
+    }
+    
+    request = {
+        "urls": "https://www.nbcnews.com/business",
+        "priority": 8,
+        "extraction_config": {
+            "type": "llm",
+            "params": {
+                "provider": "ollama/llama2",
+                "schema": schema,
+                "extraction_type": "schema",
+                "instruction": "Extract the main article information including title, summary, and main topics."
+            }
+        },
+        "extra": {"word_count_threshold": 1},
+        "crawler_params": {"verbose": True}
+    }
+    
+    try:
+        result = tester.submit_and_wait(request)
+        extracted = json.loads(result["result"]["extracted_content"])
+        print("Extracted content:", json.dumps(extracted, indent=2))
+        assert result["result"]["success"]
+    except Exception as e:
+        print(f"Ollama extraction test failed: {str(e)}")
+
+def test_cosine_extraction(tester: Crawl4AiTester):
+    print("\n=== Testing Cosine Extraction ===")
+    request = {
+        "urls": "https://www.nbcnews.com/business",
+        "priority": 8,
+        "extraction_config": {
+            "type": "cosine",
+            "params": {
+                "semantic_filter": "business finance economy",
+                "word_count_threshold": 10,
+                "max_dist": 0.2,
+                "top_k": 3
+            }
+        }
+    }
+    
+    try:
+        result = tester.submit_and_wait(request)
+        extracted = json.loads(result["result"]["extracted_content"])
+        print(f"Extracted {len(extracted)} text clusters")
+        print("First cluster tags:", extracted[0]["tags"])
+        assert result["result"]["success"]
+    except Exception as e:
+        print(f"Cosine extraction test failed: {str(e)}")
+
+def test_screenshot(tester: Crawl4AiTester):
+    print("\n=== Testing Screenshot ===")
+    request = {
+        "urls": "https://www.nbcnews.com/business",
+        "priority": 5,
+        "screenshot": True,
+        "crawler_params": {
+            "headless": True
+        }
+    }
+    
+    result = tester.submit_and_wait(request)
+    print("Screenshot captured:", bool(result["result"]["screenshot"]))
+    
+    if result["result"]["screenshot"]:
+        # Save screenshot
+        screenshot_data = base64.b64decode(result["result"]["screenshot"])
+        with open("test_screenshot.jpg", "wb") as f:
+            f.write(screenshot_data)
+        print("Screenshot saved as test_screenshot.jpg")
+    
+    assert result["result"]["success"]
+
+if __name__ == "__main__":
+    version = sys.argv[1] if len(sys.argv) > 1 else "basic"
+    # version = "full"
+    test_docker_deployment(version)
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