feat: add comprehensive type definitions and improve test coverage

Add new type definitions file with extensive Union type aliases for all core components including AsyncUrlSeeder, SeedingConfig, and various crawler strategies. Enhance test coverage with improved bot detection tests, Docker-based testing, and extended features validation. The changes provide better type safety and more robust testing infrastructure for the crawling framework.

deploy/docker/README.md

@@ -779,6 +779,144 @@ async def test_stream_crawl(token: str = None): # Made token optional
 # asyncio.run(test_stream_crawl())
 ```
 
+#### LLM Job with Chunking Strategy
+
+```python
+import requests
+import time
+
+# Example: LLM extraction with RegexChunking strategy
+# This breaks large documents into smaller chunks before LLM processing
+
+llm_job_payload = {
+    "url": "https://example.com/long-article",
+    "q": "Extract all key points and main ideas from this article",
+    "chunking_strategy": {
+        "type": "RegexChunking",
+        "params": {
+            "patterns": ["\\n\\n"],  # Split on double newlines (paragraphs)
+            "overlap": 50
+        }
+    }
+}
+
+# Submit LLM job
+response = requests.post(
+    "http://localhost:11235/llm/job",
+    json=llm_job_payload
+)
+
+if response.ok:
+    job_data = response.json()
+    job_id = job_data["task_id"]
+    print(f"Job submitted successfully. Job ID: {job_id}")
+    
+    # Poll for completion
+    while True:
+        status_response = requests.get(f"http://localhost:11235/llm/job/{job_id}")
+        if status_response.ok:
+            status_data = status_response.json()
+            if status_data["status"] == "completed":
+                print("Job completed!")
+                print("Extracted content:", status_data["result"])
+                break
+            elif status_data["status"] == "failed":
+                print("Job failed:", status_data.get("error"))
+                break
+            else:
+                print(f"Job status: {status_data['status']}")
+                time.sleep(2)  # Wait 2 seconds before checking again
+        else:
+            print(f"Error checking job status: {status_response.text}")
+            break
+else:
+    print(f"Error submitting job: {response.text}")
+```
+
+**Available Chunking Strategies:**
+
+- **IdentityChunking**: Returns the entire content as a single chunk (no splitting)
+  ```json
+  {
+    "type": "IdentityChunking",
+    "params": {}
+  }
+  ```
+
+- **RegexChunking**: Split content using regular expression patterns
+  ```json
+  {
+    "type": "RegexChunking",
+    "params": {
+      "patterns": ["\\n\\n"]
+    }
+  }
+  ```
+
+- **NlpSentenceChunking**: Split content into sentences using NLP (requires NLTK)
+  ```json
+  {
+    "type": "NlpSentenceChunking",
+    "params": {}
+  }
+  ```
+
+- **TopicSegmentationChunking**: Segment content into topics using TextTiling (requires NLTK)
+  ```json
+  {
+    "type": "TopicSegmentationChunking",
+    "params": {
+      "num_keywords": 3
+    }
+  }
+  ```
+
+- **FixedLengthWordChunking**: Split into fixed-length word chunks
+  ```json
+  {
+    "type": "FixedLengthWordChunking",
+    "params": {
+      "chunk_size": 100
+    }
+  }
+  ```
+
+- **SlidingWindowChunking**: Overlapping word chunks with configurable step size
+  ```json
+  {
+    "type": "SlidingWindowChunking",
+    "params": {
+      "window_size": 100,
+      "step": 50
+    }
+  }
+  ```
+
+- **OverlappingWindowChunking**: Fixed-size chunks with word overlap
+  ```json
+  {
+    "type": "OverlappingWindowChunking",
+    "params": {
+      "window_size": 1000,
+      "overlap": 100
+    }
+  }
+  ```
+  {
+    "type": "OverlappingWindowChunking", 
+    "params": {
+      "chunk_size": 1500,
+      "overlap": 100
+    }
+  }
+  ```
+
+**Notes:**
+- `chunking_strategy` is optional - if omitted, default token-based chunking is used
+- Chunking is applied at the API level without modifying the core SDK
+- Results from all chunks are merged into a single response
+- Each chunk is processed independently with the same LLM instruction
+
 ---
 
 ## Metrics & Monitoring