fix: allow custom LLM providers for adaptive crawler embedding config. ref: #1291

- Change embedding_llm_config from Dict to Union[LLMConfig, Dict] for type safety
  - Add backward-compatible conversion property _embedding_llm_config_dict
  - Replace all hardcoded OpenAI embedding configs with configurable options
  - Fix LLMConfig object attribute access in query expansion logic
  - Add comprehensive example demonstrating multiple provider configurations
  - Update documentation with both LLMConfig object and dictionary usage patterns

  Users can now specify any LLM provider for query expansion in embedding strategy:
  - New: embedding_llm_config=LLMConfig(provider='anthropic/claude-3', api_token='key')
  - Old: embedding_llm_config={'provider': 'openai/gpt-4', 'api_token': 'key'} (still works)

docs/md_v2/core/adaptive-crawling.md

@@ -108,7 +108,19 @@ config = AdaptiveConfig(
     embedding_min_confidence_threshold=0.1  # Stop if completely irrelevant
 )
 
-# With custom embedding provider (e.g., OpenAI)
+# With custom LLM provider for query expansion (recommended)
+from crawl4ai import LLMConfig
+
+config = AdaptiveConfig(
+    strategy="embedding",
+    embedding_llm_config=LLMConfig(
+        provider='openai/text-embedding-3-small',
+        api_token='your-api-key',
+        temperature=0.7
+    )
+)
+
+# Alternative: Dictionary format (backward compatible)
 config = AdaptiveConfig(
     strategy="embedding",
     embedding_llm_config={