#1551 : Fix casing and variable name consistency for LLMConfig in documentation

feat(ManagedBrowser): add viewport size configuration for browser launch

crawl4ai/browser_manager.py

@@ -369,6 +369,9 @@ class ManagedBrowser:
             ]
             if self.headless:
                 flags.append("--headless=new")
+            # Add viewport flag if specified in config
+            if self.browser_config.viewport_height and self.browser_config.viewport_width:
+                flags.append(f"--window-size={self.browser_config.viewport_width},{self.browser_config.viewport_height}")
             # merge common launch flags
             flags.extend(self.build_browser_flags(self.browser_config))
         elif self.browser_type == "firefox":

docs/md_v2/extraction/llm-strategies.md

@@ -20,10 +20,10 @@ In some cases, you need to extract **complex or unstructured** information from
 
 ## 2. Provider-Agnostic via LiteLLM
 
-You can use LlmConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LlmConfig [here](/api/parameters).
+You can use LLMConfig, to quickly configure multiple variations of LLMs and experiment with them to find the optimal one for your use case. You can read more about LLMConfig [here](/api/parameters).
 
 ```python
-llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
 ```
 
 Crawl4AI uses a “provider string” (e.g., `"openai/gpt-4o"`, `"ollama/llama2.0"`, `"aws/titan"`) to identify your LLM. **Any** model that LiteLLM supports is fair game. You just provide:
@@ -58,7 +58,7 @@ For structured data, `"schema"` is recommended. You provide `schema=YourPydantic
 
 Below is an overview of important LLM extraction parameters. All are typically set inside `LLMExtractionStrategy(...)`. You then put that strategy in your `CrawlerRunConfig(..., extraction_strategy=...)`.
 
-1. **`llmConfig`** (LlmConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.    
+1. **`llm_config`** (LLMConfig): e.g., `"openai/gpt-4"`, `"ollama/llama2"`.
 2. **`schema`** (dict): A JSON schema describing the fields you want. Usually generated by `YourModel.model_json_schema()`.  
 3. **`extraction_type`** (str): `"schema"` or `"block"`.  
 4. **`instruction`** (str): Prompt text telling the LLM what you want extracted. E.g., “Extract these fields as a JSON array.”  
@@ -112,7 +112,7 @@ async def main():
     # 1. Define the LLM extraction strategy
     llm_strategy = LLMExtractionStrategy(
         llm_config = LLMConfig(provider="openai/gpt-4o-mini", api_token=os.getenv('OPENAI_API_KEY')),
-        schema=Product.schema_json(), # Or use model_json_schema()
+        schema=Product.model_json_schema(), # Or use model_json_schema()
         extraction_type="schema",
         instruction="Extract all product objects with 'name' and 'price' from the content.",
         chunk_token_threshold=1000,
@@ -238,7 +238,7 @@ class KnowledgeGraph(BaseModel):
 async def main():
     # LLM extraction strategy
     llm_strat = LLMExtractionStrategy(
-        llmConfig = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
+        llm_config = LLMConfig(provider="openai/gpt-4", api_token=os.getenv('OPENAI_API_KEY')),
         schema=KnowledgeGraph.model_json_schema(),
         extraction_type="schema",
         instruction="Extract entities and relationships from the content. Return valid JSON.",