Feat/llm config (#724)

* feature: Add LlmConfig to easily configure and pass LLM configs to different strategies

* pulled in next branch and resolved conflicts

* feat: Add gemini and deepseek providers. Make ignore_cache in llm content filter to true by default to avoid confusions

* Refactor: Update LlmConfig in LLMExtractionStrategy class and deprecate old params

* updated tests, docs and readme

docs/md_v2/extraction/llm-strategies.md

@@ -71,8 +71,7 @@ Below is an overview of important LLM extraction parameters. All are typically s
 
 ```python
 extraction_strategy = LLMExtractionStrategy(
-    provider="openai/gpt-4",
-    api_token="YOUR_OPENAI_KEY",
+    llmConfig = LlmConfig(provider="openai/gpt-4", api_token="YOUR_OPENAI_KEY"),
     schema=MyModel.model_json_schema(),
     extraction_type="schema",
     instruction="Extract a list of items from the text with 'name' and 'price' fields.",
@@ -97,7 +96,7 @@ import asyncio
 import json
 from pydantic import BaseModel, Field
 from typing import List
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LlmConfig
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 
 class Product(BaseModel):
@@ -107,9 +106,8 @@ class Product(BaseModel):
 async def main():
     # 1. Define the LLM extraction strategy
     llm_strategy = LLMExtractionStrategy(
-        provider="openai/gpt-4o-mini",            # e.g. "ollama/llama2"
-        api_token=os.getenv('OPENAI_API_KEY'),
-        schema=Product.schema_json(),            # Or use model_json_schema()
+        llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv('OPENAI_API_KEY')),
+        schema=Product.schema_json(), # Or use model_json_schema()
         extraction_type="schema",
         instruction="Extract all product objects with 'name' and 'price' from the content.",
         chunk_token_threshold=1000,

docs/md_v2/extraction/no-llm-strategies.md

@@ -415,6 +415,7 @@ The schema generator is available as a static method on both `JsonCssExtractionS
 
 ```python
 from crawl4ai.extraction_strategy import JsonCssExtractionStrategy, JsonXPathExtractionStrategy
+from crawl4ai.async_configs import LlmConfig
 
 # Sample HTML with product information
 html = """
@@ -433,17 +434,15 @@ html = """
 # Option 1: Using OpenAI (requires API token)
 css_schema = JsonCssExtractionStrategy.generate_schema(
     html,
-    schema_type="css",  # This is the default
-    provider="openai/gpt-4o",  # Default provider
-    api_token="your-openai-token"  # Required for OpenAI
+    schema_type="css", 
+    llmConfig = LlmConfig(provider="openai/gpt-4o",api_token="your-openai-token")
 )
 
 # Option 2: Using Ollama (open source, no token needed)
 xpath_schema = JsonXPathExtractionStrategy.generate_schema(
     html,
     schema_type="xpath",
-    provider="ollama/llama3.3",  # Open source alternative
-    api_token=None  # Not needed for Ollama
+    llmConfig = LlmConfig(provider="ollama/llama3.3", api_token=None)  # Not needed for Ollama
 )
 
 # Use the generated schema for fast, repeated extractions