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

README.md

@@ -407,7 +407,7 @@ if __name__ == "__main__":
 ```python
 import os
 import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LlmConfig
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 from pydantic import BaseModel, Field
 
@@ -423,7 +423,7 @@ async def main():
         extraction_strategy=LLMExtractionStrategy(
             # Here you can use any provider that Litellm library supports, for instance: ollama/qwen2
             # provider="ollama/qwen2", api_token="no-token", 
-            provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY'), 
+            llmConfig = LlmConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')), 
             schema=OpenAIModelFee.schema(),
             extraction_type="schema",
             instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 

crawl4ai/async_configs.py

@@ -1,13 +1,16 @@
+import os
 from .config import (
+    DEFAULT_PROVIDER,
     MIN_WORD_THRESHOLD,
     IMAGE_DESCRIPTION_MIN_WORD_THRESHOLD,
+    PROVIDER_MODELS,
     SCREENSHOT_HEIGHT_TRESHOLD,
     PAGE_TIMEOUT,
     IMAGE_SCORE_THRESHOLD,
     SOCIAL_MEDIA_DOMAINS,
 )
 
-from .user_agent_generator import UAGen, ValidUAGenerator # , OnlineUAGenerator
+from .user_agent_generator import UAGen, ValidUAGenerator  # , OnlineUAGenerator
 from .extraction_strategy import ExtractionStrategy
 from .chunking_strategy import ChunkingStrategy, RegexChunking
 from .markdown_generation_strategy import MarkdownGenerationStrategy
@@ -21,6 +24,7 @@ import inspect
 from typing import Any, Dict, Optional
 from enum import Enum
 
+
 def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
     """
     Recursively convert an object to a serializable dictionary using {type, params} structure
@@ -35,13 +39,10 @@ def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
 
     # Handle Enum
     if isinstance(obj, Enum):
-        return {
+        return {"type": obj.__class__.__name__, "params": obj.value}
-            "type": obj.__class__.__name__,
-            "params": obj.value
-        }
 
     # Handle datetime objects
-    if hasattr(obj, 'isoformat'):
+    if hasattr(obj, "isoformat"):
         return obj.isoformat()
 
     # Handle lists, tuples, and sets, and basically any iterable
@@ -56,13 +57,13 @@ def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
     if isinstance(obj, dict):
         return {
             "type": "dict",  # Mark as plain dictionary
-            "value": {str(k): to_serializable_dict(v) for k, v in obj.items()}
+            "value": {str(k): to_serializable_dict(v) for k, v in obj.items()},
         }
 
     _type = obj.__class__.__name__
 
     # Handle class instances
-    if hasattr(obj, '__class__'):
+    if hasattr(obj, "__class__"):
         # Get constructor signature
         sig = inspect.signature(obj.__class__.__init__)
         params = sig.parameters
@@ -70,7 +71,7 @@ def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
         # Get current values
         current_values = {}
         for name, param in params.items():
-            if name == 'self':
+            if name == "self":
                 continue
 
             value = getattr(obj, name, param.default)
@@ -97,6 +98,7 @@ def to_serializable_dict(obj: Any, ignore_default_value : bool = False) -> Dict:
         
     return str(obj)
 
+
 def from_serializable_dict(data: Any) -> Any:
     """
     Recursively convert a serializable dictionary back to an object instance.
@@ -116,6 +118,7 @@ def from_serializable_dict(data: Any) -> Any:
 
         # Import from crawl4ai for class instances
         import crawl4ai
+
         cls = getattr(crawl4ai, data["type"])
 
         # Handle Enum
@@ -138,6 +141,7 @@ def from_serializable_dict(data: Any) -> Any:
 
     return data
 
+
 def is_empty_value(value: Any) -> bool:
     """Check if a value is effectively empty/null."""
     if value is None:
@@ -146,7 +150,8 @@ def is_empty_value(value: Any) -> bool:
         return True
     return False
 
-class BrowserConfig():
+
+class BrowserConfig:
     """
     Configuration class for setting up a browser instance and its context in AsyncPlaywrightCrawlerStrategy.
 
@@ -224,7 +229,7 @@ class BrowserConfig():
         viewport: dict = None,
         accept_downloads: bool = False,
         downloads_path: str = None,
-        storage_state : Union[str, dict, None]=None,
+        storage_state: Union[str, dict, None] = None,
         ignore_https_errors: bool = True,
         java_script_enabled: bool = True,
         sleep_on_close: bool = False,
@@ -381,7 +386,7 @@ class BrowserConfig():
         return to_serializable_dict(self)
 
     @staticmethod
-    def load( data: dict) -> "BrowserConfig":
+    def load(data: dict) -> "BrowserConfig":
         # Deserialize the object from a dictionary
         config = from_serializable_dict(data)
         if isinstance(config, BrowserConfig):
@@ -389,8 +394,9 @@ class BrowserConfig():
         return BrowserConfig.from_kwargs(config)
 
 
-class HTTPCrawlerConfig():
+class HTTPCrawlerConfig:
     """HTTP-specific crawler configuration"""
+
     method: str = "GET"
     headers: Optional[Dict[str, str]] = None
     data: Optional[Dict[str, Any]] = None
@@ -398,7 +404,15 @@ class HTTPCrawlerConfig():
     follow_redirects: bool = True
     verify_ssl: bool = True
 
-    def __init__(self, method: str = "GET", headers: Optional[Dict[str, str]] = None, data: Optional[Dict[str, Any]] = None, json: Optional[Dict[str, Any]] = None, follow_redirects: bool = True, verify_ssl: bool = True):
+    def __init__(
+        self,
+        method: str = "GET",
+        headers: Optional[Dict[str, str]] = None,
+        data: Optional[Dict[str, Any]] = None,
+        json: Optional[Dict[str, Any]] = None,
+        follow_redirects: bool = True,
+        verify_ssl: bool = True,
+    ):
         self.method = method
         self.headers = headers
         self.data = data
@@ -700,7 +714,6 @@ class CrawlerRunConfig():
         user_agent_generator_config: dict = {},
         # Deep Crawl Parameters
         deep_crawl_strategy: Optional[DeepCrawlStrategy] = None,
-
     ):
         # TODO: Planning to set properties dynamically based on the __init__ signature
         self.url = url
@@ -810,7 +823,6 @@ class CrawlerRunConfig():
         if self.chunking_strategy is None:
             self.chunking_strategy = RegexChunking()
 
-
         # Deep Crawl Parameters
         self.deep_crawl_strategy = deep_crawl_strategy
 
@@ -918,7 +930,6 @@ class CrawlerRunConfig():
             user_agent_generator_config=kwargs.get("user_agent_generator_config", {}),
             # Deep Crawl Parameters
             deep_crawl_strategy=kwargs.get("deep_crawl_strategy"),
-
             url=kwargs.get("url"),
         )
 
@@ -1031,3 +1042,50 @@ class CrawlerRunConfig():
         return CrawlerRunConfig.from_kwargs(config_dict)
 
 
+class LlmConfig:
+    def __init__(
+        self,
+        provider: str = DEFAULT_PROVIDER,
+        api_token: Optional[str] = None,
+        base_url: Optional[str] = None,
+    ):
+        """Configuaration class for LLM provider and API token."""
+        self.provider = provider
+        if api_token and not api_token.startswith("env:"):
+            self.api_token = api_token
+        elif api_token and api_token.startswith("env:"):
+            self.api_token = os.getenv(api_token[4:])
+        else:
+            self.api_token = PROVIDER_MODELS.get(provider, "no-token") or os.getenv(
+                "OPENAI_API_KEY"
+            )
+        self.base_url = base_url
+
+
+    @staticmethod
+    def from_kwargs(kwargs: dict) -> "LlmConfig":
+        return LlmConfig(
+            provider=kwargs.get("provider", DEFAULT_PROVIDER),
+            api_token=kwargs.get("api_token"),
+            base_url=kwargs.get("base_url"),
+        )
+
+    def to_dict(self):
+        return {
+            "provider": self.provider,
+            "api_token": self.api_token,
+            "base_url": self.base_url
+        }
+
+    def clone(self, **kwargs):
+        """Create a copy of this configuration with updated values.
+
+        Args:
+            **kwargs: Key-value pairs of configuration options to update
+
+        Returns:
+            LLMConfig: A new instance with the specified updates
+        """
+        config_dict = self.to_dict()
+        config_dict.update(kwargs)
+        return LlmConfig.from_kwargs(config_dict)

crawl4ai/config.py

@@ -21,6 +21,12 @@ PROVIDER_MODELS = {
     "anthropic/claude-3-opus-20240229": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-sonnet-20240229": os.getenv("ANTHROPIC_API_KEY"),
     "anthropic/claude-3-5-sonnet-20240620": os.getenv("ANTHROPIC_API_KEY"),
+    "gemini/gemini-pro": os.getenv("GEMINI_API_KEY"),
+    'gemini/gemini-1.5-pro': os.getenv("GEMINI_API_KEY"),
+    'gemini/gemini-2.0-flash': os.getenv("GEMINI_API_KEY"),
+    'gemini/gemini-2.0-flash-exp': os.getenv("GEMINI_API_KEY"),
+    'gemini/gemini-2.0-flash-lite-preview-02-05': os.getenv("GEMINI_API_KEY"),
+    "deepseek/deepseek-chat": os.getenv("DEEPSEEK_API_KEY"),
 }
 
 # Chunk token threshold

crawl4ai/content_filter_strategy.py

@@ -1,3 +1,4 @@
+import inspect
 import re
 import time
 from bs4 import BeautifulSoup, Tag
@@ -5,7 +6,16 @@ from typing import List, Tuple, Dict, Optional
 from rank_bm25 import BM25Okapi
 from collections import deque
 from bs4 import NavigableString, Comment
-from .utils import clean_tokens, perform_completion_with_backoff, escape_json_string, sanitize_html, get_home_folder, extract_xml_data, merge_chunks
+
+from .utils import (
+    clean_tokens,
+    perform_completion_with_backoff,
+    escape_json_string,
+    sanitize_html,
+    get_home_folder,
+    extract_xml_data,
+    merge_chunks,
+)
 from abc import ABC, abstractmethod
 import math
 from snowballstemmer import stemmer
@@ -20,10 +30,16 @@ from concurrent.futures import ThreadPoolExecutor
 from .async_logger import AsyncLogger, LogLevel
 from colorama import Fore, Style
 
+
 class RelevantContentFilter(ABC):
     """Abstract base class for content filtering strategies"""
 
-    def __init__(self, user_query: str = None, verbose: bool = False, logger: Optional[AsyncLogger] = None):
+    def __init__(
+        self,
+        user_query: str = None,
+        verbose: bool = False,
+        logger: Optional[AsyncLogger] = None,
+    ):
         """
         Initializes the RelevantContentFilter class with optional user query.
 
@@ -362,6 +378,7 @@ class RelevantContentFilter(ABC):
         except Exception:
             return str(tag)  # Fallback to original if anything fails
 
+
 class BM25ContentFilter(RelevantContentFilter):
     """
     Content filtering using BM25 algorithm with priority tag handling.
@@ -504,6 +521,7 @@ class BM25ContentFilter(RelevantContentFilter):
 
         return [self.clean_element(tag) for _, _, tag in selected_candidates]
 
+
 class PruningContentFilter(RelevantContentFilter):
     """
     Content filtering using pruning algorithm with dynamic threshold.
@@ -750,13 +768,21 @@ class PruningContentFilter(RelevantContentFilter):
                 class_id_score -= 0.5
         return class_id_score
 
+
 class LLMContentFilter(RelevantContentFilter):
     """Content filtering using LLMs to generate relevant markdown."""
+    _UNWANTED_PROPS = {
+        'provider' : 'Instead, use llmConfig=LlmConfig(provider="...")',
+        'api_token' : 'Instead, use llmConfig=LlMConfig(api_token="...")',
+        'base_url' : 'Instead, use llmConfig=LlmConfig(base_url="...")',
+        'api_base' : 'Instead, use llmConfig=LlmConfig(base_url="...")',
+    }
 
     def __init__(
         self,
         provider: str = DEFAULT_PROVIDER,
         api_token: Optional[str] = None,
+        llmConfig: "LlmConfig" = None,
         instruction: str = None,
         chunk_token_threshold: int = int(1e9),
         overlap_rate: float = OVERLAP_RATE,
@@ -768,15 +794,13 @@ class LLMContentFilter(RelevantContentFilter):
         # chunk_mode: str = "char",
         verbose: bool = False,
         logger: Optional[AsyncLogger] = None,
-        ignore_cache: bool = False,
+        ignore_cache: bool = True,
     ):
         super().__init__(None)
         self.provider = provider
-        self.api_token = (
+        self.api_token = api_token
-            api_token
+        self.base_url = base_url or api_base
-            or PROVIDER_MODELS.get(provider, "no-token")
+        self.llmConfig = llmConfig
-            or os.getenv("OPENAI_API_KEY")
-        )
         self.instruction = instruction
         self.chunk_token_threshold = chunk_token_threshold
         self.overlap_rate = overlap_rate
@@ -785,8 +809,6 @@ class LLMContentFilter(RelevantContentFilter):
         # self.char_token_rate = char_token_rate or word_token_rate / 5
         # self.token_rate = word_token_rate if chunk_mode == "word" else self.char_token_rate
         self.token_rate = word_token_rate or WORD_TOKEN_RATE
-        self.base_url = base_url
-        self.api_base = api_base or base_url
         self.extra_args = extra_args or {}
         self.ignore_cache = ignore_cache
         self.verbose = verbose
@@ -801,12 +823,13 @@ class LLMContentFilter(RelevantContentFilter):
                     **AsyncLogger.DEFAULT_ICONS,
                     "LLM": "★",  # Star for LLM operations
                     "CHUNK": "◈",  # Diamond for chunks
-                    "CACHE": "⚡", # Lightning for cache operations
+                    "CACHE": "⚡",  # Lightning for cache operations
                 },
                 colors={
                     **AsyncLogger.DEFAULT_COLORS,
-                    LogLevel.INFO: Fore.MAGENTA + Style.DIM,  # Dimmed purple for LLM ops
+                    LogLevel.INFO: Fore.MAGENTA
-                }
+                    + Style.DIM,  # Dimmed purple for LLM ops
+                },
             )
         else:
             self.logger = None
@@ -814,6 +837,17 @@ class LLMContentFilter(RelevantContentFilter):
         self.usages = []
         self.total_usage = TokenUsage()
     
+    def __setattr__(self, name, value):
+        """Handle attribute setting."""
+        # TODO: Planning to set properties dynamically based on the __init__ signature
+        sig = inspect.signature(self.__init__)
+        all_params = sig.parameters  # Dictionary of parameter names and their details
+
+        if name in self._UNWANTED_PROPS and value is not all_params[name].default:
+            raise AttributeError(f"Setting '{name}' is deprecated. {self._UNWANTED_PROPS[name]}")
+        
+        super().__setattr__(name, value)  
+        
     def _get_cache_key(self, html: str, instruction: str) -> str:
         """Generate a unique cache key based on HTML and instruction"""
         content = f"{html}{instruction}"
@@ -823,15 +857,13 @@ class LLMContentFilter(RelevantContentFilter):
         """Split text into chunks with overlap using char or word mode."""
         ov = int(self.chunk_token_threshold * self.overlap_rate)
         sections = merge_chunks(
-            docs = [text],
+            docs=[text],
-            target_size= self.chunk_token_threshold,
+            target_size=self.chunk_token_threshold,
             overlap=ov,
-            word_token_ratio=self.word_token_rate
+            word_token_ratio=self.word_token_rate,
         )
         return sections
 
-        
-
     def filter_content(self, html: str, ignore_cache: bool = True) -> List[str]:
         if not html or not isinstance(html, str):
             return []
@@ -840,8 +872,8 @@ class LLMContentFilter(RelevantContentFilter):
             self.logger.info(
                 "Starting LLM markdown content filtering process",
                 tag="LLM",
-                params={"provider": self.provider},
+                params={"provider": self.llmConfig.provider},
-                colors={"provider": Fore.CYAN}
+                colors={"provider": Fore.CYAN},
             )
 
         # Cache handling
@@ -857,17 +889,19 @@ class LLMContentFilter(RelevantContentFilter):
             if self.logger:
                 self.logger.info("Found  cached markdown result", tag="CACHE")
             try:
-                with cache_file.open('r') as f:
+                with cache_file.open("r") as f:
                     cached_data = json.load(f)
-                    usage = TokenUsage(**cached_data['usage'])
+                    usage = TokenUsage(**cached_data["usage"])
                     self.usages.append(usage)
                     self.total_usage.completion_tokens += usage.completion_tokens
                     self.total_usage.prompt_tokens += usage.prompt_tokens
                     self.total_usage.total_tokens += usage.total_tokens
-                    return cached_data['blocks']
+                    return cached_data["blocks"]
             except Exception as e:
                 if self.logger:
-                    self.logger.error(f"LLM markdown: Cache read error: {str(e)}", tag="CACHE")
+                    self.logger.error(
+                        f"LLM markdown: Cache read error: {str(e)}", tag="CACHE"
+                    )
 
         # Split into chunks
         html_chunks = self._merge_chunks(html)
@@ -876,7 +910,7 @@ class LLMContentFilter(RelevantContentFilter):
                 "LLM markdown: Split content into {chunk_count} chunks",
                 tag="CHUNK",
                 params={"chunk_count": len(html_chunks)},
-                colors={"chunk_count": Fore.YELLOW}
+                colors={"chunk_count": Fore.YELLOW},
             )
 
         start_time = time.time()
@@ -889,15 +923,13 @@ class LLMContentFilter(RelevantContentFilter):
                     self.logger.debug(
                         "LLM markdown: Processing chunk {chunk_num}/{total_chunks}",
                         tag="CHUNK",
-                        params={
+                        params={"chunk_num": i + 1, "total_chunks": len(html_chunks)},
-                            "chunk_num": i + 1,
-                            "total_chunks": len(html_chunks)
-                        }
                     )
 
                 prompt_variables = {
                     "HTML": escape_json_string(sanitize_html(chunk)),
-                    "REQUEST": self.instruction or "Convert this HTML into clean, relevant markdown, removing any noise or irrelevant content."
+                    "REQUEST": self.instruction
+                    or "Convert this HTML into clean, relevant markdown, removing any noise or irrelevant content.",
                 }
 
                 prompt = PROMPT_FILTER_CONTENT
@@ -905,37 +937,36 @@ class LLMContentFilter(RelevantContentFilter):
                     prompt = prompt.replace("{" + var + "}", value)
 
                 def _proceed_with_chunk(
-                        provider: str,
+                    provider: str,
-                        prompt: str,
+                    prompt: str,
-                        api_token: str,
+                    api_token: str,
-                        base_url: Optional[str] = None,
+                    base_url: Optional[str] = None,
-                        extra_args: Dict = {}
+                    extra_args: Dict = {},
-                    ) -> List[str]:
+                ) -> List[str]:
                     if self.logger:
                         self.logger.info(
                             "LLM Markdown: Processing chunk {chunk_num}",
                             tag="CHUNK",
-                            params={"chunk_num": i + 1}
+                            params={"chunk_num": i + 1},
                         )
                     return perform_completion_with_backoff(
                         provider,
                         prompt,
                         api_token,
                         base_url=base_url,
-                        extra_args=extra_args
+                        extra_args=extra_args,
                     )
 
                 future = executor.submit(
                     _proceed_with_chunk,
-                    self.provider,
+                    self.llmConfig.provider,
                     prompt,
-                    self.api_token,
+                    self.llmConfig.api_token,
-                    self.api_base,
+                    self.llmConfig.base_url,
-                    self.extra_args
+                    self.extra_args,
                 )
                 futures.append((i, future))
 
-
             # Collect results in order
             ordered_results = []
             for i, future in sorted(futures):
@@ -947,34 +978,39 @@ class LLMContentFilter(RelevantContentFilter):
                         completion_tokens=response.usage.completion_tokens,
                         prompt_tokens=response.usage.prompt_tokens,
                         total_tokens=response.usage.total_tokens,
-                        completion_tokens_details=response.usage.completion_tokens_details.__dict__ 
+                        completion_tokens_details=(
-                        if response.usage.completion_tokens_details else {},
+                            response.usage.completion_tokens_details.__dict__
-                        prompt_tokens_details=response.usage.prompt_tokens_details.__dict__
+                            if response.usage.completion_tokens_details
-                        if response.usage.prompt_tokens_details else {},
+                            else {}
+                        ),
+                        prompt_tokens_details=(
+                            response.usage.prompt_tokens_details.__dict__
+                            if response.usage.prompt_tokens_details
+                            else {}
+                        ),
                     )
                     self.usages.append(usage)
                     self.total_usage.completion_tokens += usage.completion_tokens
                     self.total_usage.prompt_tokens += usage.prompt_tokens
                     self.total_usage.total_tokens += usage.total_tokens
 
-                    blocks = extract_xml_data(["content"], response.choices[0].message.content)["content"]
+                    blocks = extract_xml_data(
+                        ["content"], response.choices[0].message.content
+                    )["content"]
                     if blocks:
                         ordered_results.append(blocks)
                         if self.logger:
                             self.logger.success(
                                 "LLM markdown: Successfully processed chunk {chunk_num}",
                                 tag="CHUNK",
-                                params={"chunk_num": i + 1}
+                                params={"chunk_num": i + 1},
                             )
                 except Exception as e:
                     if self.logger:
                         self.logger.error(
                             "LLM markdown: Error processing chunk {chunk_num}: {error}",
                             tag="CHUNK",
-                            params={
+                            params={"chunk_num": i + 1, "error": str(e)},
-                                "chunk_num": i + 1,
-                                "error": str(e)
-                            }
                         )
 
         end_time = time.time()
@@ -983,17 +1019,14 @@ class LLMContentFilter(RelevantContentFilter):
                 "LLM markdown: Completed processing in {time:.2f}s",
                 tag="LLM",
                 params={"time": end_time - start_time},
-                colors={"time": Fore.YELLOW}
+                colors={"time": Fore.YELLOW},
             )
 
         result = ordered_results if ordered_results else []
 
         # Cache the final result
-        cache_data = {
+        cache_data = {"blocks": result, "usage": self.total_usage.__dict__}
-            'blocks': result,
+        with cache_file.open("w") as f:
-            'usage': self.total_usage.__dict__
-        }
-        with cache_file.open('w') as f:
             json.dump(cache_data, f)
             if self.logger:
                 self.logger.info("Cached results for future use", tag="CACHE")

crawl4ai/extraction_strategy.py

@@ -1,4 +1,5 @@
 from abc import ABC, abstractmethod
+import inspect
 from typing import Any, List, Dict, Optional
 from concurrent.futures import ThreadPoolExecutor, as_completed
 import json
@@ -496,20 +497,26 @@ class LLMExtractionStrategy(ExtractionStrategy):
         usages: List of individual token usages.
         total_usage: Accumulated token usage.
     """
-
+    _UNWANTED_PROPS = {
+            'provider' : 'Instead, use llmConfig=LlmConfig(provider="...")',
+            'api_token' : 'Instead, use llmConfig=LlMConfig(api_token="...")',
+            'base_url' : 'Instead, use llmConfig=LlmConfig(base_url="...")',
+            'api_base' : 'Instead, use llmConfig=LlmConfig(base_url="...")',
+        }
     def __init__(
         self,
+        llmConfig: 'LLMConfig' = None,
+        instruction: str = None,
         provider: str = DEFAULT_PROVIDER,
         api_token: Optional[str] = None,
-        instruction: str = None,
+        base_url: str = None,
+        api_base: str = None,
         schema: Dict = None,
         extraction_type="block",
         chunk_token_threshold=CHUNK_TOKEN_THRESHOLD,
         overlap_rate=OVERLAP_RATE,
         word_token_rate=WORD_TOKEN_RATE,
         apply_chunking=True,
-        api_base: str =None,
-        base_url: str =None,
         input_format: str = "markdown",
         verbose=False,
         **kwargs,
@@ -518,6 +525,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
         Initialize the strategy with clustering parameters.
 
         Args:
+            llmConfig: The LLM configuration object.
             provider: The provider to use for extraction. It follows the format <provider_name>/<model_name>, e.g., "ollama/llama3.3".
             api_token: The API token for the provider.
             instruction: The instruction to use for the LLM model.
@@ -536,40 +544,38 @@ class LLMExtractionStrategy(ExtractionStrategy):
 
         """
         super().__init__( input_format=input_format, **kwargs)
+        self.llmConfig = llmConfig
         self.provider = provider
-        if api_token and not api_token.startswith("env:"):
+        self.api_token = api_token
-            self.api_token = api_token
+        self.base_url = base_url
-        elif api_token and api_token.startswith("env:"):
+        self.api_base = api_base
-            self.api_token = os.getenv(api_token[4:])
-        else:
-            self.api_token = (
-                PROVIDER_MODELS.get(provider, "no-token")
-                or os.getenv("OPENAI_API_KEY")
-            )
         self.instruction = instruction
         self.extract_type = extraction_type
         self.schema = schema
         if schema:
             self.extract_type = "schema"
-
         self.chunk_token_threshold = chunk_token_threshold or CHUNK_TOKEN_THRESHOLD
         self.overlap_rate = overlap_rate
         self.word_token_rate = word_token_rate
         self.apply_chunking = apply_chunking
-        self.base_url = base_url
-        self.api_base = api_base or base_url
         self.extra_args = kwargs.get("extra_args", {})
         if not self.apply_chunking:
             self.chunk_token_threshold = 1e9
-
         self.verbose = verbose
         self.usages = []  # Store individual usages
         self.total_usage = TokenUsage()  # Accumulated usage
 
-        if not self.api_token:
+    
-            raise ValueError(
+    def __setattr__(self, name, value):
-                "API token must be provided for LLMExtractionStrategy. Update the config.py or set OPENAI_API_KEY environment variable."
+        """Handle attribute setting."""
-            )
+        # TODO: Planning to set properties dynamically based on the __init__ signature
+        sig = inspect.signature(self.__init__)
+        all_params = sig.parameters  # Dictionary of parameter names and their details
+
+        if name in self._UNWANTED_PROPS and value is not all_params[name].default:
+            raise AttributeError(f"Setting '{name}' is deprecated. {self._UNWANTED_PROPS[name]}")
+        
+        super().__setattr__(name, value)  
         
     def extract(self, url: str, ix: int, html: str) -> List[Dict[str, Any]]:
         """
@@ -603,7 +609,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
             prompt_with_variables = PROMPT_EXTRACT_BLOCKS_WITH_INSTRUCTION
 
         if self.extract_type == "schema" and self.schema:
-            variable_values["SCHEMA"] = json.dumps(self.schema, indent=2)
+            variable_values["SCHEMA"] = json.dumps(self.schema, indent=2) # if type of self.schema is dict else self.schema
             prompt_with_variables = PROMPT_EXTRACT_SCHEMA_WITH_INSTRUCTION
 
         for variable in variable_values:
@@ -612,10 +618,10 @@ class LLMExtractionStrategy(ExtractionStrategy):
             )
 
         response = perform_completion_with_backoff(
-            self.provider,
+            self.llmConfig.provider,
             prompt_with_variables,
-            self.api_token,
+            self.llmConfig.api_token,
-            base_url=self.api_base or self.base_url,
+            base_url=self.llmConfig.base_url,
             extra_args=self.extra_args,
         )  # , json_response=self.extract_type == "schema")
         # Track usage
@@ -695,7 +701,7 @@ class LLMExtractionStrategy(ExtractionStrategy):
             overlap=int(self.chunk_token_threshold * self.overlap_rate),
         )
         extracted_content = []
-        if self.provider.startswith("groq/"):
+        if self.llmConfig.provider.startswith("groq/"):
             # Sequential processing with a delay
             for ix, section in enumerate(merged_sections):
                 extract_func = partial(self.extract, url)
@@ -1036,14 +1042,20 @@ class JsonElementExtractionStrategy(ExtractionStrategy):
         """Get attribute value from element"""
         pass
 
+    _GENERATE_SCHEMA_UNWANTED_PROPS = {
+        'provider': 'Instead, use llmConfig=LlmConfig(provider="...")',
+        'api_token': 'Instead, use llmConfig=LlMConfig(api_token="...")',
+    }
+
     @staticmethod
     def generate_schema(
         html: str,
         schema_type: str = "CSS", # or XPATH
         query: str = None,
         target_json_example: str = None,
-        provider: str = "gpt-4o",
+        llmConfig: 'LLMConfig' = None,
-        api_token: str = os.getenv("OPENAI_API_KEY"),
+        provider: str = None,
+        api_token: str = None,
         **kwargs
     ) -> dict:
         """
@@ -1052,8 +1064,9 @@ class JsonElementExtractionStrategy(ExtractionStrategy):
         Args:
             html (str): The HTML content to analyze
             query (str, optional): Natural language description of what data to extract
-            provider (str): LLM provider to use 
+            provider (str): Legacy Parameter. LLM provider to use 
-            api_token (str): API token for LLM provider
+            api_token (str): Legacy Parameter. API token for LLM provider
+            llmConfig (LlmConfig): LLM configuration object
             prompt (str, optional): Custom prompt template to use
             **kwargs: Additional args passed to perform_completion_with_backoff
             
@@ -1062,6 +1075,9 @@ class JsonElementExtractionStrategy(ExtractionStrategy):
         """
         from .prompts import JSON_SCHEMA_BUILDER
         from .utils import perform_completion_with_backoff
+        for name, message in JsonElementExtractionStrategy._GENERATE_SCHEMA_UNWANTED_PROPS.items():
+            if locals()[name] is not None:
+                raise AttributeError(f"Setting '{name}' is deprecated. {message}")
         
         # Use default or custom prompt
         prompt_template = JSON_SCHEMA_BUILDER if schema_type == "CSS" else JSON_SCHEMA_BUILDER_XPATH
@@ -1114,10 +1130,10 @@ In this scenario, use your best judgment to generate the schema. Try to maximize
         try:
             # Call LLM with backoff handling
             response = perform_completion_with_backoff(
-                provider=provider,
+                provider=llmConfig.provider,
                 prompt_with_variables="\n\n".join([system_message["content"], user_message["content"]]),
                 json_response = True,                
-                api_token=api_token,
+                api_token=llmConfig.api_token,
                 **kwargs
             )
             

docs/examples/extraction_strategies_examples.py

@@ -11,6 +11,7 @@ import asyncio
 import os
 
 from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.extraction_strategy import (
     LLMExtractionStrategy,
     JsonCssExtractionStrategy,
@@ -60,22 +61,19 @@ async def main():
 
     # 1. LLM Extraction with different input formats
     markdown_strategy = LLMExtractionStrategy(
-        provider="openai/gpt-4o-mini",
+        llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY")),
-        api_token=os.getenv("OPENAI_API_KEY"),
         instruction="Extract product information including name, price, and description",
     )
 
     html_strategy = LLMExtractionStrategy(
         input_format="html",
-        provider="openai/gpt-4o-mini",
+        llmConfig=LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY")),
-        api_token=os.getenv("OPENAI_API_KEY"),
         instruction="Extract product information from HTML including structured data",
     )
 
     fit_markdown_strategy = LLMExtractionStrategy(
         input_format="fit_markdown",
-        provider="openai/gpt-4o-mini",
+        llmConfig=LlmConfig(provider="openai/gpt-4o-mini",api_token=os.getenv("OPENAI_API_KEY")),
-        api_token=os.getenv("OPENAI_API_KEY"),
         instruction="Extract product information from cleaned markdown",
     )
 

docs/examples/llm_extraction_openai_pricing.py

@@ -1,3 +1,4 @@
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.extraction_strategy import *
 from crawl4ai.crawler_strategy import *
 import asyncio
@@ -25,8 +26,7 @@ async def main():
             word_count_threshold=1,
             extraction_strategy=LLMExtractionStrategy(
                 # provider= "openai/gpt-4o", api_token = os.getenv('OPENAI_API_KEY'),
-                provider="groq/llama-3.1-70b-versatile",
+                llmConfig=LlmConfig(provider="groq/llama-3.1-70b-versatile", api_token=os.getenv("GROQ_API_KEY")),
-                api_token=os.getenv("GROQ_API_KEY"),
                 schema=OpenAIModelFee.model_json_schema(),
                 extraction_type="schema",
                 instruction="From the crawled content, extract all mentioned model names along with their "

docs/examples/llm_markdown_generator.py

@@ -1,6 +1,7 @@
 import os
 import asyncio
 from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.content_filter_strategy import LLMContentFilter
 
 async def test_llm_filter():
@@ -22,8 +23,7 @@ async def test_llm_filter():
 
         # Initialize LLM filter with focused instruction
         filter = LLMContentFilter(
-            provider="openai/gpt-4o",
+            llmConfig=LlmConfig(provider="openai/gpt-4o", api_token=os.getenv('OPENAI_API_KEY')),
-            api_token=os.getenv('OPENAI_API_KEY'),
             instruction="""
             Focus on extracting the core educational content about Python classes.
             Include:
@@ -43,8 +43,7 @@ async def test_llm_filter():
         )
         
         filter = LLMContentFilter(
-            provider="openai/gpt-4o",
+            llmConfig=LlmConfig(provider="openai/gpt-4o",api_token=os.getenv('OPENAI_API_KEY')),
-            api_token=os.getenv('OPENAI_API_KEY'),
             chunk_token_threshold=2 ** 12 * 2, # 2048 * 2
             ignore_cache = True,
             instruction="""

docs/examples/quickstart_async.config.py

@@ -1,5 +1,7 @@
 import os, sys
 
+from crawl4ai.async_configs import LlmConfig
+
 sys.path.append(
     os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
 )
@@ -209,8 +211,7 @@ async def extract_structured_data_using_llm(
         word_count_threshold=1,
         page_timeout=80000,
         extraction_strategy=LLMExtractionStrategy(
-            provider=provider,
+            llmConfig=LlmConfig(provider=provider,api_token=api_token),
-            api_token=api_token,
             schema=OpenAIModelFee.model_json_schema(),
             extraction_type="schema",
             instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 

docs/examples/quickstart_async.py

@@ -1,5 +1,7 @@
 import os, sys
 
+from crawl4ai.async_configs import LlmConfig
+
 # append parent directory to system path
 sys.path.append(
     os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
@@ -145,8 +147,7 @@ async def extract_structured_data_using_llm(
             url="https://openai.com/api/pricing/",
             word_count_threshold=1,
             extraction_strategy=LLMExtractionStrategy(
-                provider=provider,
+                llmConfig=LlmConfig(provider=provider,api_token=api_token),
-                api_token=api_token,
                 schema=OpenAIModelFee.model_json_schema(),
                 extraction_type="schema",
                 instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
@@ -569,8 +570,7 @@ async def generate_knowledge_graph():
         relationships: List[Relationship]
 
     extraction_strategy = LLMExtractionStrategy(
-        provider="openai/gpt-4o-mini",  # Or any other provider, including Ollama and open source models
+        llmConfig=LlmConfig(provider="openai/gpt-4o-mini",  api_token=os.getenv("OPENAI_API_KEY")),  # In case of Ollama just pass "no-token"
-        api_token=os.getenv("OPENAI_API_KEY"),  # In case of Ollama just pass "no-token"
         schema=KnowledgeGraph.model_json_schema(),
         extraction_type="schema",
         instruction="""Extract entities and relationships from the given text.""",

docs/examples/quickstart_sync.py

@@ -1,5 +1,6 @@
 import os
 import time
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.web_crawler import WebCrawler
 from crawl4ai.chunking_strategy import *
 from crawl4ai.extraction_strategy import *
@@ -178,7 +179,7 @@ def add_llm_extraction_strategy(crawler):
     result = crawler.run(
         url="https://www.nbcnews.com/business",
         extraction_strategy=LLMExtractionStrategy(
-            provider="openai/gpt-4o", api_token=os.getenv("OPENAI_API_KEY")
+            llmConfig =  LlmConfig(provider="openai/gpt-4o", api_token=os.getenv("OPENAI_API_KEY"))
         ),
     )
     cprint(
@@ -197,8 +198,7 @@ def add_llm_extraction_strategy(crawler):
     result = crawler.run(
         url="https://www.nbcnews.com/business",
         extraction_strategy=LLMExtractionStrategy(
-            provider="openai/gpt-4o",
+            llmConfig=LlmConfig(provider="openai/gpt-4o",api_token=os.getenv("OPENAI_API_KEY")),
-            api_token=os.getenv("OPENAI_API_KEY"),
             instruction="I am interested in only financial news",
         ),
     )
@@ -210,8 +210,7 @@ def add_llm_extraction_strategy(crawler):
     result = crawler.run(
         url="https://www.nbcnews.com/business",
         extraction_strategy=LLMExtractionStrategy(
-            provider="openai/gpt-4o",
+            llmConfig=LlmConfig(provider="openai/gpt-4o",api_token=os.getenv("OPENAI_API_KEY")),
-            api_token=os.getenv("OPENAI_API_KEY"),
             instruction="Extract only content related to technology",
         ),
     )

docs/md_v2/api/parameters.md

@@ -245,11 +245,32 @@ run_config = CrawlerRunConfig(
 )
 ```
 
-## 3. Putting It All Together
+# 3. **LlmConfig** - Setting up LLM providers
+LlmConfig is useful to pass LLM provider config to strategies and functions that rely on LLMs to do extraction, filtering, schema generation etc. Currently it can be used in the following -
+
+1. LLMExtractionStrategy
+2. LLMContentFilter
+3. JsonCssExtractionStrategy.generate_schema
+4. JsonXPathExtractionStrategy.generate_schema
+
+## 3.1 Parameters
+| **Parameter**         | **Type / Default**                     | **What It Does**                                                                                                                     |
+|-----------------------|----------------------------------------|---------------------------------------------------------------------------------------------------------------------------------------|
+| **`provider`**    | `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)* | Which LLM provoder to use. 
+| **`api_token`**         |1.Optional. When not provided explicitly, api_token will be read from environment variables based on provider. For example: If a gemini model is passed as provider then,`"GEMINI_API_KEY"` will be read from environment variables  <br/> 2. API token of LLM provider <br/> eg: `api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"` <br/> 3. Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"`              | API token to use for the given provider 
+| **`base_url`**         |Optional. Custom API endpoint | If your provider has a custom endpoint
+
+## 3.2 Example Usage
+```python
+llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+```
+
+## 4. Putting It All Together
 
 - **Use** `BrowserConfig` for **global** browser settings: engine, headless, proxy, user agent.  
 - **Use** `CrawlerRunConfig` for each crawl’s **context**: how to filter content, handle caching, wait for dynamic elements, or run JS.  
 - **Pass** both configs to `AsyncWebCrawler` (the `BrowserConfig`) and then to `arun()` (the `CrawlerRunConfig`).  
+- **Use** `LlmConfig` for LLM provider configurations that can be used across all extraction, filtering, schema generation tasks. Can be used in - `LLMExtractionStrategy`, `LLMContentFilter`, `JsonCssExtractionStrategy.generate_schema` & `JsonXPathExtractionStrategy.generate_schema`
 
 ```python
 # Create a modified copy with the clone() method

docs/md_v2/api/strategies.md

@@ -131,6 +131,7 @@ OverlappingWindowChunking(
 ```python
 from pydantic import BaseModel
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
+from crawl4ai.async_configs import LlmConfig
 
 # Define schema
 class Article(BaseModel):
@@ -140,7 +141,7 @@ class Article(BaseModel):
 
 # Create strategy
 strategy = LLMExtractionStrategy(
-    provider="ollama/llama2",
+    llmConfig = LlmConfig(provider="ollama/llama2"),
     schema=Article.schema(),
     instruction="Extract article details"
 )
@@ -197,6 +198,7 @@ result = await crawler.arun(
 
 ```python
 from crawl4ai.chunking_strategy import OverlappingWindowChunking
+from crawl4ai.async_configs import LlmConfig
 
 # Create chunking strategy
 chunker = OverlappingWindowChunking(
@@ -206,7 +208,7 @@ chunker = OverlappingWindowChunking(
 
 # Use with extraction strategy
 strategy = LLMExtractionStrategy(
-    provider="ollama/llama2",
+    llmConfig = LlmConfig(provider="ollama/llama2"),
     chunking_strategy=chunker
 )
 

docs/md_v2/core/browser-crawler-config.md

@@ -1,9 +1,10 @@
-# Browser & Crawler Configuration (Quick Overview)
+# Browser, Crawler & LLM Configuration (Quick Overview)
 
 Crawl4AI’s flexibility stems from two key classes:
 
 1. **`BrowserConfig`** – Dictates **how** the browser is launched and behaves (e.g., headless or visible, proxy, user agent).  
 2. **`CrawlerRunConfig`** – Dictates **how** each **crawl** operates (e.g., caching, extraction, timeouts, JavaScript code to run, etc.).  
+3. **`LlmConfig`** - Dictates **how** LLM providers are configured. (model, api token, base url, temperature etc.)
 
 In most examples, you create **one** `BrowserConfig` for the entire crawler session, then pass a **fresh** or re-used `CrawlerRunConfig` whenever you call `arun()`. This tutorial shows the most commonly used parameters. If you need advanced or rarely used fields, see the [Configuration Parameters](../api/parameters.md).
 
@@ -234,13 +235,37 @@ The `clone()` method:
 
 ---
 
-## 3. Putting It All Together
 
-In a typical scenario, you define **one** `BrowserConfig` for your crawler session, then create **one or more** `CrawlerRunConfig` depending on each call’s needs:
+
+
+
+## 3. LlmConfig Essentials
+
+### Key fields to note
+
+1. **`provider`**:  
+- Which LLM provoder to use. 
+- Possible values are `"ollama/llama3","groq/llama3-70b-8192","groq/llama3-8b-8192", "openai/gpt-4o-mini" ,"openai/gpt-4o","openai/o1-mini","openai/o1-preview","openai/o3-mini","openai/o3-mini-high","anthropic/claude-3-haiku-20240307","anthropic/claude-3-opus-20240229","anthropic/claude-3-sonnet-20240229","anthropic/claude-3-5-sonnet-20240620","gemini/gemini-pro","gemini/gemini-1.5-pro","gemini/gemini-2.0-flash","gemini/gemini-2.0-flash-exp","gemini/gemini-2.0-flash-lite-preview-02-05","deepseek/deepseek-chat"`<br/>*(default: `"openai/gpt-4o-mini"`)*
+
+2. **`api_token`**:  
+    - Optional. When not provided explicitly, api_token will be read from environment variables based on provider. For example: If a gemini model is passed as provider then,`"GEMINI_API_KEY"` will be read from environment variables  
+    - API token of LLM provider <br/> eg: `api_token = "gsk_1ClHGGJ7Lpn4WGybR7vNWGdyb3FY7zXEw3SCiy0BAVM9lL8CQv"`
+    - Environment variable - use with prefix "env:" <br/> eg:`api_token = "env: GROQ_API_KEY"`            
+
+3. **`base_url`**:  
+   - If your provider has a custom endpoint
+
+```python
+llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv("OPENAI_API_KEY"))
+```
+
+## 4. Putting It All Together
+
+In a typical scenario, you define **one** `BrowserConfig` for your crawler session, then create **one or more** `CrawlerRunConfig` & `LlmConfig` depending on each call’s needs:
 
 ```python
 import asyncio
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode, LlmConfig
 from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
 
 async def main():
@@ -262,8 +287,39 @@ async def main():
     }
     extraction = JsonCssExtractionStrategy(schema)
 
-    # 3) Crawler run config: skip cache, use extraction
+    # 3) Example LLM content filtering
+
+    gemini_config = LlmConfig(
+        provider="gemini/gemini-1.5-pro" 
+        api_token = "env:GEMINI_API_TOKEN"
+    )
+
+    # Initialize LLM filter with specific instruction
+    filter = LLMContentFilter(
+        llmConfig=gemini_config,  # or your preferred provider
+        instruction="""
+        Focus on extracting the core educational content.
+        Include:
+        - Key concepts and explanations
+        - Important code examples
+        - Essential technical details
+        Exclude:
+        - Navigation elements
+        - Sidebars
+        - Footer content
+        Format the output as clean markdown with proper code blocks and headers.
+        """,
+        chunk_token_threshold=500,  # Adjust based on your needs
+        verbose=True
+    )
+
+    md_generator = DefaultMarkdownGenerator(
+    content_filter=filter,
+    options={"ignore_links": True}
+
+    # 4) Crawler run config: skip cache, use extraction
     run_conf = CrawlerRunConfig(
+        markdown_generator=md_generator,
         extraction_strategy=extraction,
         cache_mode=CacheMode.BYPASS,
     )
@@ -283,11 +339,11 @@ if __name__ == "__main__":
 
 ---
 
-## 4. Next Steps
+## 5. Next Steps
 
 For a **detailed list** of available parameters (including advanced ones), see:
 
-- [BrowserConfig and CrawlerRunConfig Reference](../api/parameters.md)  
+- [BrowserConfig, CrawlerRunConfig & LlmConfig Reference](../api/parameters.md)  
 
 You can explore topics like:
 
@@ -298,11 +354,12 @@ You can explore topics like:
 
 ---
 
-## 5. Conclusion
+## 6. Conclusion
 
-**BrowserConfig** and **CrawlerRunConfig** give you straightforward ways to define:
+**BrowserConfig**, **CrawlerRunConfig** and **LlmConfig** give you straightforward ways to define:
 
 - **Which** browser to launch, how it should run, and any proxy or user agent needs.  
 - **How** each crawl should behave—caching, timeouts, JavaScript code, extraction strategies, etc.
+- **Which** LLM provider to use, api token, temperature and base url for custom endpoints
 
 Use them together for **clear, maintainable** code, and when you need more specialized behavior, check out the advanced parameters in the [reference docs](../api/parameters.md). Happy crawling!

docs/md_v2/core/content-selection.md

@@ -211,7 +211,7 @@ if __name__ == "__main__":
 import asyncio
 import json
 from pydantic import BaseModel, Field
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LlmConfig
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 
 class ArticleData(BaseModel):
@@ -220,8 +220,7 @@ class ArticleData(BaseModel):
 
 async def main():
     llm_strategy = LLMExtractionStrategy(
-        provider="openai/gpt-4",
+        llmConfig = LlmConfig(provider="openai/gpt-4",api_token="sk-YOUR_API_KEY")
-        api_token="sk-YOUR_API_KEY",
         schema=ArticleData.schema(),
         extraction_type="schema",
         instruction="Extract 'headline' and a short 'summary' from the content."

docs/md_v2/core/markdown-generation.md

@@ -175,14 +175,13 @@ prune_filter = PruningContentFilter(
 For intelligent content filtering and high-quality markdown generation, you can use the **LLMContentFilter**. This filter leverages LLMs to generate relevant markdown while preserving the original content's meaning and structure:
 
 ```python
-from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig
+from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, LlmConfig
 from crawl4ai.content_filter_strategy import LLMContentFilter
 
 async def main():
     # Initialize LLM filter with specific instruction
     filter = LLMContentFilter(
-        provider="openai/gpt-4o",  # or your preferred provider
+        llmConfig = LlmConfig(provider="openai/gpt-4o",api_token="your-api-token"), #or use environment variable
-        api_token="your-api-token",  # or use environment variable
         instruction="""
         Focus on extracting the core educational content.
         Include:

docs/md_v2/core/quickstart.md

@@ -128,6 +128,7 @@ Crawl4AI can also extract structured data (JSON) using CSS or XPath selectors. B
 
 ```python
 from crawl4ai.extraction_strategy import JsonCssExtractionStrategy
+from crawl4ai.async_configs import LlmConfig
 
 # Generate a schema (one-time cost)
 html = "<div class='product'><h2>Gaming Laptop</h2><span class='price'>$999.99</span></div>"
@@ -135,15 +136,13 @@ html = "<div class='product'><h2>Gaming Laptop</h2><span class='price'>$999.99</
 # Using OpenAI (requires API token)
 schema = JsonCssExtractionStrategy.generate_schema(
     html,
-    provider="openai/gpt-4o",  # Default provider
+    llmConfig = LlmConfig(provider="openai/gpt-4o",api_token="your-openai-token")  # Required for OpenAI
-    api_token="your-openai-token"  # Required for OpenAI
 )
 
 # Or using Ollama (open source, no token needed)
 schema = JsonCssExtractionStrategy.generate_schema(
     html,
-    provider="ollama/llama3.3",  # Open source alternative
+    llmConfig = LlmConfig(provider="ollama/llama3.3", api_token=None)  # Not needed for Ollama
-    api_token=None  # Not needed for Ollama
 )
 
 # Use the schema for fast, repeated extractions
@@ -212,7 +211,7 @@ import os
 import json
 import asyncio
 from pydantic import BaseModel, Field
-from crawl4ai import AsyncWebCrawler, CrawlerRunConfig
+from crawl4ai import AsyncWebCrawler, CrawlerRunConfig, LlmConfig
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
 
 class OpenAIModelFee(BaseModel):
@@ -242,8 +241,7 @@ async def extract_structured_data_using_llm(
         word_count_threshold=1,
         page_timeout=80000,
         extraction_strategy=LLMExtractionStrategy(
-            provider=provider,
+            llmConfig = LlmConfig(provider=provider,api_token=api_token),
-            api_token=api_token,
             schema=OpenAIModelFee.model_json_schema(),
             extraction_type="schema",
             instruction="""From the crawled content, extract all mentioned model names along with their fees for input and output tokens. 
@@ -259,12 +257,6 @@ async def extract_structured_data_using_llm(
         print(result.extracted_content)
 
 if __name__ == "__main__":
-    # Use ollama with llama3.3
-    # asyncio.run(
-    #     extract_structured_data_using_llm(
-    #         provider="ollama/llama3.3", api_token="no-token"
-    #     )
-    # )
 
     asyncio.run(
         extract_structured_data_using_llm(

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",
+    llmConfig = LlmConfig(provider="openai/gpt-4", api_token="YOUR_OPENAI_KEY"),
-    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"
+        llmConfig = LlmConfig(provider="openai/gpt-4o-mini", api_token=os.getenv('OPENAI_API_KEY')),
-        api_token=os.getenv('OPENAI_API_KEY'),
+        schema=Product.schema_json(), # Or use model_json_schema()
-        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
+    schema_type="css", 
-    provider="openai/gpt-4o",  # Default provider
+    llmConfig = LlmConfig(provider="openai/gpt-4o",api_token="your-openai-token")
-    api_token="your-openai-token"  # Required for OpenAI
 )
 
 # 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
+    llmConfig = LlmConfig(provider="ollama/llama3.3", api_token=None)  # Not needed for Ollama
-    api_token=None  # Not needed for Ollama
 )
 
 # Use the generated schema for fast, repeated extractions

mkdocs.yml

@@ -18,7 +18,7 @@ nav:
     - "Command Line Interface": "core/cli.md"
     - "Simple Crawling": "core/simple-crawling.md"
     - "Crawler Result": "core/crawler-result.md"
-    - "Browser & Crawler Config": "core/browser-crawler-config.md"
+    - "Browser, Crawler & LLM Config": "core/browser-crawler-config.md"
     - "Markdown Generation": "core/markdown-generation.md"
     - "Fit Markdown": "core/fit-markdown.md"
     - "Page Interaction": "core/page-interaction.md"
@@ -46,7 +46,7 @@ nav:
     - "AsyncWebCrawler": "api/async-webcrawler.md"
     - "arun()": "api/arun.md"
     - "arun_many()": "api/arun_many.md"
-    - "Browser & Crawler Config": "api/parameters.md"
+    - "Browser, Crawler & LLM Config": "api/parameters.md"
     - "CrawlResult": "api/crawl-result.md"
     - "Strategies": "api/strategies.md"
 

tests/20241401/test_llm_filter.py

@@ -1,6 +1,7 @@
 import os
 import asyncio
 from crawl4ai import AsyncWebCrawler, BrowserConfig, CrawlerRunConfig, CacheMode
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.content_filter_strategy import LLMContentFilter
 
 async def test_llm_filter():
@@ -22,8 +23,7 @@ async def test_llm_filter():
 
         # Initialize LLM filter with focused instruction
         filter = LLMContentFilter(
-            provider="openai/gpt-4o",
+            llmConfig=LlmConfig(provider="openai/gpt-4o",api_token=os.getenv('OPENAI_API_KEY')),
-            api_token=os.getenv('OPENAI_API_KEY'),
             instruction="""
             Focus on extracting the core educational content about Python classes.
             Include:
@@ -43,8 +43,7 @@ async def test_llm_filter():
         )
         
         filter = LLMContentFilter(
-            provider="openai/gpt-4o",
+            llmConfig = LlmConfig(provider="openai/gpt-4o",api_token=os.getenv('OPENAI_API_KEY')),
-            api_token=os.getenv('OPENAI_API_KEY'),
             chunk_token_threshold=2 ** 12 * 2, # 2048 * 2
             instruction="""
             Extract the main educational content while preserving its original wording and substance completely. Your task is to:

tests/async/test_chunking_and_extraction_strategies.py

@@ -7,6 +7,7 @@ import json
 parent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
 sys.path.append(parent_dir)
 
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.async_webcrawler import AsyncWebCrawler
 from crawl4ai.chunking_strategy import RegexChunking
 from crawl4ai.extraction_strategy import LLMExtractionStrategy
@@ -48,8 +49,7 @@ async def test_llm_extraction_strategy():
     async with AsyncWebCrawler(verbose=True) as crawler:
         url = "https://www.nbcnews.com/business"
         extraction_strategy = LLMExtractionStrategy(
-            provider="openai/gpt-4o-mini",
+            llmConfig=LlmConfig(provider="openai/gpt-4o-mini",api_token=os.getenv("OPENAI_API_KEY")),
-            api_token=os.getenv("OPENAI_API_KEY"),
             instruction="Extract only content related to technology",
         )
         result = await crawler.arun(

tests/docker/test_docker.py

@@ -7,6 +7,7 @@ from crawl4ai import (
     BrowserConfig, CrawlerRunConfig, DefaultMarkdownGenerator,
     PruningContentFilter, JsonCssExtractionStrategy, LLMContentFilter, CacheMode
 )
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.docker_client import Crawl4aiDockerClient
 
 class Crawl4AiTester:
@@ -142,7 +143,7 @@ async def test_with_client():
             cache_mode=CacheMode.BYPASS,
             markdown_generator=DefaultMarkdownGenerator(
                 content_filter=LLMContentFilter(
-                    provider="openai/gpt-40",
+                    llmConfig=LlmConfig(provider="openai/gpt-40"),
                     instruction="Extract key technical concepts"
                 )
             ),

tests/docker/test_serialization.py

@@ -2,6 +2,8 @@ import inspect
 from typing import Any, Dict
 from enum import Enum
 
+from crawl4ai.async_configs import LlmConfig
+
 def to_serializable_dict(obj: Any) -> Dict:
     """
     Recursively convert an object to a serializable dictionary using {type, params} structure
@@ -222,7 +224,7 @@ if __name__ == "__main__":
     config3 = CrawlerRunConfig(
         markdown_generator=DefaultMarkdownGenerator(
             content_filter=LLMContentFilter(
-                provider="openai/gpt-4",
+                llmConfig = LlmConfig(provider="openai/gpt-4"),
                 instruction="Extract key technical concepts",
                 chunk_token_threshold=2000,
                 overlap_rate=0.1

tests/test_web_crawler.py

@@ -1,4 +1,5 @@
 import unittest, os
+from crawl4ai.async_configs import LlmConfig
 from crawl4ai.web_crawler import WebCrawler
 from crawl4ai.chunking_strategy import (
     RegexChunking,
@@ -42,7 +43,7 @@ class TestWebCrawler(unittest.TestCase):
             word_count_threshold=5,
             chunking_strategy=FixedLengthWordChunking(chunk_size=100),
             extraction_strategy=LLMExtractionStrategy(
-                provider="openai/gpt-3.5-turbo", api_token=os.getenv("OPENAI_API_KEY")
+                llmConfig=LlmConfig(provider="openai/gpt-3.5-turbo", api_token=os.getenv("OPENAI_API_KEY"))
             ),
             bypass_cache=True,
         )