Enhance BaseLLM documentation and add model parameter validation

Co-Authored-By: Joe Moura <joao@crewai.com>

docs/how-to/custom-llm.mdx

@@ -16,6 +16,16 @@ To create a custom LLM implementation, you need to:
    - `supports_function_calling()`: Whether the LLM supports function calling
    - `supports_stop_words()`: Whether the LLM supports stop words
    - `get_context_window_size()`: The context window size of the LLM
+3. Ensure you pass a model identifier string to the BaseLLM constructor using `super().__init__(model="your-model-name")`
+
+## Required Parameters
+
+When creating custom LLM implementations, the following parameters are essential:
+
+- `model`: String identifier for your model implementation.
+  - Required in the BaseLLM constructor
+  - Example values: `"gpt-4-custom"`, `"anthropic-claude-custom"`, `"custom-llm-v1.0"`
+  - Used to identify the model in logs, metrics, and other components
 
 ## Example: Basic Custom LLM
 
@@ -24,8 +34,14 @@ from crewai import BaseLLM
 from typing import Any, Dict, List, Optional, Union
 
 class CustomLLM(BaseLLM):
+    """A custom LLM implementation with basic API key authentication.
+    
+    Args:
+        api_key (str): API key for the LLM service.
+        endpoint (str): Endpoint URL for the LLM service.
+    """
     def __init__(self, api_key: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
+        super().__init__(model="custom-llm-v1.0")  # Initialize with required model parameter
         if not api_key or not isinstance(api_key, str):
             raise ValueError("Invalid API key: must be a non-empty string")
         if not endpoint or not isinstance(endpoint, str):
@@ -195,7 +211,7 @@ Always validate input parameters to prevent runtime errors:
 
 ```python
 def __init__(self, api_key: str, endpoint: str):
-    super().__init__()
+    super().__init__(model="custom-api-llm-v1.0")  # Initialize with required model parameter
     if not api_key or not isinstance(api_key, str):
         raise ValueError("Invalid API key: must be a non-empty string")
     if not endpoint or not isinstance(endpoint, str):
@@ -238,8 +254,14 @@ from crewai import BaseLLM, Agent, Task
 from typing import Any, Dict, List, Optional, Union
 
 class JWTAuthLLM(BaseLLM):
+    """A custom LLM implementation with JWT-based authentication.
+    
+    Args:
+        jwt_token (str): JWT token for authentication.
+        endpoint (str): Endpoint URL for the LLM service.
+    """
     def __init__(self, jwt_token: str, endpoint: str):
-        super().__init__()  # Initialize the base class to set default attributes
+        super().__init__(model="jwt-auth-llm-v1.0")  # Initialize with required model parameter
         if not jwt_token or not isinstance(jwt_token, str):
             raise ValueError("Invalid JWT token: must be a non-empty string")
         if not endpoint or not isinstance(endpoint, str):
@@ -386,8 +408,14 @@ import logging
 from typing import Any, Dict, List, Optional, Union
 
 class LoggingLLM(BaseLLM):
+    """A custom LLM implementation with logging capabilities.
+    
+    Args:
+        api_key (str): API key for the LLM service.
+        endpoint (str): Endpoint URL for the LLM service.
+    """
     def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
+        super().__init__(model="logging-llm-v1.0")  # Initialize with required model parameter
         self.api_key = api_key
         self.endpoint = endpoint
         self.logger = logging.getLogger("crewai.llm.custom")
@@ -419,13 +447,21 @@ import time
 from typing import Any, Dict, List, Optional, Union
 
 class RateLimitedLLM(BaseLLM):
+    """A custom LLM implementation with rate limiting capabilities.
+    
+    Args:
+        api_key (str): API key for the LLM service.
+        endpoint (str): Endpoint URL for the LLM service.
+        requests_per_minute (int, optional): Maximum number of requests allowed per minute.
+            Defaults to 60.
+    """
     def __init__(
         self, 
         api_key: str, 
         endpoint: str, 
         requests_per_minute: int = 60
     ):
-        super().__init__()
+        super().__init__(model="rate-limited-llm-v1.0")  # Initialize with required model parameter
         self.api_key = api_key
         self.endpoint = endpoint
         self.requests_per_minute = requests_per_minute
@@ -467,8 +503,14 @@ import time
 from typing import Any, Dict, List, Optional, Union
 
 class MetricsCollectingLLM(BaseLLM):
+    """A custom LLM implementation with metrics collection capabilities.
+    
+    Args:
+        api_key (str): API key for the LLM service.
+        endpoint (str): Endpoint URL for the LLM service.
+    """
     def __init__(self, api_key: str, endpoint: str):
-        super().__init__()
+        super().__init__(model="metrics-llm-v1.0")  # Initialize with required model parameter
         self.api_key = api_key
         self.endpoint = endpoint
         self.metrics: Dict[str, Any] = {

src/crewai/llms/base_llm.py

@@ -1,5 +1,5 @@
 from abc import ABC, abstractmethod
-from typing import Any, Callable, Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 
 class BaseLLM(ABC):
@@ -27,15 +27,25 @@ class BaseLLM(ABC):
         self,
         model: str,
         temperature: Optional[float] = None,
-    ):
+    ) -> None:
         """Initialize the BaseLLM with default attributes.
 
         This constructor sets default values for attributes that are expected
         by the CrewAgentExecutor and other components.
 
-        All custom LLM implementations should call super().__init__() to ensure
-        that these default attributes are properly initialized.
+        All custom LLM implementations should call super().__init__(model="model_name"),
+        where "model_name" is a string identifier for your model. This parameter
+        is required and cannot be omitted.
+
+        Args:
+            model (str): Required. A string identifier for the model.
+            temperature (Optional[float]): The sampling temperature to use.
+
+        Raises:
+            ValueError: If the model parameter is not provided or empty.
         """
+        if not model:
+            raise ValueError("model parameter is required and must be a non-empty string")
         self.model = model
         self.temperature = temperature
         self.stop = []