mirror of
https://github.com/crewAIInc/crewAI.git
synced 2026-01-08 23:58:34 +00:00
b7bf15681e
* feat: add capability to track LLM calls by task and agent This makes it possible to filter or scope LLM events by specific agents or tasks, which can be very useful for debugging or analytics in real-time application * feat: add docs about LLM tracking by Agents and Tasks * fix incompatible BaseLLM.call method signature * feat: support to filter LLM Events from Lite Agent
95 lines
3.5 KiB
Python
95 lines
3.5 KiB
Python
from abc import ABC, abstractmethod
|
|
from typing import Any, Dict, List, Optional, Union
|
|
|
|
|
|
class BaseLLM(ABC):
|
|
"""Abstract base class for LLM implementations.
|
|
|
|
This class defines the interface that all LLM implementations must follow.
|
|
Users can extend this class to create custom LLM implementations that don't
|
|
rely on litellm's authentication mechanism.
|
|
|
|
Custom LLM implementations should handle error cases gracefully, including
|
|
timeouts, authentication failures, and malformed responses. They should also
|
|
implement proper validation for input parameters and provide clear error
|
|
messages when things go wrong.
|
|
|
|
Attributes:
|
|
stop (list): A list of stop sequences that the LLM should use to stop generation.
|
|
This is used by the CrewAgentExecutor and other components.
|
|
"""
|
|
|
|
model: str
|
|
temperature: Optional[float] = None
|
|
stop: Optional[List[str]] = None
|
|
|
|
def __init__(
|
|
self,
|
|
model: str,
|
|
temperature: Optional[float] = 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.
|
|
"""
|
|
self.model = model
|
|
self.temperature = temperature
|
|
self.stop = []
|
|
|
|
@abstractmethod
|
|
def call(
|
|
self,
|
|
messages: Union[str, List[Dict[str, str]]],
|
|
tools: Optional[List[dict]] = None,
|
|
callbacks: Optional[List[Any]] = None,
|
|
available_functions: Optional[Dict[str, Any]] = None,
|
|
from_task: Optional[Any] = None,
|
|
from_agent: Optional[Any] = None,
|
|
) -> Union[str, Any]:
|
|
"""Call the LLM with the given messages.
|
|
|
|
Args:
|
|
messages: Input messages for the LLM.
|
|
Can be a string or list of message dictionaries.
|
|
If string, it will be converted to a single user message.
|
|
If list, each dict must have 'role' and 'content' keys.
|
|
tools: Optional list of tool schemas for function calling.
|
|
Each tool should define its name, description, and parameters.
|
|
callbacks: Optional list of callback functions to be executed
|
|
during and after the LLM call.
|
|
available_functions: Optional dict mapping function names to callables
|
|
that can be invoked by the LLM.
|
|
from_task: Optional task caller to be used for the LLM call.
|
|
|
|
Returns:
|
|
Either a text response from the LLM (str) or
|
|
the result of a tool function call (Any).
|
|
|
|
Raises:
|
|
ValueError: If the messages format is invalid.
|
|
TimeoutError: If the LLM request times out.
|
|
RuntimeError: If the LLM request fails for other reasons.
|
|
"""
|
|
pass
|
|
|
|
def supports_stop_words(self) -> bool:
|
|
"""Check if the LLM supports stop words.
|
|
|
|
Returns:
|
|
bool: True if the LLM supports stop words, False otherwise.
|
|
"""
|
|
return True # Default implementation assumes support for stop words
|
|
|
|
def get_context_window_size(self) -> int:
|
|
"""Get the context window size for the LLM.
|
|
|
|
Returns:
|
|
int: The number of tokens/characters the model can handle.
|
|
"""
|
|
# Default implementation - subclasses should override with model-specific values
|
|
return 4096
|