mirror of
https://github.com/crewAIInc/crewAI.git
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Refactor LLM module by extracting BaseLLM to a separate file
This commit moves the BaseLLM abstract base class from llm.py to a new file llms/base_llm.py to improve code organization. The changes include: - Creating a new file src/crewai/llms/base_llm.py - Moving the BaseLLM class to the new file - Updating imports in __init__.py and llm.py to reflect the new location - Updating test cases to use the new import path The refactoring maintains the existing functionality while improving the project's module structure.
This commit is contained in:
Lorenze Jay
@@ -4,7 +4,8 @@ from crewai.agent import Agent
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from crewai.crew import Crew
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from crewai.flow.flow import Flow
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from crewai.knowledge.knowledge import Knowledge
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from crewai.llm import LLM, BaseLLM
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from crewai.llm import LLM
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from crewai.llms.base_llm import BaseLLM
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from crewai.process import Process
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from crewai.task import Task
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@@ -4,7 +4,6 @@ import os
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import sys
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import threading
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import warnings
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from abc import ABC, abstractmethod
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from contextlib import contextmanager
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from typing import Any, Dict, List, Literal, Optional, Type, Union, cast
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@@ -27,6 +26,7 @@ with warnings.catch_warnings():
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from litellm.utils import get_supported_openai_params, supports_response_schema
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from crewai.llms.base_llm import BaseLLM
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from crewai.utilities.events import crewai_event_bus
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from crewai.utilities.exceptions.context_window_exceeding_exception import (
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LLMContextLengthExceededException,
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@@ -35,108 +35,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import (
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load_dotenv()
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class BaseLLM(ABC):
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"""Abstract base class for LLM implementations.
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This class defines the interface that all LLM implementations must follow.
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Users can extend this class to create custom LLM implementations that don't
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rely on litellm's authentication mechanism.
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Custom LLM implementations should handle error cases gracefully, including
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timeouts, authentication failures, and malformed responses. They should also
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implement proper validation for input parameters and provide clear error
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messages when things go wrong.
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Attributes:
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stop (list): A list of stop sequences that the LLM should use to stop generation.
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This is used by the CrewAgentExecutor and other components.
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"""
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def __init__(self):
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"""Initialize the BaseLLM with default attributes.
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This constructor sets default values for attributes that are expected
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by the CrewAgentExecutor and other components.
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All custom LLM implementations should call super().__init__() to ensure
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that these default attributes are properly initialized.
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"""
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self.stop = []
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@abstractmethod
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def call(
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self,
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messages: Union[str, List[Dict[str, str]]],
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tools: Optional[List[dict]] = None,
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callbacks: Optional[List[Any]] = None,
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available_functions: Optional[Dict[str, Any]] = None,
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) -> Union[str, Any]:
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"""Call the LLM with the given messages.
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Args:
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messages: Input messages for the LLM.
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Can be a string or list of message dictionaries.
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If string, it will be converted to a single user message.
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If list, each dict must have 'role' and 'content' keys.
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tools: Optional list of tool schemas for function calling.
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Each tool should define its name, description, and parameters.
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callbacks: Optional list of callback functions to be executed
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during and after the LLM call.
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available_functions: Optional dict mapping function names to callables
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that can be invoked by the LLM.
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Returns:
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Either a text response from the LLM (str) or
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the result of a tool function call (Any).
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Raises:
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ValueError: If the messages format is invalid.
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TimeoutError: If the LLM request times out.
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RuntimeError: If the LLM request fails for other reasons.
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"""
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pass
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@abstractmethod
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def supports_function_calling(self) -> bool:
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"""Check if the LLM supports function calling.
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This method should return True if the LLM implementation supports
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function calling (tools), and False otherwise. If this method returns
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True, the LLM should be able to handle the 'tools' parameter in the
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call() method.
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Returns:
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True if the LLM supports function calling, False otherwise.
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"""
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pass
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@abstractmethod
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def supports_stop_words(self) -> bool:
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"""Check if the LLM supports stop words.
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This method should return True if the LLM implementation supports
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stop words, and False otherwise. If this method returns True, the
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LLM should respect the 'stop' attribute when generating responses.
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Returns:
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True if the LLM supports stop words, False otherwise.
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"""
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pass
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@abstractmethod
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def get_context_window_size(self) -> int:
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"""Get the context window size of the LLM.
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This method should return the maximum number of tokens that the LLM
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can process in a single request. This is used by CrewAI to ensure
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that messages don't exceed the LLM's context window.
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Returns:
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The context window size as an integer.
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"""
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pass
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class FilteredStream:
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def __init__(self, original_stream):
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self._original_stream = original_stream
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104
src/crewai/llms/base_llm.py
Normal file
104
src/crewai/llms/base_llm.py
Normal file
@@ -0,0 +1,104 @@
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from abc import ABC, abstractmethod
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from typing import Any, Dict, List, Optional, Union
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class BaseLLM(ABC):
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"""Abstract base class for LLM implementations.
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This class defines the interface that all LLM implementations must follow.
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Users can extend this class to create custom LLM implementations that don't
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rely on litellm's authentication mechanism.
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Custom LLM implementations should handle error cases gracefully, including
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timeouts, authentication failures, and malformed responses. They should also
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implement proper validation for input parameters and provide clear error
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messages when things go wrong.
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Attributes:
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stop (list): A list of stop sequences that the LLM should use to stop generation.
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This is used by the CrewAgentExecutor and other components.
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"""
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def __init__(self):
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"""Initialize the BaseLLM with default attributes.
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This constructor sets default values for attributes that are expected
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by the CrewAgentExecutor and other components.
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All custom LLM implementations should call super().__init__() to ensure
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that these default attributes are properly initialized.
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"""
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self.stop = []
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@abstractmethod
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def call(
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self,
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messages: Union[str, List[Dict[str, str]]],
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tools: Optional[List[dict]] = None,
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callbacks: Optional[List[Any]] = None,
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available_functions: Optional[Dict[str, Any]] = None,
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) -> Union[str, Any]:
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"""Call the LLM with the given messages.
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Args:
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messages: Input messages for the LLM.
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Can be a string or list of message dictionaries.
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If string, it will be converted to a single user message.
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If list, each dict must have 'role' and 'content' keys.
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tools: Optional list of tool schemas for function calling.
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Each tool should define its name, description, and parameters.
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callbacks: Optional list of callback functions to be executed
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during and after the LLM call.
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available_functions: Optional dict mapping function names to callables
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that can be invoked by the LLM.
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Returns:
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Either a text response from the LLM (str) or
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the result of a tool function call (Any).
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Raises:
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ValueError: If the messages format is invalid.
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TimeoutError: If the LLM request times out.
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RuntimeError: If the LLM request fails for other reasons.
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"""
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pass
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@abstractmethod
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def supports_function_calling(self) -> bool:
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"""Check if the LLM supports function calling.
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This method should return True if the LLM implementation supports
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function calling (tools), and False otherwise. If this method returns
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True, the LLM should be able to handle the 'tools' parameter in the
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call() method.
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Returns:
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True if the LLM supports function calling, False otherwise.
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"""
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pass
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@abstractmethod
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def supports_stop_words(self) -> bool:
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"""Check if the LLM supports stop words.
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This method should return True if the LLM implementation supports
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stop words, and False otherwise. If this method returns True, the
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LLM should respect the 'stop' attribute when generating responses.
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Returns:
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True if the LLM supports stop words, False otherwise.
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"""
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pass
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@abstractmethod
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def get_context_window_size(self) -> int:
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"""Get the context window size of the LLM.
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This method should return the maximum number of tokens that the LLM
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can process in a single request. This is used by CrewAI to ensure
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that messages don't exceed the LLM's context window.
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Returns:
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The context window size as an integer.
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"""
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pass
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