From 709941c4c77ea87e106af84f21c28ba6410cf06f Mon Sep 17 00:00:00 2001 From: Lorenze Jay Date: Tue, 4 Mar 2025 15:54:46 -0800 Subject: [PATCH] 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. --- src/crewai/__init__.py | 3 +- src/crewai/llm.py | 104 +----- src/crewai/llms/base_llm.py | 104 ++++++ .../test_custom_llm_implementation.yaml | 107 ++++++ .../test_custom_llm_within_crew.yaml | 305 ++++++++++++++++++ tests/custom_llm_test.py | 260 +++++++++------ 6 files changed, 680 insertions(+), 203 deletions(-) create mode 100644 src/crewai/llms/base_llm.py create mode 100644 tests/cassettes/test_custom_llm_implementation.yaml create mode 100644 tests/cassettes/test_custom_llm_within_crew.yaml diff --git a/src/crewai/__init__.py b/src/crewai/__init__.py index 0d6b06961..c157f4c03 100644 --- a/src/crewai/__init__.py +++ b/src/crewai/__init__.py @@ -4,7 +4,8 @@ from crewai.agent import Agent from crewai.crew import Crew from crewai.flow.flow import Flow from crewai.knowledge.knowledge import Knowledge -from crewai.llm import LLM, BaseLLM +from crewai.llm import LLM +from crewai.llms.base_llm import BaseLLM from crewai.process import Process from crewai.task import Task diff --git a/src/crewai/llm.py b/src/crewai/llm.py index 7146b73ae..07483536f 100644 --- a/src/crewai/llm.py +++ b/src/crewai/llm.py @@ -4,7 +4,6 @@ import os import sys import threading import warnings -from abc import ABC, abstractmethod from contextlib import contextmanager from typing import Any, Dict, List, Literal, Optional, Type, Union, cast @@ -27,6 +26,7 @@ with warnings.catch_warnings(): from litellm.utils import get_supported_openai_params, supports_response_schema +from crewai.llms.base_llm import BaseLLM from crewai.utilities.events import crewai_event_bus from crewai.utilities.exceptions.context_window_exceeding_exception import ( LLMContextLengthExceededException, @@ -35,108 +35,6 @@ from crewai.utilities.exceptions.context_window_exceeding_exception import ( load_dotenv() -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. - """ - - def __init__(self): - """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.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, - ) -> 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. - - 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 - - @abstractmethod - def supports_function_calling(self) -> bool: - """Check if the LLM supports function calling. - - This method should return True if the LLM implementation supports - function calling (tools), and False otherwise. If this method returns - True, the LLM should be able to handle the 'tools' parameter in the - call() method. - - Returns: - True if the LLM supports function calling, False otherwise. - """ - pass - - @abstractmethod - def supports_stop_words(self) -> bool: - """Check if the LLM supports stop words. - - This method should return True if the LLM implementation supports - stop words, and False otherwise. If this method returns True, the - LLM should respect the 'stop' attribute when generating responses. - - Returns: - True if the LLM supports stop words, False otherwise. - """ - pass - - @abstractmethod - def get_context_window_size(self) -> int: - """Get the context window size of the LLM. - - This method should return the maximum number of tokens that the LLM - can process in a single request. This is used by CrewAI to ensure - that messages don't exceed the LLM's context window. - - Returns: - The context window size as an integer. - """ - pass - - class FilteredStream: def __init__(self, original_stream): self._original_stream = original_stream diff --git a/src/crewai/llms/base_llm.py b/src/crewai/llms/base_llm.py new file mode 100644 index 000000000..f4fa2e1c5 --- /dev/null +++ b/src/crewai/llms/base_llm.py @@ -0,0 +1,104 @@ +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. + """ + + def __init__(self): + """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.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, + ) -> 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. + + 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 + + @abstractmethod + def supports_function_calling(self) -> bool: + """Check if the LLM supports function calling. + + This method should return True if the LLM implementation supports + function calling (tools), and False otherwise. If this method returns + True, the LLM should be able to handle the 'tools' parameter in the + call() method. + + Returns: + True if the LLM supports function calling, False otherwise. + """ + pass + + @abstractmethod + def supports_stop_words(self) -> bool: + """Check if the LLM supports stop words. + + This method should return True if the LLM implementation supports + stop words, and False otherwise. If this method returns True, the + LLM should respect the 'stop' attribute when generating responses. + + Returns: + True if the LLM supports stop words, False otherwise. + """ + pass + + @abstractmethod + def get_context_window_size(self) -> int: + """Get the context window size of the LLM. + + This method should return the maximum number of tokens that the LLM + can process in a single request. This is used by CrewAI to ensure + that messages don't exceed the LLM's context window. + + Returns: + The context window size as an integer. + """ + pass diff --git a/tests/cassettes/test_custom_llm_implementation.yaml b/tests/cassettes/test_custom_llm_implementation.yaml new file mode 100644 index 000000000..1ec828eaf --- /dev/null +++ b/tests/cassettes/test_custom_llm_implementation.yaml @@ -0,0 +1,107 @@ +interactions: +- request: + body: '{"messages": [{"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "What is the answer to life, the universe, and everything?"}], + "model": "gpt-4o-mini", "tools": null}' + headers: + accept: + - application/json + accept-encoding: + - gzip, deflate + connection: + - keep-alive + content-length: + - '206' + content-type: + - application/json + host: + - api.openai.com + user-agent: + - OpenAI/Python 1.61.0 + x-stainless-arch: + - arm64 + x-stainless-async: + - 'false' + x-stainless-lang: + - python + x-stainless-os: + - MacOS + x-stainless-package-version: + - 1.61.0 + x-stainless-retry-count: + - '0' + x-stainless-runtime: + - CPython + x-stainless-runtime-version: + - 3.12.8 + method: POST + uri: https://api.openai.com/v1/chat/completions + response: + content: "{\n \"id\": \"chatcmpl-B7W6FS0wpfndLdg12G3H6ZAXcYhJi\",\n \"object\": + \"chat.completion\",\n \"created\": 1741131387,\n \"model\": \"gpt-4o-mini-2024-07-18\",\n + \ \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": + \"assistant\",\n \"content\": \"The answer to life, the universe, and + everything, famously found in Douglas Adams' \\\"The Hitchhiker's Guide to the + Galaxy,\\\" is the number 42. 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""" - - def __init__(self, response: str = "Custom LLM response"): + + def __init__(self, response="Default response"): """Initialize the CustomLLM with a predefined response. - + Args: response: The predefined response to return from call(). """ super().__init__() self.response = response - self.calls = [] - self.stop = [] - + self.call_count = 0 + 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, - ) -> Union[str, Any]: - """Record the call and return the predefined response. - - Args: - messages: Input messages for the LLM. - tools: Optional list of tool schemas for function calling. - callbacks: Optional list of callback functions. - available_functions: Optional dict mapping function names to callables. - - Returns: - The predefined response string. + messages, + tools=None, + callbacks=None, + available_functions=None, + ): """ - self.calls.append({ - "messages": messages, - "tools": tools, - "callbacks": callbacks, - "available_functions": available_functions - }) + Mock LLM call that returns a predefined response. + Properly formats messages to match OpenAI's expected structure. + """ + self.call_count += 1 + + # If input is a string, convert to proper message format + if isinstance(messages, str): + messages = [{"role": "user", "content": messages}] + + # Ensure each message has properly formatted content + for message in messages: + if isinstance(message["content"], str): + message["content"] = [{"type": "text", "text": message["content"]}] + + # Return predefined response in expected format + if "Thought:" in str(messages): + return f"Thought: I will say hi\nFinal Answer: {self.response}" return self.response - + def supports_function_calling(self) -> bool: - """Return True to indicate that function calling is supported. - + """Return False to indicate that function calling is not supported. + Returns: - True, indicating that this LLM supports function calling. + False, indicating that this LLM does not support function calling. """ - return True - + return False + def supports_stop_words(self) -> bool: - """Return True to indicate that stop words are supported. - + """Return False to indicate that stop words are not supported. + Returns: - True, indicating that this LLM supports stop words. + False, indicating that this LLM does not support stop words. """ - return True - + return False + def get_context_window_size(self) -> int: """Return a default context window size. - + Returns: - 8192, a typical context window size for modern LLMs. + 4096, a typical context window size for modern LLMs. """ - return 8192 + return 4096 +@pytest.mark.vcr(filter_headers=["authorization"]) def test_custom_llm_implementation(): """Test that a custom LLM implementation works with create_llm.""" custom_llm = CustomLLM(response="The answer is 42") - + # Test that create_llm returns the custom LLM instance directly result_llm = create_llm(custom_llm) - + assert result_llm is custom_llm - + # Test calling the custom LLM - response = result_llm.call("What is the answer to life, the universe, and everything?") - - # Verify that the custom LLM was called - assert len(custom_llm.calls) > 0 + response = result_llm.call( + "What is the answer to life, the universe, and everything?" + ) + # Verify that the response from the custom LLM was used - assert response == "The answer is 42" + assert "42" in response + + +@pytest.mark.vcr(filter_headers=["authorization"]) +def test_custom_llm_within_crew(): + """Test that a custom LLM implementation works with create_llm.""" + custom_llm = CustomLLM(response="Hello! Nice to meet you!") + + agent = Agent( + role="Say Hi", + goal="Say hi to the user", + backstory="""You just say hi to the user""", + llm=custom_llm, + ) + + task = Task( + description="Say hi to the user", + expected_output="A greeting to the user", + agent=agent, + ) + + crew = Crew( + agents=[agent], + tasks=[task], + process=Process.sequential, + ) + + result = crew.kickoff() + + # Assert the LLM was called + assert custom_llm.call_count > 0 + # Assert we got a response + assert "Hello!" in result.raw + + +def test_custom_llm_message_formatting(): + """Test that the custom LLM properly formats messages""" + custom_llm = CustomLLM(response="Test response") + + # Test with string input + result = custom_llm.call("Test message") + assert result == "Test response" + + # Test with message list + messages = [ + {"role": "system", "content": "System message"}, + {"role": "user", "content": "User message"}, + ] + result = custom_llm.call(messages) + assert result == "Test response" class JWTAuthLLM(BaseLLM): """Custom LLM implementation with JWT authentication.""" - + def __init__(self, jwt_token: str): super().__init__() if not jwt_token or not isinstance(jwt_token, str): @@ -103,7 +155,7 @@ class JWTAuthLLM(BaseLLM): self.jwt_token = jwt_token self.calls = [] self.stop = [] - + def call( self, messages: Union[str, List[Dict[str, str]]], @@ -112,24 +164,26 @@ class JWTAuthLLM(BaseLLM): available_functions: Optional[Dict[str, Any]] = None, ) -> Union[str, Any]: """Record the call and return a predefined response.""" - self.calls.append({ - "messages": messages, - "tools": tools, - "callbacks": callbacks, - "available_functions": available_functions - }) + self.calls.append( + { + "messages": messages, + "tools": tools, + "callbacks": callbacks, + "available_functions": available_functions, + } + ) # In a real implementation, this would use the JWT token to authenticate # with an external service return "Response from JWT-authenticated LLM" - + def supports_function_calling(self) -> bool: """Return True to indicate that function calling is supported.""" return True - + def supports_stop_words(self) -> bool: """Return True to indicate that stop words are supported.""" return True - + def get_context_window_size(self) -> int: """Return a default context window size.""" return 8192 @@ -138,15 +192,15 @@ class JWTAuthLLM(BaseLLM): def test_custom_llm_with_jwt_auth(): """Test a custom LLM implementation with JWT authentication.""" jwt_llm = JWTAuthLLM(jwt_token="example.jwt.token") - + # Test that create_llm returns the JWT-authenticated LLM instance directly result_llm = create_llm(jwt_llm) - + assert result_llm is jwt_llm - + # Test calling the JWT-authenticated LLM response = result_llm.call("Test message") - + # Verify that the JWT-authenticated LLM was called assert len(jwt_llm.calls) > 0 # Verify that the response from the JWT-authenticated LLM was used @@ -158,7 +212,7 @@ def test_jwt_auth_llm_validation(): # Test with invalid JWT token (empty string) with pytest.raises(ValueError, match="Invalid JWT token"): JWTAuthLLM(jwt_token="") - + # Test with invalid JWT token (non-string) with pytest.raises(ValueError, match="Invalid JWT token"): JWTAuthLLM(jwt_token=None) @@ -166,10 +220,10 @@ def test_jwt_auth_llm_validation(): class TimeoutHandlingLLM(BaseLLM): """Custom LLM implementation with timeout handling and retry logic.""" - + def __init__(self, max_retries: int = 3, timeout: int = 30): """Initialize the TimeoutHandlingLLM with retry and timeout settings. - + Args: max_retries: Maximum number of retry attempts. timeout: Timeout in seconds for each API call. @@ -180,7 +234,7 @@ class TimeoutHandlingLLM(BaseLLM): self.calls = [] self.stop = [] self.fail_count = 0 # Number of times to simulate failure - + def call( self, messages: Union[str, List[Dict[str, str]]], @@ -189,28 +243,30 @@ class TimeoutHandlingLLM(BaseLLM): available_functions: Optional[Dict[str, Any]] = None, ) -> Union[str, Any]: """Simulate API calls with timeout handling and retry logic. - + Args: messages: Input messages for the LLM. tools: Optional list of tool schemas for function calling. callbacks: Optional list of callback functions. available_functions: Optional dict mapping function names to callables. - + Returns: A response string based on whether this is the first attempt or a retry. - + Raises: TimeoutError: If all retry attempts fail. """ # Record the initial call - self.calls.append({ - "messages": messages, - "tools": tools, - "callbacks": callbacks, - "available_functions": available_functions, - "attempt": 0 - }) - + self.calls.append( + { + "messages": messages, + "tools": tools, + "callbacks": callbacks, + "available_functions": available_functions, + "attempt": 0, + } + ) + # Simulate retry logic for attempt in range(self.max_retries): # Skip the first attempt recording since we already did that above @@ -220,7 +276,9 @@ class TimeoutHandlingLLM(BaseLLM): self.fail_count -= 1 # If we've used all retries, raise an error if attempt == self.max_retries - 1: - raise TimeoutError(f"LLM request failed after {self.max_retries} attempts") + raise TimeoutError( + f"LLM request failed after {self.max_retries} attempts" + ) # Otherwise, continue to the next attempt (simulating backoff) continue else: @@ -229,45 +287,49 @@ class TimeoutHandlingLLM(BaseLLM): else: # This is a retry attempt (attempt > 0) # Always record retry attempts - self.calls.append({ - "retry_attempt": attempt, - "messages": messages, - "tools": tools, - "callbacks": callbacks, - "available_functions": available_functions - }) - + self.calls.append( + { + "retry_attempt": attempt, + "messages": messages, + "tools": tools, + "callbacks": callbacks, + "available_functions": available_functions, + } + ) + # Simulate a failure if fail_count > 0 if self.fail_count > 0: self.fail_count -= 1 # If we've used all retries, raise an error if attempt == self.max_retries - 1: - raise TimeoutError(f"LLM request failed after {self.max_retries} attempts") + raise TimeoutError( + f"LLM request failed after {self.max_retries} attempts" + ) # Otherwise, continue to the next attempt (simulating backoff) continue else: # Success on retry return "Response after retry" - + def supports_function_calling(self) -> bool: """Return True to indicate that function calling is supported. - + Returns: True, indicating that this LLM supports function calling. """ return True - + def supports_stop_words(self) -> bool: """Return True to indicate that stop words are supported. - + Returns: True, indicating that this LLM supports stop words. """ return True - + def get_context_window_size(self) -> int: """Return a default context window size. - + Returns: 8192, a typical context window size for modern LLMs. """ @@ -281,14 +343,14 @@ def test_timeout_handling_llm(): response = llm.call("Test message") assert response == "First attempt response" assert len(llm.calls) == 1 - + # Test successful retry llm = TimeoutHandlingLLM() llm.fail_count = 1 # Fail once, then succeed response = llm.call("Test message") assert response == "Response after retry" assert len(llm.calls) == 2 # Initial call + successful retry call - + # Test failure after all retries llm = TimeoutHandlingLLM(max_retries=2) llm.fail_count = 2 # Fail twice, which is all retries