Merge branch 'main' into fix-knowledgestorage-default-instantiation

src/crewai/agents/crew_agent_executor.py

@@ -3,7 +3,6 @@ import re
 from dataclasses import dataclass
 from typing import Any, Dict, List, Union
 
-from litellm import AuthenticationError as LiteLLMAuthenticationError
 from crewai.agents.agent_builder.base_agent import BaseAgent
 from crewai.agents.agent_builder.base_agent_executor_mixin import CrewAgentExecutorMixin
 from crewai.agents.parser import (
@@ -198,19 +197,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
             return self._invoke_loop(formatted_answer)
 
         except Exception as e:
-            if isinstance(e, LiteLLMAuthenticationError):
-                self._logger.log(
-                    level="error",
-                    message="Authentication error with litellm occurred. Please check your API key and configuration.",
-                    color="red",
-                )
-                self._logger.log(
-                    level="error",
-                    message=f"Error details: {str(e)}",
-                    color="red",
-                )
-                raise e
-            elif LLMContextLengthExceededException(str(e))._is_context_limit_error(
+            if LLMContextLengthExceededException(str(e))._is_context_limit_error(
                 str(e)
             ):
                 self._handle_context_length()

src/crewai/knowledge/knowledge.py

@@ -14,13 +14,13 @@ class Knowledge(BaseModel):
     Knowledge is a collection of sources and setup for the vector store to save and query relevant context.
     Args:
         sources: List[BaseKnowledgeSource] = Field(default_factory=list)
-        storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+        storage: Optional[KnowledgeStorage] = Field(default=None)
         embedder_config: Optional[Dict[str, Any]] = None
     """
 
     sources: List[BaseKnowledgeSource] = Field(default_factory=list)
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     embedder_config: Optional[Dict[str, Any]] = None
     collection_name: Optional[str] = None
 

src/crewai/knowledge/source/base_file_knowledge_source.py

@@ -22,7 +22,7 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
         default_factory=list, description="The path to the file"
     )
     content: Dict[Path, str] = Field(init=False, default_factory=dict)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     safe_file_paths: List[Path] = Field(default_factory=list)
 
     @field_validator("file_path", "file_paths", mode="before")
@@ -62,7 +62,10 @@ class BaseFileKnowledgeSource(BaseKnowledgeSource, ABC):
 
     def _save_documents(self):
         """Save the documents to the storage."""
-        self.storage.save(self.chunks)
+        if self.storage:
+            self.storage.save(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")
 
     def convert_to_path(self, path: Union[Path, str]) -> Path:
         """Convert a path to a Path object."""

src/crewai/knowledge/source/base_knowledge_source.py

@@ -16,7 +16,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
     chunk_embeddings: List[np.ndarray] = Field(default_factory=list)
 
     model_config = ConfigDict(arbitrary_types_allowed=True)
-    storage: KnowledgeStorage = Field(default_factory=KnowledgeStorage)
+    storage: Optional[KnowledgeStorage] = Field(default=None)
     metadata: Dict[str, Any] = Field(default_factory=dict)  # Currently unused
     collection_name: Optional[str] = Field(default=None)
 
@@ -46,4 +46,7 @@ class BaseKnowledgeSource(BaseModel, ABC):
         Save the documents to the storage.
         This method should be called after the chunks and embeddings are generated.
         """
-        self.storage.save(self.chunks)
+        if self.storage:
+            self.storage.save(self.chunks)
+        else:
+            raise ValueError("No storage found to save documents.")

tests/agent_test.py

@@ -1308,115 +1308,6 @@ def test_llm_call_with_error():
         llm.call(messages)
 
 
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_litellm_auth_error_handling():
-    """Test that LiteLLM authentication errors are handled correctly and not retried."""
-    from litellm import AuthenticationError as LiteLLMAuthenticationError
-
-    # Create an agent with a mocked LLM and max_retry_limit=0
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="gpt-4"),
-        max_retry_limit=0,  # Disable retries for authentication errors
-        max_iter=1,  # Limit to one iteration to prevent multiple calls
-    )
-
-    # Create a task
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-
-    # Mock the LLM call to raise LiteLLMAuthenticationError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
-    ):
-        mock_llm_call.side_effect = LiteLLMAuthenticationError(
-            message="Invalid API key",
-            llm_provider="openai",
-            model="gpt-4"
-        )
-        agent.execute_task(task)
-
-    # Verify the call was only made once (no retries)
-    mock_llm_call.assert_called_once()
-
-
-@pytest.mark.vcr(filter_headers=["authorization"])
-def test_crew_agent_executor_litellm_auth_error():
-    """Test that CrewAgentExecutor properly identifies and handles LiteLLM authentication errors."""
-    from litellm import AuthenticationError as LiteLLMAuthenticationError
-    from crewai.utilities import Logger
-    from crewai.agents.tools_handler import ToolsHandler
-
-    # Create an agent and executor with max_retry_limit=0
-    agent = Agent(
-        role="test role",
-        goal="test goal",
-        backstory="test backstory",
-        llm=LLM(model="gpt-4"),
-        max_retry_limit=0,  # Disable retries for authentication errors
-    )
-    task = Task(
-        description="Test task",
-        expected_output="Test output",
-        agent=agent,
-    )
-    
-    # Create executor with all required parameters
-    executor = CrewAgentExecutor(
-        agent=agent,
-        task=task,
-        llm=agent.llm,
-        crew=None,
-        prompt={
-            "system": "You are a test agent",
-            "user": "Execute the task: {input}"
-        },
-        max_iter=5,
-        tools=[],
-        tools_names="",
-        stop_words=[],
-        tools_description="",
-        tools_handler=ToolsHandler(),
-    )
-
-    # Mock the LLM call to raise LiteLLMAuthenticationError
-    with (
-        patch.object(LLM, "call") as mock_llm_call,
-        patch.object(Logger, "log") as mock_logger,
-        pytest.raises(LiteLLMAuthenticationError, match="Invalid API key"),
-    ):
-        mock_llm_call.side_effect = LiteLLMAuthenticationError(
-            message="Invalid API key",
-            llm_provider="openai",
-            model="gpt-4"
-        )
-        executor.invoke({
-            "input": "test input",
-            "tool_names": "",  # Required template variable
-            "tools": "",       # Required template variable
-        })
-
-    # Verify error handling
-    mock_logger.assert_any_call(
-        level="error",
-        message="Authentication error with litellm occurred. Please check your API key and configuration.",
-        color="red",
-    )
-    mock_logger.assert_any_call(
-        level="error",
-        message="Error details: litellm.AuthenticationError: Invalid API key",
-        color="red",
-    )
-    # Verify the call was only made once (no retries)
-    mock_llm_call.assert_called_once()
-
-
 @pytest.mark.vcr(filter_headers=["authorization"])
 def test_handle_context_length_exceeds_limit():
     agent = Agent(