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

src/crewai/flow/flow.py

@@ -219,11 +219,7 @@ class Flow(Generic[T], metaclass=FlowMeta):
         """Returns the list of all outputs from executed methods."""
         return self._method_outputs
 
-    def _initialize_state(self, inputs: Optional[Dict[str, Any]] = None) -> None:
-        """Initialize the state of the flow."""
-        if inputs is None:
-            return
-            
+    def _initialize_state(self, inputs: Dict[str, Any]) -> None:
         if isinstance(self._state, BaseModel):
             # Structured state
             try:
@@ -249,8 +245,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
             self._state.update(inputs)
         else:
             raise TypeError("State must be a BaseModel instance or a dictionary.")
-            
-        self._interpolate_inputs_in_crew(inputs)
 
     def kickoff(self, inputs: Optional[Dict[str, Any]] = None) -> Any:
         self.event_emitter.send(
@@ -412,11 +406,6 @@ class Flow(Generic[T], metaclass=FlowMeta):
 
             traceback.print_exc()
 
-    def _interpolate_inputs_in_crew(self, inputs: Dict[str, Any]) -> None:
-        """Interpolate inputs in the crew's tasks and agents if a crew is present."""
-        if hasattr(self, 'crew') and self.crew:
-            self.crew._interpolate_inputs(inputs)
-            
     def plot(self, filename: str = "crewai_flow") -> None:
         self._telemetry.flow_plotting_span(
             self.__class__.__name__, list(self._methods.keys())

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/flow_test.py

@@ -322,43 +322,3 @@ def test_router_with_multiple_conditions():
 
     # final_step should run after router_and
     assert execution_order.index("log_final_step") > execution_order.index("router_and")
-
-
-def test_flow_inputs_passed_to_tasks():
-    """Test that inputs passed to Flow's kickoff method are correctly interpolated in task descriptions."""
-    from crewai import Agent, Crew, Task
-    from crewai.llm import LLM
-
-    agent = Agent(
-        role="Test Agent",
-        goal="Test Goal",
-        backstory="Test Backstory",
-        llm=LLM(model="gpt-4o-mini")
-    )
-
-    task = Task(
-        description="Process data about {topic}",
-        expected_output="Information about {topic}",
-        agent=agent
-    )
-
-    crew = Crew(
-        agents=[agent],
-        tasks=[task]
-    )
-
-    class TestFlow(Flow):
-        def __init__(self):
-            super().__init__()
-            self.crew = crew
-
-        @start()
-        def start_process(self):
-            pass
-
-    flow = TestFlow()
-    inputs = {"topic": "artificial intelligence"}
-    flow.kickoff(inputs=inputs)
-
-    assert task.description == "Process data about artificial intelligence"
-    assert task.expected_output == "Information about artificial intelligence"