feat: Enhance agent knowledge setup with optional crew embedder

- Modify `Agent` class to add `set_knowledge` method
- Allow setting embedder from crew-level configuration
- Remove `_set_knowledge` method from initialization
- Update `Crew` class to set agent knowledge during agent setup
- Add default implementation in `BaseAgent` for compatibility

src/crewai/agent.py

@@ -114,7 +114,6 @@ class Agent(BaseAgent):
 
     @model_validator(mode="after")
     def post_init_setup(self):
-        self._set_knowledge()
         self.agent_ops_agent_name = self.role
 
         self.llm = create_llm(self.llm)
@@ -134,8 +133,11 @@ class Agent(BaseAgent):
             self.cache_handler = CacheHandler()
         self.set_cache_handler(self.cache_handler)
 
-    def _set_knowledge(self):
+    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
         try:
+            if self.embedder is None and crew_embedder:
+                self.embedder = crew_embedder
+
             if self.knowledge_sources:
                 full_pattern = re.compile(r"[^a-zA-Z0-9\-_\r\n]|(\.\.)")
                 knowledge_agent_name = f"{re.sub(full_pattern, '_', self.role)}"

src/crewai/agents/agent_builder/base_agent.py

@@ -351,3 +351,6 @@ class BaseAgent(ABC, BaseModel):
         if not self._rpm_controller:
             self._rpm_controller = rpm_controller
             self.create_agent_executor()
+
+    def set_knowledge(self, crew_embedder: Optional[Dict[str, Any]] = None):
+        pass

src/crewai/agents/crew_agent_executor.py

@@ -232,14 +232,7 @@ class CrewAgentExecutor(CrewAgentExecutorMixin):
                 self._format_answer(answer)
             except OutputParserException as e:
                 if FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE in e.error:
-                    # If both Action and Final Answer are present, prioritize the Action
-                    # by removing the Final Answer part
-                    if "Final Answer:" in answer:
-                        parts = answer.split("Final Answer:")
-                        answer = parts[0].strip()
-                    # If that doesn't work, try splitting at Observation
-                    elif "Observation:" in answer:
-                        answer = answer.split("Observation:")[0].strip()
+                    answer = answer.split("Observation:")[0].strip()
 
         return self._format_answer(answer)
 

src/crewai/crew.py

@@ -600,6 +600,7 @@ class Crew(BaseModel):
                 agent.i18n = i18n
                 # type: ignore[attr-defined] # Argument 1 to "_interpolate_inputs" of "Crew" has incompatible type "dict[str, Any] | None"; expected "dict[str, Any]"
                 agent.crew = self  # type: ignore[attr-defined]
+                agent.set_knowledge(crew_embedder=self.embedder)
                 # TODO: Create an AgentFunctionCalling protocol for future refactoring
                 if not agent.function_calling_llm:  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"
                     agent.function_calling_llm = self.function_calling_llm  # type: ignore # "BaseAgent" has no attribute "function_calling_llm"

tests/agents/test_action_final_answer_error.py

@@ -1,84 +0,0 @@
-from unittest.mock import MagicMock
-
-import pytest
-
-from crewai.agents.crew_agent_executor import CrewAgentExecutor
-from crewai.agents.parser import (
-    FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE,
-    AgentAction,
-    AgentFinish,
-    OutputParserException,
-)
-
-
-def test_process_llm_response_with_action_and_final_answer():
-    """Test that _process_llm_response correctly handles outputs with both Action and Final Answer."""
-    # Create a mock LLM
-    mock_llm = MagicMock()
-    mock_llm.supports_stop_words.return_value = False
-    
-    # Create a mock agent
-    mock_agent = MagicMock()
-    
-    # Create a CrewAgentExecutor instance
-    executor = CrewAgentExecutor(
-        llm=mock_llm,
-        task=MagicMock(),
-        crew=MagicMock(),
-        agent=mock_agent,
-        prompt={},
-        max_iter=5,
-        tools=[],
-        tools_names="",
-        stop_words=[],
-        tools_description="",
-        tools_handler=MagicMock(),
-    )
-    
-    # Test case 1: Output with both Action and Final Answer, with Final Answer after Action
-    output_with_both = """
-    Thought: I need to search for information and then provide an answer.
-    Action: search
-    Action Input: what is the temperature in SF?
-    Final Answer: The temperature is 100 degrees
-    """
-    
-    # Mock the _format_answer method to first raise an exception and then return a valid result
-    format_answer_mock = MagicMock()
-    format_answer_mock.side_effect = [
-        OutputParserException(FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE),
-        AgentAction(thought="", tool="search", tool_input="what is the temperature in SF?", text=""),
-    ]
-    executor._format_answer = format_answer_mock
-    
-    # Process the response
-    result = executor._process_llm_response(output_with_both)
-    
-    # Verify that the result is an AgentAction
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF?"
-    
-    # Test case 2: Output with both Action and Final Answer, with Observation in between
-    output_with_observation = """
-    Thought: I need to search for information.
-    Action: search
-    Action Input: what is the temperature in SF?
-    Observation: The temperature in SF is 100 degrees.
-    Final Answer: The temperature is 100 degrees
-    """
-    
-    # Reset the mock
-    format_answer_mock.reset_mock()
-    format_answer_mock.side_effect = [
-        OutputParserException(FINAL_ANSWER_AND_PARSABLE_ACTION_ERROR_MESSAGE),
-        AgentAction(thought="", tool="search", tool_input="what is the temperature in SF?", text=""),
-    ]
-    
-    # Process the response
-    result = executor._process_llm_response(output_with_observation)
-    
-    # Verify that the result is an AgentAction
-    assert isinstance(result, AgentAction)
-    assert result.tool == "search"
-    assert result.tool_input == "what is the temperature in SF?"