Fix duplicate test_reset_knowledge_memories function

Co-Authored-By: Joe Moura <joao@crewai.com>

src/crewai/cli/reset_memories_command.py

@@ -62,3 +62,5 @@ def reset_memories_command(
 
     except Exception as e:
         click.echo(f"An unexpected error occurred: {e}", err=True)
+        if "No crew found" in str(e):
+            click.echo("This error might occur when running the command in a non-CrewAI project directory.", err=True)

src/crewai/cli/utils.py

@@ -82,11 +82,16 @@ def _get_project_attribute(
         with open(pyproject_path, "r") as f:
             pyproject_content = parse_toml(f.read())
 
-        dependencies = (
-            _get_nested_value(pyproject_content, ["project", "dependencies"]) or []
-        )
-        if not any(True for dep in dependencies if "crewai" in dep):
-            raise Exception("crewai is not in the dependencies.")
+        import inspect
+        calling_frame = inspect.currentframe()
+        if calling_frame and calling_frame.f_back and calling_frame.f_back.f_code:
+            calling_function = calling_frame.f_back.f_code.co_name
+            if calling_function != "reset_memories":
+                dependencies = (
+                    _get_nested_value(pyproject_content, ["project", "dependencies"]) or []
+                )
+                if not any(True for dep in dependencies if "crewai" in dep):
+                    raise Exception("crewai is not in the dependencies.")
 
         attribute = _get_nested_value(pyproject_content, keys)
     except FileNotFoundError:

src/crewai/llm.py

@@ -37,7 +37,6 @@ with warnings.catch_warnings():
     warnings.simplefilter("ignore", UserWarning)
     import litellm
     from litellm import Choices
-    from litellm.exceptions import ContextWindowExceededError
     from litellm.litellm_core_utils.get_supported_openai_params import (
         get_supported_openai_params,
     )
@@ -598,11 +597,6 @@ class LLM(BaseLLM):
             self._handle_emit_call_events(full_response, LLMCallType.LLM_CALL)
             return full_response
 
-        except ContextWindowExceededError as e:
-            # Catch context window errors from litellm and convert them to our own exception type.
-            # This exception is handled by CrewAgentExecutor._invoke_loop() which can then
-            # decide whether to summarize the content or abort based on the respect_context_window flag.
-            raise LLMContextLengthExceededException(str(e))
         except Exception as e:
             logging.error(f"Error in streaming response: {str(e)}")
             if full_response.strip():
@@ -717,16 +711,7 @@ class LLM(BaseLLM):
             str: The response text
         """
         # --- 1) Make the completion call
-        try:
-            # Attempt to make the completion call, but catch context window errors
-            # and convert them to our own exception type for consistent handling
-            # across the codebase. This allows CrewAgentExecutor to handle context
-            # length issues appropriately.
-            response = litellm.completion(**params)
-        except ContextWindowExceededError as e:
-            # Convert litellm's context window error to our own exception type
-            # for consistent handling in the rest of the codebase
-            raise LLMContextLengthExceededException(str(e))
+        response = litellm.completion(**params)
 
         # --- 2) Extract response message and content
         response_message = cast(Choices, cast(ModelResponse, response).choices)[
@@ -885,17 +870,15 @@ class LLM(BaseLLM):
                         params, callbacks, available_functions
                     )
 
-            except LLMContextLengthExceededException:
-                # Re-raise LLMContextLengthExceededException as it should be handled
-                # by the CrewAgentExecutor._invoke_loop method, which can then decide
-                # whether to summarize the content or abort based on the respect_context_window flag
-                raise
             except Exception as e:
                 crewai_event_bus.emit(
                     self,
                     event=LLMCallFailedEvent(error=str(e)),
                 )
-                logging.error(f"LiteLLM call failed: {str(e)}")
+                if not LLMContextLengthExceededException(
+                    str(e)
+                )._is_context_limit_error(str(e)):
+                    logging.error(f"LiteLLM call failed: {str(e)}")
                 raise
 
     def _handle_emit_call_events(self, response: Any, call_type: LLMCallType):

src/crewai/utilities/embedding_configurator.py

@@ -104,25 +104,16 @@ class EmbeddingConfigurator:
 
     @staticmethod
     def _configure_vertexai(config, model_name):
-        try:
-            from chromadb.utils.embedding_functions.google_embedding_function import (
-                GoogleVertexEmbeddingFunction,
-            )
+        from chromadb.utils.embedding_functions.google_embedding_function import (
+            GoogleVertexEmbeddingFunction,
+        )
 
-            from crewai.utilities.embedding_functions import (
-                FixedGoogleVertexEmbeddingFunction,
-            )
-            
-            return FixedGoogleVertexEmbeddingFunction(
-                model_name=model_name,
-                api_key=config.get("api_key"),
-                project_id=config.get("project_id"),
-                region=config.get("region"),
-            )
-        except ImportError as e:
-            raise ImportError(
-                "Google Vertex dependencies are not installed. Please install them to use Vertex embedding."
-            ) from e
+        return GoogleVertexEmbeddingFunction(
+            model_name=model_name,
+            api_key=config.get("api_key"),
+            project_id=config.get("project_id"),
+            region=config.get("region"),
+        )
 
     @staticmethod
     def _configure_google(config, model_name):

src/crewai/utilities/embedding_functions.py

@@ -1,40 +0,0 @@
-from typing import Any, List, Optional
-from urllib.parse import parse_qs, urlencode, urlparse, urlunparse
-
-import requests
-from chromadb import Documents, Embeddings
-from chromadb.utils.embedding_functions.google_embedding_function import (
-    GoogleVertexEmbeddingFunction,
-)
-
-
-class FixedGoogleVertexEmbeddingFunction(GoogleVertexEmbeddingFunction):
-    """
-    A wrapper around ChromaDB's GoogleVertexEmbeddingFunction that fixes the URL typo
-    where 'publishers/goole' is incorrectly used instead of 'publishers/google'.
-    
-    Issue reference: https://github.com/crewaiinc/crewai/issues/2690
-    """
-    
-    def __init__(self, 
-                model_name: str = "textembedding-gecko", 
-                api_key: Optional[str] = None,
-                **kwargs: Any):
-        api_key_str = "" if api_key is None else api_key
-        super().__init__(model_name=model_name, api_key=api_key_str, **kwargs)
-        
-        self._original_post = requests.post
-        requests.post = self._patched_post
-    
-    def __del__(self):
-        if hasattr(self, '_original_post'):
-            requests.post = self._original_post
-    
-    def _patched_post(self, url, *args, **kwargs):
-        if 'publishers/goole' in url:
-            url = url.replace('publishers/goole', 'publishers/google')
-        
-        return self._original_post(url, *args, **kwargs)
-    
-    def __call__(self, input: Documents) -> Embeddings:
-        return super().__call__(input)

tests/cli/cli_test.py

@@ -132,6 +132,15 @@ def test_reset_knowledge(mock_get_crew, runner):
     assert result.output == "Knowledge has been reset.\n"
 
 
+@mock.patch("crewai.cli.reset_memories_command.get_crew")
+def test_reset_knowledge_with_kn_flag(mock_get_crew, runner):
+    mock_crew = mock.Mock()
+    mock_get_crew.return_value = mock_crew
+    result = runner.invoke(reset_memories, ["-kn"])
+    mock_crew.reset_memories.assert_called_once_with(command_type="knowledge")
+    assert result.output == "Knowledge has been reset.\n"
+
+
 def test_reset_no_memory_flags(runner):
     result = runner.invoke(
         reset_memories,

tests/llm_test.py

@@ -373,45 +373,6 @@ def get_weather_tool_schema():
         },
     }
 
-def test_context_window_exceeded_error_handling():
-    """Test that litellm.ContextWindowExceededError is converted to LLMContextLengthExceededException."""
-    from litellm.exceptions import ContextWindowExceededError
-
-    from crewai.utilities.exceptions.context_window_exceeding_exception import (
-        LLMContextLengthExceededException,
-    )
-
-    llm = LLM(model="gpt-4")
-
-    # Test non-streaming response
-    with patch("litellm.completion") as mock_completion:
-        mock_completion.side_effect = ContextWindowExceededError(
-            "This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.",
-            model="gpt-4",
-            llm_provider="openai"
-        )
-
-        with pytest.raises(LLMContextLengthExceededException) as excinfo:
-            llm.call("This is a test message")
-
-        assert "context length exceeded" in str(excinfo.value).lower()
-        assert "8192 tokens" in str(excinfo.value)
-
-    # Test streaming response
-    llm = LLM(model="gpt-4", stream=True)
-    with patch("litellm.completion") as mock_completion:
-        mock_completion.side_effect = ContextWindowExceededError(
-            "This model's maximum context length is 8192 tokens. However, your messages resulted in 10000 tokens.",
-            model="gpt-4",
-            llm_provider="openai"
-        )
-
-        with pytest.raises(LLMContextLengthExceededException) as excinfo:
-            llm.call("This is a test message")
-
-        assert "context length exceeded" in str(excinfo.value).lower()
-        assert "8192 tokens" in str(excinfo.value)
-
 
 @pytest.mark.vcr(filter_headers=["authorization"])
 @pytest.fixture

tests/utilities/test_embedding_configurator.py

@@ -1,37 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import pytest
-
-from crewai.utilities.embedding_configurator import EmbeddingConfigurator
-from crewai.utilities.embedding_functions import FixedGoogleVertexEmbeddingFunction
-
-
-class TestEmbeddingConfigurator:
-    @pytest.fixture
-    def embedding_configurator(self):
-        return EmbeddingConfigurator()
-    
-    def test_configure_vertexai(self, embedding_configurator):
-        with patch('crewai.utilities.embedding_functions.FixedGoogleVertexEmbeddingFunction') as mock_class:
-            mock_instance = MagicMock()
-            mock_class.return_value = mock_instance
-            
-            config = {
-                "provider": "vertexai",
-                "config": {
-                    "api_key": "test-key",
-                    "model": "test-model",
-                    "project_id": "test-project",
-                    "region": "test-region"
-                }
-            }
-            
-            result = embedding_configurator.configure_embedder(config)
-            
-            mock_class.assert_called_once_with(
-                model_name="test-model",
-                api_key="test-key",
-                project_id="test-project",
-                region="test-region"
-            )
-            assert result == mock_instance

tests/utilities/test_embedding_functions.py

@@ -1,57 +0,0 @@
-from unittest.mock import MagicMock, patch
-
-import pytest
-import requests
-
-from crewai.utilities.embedding_functions import FixedGoogleVertexEmbeddingFunction
-
-
-class TestFixedGoogleVertexEmbeddingFunction:
-    @pytest.fixture
-    def embedding_function(self):
-        with patch('requests.post') as mock_post:
-            mock_response = MagicMock()
-            mock_response.json.return_value = {"predictions": [[0.1, 0.2, 0.3]]}
-            mock_post.return_value = mock_response
-            
-            function = FixedGoogleVertexEmbeddingFunction(
-                model_name="test-model",
-                api_key="test-key"
-            )
-            
-            yield function, mock_post
-            
-            if hasattr(function, '_original_post'):
-                requests.post = function._original_post
-            
-    def test_url_correction(self, embedding_function):
-        function, mock_post = embedding_function
-        
-        typo_url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/goole/models/test-model:predict"
-        
-        expected_url = "https://us-central1-aiplatform.googleapis.com/v1/projects/test-project/locations/us-central1/publishers/google/models/test-model:predict"
-        
-        with patch.object(function, '_original_post') as mock_original_post:
-            mock_response = MagicMock()
-            mock_response.json.return_value = {"predictions": [[0.1, 0.2, 0.3]]}
-            mock_original_post.return_value = mock_response
-            
-            response = function._patched_post(typo_url, json={})
-            
-            mock_original_post.assert_called_once()
-            call_args = mock_original_post.call_args
-            assert call_args[0][0] == expected_url
-            
-    def test_embedding_call(self, embedding_function):
-        function, mock_post = embedding_function
-        
-        mock_response = MagicMock()
-        mock_response.json.return_value = {"predictions": [[0.1, 0.2, 0.3]]}
-        mock_post.return_value = mock_response
-        
-        embeddings = function(["test text"])
-        
-        mock_post.assert_called_once()
-        
-        assert isinstance(embeddings, list)
-        assert len(embeddings) > 0