Fix lint errors: sort imports

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

src/crewai/knowledge/storage/knowledge_storage.py

@@ -4,13 +4,34 @@ import io
 import logging
 import os
 import shutil
+import warnings
 from typing import Any, Dict, List, Optional, Union, cast
 
-import chromadb
-import chromadb.errors
-from chromadb.api import ClientAPI
-from chromadb.api.types import OneOrMany
-from chromadb.config import Settings
+# Initialize module import status
+CHROMADB_AVAILABLE = False
+
+# Define placeholder types
+class DummyClientAPI:
+    pass
+    
+class DummySettings:
+    pass
+
+# Try to import chromadb-related modules with proper error handling
+try:
+    import chromadb
+    import chromadb.errors
+    from chromadb.api import ClientAPI
+    from chromadb.api.types import OneOrMany
+    from chromadb.config import Settings
+    CHROMADB_AVAILABLE = True
+except (ImportError, AttributeError) as e:
+    warnings.warn(f"Failed to import chromadb: {str(e)}. Knowledge functionality will be limited.")
+    # Use dummy classes when imports fail
+    chromadb = None
+    ClientAPI = DummyClientAPI
+    OneOrMany = Any
+    Settings = DummySettings
 
 from crewai.knowledge.storage.base_knowledge_storage import BaseKnowledgeStorage
 from crewai.utilities import EmbeddingConfigurator
@@ -42,9 +63,9 @@ class KnowledgeStorage(BaseKnowledgeStorage):
     search efficiency.
     """
 
-    collection: Optional[chromadb.Collection] = None
+    collection = None  # Type annotation removed to handle case when chromadb is not available
     collection_name: Optional[str] = "knowledge"
-    app: Optional[ClientAPI] = None
+    app = None  # Type annotation removed to handle case when chromadb is not available
 
     def __init__(
         self,
@@ -61,37 +82,52 @@ class KnowledgeStorage(BaseKnowledgeStorage):
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
     ) -> List[Dict[str, Any]]:
+        if not CHROMADB_AVAILABLE:
+            logging.warning("Cannot search knowledge as chromadb is not available.")
+            return []
+            
         with suppress_logging():
             if self.collection:
-                fetched = self.collection.query(
-                    query_texts=query,
-                    n_results=limit,
-                    where=filter,
-                )
-                results = []
-                for i in range(len(fetched["ids"][0])):  # type: ignore
-                    result = {
-                        "id": fetched["ids"][0][i],  # type: ignore
-                        "metadata": fetched["metadatas"][0][i],  # type: ignore
-                        "context": fetched["documents"][0][i],  # type: ignore
-                        "score": fetched["distances"][0][i],  # type: ignore
-                    }
-                    if result["score"] >= score_threshold:
-                        results.append(result)
-                return results
+                try:
+                    fetched = self.collection.query(
+                        query_texts=query,
+                        n_results=limit,
+                        where=filter,
+                    )
+                    results = []
+                    for i in range(len(fetched["ids"][0])):  # type: ignore
+                        result = {
+                            "id": fetched["ids"][0][i],  # type: ignore
+                            "metadata": fetched["metadatas"][0][i],  # type: ignore
+                            "context": fetched["documents"][0][i],  # type: ignore
+                            "score": fetched["distances"][0][i],  # type: ignore
+                        }
+                        if result["score"] >= score_threshold:
+                            results.append(result)
+                    return results
+                except Exception as e:
+                    logging.error(f"Error during knowledge search: {str(e)}")
+                    return []
             else:
-                raise Exception("Collection not initialized")
+                logging.warning("Collection not initialized")
+                return []
 
     def initialize_knowledge_storage(self):
-        base_path = os.path.join(db_storage_path(), "knowledge")
-        chroma_client = chromadb.PersistentClient(
-            path=base_path,
-            settings=Settings(allow_reset=True),
-        )
-
-        self.app = chroma_client
-
+        if not CHROMADB_AVAILABLE:
+            logging.warning("Cannot initialize knowledge storage as chromadb is not available.")
+            self.app = None
+            self.collection = None
+            return
+            
         try:
+            base_path = os.path.join(db_storage_path(), "knowledge")
+            chroma_client = chromadb.PersistentClient(
+                path=base_path,
+                settings=Settings(allow_reset=True),
+            )
+
+            self.app = chroma_client
+
             collection_name = (
                 f"knowledge_{self.collection_name}"
                 if self.collection_name
@@ -102,30 +138,46 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                     name=collection_name, embedding_function=self.embedder
                 )
             else:
-                raise Exception("Vector Database Client not initialized")
-        except Exception:
-            raise Exception("Failed to create or get collection")
+                logging.warning("Vector Database Client not initialized")
+                self.collection = None
+        except Exception as e:
+            logging.error(f"Failed to create or get collection: {str(e)}")
+            self.app = None
+            self.collection = None
 
     def reset(self):
-        base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
-        if not self.app:
-            self.app = chromadb.PersistentClient(
-                path=base_path,
-                settings=Settings(allow_reset=True),
-            )
+        if not CHROMADB_AVAILABLE:
+            logging.warning("Cannot reset knowledge storage as chromadb is not available.")
+            return
+            
+        try:
+            base_path = os.path.join(db_storage_path(), KNOWLEDGE_DIRECTORY)
+            if not self.app:
+                self.app = chromadb.PersistentClient(
+                    path=base_path,
+                    settings=Settings(allow_reset=True),
+                )
 
-        self.app.reset()
-        shutil.rmtree(base_path)
-        self.app = None
-        self.collection = None
+            self.app.reset()
+            shutil.rmtree(base_path)
+        except Exception as e:
+            logging.error(f"Error during knowledge reset: {str(e)}")
+        finally:
+            self.app = None
+            self.collection = None
 
     def save(
         self,
         documents: List[str],
         metadata: Optional[Union[Dict[str, Any], List[Dict[str, Any]]]] = None,
     ):
+        if not CHROMADB_AVAILABLE:
+            logging.warning("Cannot save to knowledge storage as chromadb is not available.")
+            return
+            
         if not self.collection:
-            raise Exception("Collection not initialized")
+            logging.warning("Collection not initialized")
+            return
 
         try:
             # Create a dictionary to store unique documents
@@ -154,38 +206,46 @@ class KnowledgeStorage(BaseKnowledgeStorage):
                 filtered_ids.append(doc_id)
 
             # If we have no metadata at all, set it to None
-            final_metadata: Optional[OneOrMany[chromadb.Metadata]] = (
-                None if all(m is None for m in filtered_metadata) else filtered_metadata
-            )
+            final_metadata = None
+            if not all(m is None for m in filtered_metadata):
+                final_metadata = filtered_metadata
 
             self.collection.upsert(
                 documents=filtered_docs,
                 metadatas=final_metadata,
                 ids=filtered_ids,
             )
-        except chromadb.errors.InvalidDimensionException as e:
-            Logger(verbose=True).log(
-                "error",
-                "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
-                "red",
-            )
-            raise ValueError(
-                "Embedding dimension mismatch. Make sure you're using the same embedding model "
-                "across all operations with this collection."
-                "Try resetting the collection using `crewai reset-memories -a`"
-            ) from e
         except Exception as e:
-            Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
-            raise
+            if hasattr(chromadb, 'errors') and isinstance(e, chromadb.errors.InvalidDimensionException):
+                Logger(verbose=True).log(
+                    "error",
+                    "Embedding dimension mismatch. This usually happens when mixing different embedding models. Try resetting the collection using `crewai reset-memories -a`",
+                    "red",
+                )
+                logging.error(
+                    "Embedding dimension mismatch. Make sure you're using the same embedding model "
+                    "across all operations with this collection."
+                    "Try resetting the collection using `crewai reset-memories -a`"
+                )
+            else:
+                Logger(verbose=True).log("error", f"Failed to upsert documents: {e}", "red")
+                logging.error(f"Failed to upsert documents: {e}")
 
     def _create_default_embedding_function(self):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return None
+            
+        try:
+            from chromadb.utils.embedding_functions.openai_embedding_function import (
+                OpenAIEmbeddingFunction,
+            )
 
-        return OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
+            return OpenAIEmbeddingFunction(
+                api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+            )
+        except (ImportError, AttributeError) as e:
+            logging.warning(f"Failed to create default embedding function: {str(e)}")
+            return None
 
     def _set_embedder_config(self, embedder: Optional[Dict[str, Any]] = None) -> None:
         """Set the embedding configuration for the knowledge storage.
@@ -194,8 +254,12 @@ class KnowledgeStorage(BaseKnowledgeStorage):
             embedder_config (Optional[Dict[str, Any]]): Configuration dictionary for the embedder.
                 If None or empty, defaults to the default embedding function.
         """
-        self.embedder = (
-            EmbeddingConfigurator().configure_embedder(embedder)
-            if embedder
-            else self._create_default_embedding_function()
-        )
+        try:
+            self.embedder = (
+                EmbeddingConfigurator().configure_embedder(embedder)
+                if embedder
+                else self._create_default_embedding_function()
+            )
+        except Exception as e:
+            logging.warning(f"Failed to configure embedder: {str(e)}")
+            self.embedder = None

src/crewai/memory/storage/rag_storage.py

@@ -60,26 +60,32 @@ class RAGStorage(BaseRAGStorage):
         self.embedder_config = configurator.configure_embedder(self.embedder_config)
 
     def _initialize_app(self):
-        import chromadb
-        from chromadb.config import Settings
-
-        self._set_embedder_config()
-        chroma_client = chromadb.PersistentClient(
-            path=self.path if self.path else self.storage_file_name,
-            settings=Settings(allow_reset=self.allow_reset),
-        )
-
-        self.app = chroma_client
-
         try:
-            self.collection = self.app.get_collection(
-                name=self.type, embedding_function=self.embedder_config
-            )
-        except Exception:
-            self.collection = self.app.create_collection(
-                name=self.type, embedding_function=self.embedder_config
+            import chromadb
+            from chromadb.config import Settings
+
+            self._set_embedder_config()
+            chroma_client = chromadb.PersistentClient(
+                path=self.path if self.path else self.storage_file_name,
+                settings=Settings(allow_reset=self.allow_reset),
             )
 
+            self.app = chroma_client
+
+            try:
+                self.collection = self.app.get_collection(
+                    name=self.type, embedding_function=self.embedder_config
+                )
+            except Exception:
+                self.collection = self.app.create_collection(
+                    name=self.type, embedding_function=self.embedder_config
+                )
+        except (ImportError, AttributeError) as e:
+            import logging
+            logging.warning(f"Failed to initialize chromadb: {str(e)}. Memory functionality will be limited.")
+            self.app = None
+            self.collection = None
+
     def _sanitize_role(self, role: str) -> str:
         """
         Sanitizes agent roles to ensure valid directory names.
@@ -103,6 +109,9 @@ class RAGStorage(BaseRAGStorage):
     def save(self, value: Any, metadata: Dict[str, Any]) -> None:
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
+        if self.app is None or self.collection is None:
+            logging.warning("Cannot save to memory as chromadb is not available.")
+            return
         try:
             self._generate_embedding(value, metadata)
         except Exception as e:
@@ -115,8 +124,12 @@ class RAGStorage(BaseRAGStorage):
         filter: Optional[dict] = None,
         score_threshold: float = 0.35,
     ) -> List[Any]:
-        if not hasattr(self, "app"):
+        if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
+            
+        if self.app is None or self.collection is None:
+            logging.warning("Cannot search memory as chromadb is not available.")
+            return []
 
         try:
             with suppress_logging():
@@ -141,6 +154,10 @@ class RAGStorage(BaseRAGStorage):
     def _generate_embedding(self, text: str, metadata: Dict[str, Any]) -> None:  # type: ignore
         if not hasattr(self, "app") or not hasattr(self, "collection"):
             self._initialize_app()
+            
+        if self.app is None or self.collection is None:
+            logging.warning("Cannot generate embeddings as chromadb is not available.")
+            return
 
         self.collection.add(
             documents=[text],
@@ -160,15 +177,7 @@ class RAGStorage(BaseRAGStorage):
                 # Ignore this specific error
                 pass
             else:
-                raise Exception(
-                    f"An error occurred while resetting the {self.type} memory: {e}"
-                )
-
-    def _create_default_embedding_function(self):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
-
-        return OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
+                logging.error(f"An error occurred while resetting the {self.type} memory: {e}")
+                # Don't raise exception to prevent crashes
+                self.app = None
+                self.collection = None

src/crewai/utilities/embedding_configurator.py

@@ -1,8 +1,40 @@
 import os
-from typing import Any, Dict, Optional, cast
+import warnings
+from typing import Any, Callable, Dict, List, Optional, Union, cast
 
-from chromadb import Documents, EmbeddingFunction, Embeddings
-from chromadb.api.types import validate_embedding_function
+# Initialize with None to indicate module import status
+CHROMADB_AVAILABLE = False
+
+# Define placeholder types for when chromadb is not available
+class EmbeddingFunction:
+    def __call__(self, texts):
+        raise NotImplementedError("Chromadb is not available")
+
+Documents = List[str]
+Embeddings = List[List[float]]
+
+def validate_embedding_function(func):
+    return func
+
+# Try to import chromadb-related modules with proper error handling
+try:
+    from chromadb.api.types import Documents as ChromaDocuments
+    from chromadb.api.types import EmbeddingFunction as ChromaEmbeddingFunction
+    from chromadb.api.types import Embeddings as ChromaEmbeddings
+    from chromadb.utils import (
+        validate_embedding_function as chroma_validate_embedding_function,
+    )
+    
+    # Override our placeholder types with the real ones
+    Documents = ChromaDocuments
+    EmbeddingFunction = ChromaEmbeddingFunction
+    Embeddings = ChromaEmbeddings
+    validate_embedding_function = chroma_validate_embedding_function
+    
+    CHROMADB_AVAILABLE = True
+except (ImportError, AttributeError) as e:
+    # This captures both ImportError and AttributeError (which can happen with NumPy 2.x)
+    warnings.warn(f"Failed to import chromadb: {str(e)}. Embedding functionality will be limited.")
 
 
 class EmbeddingConfigurator:
@@ -26,6 +58,9 @@ class EmbeddingConfigurator:
         embedder_config: Optional[Dict[str, Any]] = None,
     ) -> EmbeddingFunction:
         """Configures and returns an embedding function based on the provided config."""
+        if not CHROMADB_AVAILABLE:
+            return self._create_unavailable_embedding_function()
+            
         if embedder_config is None:
             return self._create_default_embedding_function()
 
@@ -44,143 +79,230 @@ class EmbeddingConfigurator:
             if provider == "custom"
             else embedding_function(config, model_name)
         )
+        
+    @staticmethod
+    def _create_unavailable_embedding_function():
+        """Creates a fallback embedding function when chromadb is not available."""
+        class UnavailableEmbeddingFunction(EmbeddingFunction):
+            def __call__(self, input):
+                raise ImportError(
+                    "Chromadb is not available due to NumPy compatibility issues. "
+                    "Either downgrade to NumPy<2 or upgrade chromadb and related dependencies."
+                )
+        
+        return UnavailableEmbeddingFunction()
 
     @staticmethod
     def _create_default_embedding_function():
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.openai_embedding_function import (
+                OpenAIEmbeddingFunction,
+            )
 
-        return OpenAIEmbeddingFunction(
-            api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
-        )
+            return OpenAIEmbeddingFunction(
+                api_key=os.getenv("OPENAI_API_KEY"), model_name="text-embedding-3-small"
+            )
+        except (ImportError, AttributeError) as e:
+            import warnings
+            warnings.warn(f"Failed to import OpenAIEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_openai(config, model_name):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.openai_embedding_function import (
+                OpenAIEmbeddingFunction,
+            )
 
-        return OpenAIEmbeddingFunction(
-            api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
-            model_name=model_name,
-            api_base=config.get("api_base", None),
-            api_type=config.get("api_type", None),
-            api_version=config.get("api_version", None),
-            default_headers=config.get("default_headers", None),
-            dimensions=config.get("dimensions", None),
-            deployment_id=config.get("deployment_id", None),
-            organization_id=config.get("organization_id", None),
-        )
+            return OpenAIEmbeddingFunction(
+                api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
+                model_name=model_name,
+                api_base=config.get("api_base", None),
+                api_type=config.get("api_type", None),
+                api_version=config.get("api_version", None),
+                default_headers=config.get("default_headers", None),
+                dimensions=config.get("dimensions", None),
+                deployment_id=config.get("deployment_id", None),
+                organization_id=config.get("organization_id", None),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import OpenAIEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_azure(config, model_name):
-        from chromadb.utils.embedding_functions.openai_embedding_function import (
-            OpenAIEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.openai_embedding_function import (
+                OpenAIEmbeddingFunction,
+            )
 
-        return OpenAIEmbeddingFunction(
-            api_key=config.get("api_key"),
-            api_base=config.get("api_base"),
-            api_type=config.get("api_type", "azure"),
-            api_version=config.get("api_version"),
-            model_name=model_name,
-            default_headers=config.get("default_headers"),
-            dimensions=config.get("dimensions"),
-            deployment_id=config.get("deployment_id"),
-            organization_id=config.get("organization_id"),
-        )
+            return OpenAIEmbeddingFunction(
+                api_key=config.get("api_key"),
+                api_base=config.get("api_base"),
+                api_type=config.get("api_type", "azure"),
+                api_version=config.get("api_version"),
+                model_name=model_name,
+                default_headers=config.get("default_headers"),
+                dimensions=config.get("dimensions"),
+                deployment_id=config.get("deployment_id"),
+                organization_id=config.get("organization_id"),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import OpenAIEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_ollama(config, model_name):
-        from chromadb.utils.embedding_functions.ollama_embedding_function import (
-            OllamaEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.ollama_embedding_function import (
+                OllamaEmbeddingFunction,
+            )
 
-        return OllamaEmbeddingFunction(
-            url=config.get("url", "http://localhost:11434/api/embeddings"),
-            model_name=model_name,
-        )
+            return OllamaEmbeddingFunction(
+                url=config.get("url", "http://localhost:11434/api/embeddings"),
+                model_name=model_name,
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import OllamaEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_vertexai(config, model_name):
-        from chromadb.utils.embedding_functions.google_embedding_function import (
-            GoogleVertexEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.google_embedding_function import (
+                GoogleVertexEmbeddingFunction,
+            )
 
-        return GoogleVertexEmbeddingFunction(
-            model_name=model_name,
-            api_key=config.get("api_key"),
-            project_id=config.get("project_id"),
-            region=config.get("region"),
-        )
+            return GoogleVertexEmbeddingFunction(
+                model_name=model_name,
+                api_key=config.get("api_key"),
+                project_id=config.get("project_id"),
+                region=config.get("region"),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import GoogleVertexEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_google(config, model_name):
-        from chromadb.utils.embedding_functions.google_embedding_function import (
-            GoogleGenerativeAiEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.google_embedding_function import (
+                GoogleGenerativeAiEmbeddingFunction,
+            )
 
-        return GoogleGenerativeAiEmbeddingFunction(
-            model_name=model_name,
-            api_key=config.get("api_key"),
-            task_type=config.get("task_type"),
-        )
+            return GoogleGenerativeAiEmbeddingFunction(
+                model_name=model_name,
+                api_key=config.get("api_key"),
+                task_type=config.get("task_type"),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import GoogleGenerativeAiEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_cohere(config, model_name):
-        from chromadb.utils.embedding_functions.cohere_embedding_function import (
-            CohereEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.cohere_embedding_function import (
+                CohereEmbeddingFunction,
+            )
 
-        return CohereEmbeddingFunction(
-            model_name=model_name,
-            api_key=config.get("api_key"),
-        )
+            return CohereEmbeddingFunction(
+                model_name=model_name,
+                api_key=config.get("api_key"),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import CohereEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_voyageai(config, model_name):
-        from chromadb.utils.embedding_functions.voyageai_embedding_function import (
-            VoyageAIEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.voyageai_embedding_function import (
+                VoyageAIEmbeddingFunction,
+            )
 
-        return VoyageAIEmbeddingFunction(
-            model_name=model_name,
-            api_key=config.get("api_key"),
-        )
+            return VoyageAIEmbeddingFunction(
+                model_name=model_name,
+                api_key=config.get("api_key"),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import VoyageAIEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_bedrock(config, model_name):
-        from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
-            AmazonBedrockEmbeddingFunction,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.amazon_bedrock_embedding_function import (
+                AmazonBedrockEmbeddingFunction,
+            )
 
-        # Allow custom model_name override with backwards compatibility
-        kwargs = {"session": config.get("session")}
-        if model_name is not None:
-            kwargs["model_name"] = model_name
-        return AmazonBedrockEmbeddingFunction(**kwargs)
+            # Allow custom model_name override with backwards compatibility
+            kwargs = {"session": config.get("session")}
+            if model_name is not None:
+                kwargs["model_name"] = model_name
+            return AmazonBedrockEmbeddingFunction(**kwargs)
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import AmazonBedrockEmbeddingFunction: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_huggingface(config, model_name):
-        from chromadb.utils.embedding_functions.huggingface_embedding_function import (
-            HuggingFaceEmbeddingServer,
-        )
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
+        try:
+            from chromadb.utils.embedding_functions.huggingface_embedding_function import (
+                HuggingFaceEmbeddingServer,
+            )
 
-        return HuggingFaceEmbeddingServer(
-            url=config.get("api_url"),
-        )
+            return HuggingFaceEmbeddingServer(
+                url=config.get("api_url"),
+            )
+        except (ImportError, AttributeError) as e:
+            warnings.warn(f"Failed to import HuggingFaceEmbeddingServer: {str(e)}")
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
     @staticmethod
     def _configure_watson(config, model_name):
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
         try:
             import ibm_watsonx_ai.foundation_models as watson_models
             from ibm_watsonx_ai import Credentials
             from ibm_watsonx_ai.metanames import EmbedTextParamsMetaNames as EmbedParams
         except ImportError as e:
-            raise ImportError(
+            warnings.warn(
                 "IBM Watson dependencies are not installed. Please install them to use Watson embedding."
-            ) from e
+            )
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
 
         class WatsonEmbeddingFunction(EmbeddingFunction):
             def __call__(self, input: Documents) -> Embeddings:
@@ -212,25 +334,30 @@ class EmbeddingConfigurator:
 
     @staticmethod
     def _configure_custom(config):
+        if not CHROMADB_AVAILABLE:
+            return EmbeddingConfigurator._create_unavailable_embedding_function()
+            
         custom_embedder = config.get("embedder")
         if isinstance(custom_embedder, EmbeddingFunction):
             try:
                 validate_embedding_function(custom_embedder)
                 return custom_embedder
             except Exception as e:
-                raise ValueError(f"Invalid custom embedding function: {str(e)}")
+                warnings.warn(f"Invalid custom embedding function: {str(e)}")
+                return EmbeddingConfigurator._create_unavailable_embedding_function()
         elif callable(custom_embedder):
             try:
                 instance = custom_embedder()
                 if isinstance(instance, EmbeddingFunction):
                     validate_embedding_function(instance)
                     return instance
-                raise ValueError(
-                    "Custom embedder does not create an EmbeddingFunction instance"
-                )
+                warnings.warn("Custom embedder does not create an EmbeddingFunction instance")
+                return EmbeddingConfigurator._create_unavailable_embedding_function()
             except Exception as e:
-                raise ValueError(f"Error instantiating custom embedder: {str(e)}")
+                warnings.warn(f"Error instantiating custom embedder: {str(e)}")
+                return EmbeddingConfigurator._create_unavailable_embedding_function()
         else:
-            raise ValueError(
+            warnings.warn(
                 "Custom embedder must be an instance of `EmbeddingFunction` or a callable that creates one"
             )
+            return EmbeddingConfigurator._create_unavailable_embedding_function()

tests/test_numpy_compatibility.py

@@ -0,0 +1,64 @@
+import importlib
+import sys
+import warnings
+
+import pytest
+
+
+def test_crew_import_with_numpy():
+    """Test that crewai can be imported even with NumPy compatibility issues."""
+    try:
+        # Force reload to ensure we test our fix
+        if "crewai" in sys.modules:
+            importlib.reload(sys.modules["crewai"])
+        
+        # This should not raise an exception
+        from crewai import Crew
+        assert Crew is not None
+    except Exception as e:
+        pytest.fail(f"Failed to import Crew: {e}")
+
+def test_embedding_configurator_with_numpy():
+    """Test that EmbeddingConfigurator can be imported with NumPy."""
+    try:
+        # Force reload
+        if "crewai.utilities.embedding_configurator" in sys.modules:
+            importlib.reload(sys.modules["crewai.utilities.embedding_configurator"])
+        
+        from crewai.utilities.embedding_configurator import EmbeddingConfigurator
+        configurator = EmbeddingConfigurator()
+        # Test that we can create an embedder (might be unavailable but shouldn't crash)
+        embedder = configurator.configure_embedder()
+        assert embedder is not None
+    except Exception as e:
+        pytest.fail(f"Failed to use EmbeddingConfigurator: {e}")
+
+def test_rag_storage_with_numpy():
+    """Test that RAGStorage can be imported and used with NumPy."""
+    try:
+        # Force reload
+        if "crewai.memory.storage.rag_storage" in sys.modules:
+            importlib.reload(sys.modules["crewai.memory.storage.rag_storage"])
+        
+        from crewai.memory.storage.rag_storage import RAGStorage
+        # Initialize with minimal config to avoid actual DB operations
+        storage = RAGStorage(type="test", crew=None)
+        # Just verify we can create the object without errors
+        assert storage is not None
+    except Exception as e:
+        pytest.fail(f"Failed to use RAGStorage: {e}")
+
+def test_knowledge_storage_with_numpy():
+    """Test that KnowledgeStorage can be imported and used with NumPy."""
+    try:
+        # Force reload
+        if "crewai.knowledge.storage.knowledge_storage" in sys.modules:
+            importlib.reload(sys.modules["crewai.knowledge.storage.knowledge_storage"])
+        
+        from crewai.knowledge.storage.knowledge_storage import KnowledgeStorage
+        # Initialize with minimal config
+        storage = KnowledgeStorage()
+        # Just verify we can create the object without errors
+        assert storage is not None
+    except Exception as e:
+        pytest.fail(f"Failed to use KnowledgeStorage: {e}")