diff --git a/src/crewai/memory/entity/entity_memory.py b/src/crewai/memory/entity/entity_memory.py
index d4f8d9aae..4de0594c7 100644
--- a/src/crewai/memory/entity/entity_memory.py
+++ b/src/crewai/memory/entity/entity_memory.py
@@ -16,7 +16,7 @@ class EntityMemory(Memory):
             if storage
             else RAGStorage(
                 type="entities",
-                allow_reset=False,
+                allow_reset=True,
                 embedder_config=embedder_config,
                 crew=crew,
             )
diff --git a/src/crewai/memory/storage/rag_storage.py b/src/crewai/memory/storage/rag_storage.py
index ca67492c4..56b7373d9 100644
--- a/src/crewai/memory/storage/rag_storage.py
+++ b/src/crewai/memory/storage/rag_storage.py
@@ -9,6 +9,8 @@ from crewai.memory.storage.base_rag_storage import BaseRAGStorage
 from crewai.utilities.paths import db_storage_path
 from chromadb.api import ClientAPI
 from chromadb.api.types import validate_embedding_function
+from chromadb import Documents, EmbeddingFunction, Embeddings
+from typing import cast
 
 
 @contextlib.contextmanager
@@ -47,64 +49,64 @@ class RAGStorage(BaseRAGStorage):
         self._initialize_app()
 
     def _set_embedder_config(self):
+        import chromadb.utils.embedding_functions as embedding_functions
+
         if self.embedder_config is None:
             self.embedder_config = self._create_default_embedding_function()
+
         if isinstance(self.embedder_config, dict):
             provider = self.embedder_config.get("provider")
             config = self.embedder_config.get("config", {})
             model_name = config.get("model")
             if provider == "openai":
-                import chromadb.utils.embedding_functions as embedding_functions
-
                 self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
                     api_key=config.get("api_key") or os.getenv("OPENAI_API_KEY"),
                     model_name=model_name,
                 )
             elif provider == "azure":
-                from chromadb.utils.embedding_functions.openai_embedding_function import (
-                    OpenAIEmbeddingFunction,
-                )
-
-                self.embedder_config = OpenAIEmbeddingFunction(
+                self.embedder_config = embedding_functions.OpenAIEmbeddingFunction(
                     api_key=config.get("api_key"),
                     api_base=config.get("api_base"),
-                    api_type=config.get("api_type"),
+                    api_type=config.get("api_type", "azure"),
                     api_version=config.get("api_version"),
                     model_name=model_name,
                 )
             elif provider == "ollama":
-                from chromadb.utils.embedding_functions.ollama_embedding_function import (
-                    OllamaEmbeddingFunction,
-                )
+                print("using this ollama")
+                from openai import OpenAI
 
-                self.embedder_config = OllamaEmbeddingFunction(
-                    model_name=config.get("model"),
-                    url=config.get("url") or "http://localhost:11434",
-                )
+                class OllamaEmbeddingFunction(EmbeddingFunction):
+                    def __call__(self, input: Documents) -> Embeddings:
+                        client = OpenAI(
+                            base_url="http://localhost:11434/v1",
+                            api_key=config.get("api_key", "ollama"),
+                        )
+                        try:
+                            response = client.embeddings.create(
+                                input=input, model=model_name
+                            )
+                            embeddings = [item.embedding for item in response.data]
+                            return cast(Embeddings, embeddings)
+                        except Exception as e:
+                            raise e
+
+                self.embedder_config = OllamaEmbeddingFunction()
             elif provider == "vertexai":
-                from chromadb.utils.embedding_functions.google_embedding_function import (
-                    GoogleVertexEmbeddingFunction,
-                )
-
-                self.embedder_config = GoogleVertexEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
+                self.embedder_config = (
+                    embedding_functions.GoogleVertexEmbeddingFunction(
+                        model_name=model_name,
+                        api_key=config.get("api_key"),
+                    )
                 )
             elif provider == "google":
-                from chromadb.utils.embedding_functions.google_embedding_function import (
-                    GoogleGenerativeAiEmbeddingFunction,
-                )
-
-                self.embedder_config = GoogleGenerativeAiEmbeddingFunction(
-                    model_name=model_name,
-                    api_key=config.get("api_key"),
+                self.embedder_config = (
+                    embedding_functions.GoogleGenerativeAiEmbeddingFunction(
+                        model_name=model_name,
+                        api_key=config.get("api_key"),
+                    )
                 )
             elif provider == "cohere":
-                from chromadb.utils.embedding_functions.cohere_embedding_function import (
-                    CohereEmbeddingFunction,
-                )
-
-                self.embedder_config = CohereEmbeddingFunction(
+                self.embedder_config = embedding_functions.CohereEmbeddingFunction(
                     model_name=model_name,
                     api_key=config.get("api_key"),
                 )