diff --git a/redisvl/utils/utils.py b/redisvl/utils/utils.py
index caa48889..0e0ec28d 100644
--- a/redisvl/utils/utils.py
+++ b/redisvl/utils/utils.py
@@ -8,7 +8,7 @@
 from enum import Enum
 from functools import wraps
 from time import time
-from typing import Any, Callable, Coroutine, Sequence, TypeVar
+from typing import Any, Callable, Coroutine, ParamSpec, Sequence, TypeVar, cast
 from warnings import warn
 
 from pydantic import BaseModel
@@ -17,6 +17,9 @@
 from redisvl.types import SyncRedisClient
 
 T = TypeVar("T")
+R = TypeVar("R")
+P = ParamSpec("P")
+C = TypeVar("C", bound=type)
 
 
 def create_ulid() -> str:
@@ -76,7 +79,9 @@ def deserialize(data: str) -> Any:
     return json.loads(data)
 
 
-def deprecated_argument(argument: str, replacement: str | None = None) -> Callable:
+def deprecated_argument(
+    argument: str, replacement: str | None = None
+) -> Callable[[Callable[P, R]], Callable[P, R]]:
     """
     Decorator to warn if a deprecated argument is passed.
 
@@ -100,13 +105,16 @@ def test_method(cls, old_arg=None, new_arg=None):
     if replacement:
         message += f" Use {replacement} instead."
 
-    def decorator(func):
-        # Check if the function is a classmethod or staticmethod
+    def decorator(func: Callable[P, R]) -> Callable[P, R]:
+        # The documented usage puts this decorator inside
+        # @classmethod/@staticmethod, so this branch only covers the reversed
+        # order; a classmethod object is not a Callable to the type system,
+        # hence the casts.
         if isinstance(func, (classmethod, staticmethod)):
-            underlying = func.__func__
+            underlying = cast(Callable[P, R], func.__func__)
 
             @wraps(underlying)
-            def inner_wrapped(*args, **kwargs):
+            def inner_wrapped(*args: P.args, **kwargs: P.kwargs) -> R:
                 if argument in kwargs:
                     warn(message, DeprecationWarning, stacklevel=2)
                 else:
@@ -117,13 +125,13 @@ def inner_wrapped(*args, **kwargs):
                 return underlying(*args, **kwargs)
 
             if isinstance(func, classmethod):
-                return classmethod(inner_wrapped)
+                return cast(Callable[P, R], classmethod(inner_wrapped))
             else:
-                return staticmethod(inner_wrapped)
+                return cast(Callable[P, R], staticmethod(inner_wrapped))
         else:
 
             @wraps(func)
-            def inner_normal(*args, **kwargs):
+            def inner_normal(*args: P.args, **kwargs: P.kwargs) -> R:
                 if argument in kwargs:
                     warn(message, DeprecationWarning, stacklevel=2)
                 else:
@@ -148,7 +156,9 @@ def assert_no_warnings():
         yield
 
 
-def deprecated_function(name: str | None = None, replacement: str | None = None):
+def deprecated_function(
+    name: str | None = None, replacement: str | None = None
+) -> Callable[[Callable[P, R]], Callable[P, R]]:
     """
     Decorator to mark a function as deprecated.
 
@@ -156,7 +166,7 @@ def deprecated_function(name: str | None = None, replacement: str | None = None)
     warning.
     """
 
-    def decorator(func):
+    def decorator(func: Callable[P, R]) -> Callable[P, R]:
         fn_name = name or func.__name__
         warning_message = (
             f"Function {fn_name} is deprecated and will be "
@@ -166,7 +176,7 @@ def decorator(func):
             warning_message += replacement
 
         @wraps(func)
-        def wrapper(*args, **kwargs):
+        def wrapper(*args: P.args, **kwargs: P.kwargs) -> R:
             warn(warning_message, category=DeprecationWarning, stacklevel=3)
             return func(*args, **kwargs)
 
@@ -175,7 +185,9 @@ def wrapper(*args, **kwargs):
     return decorator
 
 
-def deprecated_class(name: str | None = None, replacement: str | None = None):
+def deprecated_class(
+    name: str | None = None, replacement: str | None = None
+) -> Callable[[C], C]:
     """
     Decorator to mark a class as deprecated.
 
@@ -193,7 +205,7 @@ class OldClass:
             pass
     """
 
-    def decorator(cls):
+    def decorator(cls: C) -> C:
         class_name = name or cls.__name__
         warning_message = (
             f"Class {class_name} is deprecated and will be "
@@ -202,10 +214,12 @@ def decorator(cls):
         if replacement:
             warning_message += replacement
 
-        original_init = cls.__init__
+        # getattr/setattr rather than attribute access: `cls` is typed as a
+        # class object, so mypy resolves `cls.__init__` to the metaclass slot.
+        original_init = getattr(cls, "__init__")
 
         @wraps(original_init)
-        def new_init(self, *args, **kwargs):
+        def new_init(self: Any, *args: Any, **kwargs: Any) -> None:
             # Emit only once per instance. When a deprecated subclass wraps a
             # deprecated parent, both __init__ wrappers run via super().__init__;
             # the sentinel keeps that to a single warning.
@@ -217,7 +231,7 @@ def new_init(self, *args, **kwargs):
                     pass
             original_init(self, *args, **kwargs)
 
-        cls.__init__ = new_init
+        setattr(cls, "__init__", new_init)
         return cls
 
     return decorator
diff --git a/redisvl/utils/vectorize/base.py b/redisvl/utils/vectorize/base.py
index ca4d72be..c3652cd6 100644
--- a/redisvl/utils/vectorize/base.py
+++ b/redisvl/utils/vectorize/base.py
@@ -184,7 +184,7 @@ def embed_many(
         if cache_misses:
             cache_metadata = kwargs.pop("metadata", {})
             new_embeddings = self._embed_many(
-                contents=cache_misses, batch_size=batch_size, **kwargs
+                cache_misses, batch_size=batch_size, **kwargs
             )
 
             # Store new embeddings in cache
@@ -317,7 +317,7 @@ async def aembed_many(
         if cache_misses:
             cache_metadata = kwargs.pop("metadata", {})
             new_embeddings = await self._aembed_many(
-                contents=cache_misses, batch_size=batch_size, **kwargs
+                cache_misses, batch_size=batch_size, **kwargs
             )
 
             # Store new embeddings in cache
@@ -332,45 +332,36 @@ async def aembed_many(
         # Process and return results
         return [self._process_embedding(emb, as_buffer, self.dtype) for emb in results]
 
-    @deprecated_argument("text", "content")
-    def _embed(self, text: Any = "", content: Any = "", **kwargs) -> list[float]:
+    # The four hooks below are the provider extension points. Every caller —
+    # the public embed/embed_many/aembed/aembed_many above, and each provider's
+    # own _set_model_dims probe — passes the content as the first positional
+    # argument. None passes the deprecated `text`/`texts` alias, because the
+    # public methods resolve it before dispatching here.
+    def _embed(self, content: Any, **kwargs) -> list[float]:
         """Generate a vector embedding for a single item."""
         raise NotImplementedError
 
-    @deprecated_argument("texts", "contents")
     def _embed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        batch_size: int = 10,
-        **kwargs,
+        self, contents: list[Any], batch_size: int = 10, **kwargs
     ) -> list[list[float]]:
         """Generate vector embeddings for a batch of items."""
         raise NotImplementedError
 
-    @deprecated_argument("text", "content")
-    async def _aembed(self, content: Any = "", text: Any = "", **kwargs) -> list[float]:
+    async def _aembed(self, content: Any, **kwargs) -> list[float]:
         """Asynchronously generate a vector embedding for a single item."""
         logger.warning(
             "This vectorizer has no async embed method. Falling back to sync."
         )
-        return self._embed(content=content or text, **kwargs)
+        return self._embed(content, **kwargs)
 
-    @deprecated_argument("texts", "contents")
     async def _aembed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        batch_size: int = 10,
-        **kwargs,
+        self, contents: list[Any], batch_size: int = 10, **kwargs
     ) -> list[list[float]]:
         """Asynchronously generate vector embeddings for a batch of items."""
         logger.warning(
             "This vectorizer has no async embed_many method. Falling back to sync."
         )
-        return self._embed_many(
-            contents=contents or texts, batch_size=batch_size, **kwargs
-        )
+        return self._embed_many(contents, batch_size=batch_size, **kwargs)
 
     def _get_from_cache_batch(
         self, contents: list[Any], skip_cache: bool
diff --git a/redisvl/utils/vectorize/text/azureopenai.py b/redisvl/utils/vectorize/text/azureopenai.py
index 9640d1b8..46ef8692 100644
--- a/redisvl/utils/vectorize/text/azureopenai.py
+++ b/redisvl/utils/vectorize/text/azureopenai.py
@@ -8,7 +8,6 @@
 if TYPE_CHECKING:
     from redisvl.extensions.cache.embeddings.embeddings import EmbeddingsCache
 
-from redisvl.utils.utils import deprecated_argument
 from redisvl.utils.vectorize.base import BaseVectorizer
 
 # ignore that openai isn't imported
@@ -206,30 +205,27 @@ def _set_model_dims(self) -> int:
             # fall back (TODO get more specific)
             raise ValueError(f"Error setting embedding model dimensions: {str(e)}")
 
-    @deprecated_argument("text", "content")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
         retry=retry_if_not_exception_type(TypeError),
         reraise=True,
     )
-    def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    def _embed(self, content: str, **kwargs) -> list[float]:
         """
         Generate a vector embedding for a single text using the AzureOpenAI API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the AzureOpenAI API
 
         Returns:
             List[float]: Vector embedding as a list of floats
 
         Raises:
-            TypeError: If text is not a string
+            TypeError: If content is not a string
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -241,7 +237,6 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
         except Exception as e:
             raise ValueError(f"Embedding text failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -250,8 +245,7 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
     )
     def _embed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -260,7 +254,6 @@ def _embed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the AzureOpenAI API
 
@@ -271,7 +264,6 @@ def _embed_many(
             TypeError: If contents is not a list of strings
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
@@ -288,20 +280,18 @@ def _embed_many(
         except Exception as e:
             raise ValueError(f"Embedding texts failed: {e}")
 
-    @deprecated_argument("text", "content")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
         retry=retry_if_not_exception_type(TypeError),
         reraise=True,
     )
-    async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    async def _aembed(self, content: str, **kwargs) -> list[float]:
         """
         Asynchronously generate a vector embedding for a single text using the AzureOpenAI API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the AzureOpenAI API
 
         Returns:
@@ -311,7 +301,6 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
             TypeError: If content is not a string
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -323,7 +312,6 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
         except Exception as e:
             raise ValueError(f"Embedding text failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -332,8 +320,7 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
     )
     async def _aembed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -342,7 +329,6 @@ async def _aembed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the AzureOpenAI API
 
@@ -353,7 +339,6 @@ async def _aembed_many(
             TypeError: If contents is not a list of strings
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
diff --git a/redisvl/utils/vectorize/text/bedrock.py b/redisvl/utils/vectorize/text/bedrock.py
index ced3cc50..51490bbd 100644
--- a/redisvl/utils/vectorize/text/bedrock.py
+++ b/redisvl/utils/vectorize/text/bedrock.py
@@ -1,6 +1,4 @@
-from typing import Any
-
-from redisvl.utils.utils import deprecated_argument, deprecated_class
+from redisvl.utils.utils import deprecated_class
 from redisvl.utils.vectorize.bedrock import BedrockVectorizer
 
 
@@ -8,27 +6,8 @@
     name="BedrockTextVectorizer", replacement="Use BedrockVectorizer instead."
 )
 class BedrockTextVectorizer(BedrockVectorizer):
-    """A backwards-compatible alias for BedrockVectorizer."""
-
-    @deprecated_argument("text", "content")
-    def embed(self, content: Any = "", text: Any = "", **kwargs) -> list[float] | bytes:
-        """Generate a vector embedding for a single input using the AWS Bedrock API.
-
-        Deprecated: Use `BedrockVectorizer.embed` instead.
-        """
-        content = content or text
-        return super().embed(content=content, **kwargs)
-
-    @deprecated_argument("texts", "contents")
-    def embed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        **kwargs,
-    ) -> list[list[float]]:
-        """Generate vector embeddings for a batch of inputs using the AWS Bedrock API.
+    """A backwards-compatible alias for BedrockVectorizer.
 
-        Deprecated: Use `BedrockVectorizer.embed_many` instead.
-        """
-        contents = contents or texts
-        return super().embed_many(contents=contents, **kwargs)
+    The `text`/`texts` keyword arguments still work, and still warn, via
+    BaseVectorizer.
+    """
diff --git a/redisvl/utils/vectorize/text/cohere.py b/redisvl/utils/vectorize/text/cohere.py
index d5b2270a..bdedbc74 100644
--- a/redisvl/utils/vectorize/text/cohere.py
+++ b/redisvl/utils/vectorize/text/cohere.py
@@ -9,7 +9,6 @@
 if TYPE_CHECKING:
     from redisvl.extensions.cache.embeddings.embeddings import EmbeddingsCache
 
-from redisvl.utils.utils import deprecated_argument
 from redisvl.utils.vectorize.base import BaseVectorizer
 
 # ignore that cohere isn't imported
@@ -205,14 +204,12 @@ def _validate_input_type(self, input_type) -> None:
                 "See https://docs.cohere.com/reference/embed."
             )
 
-    @deprecated_argument("text", "content")
-    def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float | int]:
+    def _embed(self, content: str, **kwargs) -> list[float | int]:
         """
         Generate a vector embedding for a single text using the Cohere API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the Cohere API,
                       must include 'input_type'
 
@@ -224,10 +221,9 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float | in
               - For dtype="int8" or "uint8": Returns a list of integers
 
         Raises:
-            TypeError: If text is not a string or input_type is not provided
+            TypeError: If content is not a string or input_type is not provided
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -267,7 +263,6 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float | in
         except Exception as e:
             raise ValueError(f"Embedding text failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -275,8 +270,7 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float | in
     )
     def _embed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float | int]]:
@@ -285,7 +279,6 @@ def _embed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the Cohere API,
                       must include 'input_type'
@@ -297,7 +290,6 @@ def _embed_many(
             TypeError: If contents is not a list of strings or input_type is not provided
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
diff --git a/redisvl/utils/vectorize/text/custom.py b/redisvl/utils/vectorize/text/custom.py
index 734a6d7a..743b2135 100644
--- a/redisvl/utils/vectorize/text/custom.py
+++ b/redisvl/utils/vectorize/text/custom.py
@@ -1,6 +1,4 @@
-from typing import Any
-
-from redisvl.utils.utils import deprecated_argument, deprecated_class
+from redisvl.utils.utils import deprecated_class
 from redisvl.utils.vectorize.custom import CustomVectorizer
 
 
@@ -8,50 +6,8 @@
     name="CustomTextVectorizer", replacement="Use CustomVectorizer instead."
 )
 class CustomTextVectorizer(CustomVectorizer):
-    """A backwards-compatible alias for CustomVectorizer."""
-
-    @deprecated_argument("text", "content")
-    def embed(self, content: Any = "", text: Any = "", **kwargs) -> list[float]:
-        """Generate a vector embedding for a single input using the custom function.
-
-        Deprecated: Use `CustomVectorizer.embed` instead.
-        """
-        content = content or text
-        return super().embed(content=content, **kwargs)
-
-    @deprecated_argument("texts", "contents")
-    def embed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        **kwargs,
-    ) -> list[list[float]]:
-        """Generate vector embeddings for a batch of inputs using the custom function.
-
-        Deprecated: Use `CustomVectorizer.embed_many` instead.
-        """
-        contents = contents or texts
-        return super().embed_many(contents=contents, **kwargs)
-
-    @deprecated_argument("text", "content")
-    async def aembed(self, content: Any = "", text: Any = "", **kwargs) -> list[float]:
-        """Asynchronously generate a vector embedding for a single input using the custom function.
-
-        Deprecated: Use `CustomVectorizer.aembed` instead.
-        """
-        content = content or text
-        return await super().aembed(content=content, **kwargs)
-
-    @deprecated_argument("texts", "contents")
-    async def aembed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        **kwargs,
-    ) -> list[list[float]]:
-        """Asynchronously generate vector embeddings for a batch of inputs using the custom function.
+    """A backwards-compatible alias for CustomVectorizer.
 
-        Deprecated: Use `CustomVectorizer.aembed_many` instead.
-        """
-        contents = contents or texts
-        return await super().aembed_many(contents=contents, **kwargs)
+    The `text`/`texts` keyword arguments still work, and still warn, via
+    BaseVectorizer.
+    """
diff --git a/redisvl/utils/vectorize/text/huggingface.py b/redisvl/utils/vectorize/text/huggingface.py
index 5fb6e8bc..0ff1b6f8 100644
--- a/redisvl/utils/vectorize/text/huggingface.py
+++ b/redisvl/utils/vectorize/text/huggingface.py
@@ -5,7 +5,6 @@
 if TYPE_CHECKING:
     from redisvl.extensions.cache.embeddings.embeddings import EmbeddingsCache
 
-from redisvl.utils.utils import deprecated_argument
 from redisvl.utils.vectorize.base import BaseVectorizer
 
 
@@ -125,19 +124,16 @@ def _set_model_dims(self):
             raise ValueError(f"Error setting embedding model dimensions: {str(e)}")
         return len(embedding)
 
-    @deprecated_argument("text", "content")
-    def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    def _embed(self, content: str, **kwargs) -> list[float]:
         """Generate a vector embedding for a single text using the Hugging Face model.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional model-specific parameters
 
         Returns:
             List[float]: Vector embedding as a list of floats
         """
-        content = content or text
         if "show_progress_bar" not in kwargs:
             # disable annoying tqdm by default
             kwargs["show_progress_bar"] = False
@@ -145,11 +141,9 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
         embedding = self._client.encode([content], **kwargs)[0]
         return embedding.tolist()
 
-    @deprecated_argument("texts", "contents")
     def _embed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -157,14 +151,12 @@ def _embed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each batch
             **kwargs: Additional model-specific parameters
 
         Returns:
             List[List[float]]: List of vector embeddings as lists of floats
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of values to embed.")
         if "show_progress_bar" not in kwargs:
diff --git a/redisvl/utils/vectorize/text/mistral.py b/redisvl/utils/vectorize/text/mistral.py
index b1ba36da..403097b2 100644
--- a/redisvl/utils/vectorize/text/mistral.py
+++ b/redisvl/utils/vectorize/text/mistral.py
@@ -8,7 +8,6 @@
 if TYPE_CHECKING:
     from redisvl.extensions.cache.embeddings.embeddings import EmbeddingsCache
 
-from redisvl.utils.utils import deprecated_argument
 from redisvl.utils.vectorize.base import BaseVectorizer
 
 # ignore that mistralai isn't imported
@@ -163,19 +162,17 @@ def _set_model_dims(self) -> int:
             # fall back (TODO get more specific)
             raise ValueError(f"Error setting embedding model dimensions: {str(e)}")
 
-    @deprecated_argument("text", "content")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
         retry=retry_if_not_exception_type(TypeError),
     )
-    def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    def _embed(self, content: str, **kwargs) -> list[float]:
         """
         Generate a vector embedding for a single text using the MistralAI API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the MistralAI API
 
         Returns:
@@ -185,7 +182,6 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
             TypeError: If content is not a string
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -197,7 +193,6 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
         except Exception as e:
             raise ValueError(f"Embedding text failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -205,8 +200,7 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
     )
     def _embed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -215,7 +209,6 @@ def _embed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the MistralAI API
 
@@ -226,7 +219,6 @@ def _embed_many(
             TypeError: If contents is not a list of strings
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
@@ -243,19 +235,17 @@ def _embed_many(
         except Exception as e:
             raise ValueError(f"Embedding texts failed: {e}")
 
-    @deprecated_argument("text", "content")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
         retry=retry_if_not_exception_type(TypeError),
     )
-    async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    async def _aembed(self, content: str, **kwargs) -> list[float]:
         """
         Asynchronously generate a vector embedding for a single text using the MistralAI API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the MistralAI API
 
         Returns:
@@ -265,7 +255,6 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
             TypeError: If `content` is not a string
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -277,7 +266,6 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
         except Exception as e:
             raise ValueError(f"Embedding content failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -285,8 +273,7 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
     )
     async def _aembed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -295,7 +282,6 @@ async def _aembed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the MistralAI API
 
@@ -303,10 +289,9 @@ async def _aembed_many(
             List[List[float]]: List of vector embeddings as lists of floats
 
         Raises:
-            TypeError: If texts is not a list of strings
+            TypeError: If contents is not a list of strings
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
diff --git a/redisvl/utils/vectorize/text/openai.py b/redisvl/utils/vectorize/text/openai.py
index 5102a8f8..cecb65cb 100644
--- a/redisvl/utils/vectorize/text/openai.py
+++ b/redisvl/utils/vectorize/text/openai.py
@@ -8,7 +8,6 @@
 if TYPE_CHECKING:
     from redisvl.extensions.cache.embeddings.embeddings import EmbeddingsCache
 
-from redisvl.utils.utils import deprecated_argument
 from redisvl.utils.vectorize.base import BaseVectorizer
 
 # ignore that openai isn't imported
@@ -162,28 +161,25 @@ def _set_model_dims(self) -> int:
             # fall back (TODO get more specific)
             raise ValueError(f"Error setting embedding model dimensions: {str(e)}")
 
-    @deprecated_argument("text", "content")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
         retry=retry_if_not_exception_type(TypeError),
     )
-    def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    def _embed(self, content: str, **kwargs) -> list[float]:
         """Generate a vector embedding for a single text using the OpenAI API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the OpenAI API
 
         Returns:
             List[float]: Vector embedding as a list of floats
 
         Raises:
-            TypeError: If text is not a string
+            TypeError: If content is not a string
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -195,7 +191,6 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
         except Exception as e:
             raise ValueError(f"Embedding text failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -203,8 +198,7 @@ def _embed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
     )
     def _embed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -212,7 +206,6 @@ def _embed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the OpenAI API
 
@@ -223,7 +216,6 @@ def _embed_many(
             TypeError: If contents is not a list of strings
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
@@ -240,18 +232,16 @@ def _embed_many(
                 raise ValueError(f"Embedding texts failed: {e}")
         return embeddings
 
-    @deprecated_argument("text", "content")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
         retry=retry_if_not_exception_type(TypeError),
     )
-    async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[float]:
+    async def _aembed(self, content: str, **kwargs) -> list[float]:
         """Asynchronously generate a vector embedding for a single text using the OpenAI API.
 
         Args:
             content: Text to embed
-            text: Text to embed (deprecated - use `content` instead)
             **kwargs: Additional parameters to pass to the OpenAI API
 
         Returns:
@@ -261,7 +251,6 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
             TypeError: If content is not a string
             ValueError: If embedding fails
         """
-        content = content or text
         if not isinstance(content, str):
             raise TypeError("Must pass in a str value to embed.")
 
@@ -273,7 +262,6 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
         except Exception as e:
             raise ValueError(f"Embedding text failed: {e}")
 
-    @deprecated_argument("texts", "contents")
     @retry(
         wait=wait_random_exponential(min=1, max=60),
         stop=stop_after_attempt(6),
@@ -281,8 +269,7 @@ async def _aembed(self, content: str = "", text: str = "", **kwargs) -> list[flo
     )
     async def _aembed_many(
         self,
-        contents: list[str] | None = None,
-        texts: list[str] | None = None,
+        contents: list[str],
         batch_size: int = 10,
         **kwargs,
     ) -> list[list[float]]:
@@ -290,7 +277,6 @@ async def _aembed_many(
 
         Args:
             contents: List of texts to embed
-            texts: List of texts to embed (deprecated - use `contents` instead)
             batch_size: Number of texts to process in each API call
             **kwargs: Additional parameters to pass to the OpenAI API
 
@@ -301,7 +287,6 @@ async def _aembed_many(
             TypeError: If contents is not a list of strings
             ValueError: If embedding fails
         """
-        contents = contents or texts
         if not isinstance(contents, list):
             raise TypeError("Must pass in a list of str values to embed.")
         if contents and not isinstance(contents[0], str):
diff --git a/redisvl/utils/vectorize/text/vertexai.py b/redisvl/utils/vectorize/text/vertexai.py
index 8e1a77f5..3d262030 100644
--- a/redisvl/utils/vectorize/text/vertexai.py
+++ b/redisvl/utils/vectorize/text/vertexai.py
@@ -1,6 +1,4 @@
-from typing import Any
-
-from redisvl.utils.utils import deprecated_argument, deprecated_class
+from redisvl.utils.utils import deprecated_class
 from redisvl.utils.vectorize.vertexai import VertexAIVectorizer
 
 
@@ -9,27 +7,9 @@
     replacement="Use GoogleGenAIVectorizer instead.",
 )
 class VertexAITextVectorizer(VertexAIVectorizer):
-    """A backwards-compatible alias for VertexAIVectorizer."""
-
-    @deprecated_argument("text", "content")
-    def embed(self, content: str = "", text: Any = "", **kwargs) -> list[float]:
-        """Generate a vector embedding for a single input using the VertexAI API.
-
-        Deprecated: Use `VertexAIVectorizer.embed` instead.
-        """
-        content = content or text
-        return super().embed(content=content, **kwargs)
-
-    @deprecated_argument("texts", "contents")
-    def embed_many(
-        self,
-        contents: list[str] | None = None,
-        texts: list[Any] | None = None,
-        **kwargs,
-    ) -> list[list[float]]:
-        """Generate vector embeddings for a batch of inputs using the VertexAI API.
+    """A backwards-compatible alias for VertexAIVectorizer, which is itself
+    deprecated: use GoogleGenAIVectorizer instead.
 
-        Deprecated: Use `VertexAIVectorizer.embed_many` instead.
-        """
-        contents = contents or texts
-        return super().embed_many(contents=contents, **kwargs)
+    The `text`/`texts` keyword arguments still work, and still warn, via
+    BaseVectorizer.
+    """
diff --git a/redisvl/utils/vectorize/text/voyageai.py b/redisvl/utils/vectorize/text/voyageai.py
index 45db4c6d..c7f067a6 100644
--- a/redisvl/utils/vectorize/text/voyageai.py
+++ b/redisvl/utils/vectorize/text/voyageai.py
@@ -1,6 +1,4 @@
-from typing import Any
-
-from redisvl.utils.utils import deprecated_argument, deprecated_class
+from redisvl.utils.utils import deprecated_class
 from redisvl.utils.vectorize.voyageai import VoyageAIVectorizer
 
 
@@ -8,50 +6,8 @@
     name="VoyageAITextVectorizer", replacement="Use VoyageAIVectorizer instead."
 )
 class VoyageAITextVectorizer(VoyageAIVectorizer):
-    """A backwards-compatible alias for VoyageAIVectorizer."""
-
-    @deprecated_argument("text", "content")
-    def embed(self, content: Any = "", text: Any = "", **kwargs) -> list[float]:
-        """Generate a vector embedding for a single text using the VoyageAI API.
-
-        Deprecated: Use `VoyageAIVectorizer.embed` instead.
-        """
-        content = content or text
-        return super().embed(content=content, **kwargs)
-
-    @deprecated_argument("texts", "contents")
-    def embed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        **kwargs,
-    ) -> list[list[float]]:
-        """Generate vector embeddings for a batch of texts using the VoyageAI API.
-
-        Deprecated: Use `VoyageAIVectorizer.embed_many` instead.
-        """
-        contents = contents or texts
-        return super().embed_many(contents=contents, **kwargs)
-
-    @deprecated_argument("text", "content")
-    async def aembed(self, content: Any = "", text: Any = "", **kwargs) -> list[float]:
-        """Asynchronously generate a vector embedding for a single text using the VoyageAI API.
-
-        Deprecated: Use `VoyageAIVectorizer.aembed` instead.
-        """
-        content = content or text
-        return await super().aembed(content=content, **kwargs)
-
-    @deprecated_argument("texts", "contents")
-    async def aembed_many(
-        self,
-        contents: list[Any] | None = None,
-        texts: list[Any] | None = None,
-        **kwargs,
-    ) -> list[list[float]]:
-        """Asynchronously generate vector embeddings for a batch of texts using the VoyageAI API.
+    """A backwards-compatible alias for VoyageAIVectorizer.
 
-        Deprecated: Use `VoyageAIVectorizer.aembed_many` instead.
-        """
-        contents = contents or texts
-        return await super().aembed_many(contents=contents, **kwargs)
+    The `text`/`texts` keyword arguments still work, and still warn, via
+    BaseVectorizer.
+    """