diff --git a/chatlab/conversation.py b/chatlab/conversation.py
index 646afad..be88fce 100644
--- a/chatlab/conversation.py
+++ b/chatlab/conversation.py
@@ -14,13 +14,13 @@
 import asyncio
 import logging
 import os
-from dataclasses import dataclass
-from typing import Callable, Dict, Iterable, List, Optional, Tuple, Type, Union, cast, overload
+from typing import AsyncIterator, Callable, List, Optional, Tuple, Type, Union, overload
 
 import openai
-import openai.error
 from deprecation import deprecated
 from IPython.core.async_helpers import get_asyncio_loop
+from openai import AsyncOpenAI
+from openai.types.chat import ChatCompletion, ChatCompletionChunk, ChatCompletionMessageParam
 from pydantic import BaseModel
 
 from chatlab.views.assistant_function_call import AssistantFunctionCallView
@@ -28,55 +28,13 @@
 from ._version import __version__
 from .display import ChatFunctionCall
 from .errors import ChatLabError
-from .messaging import (
-    ChatCompletion,
-    Message,
-    StreamCompletion,
-    human,
-    is_full_choice,
-    is_function_call,
-    is_stream_choice,
-)
-from .registry import FunctionRegistry, PythonHallucinationFunction
+from .messaging import human
+from .registry import FunctionRegistry, FunctionSchema, PythonHallucinationFunction
 from .views.assistant import AssistantMessageView
 
 logger = logging.getLogger(__name__)
 
 
-@dataclass
-class ContentDelta:
-    """A delta that contains markdown."""
-
-    content: str
-
-
-@dataclass
-class FunctionCallArgumentsDelta:
-    """A delta that contains function call arguments."""
-
-    arguments: str
-
-
-@dataclass
-class FunctionCallNameDelta:
-    """A delta that contains function call name."""
-
-    name: str
-
-
-def process_delta(delta):
-    """Process a delta."""
-    if 'content' in delta and delta['content'] is not None:
-        yield ContentDelta(delta['content'])
-
-    elif 'function_call' in delta:  # If the delta contains a function call
-        if 'name' in delta['function_call']:
-            yield FunctionCallNameDelta(delta['function_call']['name'])
-
-        if 'arguments' in delta['function_call']:
-            yield FunctionCallArgumentsDelta(delta['function_call']['arguments'])
-
-
 class Chat:
     """Interactive chats inside of computational notebooks, relying on OpenAI's API.
 
@@ -85,7 +43,7 @@ class Chat:
     History is tracked and can be used to continue a conversation.
 
     Args:
-        initial_context (str | Message): The initial context for the conversation.
+        initial_context (str | ChatCompletionMessageParam): The initial context for the conversation.
 
         model (str): The model to use for the conversation.
 
@@ -102,14 +60,14 @@ class Chat:
 
     """
 
-    messages: List[Message]
+    messages: List[ChatCompletionMessageParam]
     model: str
     function_registry: FunctionRegistry
     allow_hallucinated_python: bool
 
     def __init__(
         self,
-        *initial_context: Union[Message, str],
+        *initial_context: Union[ChatCompletionMessageParam, str],
         model="gpt-3.5-turbo-0613",
         function_registry: Optional[FunctionRegistry] = None,
         chat_functions: Optional[List[Callable]] = None,
@@ -141,7 +99,7 @@ def __init__(
         if initial_context is None:
             initial_context = []  # type: ignore
 
-        self.messages: List[Message] = []
+        self.messages: List[ChatCompletionMessageParam] = []
 
         self.append(*initial_context)
         self.model = model
@@ -164,7 +122,7 @@ def __init__(
     )
     def chat(
         self,
-        *messages: Union[Message, str],
+        *messages: Union[ChatCompletionMessageParam, str],
     ):
         """Send messages to the chat model and display the response.
 
@@ -172,23 +130,19 @@ def chat(
         """
         raise Exception("This method is deprecated. Use `submit` instead.")
 
-    async def __call__(self, *messages: Union[Message, str], stream: bool = True, **kwargs):
+    async def __call__(self, *messages: Union[ChatCompletionMessageParam, str], stream=True, **kwargs):
         """Send messages to the chat model and display the response."""
         return await self.submit(*messages, stream=stream, **kwargs)
 
     async def __process_stream(
-        self, resp: Iterable[Union[StreamCompletion, ChatCompletion]]
+        self, resp: AsyncIterator[ChatCompletionChunk]
     ) -> Tuple[str, Optional[AssistantFunctionCallView]]:
         assistant_view: AssistantMessageView = AssistantMessageView()
         function_view: Optional[AssistantFunctionCallView] = None
         finish_reason = None
 
-        for result in resp:  # Go through the results of the stream
-            if not isinstance(result, dict):
-                logger.warning(f"Unknown result type: {type(result)}: {result}")
-                continue
-
-            choices = result.get('choices', [])
+        async for result in resp:  # Go through the results of the stream
+            choices = result.choices
 
             if len(choices) == 0:
                 logger.warning(f"Result has no choices: {result}")
@@ -196,33 +150,24 @@ async def __process_stream(
 
             choice = choices[0]
 
-            if is_stream_choice(choice):  # If there is a delta in the result
-                delta = choice['delta']
-
-                for event in process_delta(delta):
-                    if isinstance(event, ContentDelta):
-                        assistant_view.append(event.content)
-                    elif isinstance(event, FunctionCallNameDelta):
+            # Is stream choice?
+            if choice.delta is not None:
+                if choice.delta.content is not None:
+                    assistant_view.append(choice.delta.content)
+                elif choice.delta.function_call is not None:
+                    function_call = choice.delta.function_call
+                    if function_call.name is not None:
                         if assistant_view.in_progress():
                             # Flush out the finished assistant message
                             message = assistant_view.flush()
                             self.append(message)
-                        function_view = AssistantFunctionCallView(event.name)
-                    elif isinstance(event, FunctionCallArgumentsDelta):
+                        function_view = AssistantFunctionCallView(function_call.name)
+                    if function_call.arguments is not None:
                         if function_view is None:
                             raise ValueError("Function arguments provided without function name")
-                        function_view.append(event.arguments)
-            elif is_full_choice(choice):
-                message = choice['message']
-
-                if is_function_call(message):
-                    function_view = AssistantFunctionCallView(message['function_call']['name'])
-                    function_view.append(message['function_call']['arguments'])
-                elif 'content' in message and message['content'] is not None:
-                    assistant_view.append(message['content'])
-
-            if 'finish_reason' in choice and choice['finish_reason'] is not None:
-                finish_reason = choice['finish_reason']
+                        function_view.append(function_call.arguments)
+            if choice.finish_reason is not None:
+                finish_reason = choice.finish_reason
                 break
 
         # Wrap up the previous assistant
@@ -236,7 +181,29 @@ async def __process_stream(
 
         return (finish_reason, function_view)
 
-    async def submit(self, *messages: Union[Message, str], stream: bool = True, **kwargs):
+    async def __process_full_completion(self, resp: ChatCompletion) -> Tuple[str, Optional[AssistantFunctionCallView]]:
+        assistant_view: AssistantMessageView = AssistantMessageView()
+        function_view: Optional[AssistantFunctionCallView] = None
+
+        if len(resp.choices) == 0:
+            logger.warning(f"Result has no choices: {resp}")
+            return ("stop", None)  # TODO
+
+        choice = resp.choices[0]
+
+        message = choice.message
+
+        if message.content is not None:
+            assistant_view.append(message.content)
+            self.append(assistant_view.flush())
+        if message.function_call is not None:
+            function_call = message.function_call
+            function_view = AssistantFunctionCallView(function_name=function_call.name)
+            function_view.append(function_call.arguments)
+
+        return choice.finish_reason, function_view
+
+    async def submit(self, *messages: Union[ChatCompletionMessageParam, str], stream=True, **kwargs):
         """Send messages to the chat model and display the response.
 
         Side effects:
@@ -245,13 +212,13 @@ async def submit(self, *messages: Union[Message, str], stream: bool = True, **kw
             - chat.messages are updated with response(s).
 
         Args:
-            messages (str | Message): One or more messages to send to the chat, can be strings or Message objects.
+            messages (str | ChatCompletionMessageParam): One or more messages to send to the chat, can be strings or
+            ChatCompletionMessageParam objects.
 
-            stream (bool): Whether to stream chat into markdown or not. If False, the entire chat will be sent once.
+            stream: Whether to stream chat into markdown or not. If False, the entire chat will be sent once.
 
         """
-
-        full_messages: List[Message] = []
+        full_messages: List[ChatCompletionMessageParam] = []
         full_messages.extend(self.messages)
         for message in messages:
             if isinstance(message, str):
@@ -260,14 +227,33 @@ async def submit(self, *messages: Union[Message, str], stream: bool = True, **kw
                 full_messages.append(message)
 
         try:
-            resp = openai.ChatCompletion.create(
-                model=self.model,
-                messages=full_messages,
-                **self.function_registry.api_manifest(),
-                stream=stream,
-                temperature=kwargs.get("temperature", 0),
-            )
-        except openai.error.RateLimitError as e:
+            client = AsyncOpenAI()
+
+            manifest = self.function_registry.api_manifest()
+
+            # Due to the strict response typing based on `Literal` typing on `stream`, we have to process these two cases separately
+            if stream:
+                streaming_response = await client.chat.completions.create(
+                    model=self.model,
+                    messages=full_messages,
+                    **manifest,
+                    stream=True,
+                    temperature=kwargs.get("temperature", 0),
+                )
+
+                finish_reason, function_call_request = await self.__process_stream(streaming_response)
+            else:
+                full_response = await client.chat.completions.create(
+                    model=self.model,
+                    messages=full_messages,
+                    **manifest,
+                    stream=False,
+                    temperature=kwargs.get("temperature", 0),
+                )
+
+                finish_reason, function_call_request = await self.__process_full_completion(full_response)
+
+        except openai.RateLimitError as e:
             logger.error(f"Rate limited: {e}. Waiting 5 seconds and trying again.")
             await asyncio.sleep(5)
             await self.submit(*messages, stream=stream, **kwargs)
@@ -276,13 +262,6 @@ async def submit(self, *messages: Union[Message, str], stream: bool = True, **kw
 
         self.append(*messages)
 
-        if not stream:
-            resp = [resp]
-
-        resp = cast(Iterable[Union[StreamCompletion, ChatCompletion]], resp)
-
-        finish_reason, function_call_request = await self.__process_stream(resp)
-
         if finish_reason == "function_call":
             if function_call_request is None:
                 raise ValueError(
@@ -317,13 +296,13 @@ async def submit(self, *messages: Union[Message, str], stream: bool = True, **kw
                 f"UNKNOWN FINISH REASON: '{finish_reason}'. If you see this message, report it as an issue to https://github.com/rgbkrk/chatlab/issues"  # noqa: E501
             )
 
-    def append(self, *messages: Union[Message, str]):
+    def append(self, *messages: Union[ChatCompletionMessageParam, str]):
         """Append messages to the conversation history.
 
         Note: this does not send the messages on until `chat` is called.
 
         Args:
-            messages (str | Message): One or more messages to append to the conversation.
+            messages (str | ChatCompletionMessageParam): One or more messages to append to the conversation.
 
         """
         # Messages are either a dict respecting the {role, content} format or a str that we convert to a human message
@@ -340,13 +319,17 @@ def register(
         ...
 
     @overload
-    def register(self, function: Callable, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None) -> Dict:
+    def register(
+        self, function: Callable, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None
+    ) -> FunctionSchema:
         ...
 
     def register(
         self, function: Optional[Callable] = None, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None
-    ) -> Union[Callable, Dict]:
-        """Register a function with the ChatLab instance. This can be used as a decorator like so:
+    ) -> Union[Callable, FunctionSchema]:
+        """Register a function with the ChatLab instance.
+
+        This can be used as a decorator like so:
 
         >>> from chatlab import Chat
         >>> chat = Chat()
diff --git a/chatlab/display.py b/chatlab/display.py
index a4f11cc..6a56995 100644
--- a/chatlab/display.py
+++ b/chatlab/display.py
@@ -2,8 +2,10 @@
 
 from typing import Optional
 
+from openai.types.chat import ChatCompletionMessageParam
+
 from .components.function_details import ChatFunctionComponent
-from .messaging import Message, function_result, system
+from .messaging import function_result, system
 from .registry import FunctionArgumentError, FunctionRegistry, UnknownFunctionError
 from .views.abstracts import AutoDisplayer
 
@@ -35,7 +37,7 @@ def __init__(
         self._display_id = display_id
         self.update_displays()
 
-    async def call(self) -> Message:
+    async def call(self) -> ChatCompletionMessageParam:
         """Call the function and return a stack of messages for LLM and human consumption."""
         function_name = self.function_name
         function_args = self.function_args
diff --git a/chatlab/messaging.py b/chatlab/messaging.py
index af9cc6d..032d488 100644
--- a/chatlab/messaging.py
+++ b/chatlab/messaging.py
@@ -10,117 +10,12 @@
 
 """
 
-from typing import List, Optional, TypedDict, Union
-
-from typing_extensions import TypeGuard
-
-BasicMessage = TypedDict(
-    "BasicMessage",
-    {
-        "role": str,
-        "content": str,
-    },
-)
+from typing import Optional
 
+from openai.types.chat import ChatCompletionMessageParam
 
-FunctionCall = TypedDict(
-    "FunctionCall",
-    {
-        "name": str,
-        "arguments": str,
-    },
-)
 
-FunctionCallMessage = TypedDict(
-    "FunctionCallMessage",
-    {
-        "role": str,
-        "content": Optional[str],
-        "function_call": FunctionCall,
-    },
-)
-
-FunctionResultMessage = TypedDict(
-    "FunctionResultMessage",
-    {
-        "role": str,
-        "content": str,
-        "name": str,
-    },
-)
-
-
-Message = Union[BasicMessage, FunctionCallMessage, FunctionResultMessage]
-
-
-def is_function_call(message: Message) -> TypeGuard[FunctionCallMessage]:
-    """Check if a message is a function call message."""
-    return 'function_call' in message
-
-
-def is_basic_message(message: Message) -> TypeGuard[BasicMessage]:
-    """Check if a message is a basic message."""
-    return 'content' in message and 'role' in message and 'function_call' not in message
-
-
-#### STREAMING ####
-
-Delta = TypedDict(
-    "Delta",
-    {
-        "function_call": FunctionCall,
-        "content": Optional[str],
-    },
-    total=False,
-)
-
-
-StreamChoice = TypedDict(
-    "StreamChoice",
-    {
-        "finish_reason": Optional[str],
-        "delta": Delta,
-    },
-)
-
-StreamCompletion = TypedDict(
-    "StreamCompletion",
-    {
-        "choices": List[StreamChoice],
-    },
-    total=False,
-)
-
-#### NON STREAMING ####
-
-FullChoice = TypedDict(
-    "FullChoice",
-    {
-        "finish_reason": Optional[str],
-        "message": Message,
-    },
-)
-
-ChatCompletion = TypedDict(
-    "ChatCompletion",
-    {
-        "choices": List[FullChoice],
-    },
-    total=False,
-)
-
-
-def is_stream_choice(choice: Union[StreamChoice, FullChoice]) -> TypeGuard[StreamChoice]:
-    """Check if a choice is a stream choice."""
-    return 'delta' in choice
-
-
-def is_full_choice(choice: Union[StreamChoice, FullChoice]) -> TypeGuard[FullChoice]:
-    """Check if a choice is a regular choice."""
-    return 'message' in choice
-
-
-def assistant(content: str) -> BasicMessage:
+def assistant(content: str) -> ChatCompletionMessageParam:
     """Create a message from the assistant.
 
     Args:
@@ -135,7 +30,7 @@ def assistant(content: str) -> BasicMessage:
     }
 
 
-def user(content: str) -> BasicMessage:
+def user(content: str) -> ChatCompletionMessageParam:
     """Create a message from the user.
 
     Args:
@@ -150,7 +45,7 @@ def user(content: str) -> BasicMessage:
     }
 
 
-def system(content: str) -> BasicMessage:
+def system(content: str) -> ChatCompletionMessageParam:
     """Create a message from the system.
 
     Args:
@@ -165,7 +60,7 @@ def system(content: str) -> BasicMessage:
     }
 
 
-def assistant_function_call(name: str, arguments: Optional[str] = None) -> FunctionCallMessage:
+def assistant_function_call(name: str, arguments: Optional[str] = None) -> ChatCompletionMessageParam:
     """Create a function call message from the assistant.
 
     Args:
@@ -188,7 +83,7 @@ def assistant_function_call(name: str, arguments: Optional[str] = None) -> Funct
     }
 
 
-def function_result(name: str, content: str) -> FunctionResultMessage:
+def function_result(name: str, content: str) -> ChatCompletionMessageParam:
     """Create a function result message.
 
     Args:
diff --git a/chatlab/models.py b/chatlab/models.py
index dd36365..1e5100a 100644
--- a/chatlab/models.py
+++ b/chatlab/models.py
@@ -2,8 +2,6 @@
 
 from enum import Enum
 
-import openai
-
 
 class ChatModel(Enum):
     """Models available for use with chatlab."""
@@ -42,15 +40,3 @@ class FunctionCompatibleModel(Enum):
 GPT_3_5_TURBO_0613 = ChatModel.GPT_3_5_TURBO_0613.value
 GPT_3_5_TURBO_16K = ChatModel.GPT_3_5_TURBO_16K.value
 GPT_3_5_TURBO_16K_0613 = ChatModel.GPT_3_5_TURBO_16K_0613.value
-
-
-def list_enabled_chat_models() -> list:
-    """Return a list of valid models for use with chatlab."""
-    all_models = openai.Model.list()
-    return [model for model in all_models if model.id in ChatModel]
-
-
-def list_enabled_function_compatible_models() -> list:
-    """Return a list of valid models for use with chatlab."""
-    all_models = openai.Model.list()
-    return [model for model in all_models if model.id in FunctionCompatibleModel]
diff --git a/chatlab/registry.py b/chatlab/registry.py
index 95fbde4..cc7063f 100644
--- a/chatlab/registry.py
+++ b/chatlab/registry.py
@@ -42,27 +42,28 @@ class WhatTime(BaseModel):
 import asyncio
 import inspect
 import json
-from enum import Enum
-from typing import (
-    Any,
-    Callable,
-    Dict,
-    Iterable,
-    List,
-    Literal,
-    Optional,
-    Type,
-    Union,
-    get_args,
-    get_origin,
-    overload,
-)
+from typing import Any, Callable, Dict, Iterable, List, Optional, Type, TypedDict, Union, get_args, get_origin, overload
 
+from openai.types.chat.completion_create_params import Function as FunctionSchema
+from openai.types.chat.completion_create_params import FunctionCall as FunctionCallOption
 from pydantic import BaseModel, create_model
 
 from .decorators import ChatlabMetadata
 
 
+class APIManifest(TypedDict, total=False):
+    """The schema for the API."""
+
+    functions: List[FunctionSchema]
+    """A list of functions that the model can call during the conversation."""
+
+    function_call: FunctionCallOption
+    """The policy for when to call functions.
+
+    One of "auto", "none", or a dictionary with a "name" key.
+    """
+
+
 class FunctionArgumentError(Exception):
     """Exception raised when a function is called with invalid arguments."""
 
@@ -109,7 +110,7 @@ class FunctionSchemaConfig:
 def generate_function_schema(
     function: Callable,
     parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None,
-):
+) -> FunctionSchema:
     """Generate a function schema for sending to OpenAI."""
     doc = function.__doc__
     func_name = function.__name__
@@ -121,11 +122,11 @@ def generate_function_schema(
     if not doc:
         raise Exception("Only functions with docstrings can be registered")
 
-    schema = {
-        "name": func_name,
-        "description": doc,
-        "parameters": {},
-    }
+    schema = FunctionSchema(
+        name=func_name,
+        description=doc,
+        parameters={},
+    )
 
     if isinstance(parameter_schema, dict):
         parameters = parameter_schema
@@ -144,9 +145,7 @@ def generate_function_schema(
             # determine type annotation
             if param.annotation == inspect.Parameter.empty:
                 # no annotation, raise instead of falling back to Any
-                raise Exception(
-                    f"`{name}` parameter of {func_name} must have a JSON-serializable type annotation"
-                )
+                raise Exception(f"`{name}` parameter of {func_name} must have a JSON-serializable type annotation")
             type_annotation = param.annotation
 
             # get the default value, otherwise set as required
@@ -160,7 +159,7 @@ def generate_function_schema(
         # function schema used by OpenAI
         model = create_model(
             function.__name__,
-            __config__=FunctionSchemaConfig,
+            __config__=FunctionSchemaConfig,  # type: ignore
             **fields,  # type: ignore
         )
         parameters: dict = model.schema()  # type: ignore
@@ -221,7 +220,7 @@ class WhatTime(BaseModel):
     """
 
     __functions: dict[str, Callable]
-    __schemas: dict[str, dict]
+    __schemas: dict[str, FunctionSchema]
 
     # Allow passing in a callable that accepts a single string for the python
     # hallucination function. This is useful for testing.
@@ -232,9 +231,7 @@ def __init__(self, python_hallucination_function: Optional[PythonHallucinationFu
 
         self.python_hallucination_function = python_hallucination_function
 
-    def decorator(
-        self, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None
-    ) -> Callable:
+    def decorator(self, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None) -> Callable:
         """Create a decorator for registering functions with a schema."""
 
         def decorator(function):
@@ -254,14 +251,14 @@ def register(
     @overload
     def register(
         self, function: Callable, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None
-    ) -> Dict:
+    ) -> FunctionSchema:
         ...
 
     def register(
         self,
         function: Optional[Callable] = None,
         parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None,
-    ) -> Union[Callable, Dict]:
+    ) -> Union[Callable, FunctionSchema]:
         """Register a function for use in `Chat`s. Can be used as a decorator or directly to register a function.
 
         >>> registry = FunctionRegistry()
@@ -290,7 +287,7 @@ def register(
 
     def register_function(
         self, function: Callable, parameter_schema: Optional[Union[Type["BaseModel"], dict]] = None
-    ) -> Dict:
+    ) -> FunctionSchema:
         """Register a single function."""
         final_schema = generate_function_schema(function, parameter_schema)
 
@@ -314,7 +311,7 @@ def get(self, function_name) -> Optional[Callable]:
 
         return self.__functions.get(function_name)
 
-    def get_schema(self, function_name) -> Optional[dict]:
+    def get_schema(self, function_name) -> Optional[FunctionSchema]:
         """Get a function schema by name."""
         return self.__schemas.get(function_name)
 
@@ -328,9 +325,9 @@ def get_chatlab_metadata(self, function_name) -> ChatlabMetadata:
         chatlab_metadata = getattr(function, "chatlab_metadata", ChatlabMetadata())
         return chatlab_metadata
 
-    def api_manifest(self, function_call_option: Union[str, dict] = "auto"):
-        """
-        Get a dictionary containing function definitions and calling options.
+    def api_manifest(self, function_call_option: FunctionCallOption = "auto") -> APIManifest:
+        """Get a dictionary containing function definitions and calling options.
+
         This is designed to be used with OpenAI's Chat Completion API, where the
         dictionary can be passed as keyword arguments to set the `functions` and
         `function_call` parameters.
@@ -415,9 +412,7 @@ async def call(self, name: str, arguments: Optional[str] = None) -> Any:
                 parameters = json.loads(arguments)
                 # TODO: Validate parameters against schema
             except json.JSONDecodeError:
-                raise FunctionArgumentError(
-                    f"Invalid Function call on {name}. Arguments must be a valid JSON object"
-                )
+                raise FunctionArgumentError(f"Invalid Function call on {name}. Arguments must be a valid JSON object")
 
         if function is None:
             raise UnknownFunctionError(f"Function {name} is not registered")
@@ -435,6 +430,6 @@ def __contains__(self, name) -> bool:
         return name in self.__functions
 
     @property
-    def function_definitions(self) -> list[dict]:
+    def function_definitions(self) -> list[FunctionSchema]:
         """Get a list of function definitions."""
         return list(self.__schemas.values())
diff --git a/chatlab/views/abstracts.py b/chatlab/views/abstracts.py
index ea09762..b3be973 100644
--- a/chatlab/views/abstracts.py
+++ b/chatlab/views/abstracts.py
@@ -4,8 +4,7 @@
 from binascii import hexlify
 
 from IPython.core import display_functions
-
-from ..messaging import Message
+from openai.types.chat import ChatCompletionMessageParam
 
 
 class AutoDisplayer(ABC):
@@ -85,11 +84,11 @@ def in_progress(self):
         return self.active and not self.is_empty()
 
     @abstractmethod
-    def get_message(self) -> Message:
+    def get_message(self) -> ChatCompletionMessageParam:
         """Returns the crafted message. To be overridden in subclasses."""
         pass
 
-    def flush(self):
+    def flush(self) -> ChatCompletionMessageParam:
         """Flushes the message buffer."""
         message = self.get_message()
         self.buffer = self.create_buffer()
diff --git a/poetry.lock b/poetry.lock
index 13dc1df..3bf4bf3 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -11,133 +11,11 @@ files = [
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-
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-
 [[package]]
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 version = "3.7.1"
 description = "High level compatibility layer for multiple asynchronous event loop implementations"
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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 [package.extras]
 test = ["astroid", "pytest"]
 
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-description = "Timeout context manager for asyncio programs"
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 version = "22.2.0"
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 ]
 
+[[package]]
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+version = "1.8.0"
+description = "Distro - an OS platform information API"
+optional = false
+python-versions = ">=3.6"
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+]
+
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 name = "docutils"
 version = "0.19"
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 pydocstyle = ">=2.1"
 
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 name = "h11"
 version = "0.14.0"
 description = "A pure-Python, bring-your-own-I/O implementation of HTTP/1.1"
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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 version = "0.17.3"
 description = "A minimal low-level HTTP client."
-optional = true
+optional = false
 python-versions = ">=3.7"
 files = [
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 name = "httpx"
 version = "0.24.1"
 description = "The next generation HTTP client."
-optional = true
+optional = false
 python-versions = ">=3.7"
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-description = "Python client library for the OpenAI API"
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+datalib = ["numpy (>=1)", "pandas (>=1.2.3)", "pandas-stubs (>=1.1.0.11)"]
 
 [[package]]
 name = "orjson"
@@ -2619,7 +2331,7 @@ files = [
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 version = "1.3.0"
 description = "Sniff out which async library your code is running under"
-optional = true
+optional = false
 python-versions = ">=3.7"
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-    {file = "yarl-1.8.2.tar.gz", hash = "sha256:49d43402c6e3013ad0978602bf6bf5328535c48d192304b91b97a3c6790b1562"},
-]
-
-[package.dependencies]
-idna = ">=2.0"
-multidict = ">=4.0"
-
 [[package]]
 name = "zipp"
 version = "3.15.0"
@@ -3088,4 +2713,4 @@ test = []
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9.0,<3.12"
-content-hash = "d93487b5fd5cd5336df48ca90f54dc0e3b089481daf47f4bf4a699710141232a"
+content-hash = "c045ecec8c6ed15e289c46913bffe04bbc964d49c43ac0cd63b27040a7359946"
diff --git a/pyproject.toml b/pyproject.toml
index 8c69575..0d9dbca 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -35,7 +35,7 @@ packages = [
 [tool.poetry.dependencies]
 python = ">=3.9.0,<3.12"
 ipython = ">=7.0.0,<9.0.0"
-openai = "^0.27.4"
+openai = {version = "^1.0.0b2", allow-prereleases = true}
 pydantic = ">=1.9.0,<2.0.0"
 vdom = "^1.0.0"
 deprecation = "^2.1.0"
diff --git a/tests/test_registry.py b/tests/test_registry.py
index 9c160f8..2711edf 100644
--- a/tests/test_registry.py
+++ b/tests/test_registry.py
@@ -1,17 +1,13 @@
 # flake8: noqa
 import uuid
+from typing import Optional
 from unittest import mock
 from unittest.mock import MagicMock, patch
 
 import pytest
 from pydantic import BaseModel, Field
 
-from chatlab.registry import (
-    FunctionArgumentError,
-    FunctionRegistry,
-    UnknownFunctionError,
-    generate_function_schema,
-)
+from chatlab.registry import FunctionArgumentError, FunctionRegistry, UnknownFunctionError, generate_function_schema
 
 
 # Define a function to use in testing
@@ -36,7 +32,7 @@ def simple_func_with_model_arg(
     x: int,
     y: str,
     z: bool = False,
-    model: SimpleModel = None,
+    model: Optional[SimpleModel] = None,
 ) -> str:
     """A simple test function with a model argument"""
     return f"{x}, {y}, {z}, {model}"
@@ -53,8 +49,8 @@ def simple_func_with_model_args(
     x: int,
     y: str,
     z: bool = False,
-    model: SimpleModel = None,
-    nested_model: NestedModel = None,
+    model: Optional[SimpleModel] = None,
+    nested_model: Optional[NestedModel] = None,
 ) -> str:
     """A simple test function with model arguments"""
     return f"{x}, {y}, {z}, {model}, {nested_model}"
@@ -64,7 +60,7 @@ def simple_func_with_uuid_arg(
     x: int,
     y: str,
     z: bool = False,
-    uuid: uuid.UUID = None,
+    uuid: Optional[uuid.UUID] = None,
 ) -> str:
     """A simple test function with a uuid argument"""
     return f"{x}, {y}, {z}, {uuid}"
@@ -356,8 +352,8 @@ def func_with_optional_args(x: int, y: str, z: bool = False):
         return f"{x}, {y}, {z}"
 
     schema = generate_function_schema(func_with_optional_args)
-    assert "z" in schema["parameters"]["properties"]
-    assert "z" not in schema["parameters"]["required"]
+    assert "z" in schema["parameters"]["properties"]  # type: ignore
+    assert "z" not in schema["parameters"]["required"]  # type: ignore
 
 
 # Test the generate_function_schema for function with no arguments