# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ========= Copyright 2023-2026 @ CAMEL-AI.org. All Rights Reserved. =========
import copy
import json
import os
import warnings
from typing import (
    Any,
    AsyncGenerator,
    Dict,
    Generator,
    List,
    Literal,
    Optional,
    Type,
    Union,
    cast,
)

from openai import AsyncOpenAI, AsyncStream, OpenAI, Stream
from openai.lib.streaming.chat import (
    AsyncChatCompletionStreamManager,
    ChatCompletionStreamManager,
)
from pydantic import BaseModel

from camel.configs import ChatGPTConfig
from camel.logger import get_logger
from camel.messages import OpenAIMessage
from camel.models import BaseModelBackend
from camel.types import (
    ChatCompletion,
    ChatCompletionChunk,
    ModelType,
)
from camel.utils import (
    BaseTokenCounter,
    OpenAITokenCounter,
    api_keys_required,
    get_current_agent_session_id,
    is_langfuse_available,
)

logger = get_logger(__name__)

if os.environ.get("LANGFUSE_ENABLED", "False").lower() == "true":
    try:
        from langfuse.decorators import observe
    except ImportError:
        from camel.utils import observe
elif os.environ.get("TRACEROOT_ENABLED", "False").lower() == "true":
    try:
        from traceroot import trace as observe  # type: ignore[import]
    except ImportError:
        from camel.utils import observe
else:
    from camel.utils import observe


UNSUPPORTED_PARAMS = {
    "temperature",
    "top_p",
    "presence_penalty",
    "frequency_penalty",
    "logprobs",
    "top_logprobs",
    "logit_bias",
}


class OpenAIModel(BaseModelBackend):
    r"""OpenAI API in a unified BaseModelBackend interface.

    Args:
        model_type (Union[ModelType, str]): Model for which a backend is
            created, one of GPT_* series.
        model_config_dict (Optional[Dict[str, Any]], optional): A dictionary
            that will be fed into:obj:`openai.ChatCompletion.create()`. If
            :obj:`None`, :obj:`ChatGPTConfig().as_dict()` will be used.
            (default: :obj:`None`)
        api_key (Optional[str], optional): The API key for authenticating
            with the OpenAI service. (default: :obj:`None`)
        url (Optional[str], optional): The url to the OpenAI service.
            (default: :obj:`None`)
        token_counter (Optional[BaseTokenCounter], optional): Token counter to
            use for the model. If not provided, :obj:`OpenAITokenCounter` will
            be used. (default: :obj:`None`)
        timeout (Optional[float], optional): The timeout value in seconds for
            API calls. If not provided, will fall back to the MODEL_TIMEOUT
            environment variable or default to 180 seconds.
            (default: :obj:`None`)
        max_retries (int, optional): Maximum number of retries for API calls.
            (default: :obj:`3`)
        client (Optional[Any], optional): A custom synchronous OpenAI client
            instance. If provided, this client will be used instead of
            creating a new one. Useful for RL frameworks like AReaL or rLLM
            that provide OpenAI-compatible clients. The client should
            implement the OpenAI client interface with
            `.chat.completions.create()` and `.beta.chat.completions.parse()`
            methods. (default: :obj:`None`)
        async_client (Optional[Any], optional): A custom asynchronous OpenAI
            client instance. If provided, this client will be used instead of
            creating a new one. The client should implement the AsyncOpenAI
            client interface. (default: :obj:`None`)
        api_mode (Literal["chat_completions", "responses"], optional):
            OpenAI API mode to use. Supported values:
            `"chat_completions"` (default) and `"responses"`.
        **kwargs (Any): Additional arguments to pass to the
            OpenAI client initialization. These can include parameters like
            'organization', 'default_headers', 'http_client', etc.
            Ignored if custom clients are provided.
    """

    @api_keys_required(
        [
            ("api_key", "OPENAI_API_KEY"),
        ]
    )
    def __init__(
        self,
        model_type: Union[ModelType, str],
        model_config_dict: Optional[Dict[str, Any]] = None,
        api_key: Optional[str] = None,
        url: Optional[str] = None,
        token_counter: Optional[BaseTokenCounter] = None,
        timeout: Optional[float] = None,
        max_retries: int = 3,
        client: Optional[Any] = None,
        async_client: Optional[Any] = None,
        api_mode: Literal["chat_completions", "responses"] = (
            "chat_completions"
        ),
        **kwargs: Any,
    ) -> None:
        if model_config_dict is None:
            model_config_dict = ChatGPTConfig().as_dict()
        api_key = api_key or os.environ.get("OPENAI_API_KEY")
        url = url or os.environ.get("OPENAI_API_BASE_URL")
        timeout = timeout or float(os.environ.get("MODEL_TIMEOUT", 180))
        if api_mode not in {"chat_completions", "responses"}:
            raise ValueError(
                "api_mode must be 'chat_completions' or 'responses', "
                f"got: {api_mode}"
            )
        self._api_mode = api_mode
        self._responses_previous_response_id_by_session: Dict[str, str] = {}
        self._responses_last_message_count_by_session: Dict[str, int] = {}

        # Store additional client args for later use
        self._max_retries = max_retries

        super().__init__(
            model_type, model_config_dict, api_key, url, token_counter, timeout
        )

        # Use custom clients if provided, otherwise create new ones
        if client is not None:
            # Use the provided custom sync client
            self._client = client
        else:
            # Create default sync client
            if is_langfuse_available():
                from langfuse.openai import OpenAI as LangfuseOpenAI

                self._client = LangfuseOpenAI(
                    timeout=self._timeout,
                    max_retries=self._max_retries,
                    base_url=self._url,
                    api_key=self._api_key,
                    **kwargs,
                )
            else:
                self._client = OpenAI(
                    timeout=self._timeout,
                    max_retries=self._max_retries,
                    base_url=self._url,
                    api_key=self._api_key,
                    **kwargs,
                )

        if async_client is not None:
            # Use the provided custom async client
            self._async_client = async_client
        else:
            # Create default async client
            if is_langfuse_available():
                from langfuse.openai import AsyncOpenAI as LangfuseAsyncOpenAI

                self._async_client = LangfuseAsyncOpenAI(
                    timeout=self._timeout,
                    max_retries=self._max_retries,
                    base_url=self._url,
                    api_key=self._api_key,
                    **kwargs,
                )
            else:
                self._async_client = AsyncOpenAI(
                    timeout=self._timeout,
                    max_retries=self._max_retries,
                    base_url=self._url,
                    api_key=self._api_key,
                    **kwargs,
                )

    def _sanitize_config(self, config_dict: Dict[str, Any]) -> Dict[str, Any]:
        r"""Sanitize the model configuration for O1 models."""

        if self.model_type in [
            ModelType.O1,
            ModelType.O1_MINI,
            ModelType.O1_PREVIEW,
            ModelType.O3_MINI,
            ModelType.O3,
            ModelType.O4_MINI,
            ModelType.O3_PRO,
        ]:
            warnings.warn(
                "Warning: You are using an reasoning model (O series), "
                "which has certain limitations, reference: "
                "`https://platform.openai.com/docs/guides/reasoning`.",
                UserWarning,
            )
            return {
                k: v
                for k, v in config_dict.items()
                if k not in UNSUPPORTED_PARAMS
            }
        return config_dict

    def _adapt_messages_for_o1_models(
        self, messages: List[OpenAIMessage]
    ) -> List[OpenAIMessage]:
        r"""Adjust message roles to comply with O1 model requirements by
        converting 'system' or 'developer' to 'user' role.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.

        Returns:
            processed_messages (List[OpenAIMessage]): Return a new list of
                messages to avoid mutating input.
        """

        # Define supported O1 model types as a class constant would be better
        O1_MODEL_TYPES = {ModelType.O1_MINI, ModelType.O1_PREVIEW}

        if self.model_type not in O1_MODEL_TYPES:
            return messages.copy()

        # Issue warning only once using class state
        if not hasattr(self, "_o1_warning_issued"):
            warnings.warn(
                "O1 models (O1_MINI/O1_PREVIEW) have role limitations: "
                "System or Developer messages will be converted to user role."
                "Reference: https://community.openai.com/t/"
                "developer-role-not-accepted-for-o1-o1-mini-o3-mini/1110750/7",
                UserWarning,
                stacklevel=2,
            )
            self._o1_warning_issued = True

        # Create new message list to avoid mutating input
        processed_messages = []
        for message in messages:
            processed_message = message.copy()
            if (
                processed_message["role"] == "system"
                or processed_message["role"] == "developer"
            ):
                processed_message["role"] = "user"  # type: ignore[arg-type]
            processed_messages.append(processed_message)

        return processed_messages

    @property
    def token_counter(self) -> BaseTokenCounter:
        r"""Initialize the token counter for the model backend.

        Returns:
            BaseTokenCounter: The token counter following the model's
                tokenization style.
        """
        if not self._token_counter:
            self._token_counter = OpenAITokenCounter(self.model_type)
        return self._token_counter

    @observe()
    def _run(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]] = None,
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[
        ChatCompletion,
        Stream[ChatCompletionChunk],
        ChatCompletionStreamManager[BaseModel],
    ]:
        r"""Runs inference of OpenAI chat completion.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.
            response_format (Optional[Type[BaseModel]]): The format of the
                response.
            tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
                use for the request.

        Returns:
            Union[ChatCompletion, Stream[ChatCompletionChunk],
                ChatCompletionStreamManager[BaseModel]]:
                `ChatCompletion` in the non-stream mode,
                `Stream[ChatCompletionChunk]`in the stream mode,
                or `ChatCompletionStreamManager[BaseModel]` for
                structured output streaming.
        """
        self._log_and_trace()

        messages = self._adapt_messages_for_o1_models(messages)
        response_format = response_format or self.model_config_dict.get(
            "response_format", None
        )

        # Check if streaming is enabled
        is_streaming = self.model_config_dict.get("stream", False)

        if response_format:
            if is_streaming:
                if self._api_mode == "responses":
                    return cast(
                        Stream[ChatCompletionChunk],
                        self._request_responses_stream(
                            messages, response_format, tools
                        ),
                    )
                return self._request_stream_parse(
                    messages, response_format, tools
                )
            else:
                if self._api_mode == "responses":
                    return self._request_responses(
                        messages, response_format, tools
                    )
                return self._request_parse(messages, response_format, tools)
        else:
            result: Union[ChatCompletion, Stream[ChatCompletionChunk]]
            if self._api_mode == "responses":
                if is_streaming:
                    result = cast(
                        Stream[ChatCompletionChunk],
                        self._request_responses_stream(messages, None, tools),
                    )
                else:
                    result = self._request_responses(messages, None, tools)
            else:
                result = self._request_chat_completion(messages, tools)

        return result

    @observe()
    async def _arun(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]] = None,
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[
        ChatCompletion,
        AsyncStream[ChatCompletionChunk],
        AsyncChatCompletionStreamManager[BaseModel],
    ]:
        r"""Runs inference of OpenAI chat completion in async mode.

        Args:
            messages (List[OpenAIMessage]): Message list with the chat history
                in OpenAI API format.
            response_format (Optional[Type[BaseModel]]): The format of the
                response.
            tools (Optional[List[Dict[str, Any]]]): The schema of the tools to
                use for the request.

        Returns:
            Union[ChatCompletion, AsyncStream[ChatCompletionChunk],
                  AsyncChatCompletionStreamManager[BaseModel]]:
                `ChatCompletion` in the non-stream mode,
                `AsyncStream[ChatCompletionChunk]` in the stream mode, or
                `AsyncChatCompletionStreamManager[BaseModel]` for
                structured output streaming.
        """
        self._log_and_trace()

        messages = self._adapt_messages_for_o1_models(messages)
        response_format = response_format or self.model_config_dict.get(
            "response_format", None
        )

        # Check if streaming is enabled
        is_streaming = self.model_config_dict.get("stream", False)

        if response_format:
            if is_streaming:
                if self._api_mode == "responses":
                    return cast(
                        AsyncStream[ChatCompletionChunk],
                        await self._arequest_responses_stream(
                            messages, response_format, tools
                        ),
                    )
                return await self._arequest_stream_parse(
                    messages, response_format, tools
                )
            else:
                if self._api_mode == "responses":
                    return await self._arequest_responses(
                        messages, response_format, tools
                    )
                return await self._arequest_parse(
                    messages, response_format, tools
                )
        else:
            result: Union[
                ChatCompletion,
                AsyncStream[ChatCompletionChunk],
            ]
            if self._api_mode == "responses":
                if is_streaming:
                    result = cast(
                        AsyncStream[ChatCompletionChunk],
                        await self._arequest_responses_stream(
                            messages, None, tools
                        ),
                    )
                else:
                    result = await self._arequest_responses(
                        messages, None, tools
                    )
            else:
                result = await self._arequest_chat_completion(messages, tools)

        return result

    def _prepare_responses_request_config(
        self,
        tools: Optional[List[Dict[str, Any]]] = None,
        response_format: Optional[Type[BaseModel]] = None,
        stream: bool = False,
    ) -> Dict[str, Any]:
        request_config = self._prepare_request_config(tools)
        request_config = self._sanitize_config(request_config)

        # Translate chat-completions style parameters to responses style.
        max_tokens = request_config.pop("max_tokens", None)
        if (
            max_tokens is not None
            and "max_output_tokens" not in request_config
        ):
            request_config["max_output_tokens"] = max_tokens

        # `n` is unsupported in responses. Keep backward compatibility.
        if request_config.get("n") not in (None, 1):
            warnings.warn(
                "OpenAI Responses API does not support `n`; "
                "ignoring configured value.",
                UserWarning,
            )
        request_config.pop("n", None)

        request_config.pop("response_format", None)
        request_config.pop("stream_options", None)
        request_config["stream"] = stream
        # previous_response_id chaining requires stored responses.
        # Only force store=True when not explicitly set by the user.
        if "store" not in request_config:
            request_config["store"] = True
        elif request_config.get("store") is False:
            warnings.warn(
                "Setting `store=False` will disable "
                "`previous_response_id` chaining.",
                UserWarning,
            )

        if request_config.get("tools"):
            request_config["tools"] = self._normalize_tools_for_responses_api(
                request_config["tools"]
            )

        if response_format is not None:
            schema = copy.deepcopy(response_format.model_json_schema())
            self._enforce_object_additional_properties_false(schema)
            request_config["text"] = {
                "format": {
                    "type": "json_schema",
                    "name": response_format.__name__,
                    "schema": schema,
                }
            }

        return request_config

    @staticmethod
    def _normalize_tools_for_responses_api(
        tools: List[Dict[str, Any]],
    ) -> List[Dict[str, Any]]:
        r"""Convert chat-completions style function tools to Responses style.

        Input:
            {"type":"function","function":{"name":"foo",...}}
        Output:
            {"type":"function","name":"foo",...}
        """
        normalized_tools: List[Dict[str, Any]] = []
        for tool in tools:
            if (
                isinstance(tool, dict)
                and tool.get("type") == "function"
                and isinstance(tool.get("function"), dict)
            ):
                normalized_tools.append(
                    {"type": "function", **tool["function"]}
                )
            else:
                normalized_tools.append(tool)
        return normalized_tools

    @staticmethod
    def _enforce_object_additional_properties_false(schema: Any) -> None:
        r"""Recursively enforce strict object schema for Responses API."""
        if isinstance(schema, dict):
            if (
                schema.get("type") == "object"
                and "additionalProperties" not in schema
            ):
                schema["additionalProperties"] = False

            for value in schema.values():
                OpenAIModel._enforce_object_additional_properties_false(value)
        elif isinstance(schema, list):
            for item in schema:
                OpenAIModel._enforce_object_additional_properties_false(item)

    def _get_response_chain_session_key(self) -> str:
        return get_current_agent_session_id() or "__default__"

    def _clear_response_chain_state(self, session_key: str) -> None:
        self._responses_previous_response_id_by_session.pop(session_key, None)
        self._responses_last_message_count_by_session.pop(session_key, None)

    @staticmethod
    def _responses_chain_enabled(request_config: Dict[str, Any]) -> bool:
        return request_config.get("store") is not False

    def _prepare_responses_input_and_chain(
        self,
        messages: List[OpenAIMessage],
        chain_enabled: bool = True,
    ) -> Dict[str, Any]:
        session_key = self._get_response_chain_session_key()
        if chain_enabled:
            previous_response_id = (
                self._responses_previous_response_id_by_session.get(
                    session_key
                )
            )
            last_message_count = (
                self._responses_last_message_count_by_session.get(
                    session_key, 0
                )
            )
        else:
            self._clear_response_chain_state(session_key)
            previous_response_id = None
            last_message_count = 0

        # If memory was reset/truncated, reset chain and send full context.
        if len(messages) < last_message_count:
            previous_response_id = None
            last_message_count = 0
            self._clear_response_chain_state(session_key)

        if previous_response_id and last_message_count > 0:
            delta_messages = messages[last_message_count:]
            input_messages = (
                delta_messages if delta_messages else [messages[-1]]
            )
        else:
            input_messages = messages

        input_items = self._convert_messages_to_responses_input(input_messages)

        return {
            "session_key": session_key,
            "previous_response_id": previous_response_id,
            "input_messages": input_items,
            "message_count": len(messages),
        }

    @staticmethod
    def _convert_messages_to_responses_input(
        messages: List[OpenAIMessage],
    ) -> List[Dict[str, Any]]:
        r"""Convert chat-completions style messages to Responses input items.

        This specifically rewrites tool-calling history:
        - assistant message with `tool_calls` -> one or more `function_call`
          items (+ optional assistant message when content exists)
        - tool message -> `function_call_output` item
        """
        input_items: List[Dict[str, Any]] = []
        for message in messages:
            role = message.get("role")
            content = message.get("content", "")

            if role == "tool":
                input_items.append(
                    {
                        "type": "function_call_output",
                        "call_id": message.get("tool_call_id", "null"),
                        "output": content
                        if isinstance(content, str)
                        else str(content),
                    }
                )
                continue

            if role == "assistant" and message.get("tool_calls"):
                if content not in (None, "", []):
                    input_items.append(
                        {
                            "role": "assistant",
                            "content": content,
                        }
                    )

                tool_calls = message.get("tool_calls", [])
                if not isinstance(tool_calls, list):
                    tool_calls = [tool_calls]
                for tool_call in tool_calls:
                    function_data = (
                        tool_call.get("function", {})
                        if isinstance(tool_call, dict)
                        else {}
                    )
                    if not isinstance(function_data, dict):
                        continue
                    arguments = function_data.get("arguments", "{}")
                    if not isinstance(arguments, str):
                        arguments = json.dumps(arguments, ensure_ascii=False)
                    input_items.append(
                        {
                            "type": "function_call",
                            "call_id": (
                                tool_call.get("id", "null")
                                if isinstance(tool_call, dict)
                                else "null"
                            ),
                            "name": function_data.get("name", ""),
                            "arguments": arguments,
                        }
                    )
                continue

            input_items.append(
                {
                    "role": role,
                    "content": content,
                }
            )

        return input_items

    def _save_response_chain_state(
        self,
        session_key: str,
        response_id: Optional[str],
        message_count: int,
    ) -> None:
        if response_id:
            self._responses_previous_response_id_by_session[session_key] = (
                response_id
            )
        self._responses_last_message_count_by_session[session_key] = (
            message_count
        )

    def _request_responses(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        from camel.models.openai_responses_adapter import (
            response_to_chat_completion,
        )

        request_config = self._prepare_responses_request_config(
            tools=tools, response_format=response_format, stream=False
        )
        chain_enabled = self._responses_chain_enabled(request_config)
        chain_state = self._prepare_responses_input_and_chain(
            messages, chain_enabled=chain_enabled
        )
        if chain_enabled and chain_state["previous_response_id"]:
            request_config["previous_response_id"] = chain_state[
                "previous_response_id"
            ]
        response = self._client.responses.create(
            input=chain_state["input_messages"],
            model=self.model_type,
            **request_config,
        )
        if chain_enabled:
            self._save_response_chain_state(
                session_key=chain_state["session_key"],
                response_id=getattr(response, "id", None)
                if not isinstance(response, dict)
                else response.get("id"),
                message_count=chain_state["message_count"],
            )
        return response_to_chat_completion(
            response=response,
            model=str(self.model_type),
            response_format=response_format,
        )

    async def _arequest_responses(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        from camel.models.openai_responses_adapter import (
            response_to_chat_completion,
        )

        request_config = self._prepare_responses_request_config(
            tools=tools, response_format=response_format, stream=False
        )
        chain_enabled = self._responses_chain_enabled(request_config)
        chain_state = self._prepare_responses_input_and_chain(
            messages, chain_enabled=chain_enabled
        )
        if chain_enabled and chain_state["previous_response_id"]:
            request_config["previous_response_id"] = chain_state[
                "previous_response_id"
            ]
        response = await self._async_client.responses.create(
            input=chain_state["input_messages"],
            model=self.model_type,
            **request_config,
        )
        if chain_enabled:
            self._save_response_chain_state(
                session_key=chain_state["session_key"],
                response_id=getattr(response, "id", None)
                if not isinstance(response, dict)
                else response.get("id"),
                message_count=chain_state["message_count"],
            )
        return response_to_chat_completion(
            response=response,
            model=str(self.model_type),
            response_format=response_format,
        )

    def _request_responses_stream(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Generator[ChatCompletionChunk, None, None]:
        from camel.models.openai_responses_adapter import (
            iter_response_events_to_chat_chunks,
        )

        request_config = self._prepare_responses_request_config(
            tools=tools, response_format=response_format, stream=True
        )
        chain_enabled = self._responses_chain_enabled(request_config)
        chain_state = self._prepare_responses_input_and_chain(
            messages, chain_enabled=chain_enabled
        )
        if chain_enabled and chain_state["previous_response_id"]:
            request_config["previous_response_id"] = chain_state[
                "previous_response_id"
            ]
        event_stream = self._client.responses.create(
            input=chain_state["input_messages"],
            model=self.model_type,
            **request_config,
        )

        def _on_response_completed(response_id: str) -> None:
            if chain_enabled:
                self._save_response_chain_state(
                    session_key=chain_state["session_key"],
                    response_id=response_id,
                    message_count=chain_state["message_count"],
                )

        return iter_response_events_to_chat_chunks(
            event_stream=event_stream,
            model=str(self.model_type),
            on_response_completed=_on_response_completed,
        )

    async def _arequest_responses_stream(
        self,
        messages: List[OpenAIMessage],
        response_format: Optional[Type[BaseModel]],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> AsyncGenerator[ChatCompletionChunk, None]:
        from camel.models.openai_responses_adapter import (
            aiter_response_events_to_chat_chunks,
        )

        request_config = self._prepare_responses_request_config(
            tools=tools, response_format=response_format, stream=True
        )
        chain_enabled = self._responses_chain_enabled(request_config)
        chain_state = self._prepare_responses_input_and_chain(
            messages, chain_enabled=chain_enabled
        )
        if chain_enabled and chain_state["previous_response_id"]:
            request_config["previous_response_id"] = chain_state[
                "previous_response_id"
            ]
        event_stream = await self._async_client.responses.create(
            input=chain_state["input_messages"],
            model=self.model_type,
            **request_config,
        )

        def _on_response_completed(response_id: str) -> None:
            if chain_enabled:
                self._save_response_chain_state(
                    session_key=chain_state["session_key"],
                    response_id=response_id,
                    message_count=chain_state["message_count"],
                )

        return aiter_response_events_to_chat_chunks(
            event_stream=event_stream,
            model=str(self.model_type),
            on_response_completed=_on_response_completed,
        )

    def _request_chat_completion(
        self,
        messages: List[OpenAIMessage],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[ChatCompletion, Stream[ChatCompletionChunk]]:
        request_config = self._prepare_request_config(tools)
        request_config = self._sanitize_config(request_config)

        return self._call_client(
            self._client.chat.completions.create,
            messages=messages,
            model=self.model_type,
            **request_config,
        )

    async def _arequest_chat_completion(
        self,
        messages: List[OpenAIMessage],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> Union[ChatCompletion, AsyncStream[ChatCompletionChunk]]:
        request_config = self._prepare_request_config(tools)
        request_config = self._sanitize_config(request_config)

        return await self._acall_client(
            self._async_client.chat.completions.create,
            messages=messages,
            model=self.model_type,
            **request_config,
        )

    def _request_parse(
        self,
        messages: List[OpenAIMessage],
        response_format: Type[BaseModel],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        request_config = self._prepare_request_config(tools)
        request_config["response_format"] = response_format
        # Remove stream from request config since OpenAI does not support it
        # with structured response
        request_config.pop("stream", None)
        request_config = self._sanitize_config(request_config)

        return self._call_client(
            self._client.beta.chat.completions.parse,
            messages=messages,
            model=self.model_type,
            **request_config,
        )

    async def _arequest_parse(
        self,
        messages: List[OpenAIMessage],
        response_format: Type[BaseModel],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletion:
        request_config = self._prepare_request_config(tools)
        request_config["response_format"] = response_format
        # Remove stream from request config since OpenAI does not support it
        # with structured response
        request_config.pop("stream", None)
        request_config = self._sanitize_config(request_config)

        return await self._acall_client(
            self._async_client.beta.chat.completions.parse,
            messages=messages,
            model=self.model_type,
            **request_config,
        )

    def _request_stream_parse(
        self,
        messages: List[OpenAIMessage],
        response_format: Type[BaseModel],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> ChatCompletionStreamManager[BaseModel]:
        r"""Request streaming structured output parsing.

        Note: This uses OpenAI's beta streaming API for structured outputs.
        """
        request_config = self._prepare_request_config(tools)
        # Remove stream from config as it's handled by the stream method
        request_config.pop("stream", None)
        request_config = self._sanitize_config(request_config)

        # Use the beta streaming API for structured outputs
        return self._call_client(
            self._client.beta.chat.completions.stream,
            messages=messages,
            model=self.model_type,
            response_format=response_format,
            **request_config,
        )

    async def _arequest_stream_parse(
        self,
        messages: List[OpenAIMessage],
        response_format: Type[BaseModel],
        tools: Optional[List[Dict[str, Any]]] = None,
    ) -> AsyncChatCompletionStreamManager[BaseModel]:
        r"""Request async streaming structured output parsing.

        Note: This uses OpenAI's beta streaming API for structured outputs.
        """
        request_config = self._prepare_request_config(tools)
        # Remove stream from config as it's handled by the stream method
        request_config.pop("stream", None)

        request_config = self._sanitize_config(request_config)

        # Use the beta streaming API for structured outputs
        return self._call_client(
            self._async_client.beta.chat.completions.stream,
            messages=messages,
            model=self.model_type,
            response_format=response_format,
            **request_config,
        )

    @property
    def stream(self) -> bool:
        r"""Returns whether the model is in stream mode, which sends partial
        results each time.

        Returns:
            bool: Whether the model is in stream mode.
        """
        return self.model_config_dict.get('stream', False)
