"""ZhipuAI chat models wrapper."""

from __future__ import annotations

import json
import logging
import time
from collections.abc import AsyncIterator, Iterator
from contextlib import asynccontextmanager, contextmanager
from operator import itemgetter
from typing import (
    Any,
    Callable,
    Dict,
    List,
    Literal,
    Optional,
    Sequence,
    Tuple,
    Type,
    Union,
)

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
    BaseChatModel,
    agenerate_from_stream,
    generate_from_stream,
)
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    BaseMessageChunk,
    ChatMessage,
    ChatMessageChunk,
    HumanMessage,
    HumanMessageChunk,
    SystemMessage,
    SystemMessageChunk,
    ToolMessage,
)
from langchain_core.output_parsers.base import OutputParserLike
from langchain_core.output_parsers.openai_tools import (
    JsonOutputKeyToolsParser,
    PydanticToolsParser,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import get_from_dict_or_env
from langchain_core.utils.function_calling import convert_to_openai_tool
from pydantic import BaseModel, ConfigDict, Field, model_validator

logger = logging.getLogger(__name__)

API_TOKEN_TTL_SECONDS = 3 * 60
ZHIPUAI_API_BASE = "https://open.bigmodel.cn/api/paas/v4/chat/completions"


def _is_pydantic_class(obj: Any) -> bool:
    return isinstance(obj, type) and issubclass(obj, BaseModel)


@contextmanager
def connect_sse(client: Any, method: str, url: str, **kwargs: Any) -> Iterator:
    """Context manager for connecting to an SSE stream.

    Args:
        client: The HTTP client.
        method: The HTTP method.
        url: The URL.
        kwargs: Additional keyword arguments.

    Yields:
        The event source.
    """
    from httpx_sse import EventSource

    with client.stream(method, url, **kwargs) as response:
        yield EventSource(response)


@asynccontextmanager
async def aconnect_sse(
    client: Any, method: str, url: str, **kwargs: Any
) -> AsyncIterator:
    """Async context manager for connecting to an SSE stream.

    Args:
        client: The HTTP client.
        method: The HTTP method.
        url: The URL.
        kwargs: Additional keyword arguments.

    Yields:
        The event source.
    """
    from httpx_sse import EventSource

    async with client.stream(method, url, **kwargs) as response:
        yield EventSource(response)


def _get_jwt_token(api_key: str) -> str:
    """Gets JWT token for ZhipuAI API.

    See 'https://open.bigmodel.cn/dev/api#nosdk'.

    Args:
        api_key: The API key for ZhipuAI API.

    Returns:
        The JWT token.
    """
    try:
        import jwt
    except ImportError:
        raise ImportError(
            "jwt package not found, please install it with`pip install pyjwt`"
        )

    try:
        id, secret = api_key.split(".")
    except ValueError as err:
        raise ValueError(f"Invalid API key: {api_key}") from err

    payload = {
        "api_key": id,
        "exp": int(round(time.time() * 1000)) + API_TOKEN_TTL_SECONDS * 1000,
        "timestamp": int(round(time.time() * 1000)),
    }

    return jwt.encode(
        payload,
        secret,
        algorithm="HS256",
        headers={"alg": "HS256", "sign_type": "SIGN"},
    )


def _convert_dict_to_message(dct: Dict[str, Any]) -> BaseMessage:
    role = dct.get("role")
    content = dct.get("content", "")
    if role == "system":
        return SystemMessage(content=content)
    if role == "user":
        return HumanMessage(content=content)
    if role == "assistant":
        additional_kwargs = {}
        tool_calls = dct.get("tool_calls", None)
        if tool_calls is not None:
            additional_kwargs["tool_calls"] = tool_calls
        return AIMessage(content=content, additional_kwargs=additional_kwargs)
    if role == "tool":
        additional_kwargs = {}
        if "name" in dct:
            additional_kwargs["name"] = dct["name"]
        return ToolMessage(
            content=content,
            tool_call_id=dct.get("tool_call_id"),
            additional_kwargs=additional_kwargs,
        )
    return ChatMessage(role=role, content=content)  # type: ignore[arg-type]


def _convert_message_to_dict(message: BaseMessage) -> Dict[str, Any]:
    """Convert a LangChain message to a dictionary.

    Args:
        message: The LangChain message.

    Returns:
        The dictionary.
    """
    message_dict: Dict[str, Any]
    if isinstance(message, ChatMessage):
        message_dict = {"role": message.role, "content": message.content}
    elif isinstance(message, SystemMessage):
        message_dict = {"role": "system", "content": message.content}
    elif isinstance(message, HumanMessage):
        message_dict = {"role": "user", "content": message.content}
    elif isinstance(message, AIMessage):
        message_dict = {"role": "assistant", "content": message.content}
    elif isinstance(message, ToolMessage):
        message_dict = {
            "role": "tool",
            "content": message.content,
            "tool_call_id": message.tool_call_id,
            "name": message.name or message.additional_kwargs.get("name"),
        }
    else:
        raise TypeError(f"Got unknown type '{message.__class__.__name__}'.")
    return message_dict


def _convert_delta_to_message_chunk(
    dct: Dict[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
    role = dct.get("role")
    content = dct.get("content", "")
    additional_kwargs = {}
    tool_calls = dct.get("tool_calls", None)
    if tool_calls is not None:
        additional_kwargs["tool_calls"] = tool_calls

    if role == "system" or default_class == SystemMessageChunk:
        return SystemMessageChunk(content=content)
    if role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=content)
    if role == "assistant" or default_class == AIMessageChunk:
        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
    if role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=content, role=role)  # type: ignore[arg-type]
    return default_class(content=content)  # type: ignore[call-arg]


def _truncate_params(payload: Dict[str, Any]) -> None:
    """Truncate temperature and top_p parameters between [0.01, 0.99].

    ZhipuAI only support temperature / top_p between (0, 1) open interval,
    so we truncate them to [0.01, 0.99].
    """
    temperature = payload.get("temperature")
    top_p = payload.get("top_p")
    if temperature is not None:
        payload["temperature"] = max(0.01, min(0.99, temperature))
    if top_p is not None:
        payload["top_p"] = max(0.01, min(0.99, top_p))


class ChatZhipuAI(BaseChatModel):
    """ZhipuAI chat model integration.

    Setup:
        Install ``PyJWT`` and set environment variable ``ZHIPUAI_API_KEY``

        .. code-block:: bash

            pip install pyjwt
            export ZHIPUAI_API_KEY="your-api-key"

    Key init args — completion params:
        model: Optional[str]
            Name of ZhipuAI model to use.
        temperature: float
            Sampling temperature.
        max_tokens: Optional[int]
            Max number of tokens to generate.

    Key init args — client params:
        api_key: Optional[str]
            ZhipuAI API key. If not passed in will be read from env var ZHIPUAI_API_KEY.
        api_base: Optional[str]
            Base URL for API requests.

    See full list of supported init args and their descriptions in the params section.

    Instantiate:
        .. code-block:: python

            from langchain_community.chat_models import ChatZhipuAI

            zhipuai_chat = ChatZhipuAI(
                temperature=0.5,
                api_key="your-api-key",
                model="glm-4",
                # api_base="...",
                # other params...
            )

    Invoke:
        .. code-block:: python

            messages = [
                ("system", "你是一名专业的翻译家，可以将用户的中文翻译为英文。"),
                ("human", "我喜欢编程。"),
            ]
            zhipuai_chat.invoke(messages)

        .. code-block:: python

            AIMessage(content='I enjoy programming.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 23, 'total_tokens': 29}, 'model_name': 'glm-4', 'finish_reason': 'stop'}, id='run-c5d9af91-55c6-470e-9545-02b2fa0d7f9d-0')

    Stream:
        .. code-block:: python

            for chunk in zhipuai_chat.stream(messages):
                print(chunk)

        .. code-block:: python

            content='I' id='run-4df71729-618f-4e2b-a4ff-884682723082'
            content=' enjoy' id='run-4df71729-618f-4e2b-a4ff-884682723082'
            content=' programming' id='run-4df71729-618f-4e2b-a4ff-884682723082'
            content='.' id='run-4df71729-618f-4e2b-a4ff-884682723082'
            content='' response_metadata={'finish_reason': 'stop'} id='run-4df71729-618f-4e2b-a4ff-884682723082'

        .. code-block:: python

            stream = zhipuai_chat.stream(messages)
            full = next(stream)
            for chunk in stream:
                full += chunk
            full

        .. code-block::

            AIMessageChunk(content='I enjoy programming.', response_metadata={'finish_reason': 'stop'}, id='run-20b05040-a0b4-4715-8fdc-b39dba9bfb53')

    Async:
        .. code-block:: python

            await zhipuai_chat.ainvoke(messages)

            # stream:
            # async for chunk in zhipuai_chat.astream(messages):
            #    print(chunk)

            # batch:
            # await zhipuai_chat.abatch([messages])

        .. code-block:: python

            [AIMessage(content='I enjoy programming.', response_metadata={'token_usage': {'completion_tokens': 6, 'prompt_tokens': 23, 'total_tokens': 29}, 'model_name': 'glm-4', 'finish_reason': 'stop'}, id='run-ba06af9d-4baa-40b2-9298-be9c62aa0849-0')]

    Tool calling:
        .. code-block:: python

            from pydantic import BaseModel, Field


            class GetWeather(BaseModel):
                '''Get the current weather in a given location'''

                location: str = Field(
                    ..., description="The city and state, e.g. San Francisco, CA"
                )


            class GetPopulation(BaseModel):
                '''Get the current population in a given location'''

                location: str = Field(
                    ..., description="The city and state, e.g. San Francisco, CA"
                )

            chat_with_tools = zhipuai_chat.bind_tools([GetWeather, GetPopulation])
            ai_msg = chat_with_tools.invoke(
                "Which city is hotter today and which is bigger: LA or NY?"
            )
            ai_msg.tool_calls

        .. code-block:: python

            [
                {
                    'name': 'GetWeather',
                    'args': {'location': 'Los Angeles, CA'},
                    'id': 'call_202408222146464ea49ec8731145a9',
                    'type': 'tool_call'
                }
            ]

    Structured output:
        .. code-block:: python

            from typing import Optional

            from pydantic import BaseModel, Field


            class Joke(BaseModel):
                '''Joke to tell user.'''

                setup: str = Field(description="The setup of the joke")
                punchline: str = Field(description="The punchline to the joke")
                rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10")


            structured_chat = zhipuai_chat.with_structured_output(Joke)
            structured_chat.invoke("Tell me a joke about cats")

        .. code-block:: python

            Joke(setup='What do cats like to eat for breakfast?', punchline='Mice Krispies!', rating=None)

    Response metadata
        .. code-block:: python

            ai_msg = zhipuai_chat.invoke(messages)
            ai_msg.response_metadata

        .. code-block:: python

            {'token_usage': {'completion_tokens': 6,
              'prompt_tokens': 23,
              'total_tokens': 29},
              'model_name': 'glm-4',
              'finish_reason': 'stop'}

    """  # noqa: E501

    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"zhipuai_api_key": "ZHIPUAI_API_KEY"}

    @classmethod
    def get_lc_namespace(cls) -> List[str]:
        """Get the namespace of the langchain object."""
        return ["langchain", "chat_models", "zhipuai"]

    @property
    def lc_attributes(self) -> Dict[str, Any]:
        attributes: Dict[str, Any] = {}

        if self.zhipuai_api_base:
            attributes["zhipuai_api_base"] = self.zhipuai_api_base

        return attributes

    @property
    def _llm_type(self) -> str:
        """Return the type of chat model."""
        return "zhipuai-chat"

    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling OpenAI API."""
        params = {
            "model": self.model_name,
            "stream": self.streaming,
            "temperature": self.temperature,
        }
        if self.max_tokens is not None:
            params["max_tokens"] = self.max_tokens
        return params

    # client:
    zhipuai_api_key: Optional[str] = Field(default=None, alias="api_key")
    """Automatically inferred from env var `ZHIPUAI_API_KEY` if not provided."""
    zhipuai_api_base: Optional[str] = Field(default=None, alias="api_base")
    """Base URL path for API requests, leave blank if not using a proxy or service
        emulator.
    """

    model_name: Optional[str] = Field(default="glm-4", alias="model")
    """
    Model name to use, see 'https://open.bigmodel.cn/dev/api#language'.
    Alternatively, you can use any fine-tuned model from the GLM series.
    """

    temperature: float = 0.95
    """
    What sampling temperature to use. The value ranges from 0.0 to 1.0 and cannot
    be equal to 0.
    The larger the value, the more random and creative the output; The smaller
    the value, the more stable or certain the output will be.
    You are advised to adjust top_p or temperature parameters based on application
    scenarios, but do not adjust the two parameters at the same time.
    """

    top_p: float = 0.7
    """
    Another method of sampling temperature is called nuclear sampling. The value
    ranges from 0.0 to 1.0 and cannot be equal to 0 or 1.
    The model considers the results with top_p probability quality tokens.
    For example, 0.1 means that the model decoder only considers tokens from the
    top 10% probability of the candidate set.
    You are advised to adjust top_p or temperature parameters based on application
    scenarios, but do not adjust the two parameters at the same time.
    """

    streaming: bool = False
    """Whether to stream the results or not."""
    max_tokens: Optional[int] = None
    """Maximum number of tokens to generate."""

    model_config = ConfigDict(
        populate_by_name=True,
    )

    @model_validator(mode="before")
    @classmethod
    def validate_environment(cls, values: Dict[str, Any]) -> Any:
        values["zhipuai_api_key"] = get_from_dict_or_env(
            values, ["zhipuai_api_key", "api_key"], "ZHIPUAI_API_KEY"
        )
        values["zhipuai_api_base"] = get_from_dict_or_env(
            values, "zhipuai_api_base", "ZHIPUAI_API_BASE", default=ZHIPUAI_API_BASE
        )

        return values

    def _create_message_dicts(
        self, messages: List[BaseMessage], stop: Optional[List[str]]
    ) -> Tuple[List[Dict[str, Any]], Dict[str, Any]]:
        params = self._default_params
        if stop is not None:
            params["stop"] = stop
        message_dicts = [_convert_message_to_dict(m) for m in messages]
        return message_dicts, params

    def _create_chat_result(self, response: Union[dict, BaseModel]) -> ChatResult:
        generations = []
        if not isinstance(response, dict):
            response = response.dict()
        for res in response["choices"]:
            message = _convert_dict_to_message(res["message"])
            generation_info = dict(finish_reason=res.get("finish_reason"))
            generations.append(
                ChatGeneration(message=message, generation_info=generation_info)
            )
        token_usage = response.get("usage", {})
        llm_output = {
            "token_usage": token_usage,
            "model_name": self.model_name,
        }
        return ChatResult(generations=generations, llm_output=llm_output)

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        stream: Optional[bool] = None,
        **kwargs: Any,
    ) -> ChatResult:
        """Generate a chat response."""
        should_stream = stream if stream is not None else self.streaming
        if should_stream:
            stream_iter = self._stream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return generate_from_stream(stream_iter)

        if self.zhipuai_api_key is None:
            raise ValueError("Did not find zhipuai_api_key.")
        message_dicts, params = self._create_message_dicts(messages, stop)
        payload = {
            **params,
            **kwargs,
            "messages": message_dicts,
            "stream": False,
        }
        _truncate_params(payload)
        headers = {
            "Authorization": _get_jwt_token(self.zhipuai_api_key),
            "Accept": "application/json",
        }
        import httpx

        with httpx.Client(headers=headers, timeout=60) as client:
            response = client.post(self.zhipuai_api_base, json=payload)  # type: ignore[arg-type]
            response.raise_for_status()
        return self._create_chat_result(response.json())

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        """Stream the chat response in chunks."""
        if self.zhipuai_api_key is None:
            raise ValueError("Did not find zhipuai_api_key.")
        if self.zhipuai_api_base is None:
            raise ValueError("Did not find zhipu_api_base.")
        message_dicts, params = self._create_message_dicts(messages, stop)
        payload = {**params, **kwargs, "messages": message_dicts, "stream": True}
        _truncate_params(payload)
        headers = {
            "Authorization": _get_jwt_token(self.zhipuai_api_key),
            "Accept": "application/json",
        }

        default_chunk_class = AIMessageChunk
        import httpx

        with httpx.Client(headers=headers, timeout=60) as client:
            with connect_sse(
                client, "POST", self.zhipuai_api_base, json=payload
            ) as event_source:
                for sse in event_source.iter_sse():
                    chunk = json.loads(sse.data)
                    if len(chunk["choices"]) == 0:
                        continue
                    choice = chunk["choices"][0]
                    usage = chunk.get("usage", None)
                    model_name = chunk.get("model", "")
                    chunk = _convert_delta_to_message_chunk(
                        choice["delta"], default_chunk_class
                    )
                    finish_reason = choice.get("finish_reason", None)

                    generation_info = (
                        {
                            "finish_reason": finish_reason,
                            "token_usage": usage,
                            "model_name": model_name,
                        }
                        if finish_reason is not None
                        else None
                    )
                    chunk = ChatGenerationChunk(
                        message=chunk, generation_info=generation_info
                    )
                    if run_manager:
                        run_manager.on_llm_new_token(chunk.text, chunk=chunk)
                    yield chunk

                    if finish_reason is not None:
                        break

    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        stream: Optional[bool] = None,
        **kwargs: Any,
    ) -> ChatResult:
        should_stream = stream if stream is not None else self.streaming
        if should_stream:
            stream_iter = self._astream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return await agenerate_from_stream(stream_iter)

        if self.zhipuai_api_key is None:
            raise ValueError("Did not find zhipuai_api_key.")
        message_dicts, params = self._create_message_dicts(messages, stop)
        payload = {
            **params,
            **kwargs,
            "messages": message_dicts,
            "stream": False,
        }
        _truncate_params(payload)
        headers = {
            "Authorization": _get_jwt_token(self.zhipuai_api_key),
            "Accept": "application/json",
        }
        import httpx

        async with httpx.AsyncClient(headers=headers, timeout=60) as client:
            response = await client.post(self.zhipuai_api_base, json=payload)  # type: ignore[arg-type]
            response.raise_for_status()
        return self._create_chat_result(response.json())

    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        if self.zhipuai_api_key is None:
            raise ValueError("Did not find zhipuai_api_key.")
        if self.zhipuai_api_base is None:
            raise ValueError("Did not find zhipu_api_base.")
        message_dicts, params = self._create_message_dicts(messages, stop)
        payload = {**params, **kwargs, "messages": message_dicts, "stream": True}
        _truncate_params(payload)
        headers = {
            "Authorization": _get_jwt_token(self.zhipuai_api_key),
            "Accept": "application/json",
        }

        default_chunk_class = AIMessageChunk
        import httpx

        async with httpx.AsyncClient(headers=headers, timeout=60) as client:
            async with aconnect_sse(
                client, "POST", self.zhipuai_api_base, json=payload
            ) as event_source:
                async for sse in event_source.aiter_sse():
                    chunk = json.loads(sse.data)
                    if len(chunk["choices"]) == 0:
                        continue
                    choice = chunk["choices"][0]
                    usage = chunk.get("usage", None)
                    model_name = chunk.get("model", "")
                    chunk = _convert_delta_to_message_chunk(
                        choice["delta"], default_chunk_class
                    )
                    finish_reason = choice.get("finish_reason", None)

                    generation_info = (
                        {
                            "finish_reason": finish_reason,
                            "token_usage": usage,
                            "model_name": model_name,
                        }
                        if finish_reason is not None
                        else None
                    )
                    chunk = ChatGenerationChunk(
                        message=chunk, generation_info=generation_info
                    )
                    if run_manager:
                        await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
                    yield chunk

                    if finish_reason is not None:
                        break

    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
        *,
        tool_choice: Optional[
            Union[dict, str, Literal["auto", "any", "none"], bool]
        ] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, AIMessage]:
        """Bind tool-like objects to this chat model.
        Args:
            tools: A list of tool definitions to bind to this chat model.
                Can be  a dictionary, pydantic model, callable, or BaseTool. Pydantic
                models, callables, and BaseTools will be automatically converted to
                their schema dictionary representation.
            tool_choice: Currently this can only be auto for this chat model.
            **kwargs: Any additional parameters to pass to the
                :class:`~langchain.runnable.Runnable` constructor.
        """
        if self.model_name == "glm-4v":
            raise ValueError("glm-4v currently does not support tool calling")

        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
        if tool_choice and tool_choice != "auto":
            raise ValueError("ChatZhipuAI currently only supports `auto` tool choice")
        elif tool_choice and tool_choice == "auto":
            kwargs["tool_choice"] = tool_choice
        return self.bind(tools=formatted_tools, **kwargs)

    def with_structured_output(
        self,
        schema: Optional[Union[Dict, Type[BaseModel]]] = None,
        *,
        method: Literal["function_calling", "json_mode"] = "function_calling",
        include_raw: bool = False,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, Union[Dict, BaseModel]]:
        """Model wrapper that returns outputs formatted to match the given schema.

        Args:
            schema: The output schema as a dict or a Pydantic class. If a Pydantic class
                then the model output will be an object of that class. If a dict then
                the model output will be a dict. With a Pydantic class the returned
                attributes will be validated, whereas with a dict they will not be. If
                `method` is "function_calling" and `schema` is a dict, then the dict
                must match the OpenAI function-calling spec.
            method: The method for steering model generation, either "function_calling"
                or "json_mode". ZhipuAI only supports "function_calling" which
                converts the schema to a OpenAI function and the model will make use of the
                function-calling API.
            include_raw:
                If `False` then only the parsed structured output is returned.

                If an error occurs during model output parsing it will be raised.

                If `True` then both the raw model response (a `BaseMessage`) and the
                parsed model response will be returned.

                If an error occurs during output parsing it will be caught and returned
                as well.

                The final output is always a `dict` with keys `'raw'`, `'parsed'`, and
                `'parsing_error'`.

        Returns:
            A Runnable that takes any ChatModel input and returns as output:

                If include_raw is True then a dict with keys:
                    raw: BaseMessage
                    parsed: Optional[_DictOrPydantic]
                    parsing_error: Optional[BaseException]

                If include_raw is False then just _DictOrPydantic is returned,
                where _DictOrPydantic depends on the schema:

                If schema is a Pydantic class then _DictOrPydantic is the Pydantic
                    class.

                If schema is a dict then _DictOrPydantic is a dict.

        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=False):
            .. code-block:: python

                from langchain_community.chat_models import ChatZhipuAI
                from pydantic import BaseModel

                class AnswerWithJustification(BaseModel):
                    '''An answer to the user question along with justification for the answer.'''
                    answer: str
                    justification: str

                llm = ChatZhipuAI(temperature=0)
                structured_llm = llm.with_structured_output(AnswerWithJustification)

                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
                # -> AnswerWithJustification(
                #     answer='A pound of bricks and a pound of feathers weigh the same.'
                #     justification="Both a pound of bricks and a pound of feathers have been defined to have the same weight. The 'pound' is a unit of weight, so any two things that are described as weighing a pound will weigh the same."
                # )

        Example: Function-calling, Pydantic schema (method="function_calling", include_raw=True):
            .. code-block:: python

                from langchain_community.chat_models import ChatZhipuAI
                from pydantic import BaseModel

                class AnswerWithJustification(BaseModel):
                    '''An answer to the user question along with justification for the answer.'''
                    answer: str
                    justification: str

                llm = ChatZhipuAI(temperature=0)
                structured_llm = llm.with_structured_output(AnswerWithJustification, include_raw=True)

                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
                # -> {
                #     'raw': AIMessage(content='', additional_kwargs={'tool_calls': [{'id': 'call_01htjn3cspevxbqc1d7nkk8wab', 'function': {'arguments': '{"answer": "A pound of bricks and a pound of feathers weigh the same.", "justification": "Both a pound of bricks and a pound of feathers have been defined to have the same weight. The \'pound\' is a unit of weight, so any two things that are described as weighing a pound will weigh the same.", "unit": "pounds"}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}, id='run-456beee6-65f6-4e80-88af-a6065480822c-0'),
                #     'parsed': AnswerWithJustification(answer='A pound of bricks and a pound of feathers weigh the same.', justification="Both a pound of bricks and a pound of feathers have been defined to have the same weight. The 'pound' is a unit of weight, so any two things that are described as weighing a pound will weigh the same."),
                #     'parsing_error': None
                # }

        Example: Function-calling, dict schema (method="function_calling", include_raw=False):
            .. code-block:: python

                from langchain_community.chat_models import ChatZhipuAI
                from pydantic import BaseModel
                from langchain_core.utils.function_calling import convert_to_openai_tool

                class AnswerWithJustification(BaseModel):
                    '''An answer to the user question along with justification for the answer.'''
                    answer: str
                    justification: str

                dict_schema = convert_to_openai_tool(AnswerWithJustification)
                llm = ChatZhipuAI(temperature=0)
                structured_llm = llm.with_structured_output(dict_schema)

                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
                # -> {
                #     'answer': 'A pound of bricks and a pound of feathers weigh the same.',
                #     'justification': "Both a pound of bricks and a pound of feathers have been defined to have the same weight. The 'pound' is a unit of weight, so any two things that are described as weighing a pound will weigh the same.", 'unit': 'pounds'}
                # }

        """  # noqa: E501
        if kwargs:
            raise ValueError(f"Received unsupported arguments {kwargs}")
        is_pydantic_schema = _is_pydantic_class(schema)
        if method == "function_calling":
            if schema is None:
                raise ValueError(
                    "schema must be specified when method is 'function_calling'. "
                    "Received None."
                )
            tool_name = convert_to_openai_tool(schema)["function"]["name"]
            llm = self.bind_tools([schema], tool_choice="auto")
            if is_pydantic_schema:
                output_parser: OutputParserLike = PydanticToolsParser(
                    tools=[schema],  # type: ignore[list-item]
                    first_tool_only=True,
                )
            else:
                output_parser = JsonOutputKeyToolsParser(
                    key_name=tool_name, first_tool_only=True
                )
        else:
            raise ValueError(
                f"""Unrecognized method argument. Expected 'function_calling'.
                Received: '{method}'"""
            )

        if include_raw:
            parser_assign = RunnablePassthrough.assign(
                parsed=itemgetter("raw") | output_parser, parsing_error=lambda _: None
            )
            parser_none = RunnablePassthrough.assign(parsed=lambda _: None)
            parser_with_fallback = parser_assign.with_fallbacks(
                [parser_none], exception_key="parsing_error"
            )
            return RunnableMap(raw=llm) | parser_with_fallback
        else:
            return llm | output_parser