langchain/libs/community/langchain_community/chat_models/baidu_qianfan_endpoint.py

import logging
import uuid
from operator import itemgetter
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Mapping,
    Optional,
    Sequence,
    Type,
    Union,
    cast,
)

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
    AIMessage,
    AIMessageChunk,
    BaseMessage,
    ChatMessage,
    FunctionMessage,
    HumanMessage,
    SystemMessage,
    ToolMessage,
)
from langchain_core.messages.ai import UsageMetadata
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.pydantic_v1 import BaseModel, Field, SecretStr, root_validator
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env
from langchain_core.utils.function_calling import convert_to_openai_tool

logger = logging.getLogger(__name__)


def convert_message_to_dict(message: BaseMessage) -> dict:
    """Convert a message to a dictionary that can be passed to the API."""
    message_dict: Dict[str, Any]
    if isinstance(message, ChatMessage):
        message_dict = {"role": message.role, "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}
        if "function_call" in message.additional_kwargs:
            message_dict["function_call"] = message.additional_kwargs["function_call"]
            # If function call only, content is None not empty string
            if message_dict["content"] == "":
                message_dict["content"] = None
    elif isinstance(message, (FunctionMessage, ToolMessage)):
        message_dict = {
            "role": "function",
            "content": message.content,
            "name": message.name or message.additional_kwargs.get("name"),
        }
    else:
        raise TypeError(f"Got unknown type {message}")

    return message_dict


def _convert_dict_to_message(_dict: Mapping[str, Any]) -> AIMessage:
    content = _dict.get("result", "") or ""
    additional_kwargs: Mapping[str, Any] = {}
    if _dict.get("function_call"):
        additional_kwargs = {"function_call": dict(_dict["function_call"])}
        if "thoughts" in additional_kwargs["function_call"]:
            # align to api sample, which affects the llm function_call output
            additional_kwargs["function_call"].pop("thoughts")

    additional_kwargs = {**_dict.get("body", {}), **additional_kwargs}
    msg_additional_kwargs = dict(
        finish_reason=additional_kwargs.get("finish_reason", ""),
        request_id=additional_kwargs["id"],
        object=additional_kwargs.get("object", ""),
        search_info=additional_kwargs.get("search_info", []),
        usage=additional_kwargs.get("usage", None),
    )

    if additional_kwargs.get("function_call", {}):
        msg_additional_kwargs["function_call"] = additional_kwargs.get(
            "function_call", {}
        )
        msg_additional_kwargs["tool_calls"] = [
            {
                "type": "function",
                "function": additional_kwargs.get("function_call", {}),
                "id": str(uuid.uuid4()),
            }
        ]

    if usage := additional_kwargs.get("usage", None):
        return AIMessage(
            content=content,
            additional_kwargs=msg_additional_kwargs,
            usage_metadata=UsageMetadata(
                input_tokens=usage.prompt_tokens,
                output_tokens=usage.completion_tokens,
                total_tokens=usage.total_tokens,
            ),
        )

    return AIMessage(
        content=content,
        additional_kwargs=msg_additional_kwargs,
    )


class QianfanChatEndpoint(BaseChatModel):
    """Baidu Qianfan chat model integration.

    Setup:
        Install ``qianfan`` and set environment variables ``QIANFAN_AK``, ``QIANFAN_SK``.

        .. code-block:: bash

            pip install qianfan
            export QIANFAN_AK="your-api-key"
            export QIANFAN_SK="your-secret_key"

    Key init args — completion params:
        model: str
            Name of Qianfan model to use.
        temperature: Optional[float]
            Sampling temperature.
        endpoint: Optional[str]
            Endpoint of the Qianfan LLM
        top_p: Optional[float]
            What probability mass to use.

    Key init args — client params:
        timeout: Optional[int]
            Timeout for requests.
        api_key: Optional[str]
            Qianfan API KEY. If not passed in will be read from env var QIANFAN_AK.
        secret_key: Optional[str]
            Qianfan SECRET KEY. If not passed in will be read from env var QIANFAN_SK.

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

    Instantiate:
        .. code-block:: python

            from langchain_community.chat_models import QianfanChatEndpoint

            qianfan_chat = QianfanChatEndpoint(
                model="ERNIE-3.5-8K",
                temperature=0.2,
                timeout=30,
                # api_key="...",
                # secret_key="...",
                # top_p="...",
                # 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。"),
            ]
            qianfan_chat.invoke(message)

        .. code-block:: python

            AIMessage(content='I enjoy programming.', additional_kwargs={'finish_reason': 'normal', 'request_id': 'as-7848zeqn1c', 'object': 'chat.completion', 'search_info': []}, response_metadata={'token_usage': {'prompt_tokens': 16, 'completion_tokens': 4, 'total_tokens': 20}, 'model_name': 'ERNIE-3.5-8K', 'finish_reason': 'normal', 'id': 'as-7848zeqn1c', 'object': 'chat.completion', 'created': 1719153606, 'result': 'I enjoy programming.', 'is_truncated': False, 'need_clear_history': False, 'usage': {'prompt_tokens': 16, 'completion_tokens': 4, 'total_tokens': 20}}, id='run-4bca0c10-5043-456b-a5be-2f62a980f3f0-0')

    Stream:
        .. code-block:: python

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

        .. code-block:: python

            content='I enjoy' response_metadata={'finish_reason': 'normal', 'request_id': 'as-yz0yz1w1rq', 'object': 'chat.completion', 'search_info': []} id='run-0fa9da50-003e-4a26-ba16-dbfe96249b8b' role='assistant'
            content=' programming.' response_metadata={'finish_reason': 'normal', 'request_id': 'as-yz0yz1w1rq', 'object': 'chat.completion', 'search_info': []} id='run-0fa9da50-003e-4a26-ba16-dbfe96249b8b' role='assistant'

        .. code-block:: python

            full = next(stream)
            for chunk in stream:
                full += chunk
            full

        .. code-block::

            AIMessageChunk(content='I enjoy programming.', response_metadata={'finish_reason': 'normalnormal', 'request_id': 'as-p63cnn3ppnas-p63cnn3ppn', 'object': 'chat.completionchat.completion', 'search_info': []}, id='run-09a8cbbd-5ded-4529-981d-5bc9d1206404')

    Async:
        .. code-block:: python

            await qianfan_chat.ainvoke(messages)

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

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

        .. code-block:: python

            [AIMessage(content='I enjoy programming.', additional_kwargs={'finish_reason': 'normal', 'request_id': 'as-mpqa8qa1qb', 'object': 'chat.completion', 'search_info': []}, response_metadata={'token_usage': {'prompt_tokens': 16, 'completion_tokens': 4, 'total_tokens': 20}, 'model_name': 'ERNIE-3.5-8K', 'finish_reason': 'normal', 'id': 'as-mpqa8qa1qb', 'object': 'chat.completion', 'created': 1719155120, 'result': 'I enjoy programming.', 'is_truncated': False, 'need_clear_history': False, 'usage': {'prompt_tokens': 16, 'completion_tokens': 4, 'total_tokens': 20}}, id='run-443b2231-08f9-4725-b807-b77d0507ad44-0')]

    Tool calling:
        .. code-block:: python

            from langchain_core.pydantic_v1 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 = qianfan_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': '533e5f63-a3dc-40f2-9d9c-22b1feee62e0'
                }
            ]

    Structured output:
        .. code-block:: python

            from typing import Optional

            from langchain_core.pydantic_v1 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 = qianfan_chat.with_structured_output(Joke)
            structured_chat.invoke("Tell me a joke about cats")

        .. code-block:: python

            Joke(
                setup='A cat is sitting in front of a mirror and sees another cat. What does the cat think?',
                punchline="The cat doesn't think it's another cat, it thinks it's another mirror.",
                rating=None
            )

    Response metadata
        .. code-block:: python

            ai_msg = qianfan_chat.invoke(messages)
            ai_msg.response_metadata

        .. code-block:: python
            {
                'token_usage': {
                    'prompt_tokens': 16,
                    'completion_tokens': 4,
                    'total_tokens': 20},
                    'model_name': 'ERNIE-3.5-8K',
                    'finish_reason': 'normal',
                    'id': 'as-qbzwtydqmi',
                    'object': 'chat.completion',
                    'created': 1719158153,
                    'result': 'I enjoy programming.',
                    'is_truncated': False,
                    'need_clear_history': False,
                    'usage': {
                        'prompt_tokens': 16,
                        'completion_tokens': 4,
                        'total_tokens': 20
                    }
            }

    """  # noqa: E501

    init_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """init kwargs for qianfan client init, such as `query_per_second` which is 
        associated with qianfan resource object to limit QPS"""

    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """extra params for model invoke using with `do`."""

    client: Any  #: :meta private:

    qianfan_ak: SecretStr = Field(alias="api_key")
    """Qianfan API KEY"""
    qianfan_sk: Optional[SecretStr] = Field(default=None, alias="secret_key")
    """Qianfan SECRET KEY"""
    streaming: Optional[bool] = False
    """Whether to stream the results or not."""

    request_timeout: Optional[int] = Field(60, alias="timeout")
    """request timeout for chat http requests"""

    top_p: Optional[float] = 0.8
    """What probability mass to use."""
    temperature: Optional[float] = 0.95
    """What sampling temperature to use."""
    penalty_score: Optional[float] = 1
    """Model params, only supported in ERNIE-Bot and ERNIE-Bot-turbo.
    In the case of other model, passing these params will not affect the result.
    """

    model: str = "ERNIE-Bot-turbo"
    """Model name.
    you could get from https://cloud.baidu.com/doc/WENXINWORKSHOP/s/Nlks5zkzu
    
    preset models are mapping to an endpoint.
    `model` will be ignored if `endpoint` is set.
    Default is ERNIE-Bot-turbo.
    """

    endpoint: Optional[str] = None
    """Endpoint of the Qianfan LLM, required if custom model used."""

    class Config:
        """Configuration for this pydantic object."""

        allow_population_by_field_name = True

    @root_validator(pre=True)
    def validate_environment(cls, values: Dict) -> Dict:
        values["qianfan_ak"] = convert_to_secret_str(
            get_from_dict_or_env(
                values,
                ["qianfan_ak", "api_key"],
                "QIANFAN_AK",
            )
        )
        values["qianfan_sk"] = convert_to_secret_str(
            get_from_dict_or_env(
                values,
                ["qianfan_sk", "secret_key"],
                "QIANFAN_SK",
            )
        )

        default_values = {
            name: field.default
            for name, field in cls.__fields__.items()
            if field.default is not None
        }
        default_values.update(values)
        params = {
            **values.get("init_kwargs", {}),
            "model": default_values.get("model"),
            "stream": default_values.get("streaming"),
        }
        if values["qianfan_ak"].get_secret_value() != "":
            params["ak"] = values["qianfan_ak"].get_secret_value()
        if values["qianfan_sk"].get_secret_value() != "":
            params["sk"] = values["qianfan_sk"].get_secret_value()
        if (
            default_values.get("endpoint") is not None
            and default_values["endpoint"] != ""
        ):
            params["endpoint"] = default_values["endpoint"]
        try:
            import qianfan

            values["client"] = qianfan.ChatCompletion(**params)
        except ImportError:
            raise ImportError(
                "qianfan package not found, please install it with "
                "`pip install qianfan`"
            )
        return values

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        return {
            **{"endpoint": self.endpoint, "model": self.model},
            **super()._identifying_params,
        }

    @property
    def _llm_type(self) -> str:
        """Return type of chat_model."""
        return "baidu-qianfan-chat"

    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling Qianfan API."""
        normal_params = {
            "model": self.model,
            "endpoint": self.endpoint,
            "stream": self.streaming,
            "request_timeout": self.request_timeout,
            "top_p": self.top_p,
            "temperature": self.temperature,
            "penalty_score": self.penalty_score,
        }

        return {**normal_params, **self.model_kwargs}

    def _convert_prompt_msg_params(
        self,
        messages: List[BaseMessage],
        **kwargs: Any,
    ) -> Dict[str, Any]:
        """
        Converts a list of messages into a dictionary containing the message content
        and default parameters.

        Args:
            messages (List[BaseMessage]): The list of messages.
            **kwargs (Any): Optional arguments to add additional parameters to the
            resulting dictionary.

        Returns:
            Dict[str, Any]: A dictionary containing the message content and default
            parameters.

        """
        messages_dict: Dict[str, Any] = {
            "messages": [
                convert_message_to_dict(m)
                for m in messages
                if not isinstance(m, SystemMessage)
            ]
        }
        for i in [i for i, m in enumerate(messages) if isinstance(m, SystemMessage)]:
            if "system" not in messages_dict:
                messages_dict["system"] = ""
            messages_dict["system"] += cast(str, messages[i].content) + "\n"

        return {
            **messages_dict,
            **self._default_params,
            **kwargs,
        }

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        """Call out to an qianfan models endpoint for each generation with a prompt.
        Args:
            messages: The messages to pass into the model.
            stop: Optional list of stop words to use when generating.
        Returns:
            The string generated by the model.

        Example:
            .. code-block:: python
                response = qianfan_model.invoke("Tell me a joke.")
        """
        if self.streaming:
            completion = ""
            chat_generation_info: Dict = {}
            for chunk in self._stream(messages, stop, run_manager, **kwargs):
                chat_generation_info = (
                    chunk.generation_info
                    if chunk.generation_info is not None
                    else chat_generation_info
                )
                completion += chunk.text
            lc_msg = AIMessage(content=completion, additional_kwargs={})
            gen = ChatGeneration(
                message=lc_msg,
                generation_info=dict(finish_reason="stop"),
            )
            return ChatResult(
                generations=[gen],
                llm_output={
                    "token_usage": chat_generation_info.get("usage", {}),
                    "model_name": self.model,
                },
            )
        params = self._convert_prompt_msg_params(messages, **kwargs)
        params["stop"] = stop
        response_payload = self.client.do(**params)
        lc_msg = _convert_dict_to_message(response_payload)
        gen = ChatGeneration(
            message=lc_msg,
            generation_info={
                "finish_reason": "stop",
                **response_payload.get("body", {}),
            },
        )
        token_usage = response_payload.get("usage", {})
        llm_output = {"token_usage": token_usage, "model_name": self.model}
        return ChatResult(generations=[gen], llm_output=llm_output)

    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        if self.streaming:
            completion = ""
            chat_generation_info: Dict = {}
            async for chunk in self._astream(messages, stop, run_manager, **kwargs):
                chat_generation_info = (
                    chunk.generation_info
                    if chunk.generation_info is not None
                    else chat_generation_info
                )
                completion += chunk.text

            lc_msg = AIMessage(content=completion, additional_kwargs={})
            gen = ChatGeneration(
                message=lc_msg,
                generation_info=dict(finish_reason="stop"),
            )
            return ChatResult(
                generations=[gen],
                llm_output={
                    "token_usage": chat_generation_info.get("usage", {}),
                    "model_name": self.model,
                },
            )
        params = self._convert_prompt_msg_params(messages, **kwargs)
        params["stop"] = stop
        response_payload = await self.client.ado(**params)
        lc_msg = _convert_dict_to_message(response_payload)
        generations = []
        gen = ChatGeneration(
            message=lc_msg,
            generation_info={
                "finish_reason": "stop",
                **response_payload.get("body", {}),
            },
        )
        generations.append(gen)
        token_usage = response_payload.get("usage", {})
        llm_output = {"token_usage": token_usage, "model_name": self.model}
        return ChatResult(generations=generations, llm_output=llm_output)

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        params = self._convert_prompt_msg_params(messages, **kwargs)
        params["stop"] = stop
        params["stream"] = True
        for res in self.client.do(**params):
            if res:
                msg = _convert_dict_to_message(res)
                additional_kwargs = msg.additional_kwargs.get("function_call", {})
                chunk = ChatGenerationChunk(
                    text=res["result"],
                    message=AIMessageChunk(  # type: ignore[call-arg]
                        content=msg.content,
                        role="assistant",
                        additional_kwargs=additional_kwargs,
                        usage_metadata=msg.usage_metadata,
                    ),
                    generation_info=msg.additional_kwargs,
                )
                if run_manager:
                    run_manager.on_llm_new_token(chunk.text, chunk=chunk)
                yield chunk

    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        params = self._convert_prompt_msg_params(messages, **kwargs)
        params["stop"] = stop
        params["stream"] = True
        async for res in await self.client.ado(**params):
            if res:
                msg = _convert_dict_to_message(res)
                additional_kwargs = msg.additional_kwargs.get("function_call", {})
                chunk = ChatGenerationChunk(
                    text=res["result"],
                    message=AIMessageChunk(  # type: ignore[call-arg]
                        content=msg.content,
                        role="assistant",
                        additional_kwargs=additional_kwargs,
                        usage_metadata=msg.usage_metadata,
                    ),
                    generation_info=msg.additional_kwargs,
                )
                if run_manager:
                    await run_manager.on_llm_new_token(chunk.text, chunk=chunk)
                yield chunk

    def bind_tools(
        self,
        tools: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, BaseMessage]:
        """Bind tool-like objects to this chat model.

        Assumes model is compatible with OpenAI tool-calling API.

        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.
            **kwargs: Any additional parameters to pass to the
                :class:`~langchain.runnable.Runnable` constructor.
        """

        formatted_tools = [convert_to_openai_tool(tool)["function"] for tool in tools]
        return super().bind(functions=formatted_tools, **kwargs)

    def with_structured_output(
        self,
        schema: Union[Dict, Type[BaseModel]],
        *,
        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.
            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_mistralai import QianfanChatEndpoint
                from langchain_core.pydantic_v1 import BaseModel

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

                llm = QianfanChatEndpoint(endpoint="ernie-3.5-8k-0329")
                structured_llm = llm.with_structured_output(AnswerWithJustification)

                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")

                # -> AnswerWithJustification(
                #     answer='They weigh the same',
                #     justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'
                # )

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

                from langchain_mistralai import QianfanChatEndpoint
                from langchain_core.pydantic_v1 import BaseModel

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

                llm = QianfanChatEndpoint(endpoint="ernie-3.5-8k-0329")
                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_Ao02pnFYXD6GN1yzc0uXPsvF', 'function': {'arguments': '{"answer":"They weigh the same.","justification":"Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ."}', 'name': 'AnswerWithJustification'}, 'type': 'function'}]}),
                #     'parsed': AnswerWithJustification(answer='They weigh the same.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume or density of the objects may differ.'),
                #     'parsing_error': None
                # }

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

                from langchain_mistralai import QianfanChatEndpoint
                from langchain_core.pydantic_v1 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 = QianfanChatEndpoint(endpoint="ernie-3.5-8k-0329")
                structured_llm = llm.with_structured_output(dict_schema)

                structured_llm.invoke("What weighs more a pound of bricks or a pound of feathers")
                # -> {
                #     'answer': 'They weigh the same',
                #     'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The weight is the same, but the volume and density of the two substances differ.'
                # }

        """  # noqa: E501
        if kwargs:
            raise ValueError(f"Received unsupported arguments {kwargs}")
        is_pydantic_schema = isinstance(schema, type) and issubclass(schema, BaseModel)
        llm = self.bind_tools([schema])
        if is_pydantic_schema:
            output_parser: OutputParserLike = PydanticToolsParser(
                tools=[schema],  # type: ignore[list-item]
                first_tool_only=True,  # type: ignore[list-item]
            )
        else:
            key_name = convert_to_openai_tool(schema)["function"]["name"]
            output_parser = JsonOutputKeyToolsParser(
                key_name=key_name, first_tool_only=True
            )

        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