langchain/libs/partners/groq/langchain_groq/chat_models.py

"""Groq Chat wrapper."""

from __future__ import annotations

import os
import warnings
from operator import itemgetter
from typing import (
    Any,
    AsyncIterator,
    Callable,
    Dict,
    Iterator,
    List,
    Literal,
    Mapping,
    Optional,
    Sequence,
    Tuple,
    Type,
    TypedDict,
    Union,
    cast,
)

from langchain_core._api import beta
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,
    FunctionMessage,
    FunctionMessageChunk,
    HumanMessage,
    HumanMessageChunk,
    SystemMessage,
    SystemMessageChunk,
    ToolMessage,
    ToolMessageChunk,
)
from langchain_core.output_parsers import (
    JsonOutputParser,
    PydanticOutputParser,
)
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,
    get_pydantic_field_names,
)
from langchain_core.utils.function_calling import (
    convert_to_openai_function,
    convert_to_openai_tool,
)


class ChatGroq(BaseChatModel):
    """`Groq` Chat large language models API.

    To use, you should have the
    environment variable ``GROQ_API_KEY`` set with your API key.

    Any parameters that are valid to be passed to the groq.create call can be passed
    in, even if not explicitly saved on this class.

    Example:
        .. code-block:: python

            from langchain_groq import ChatGroq

            model = ChatGroq(model_name="mixtral-8x7b-32768")
    """

    client: Any = Field(default=None, exclude=True)  #: :meta private:
    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
    model_name: str = Field(default="mixtral-8x7b-32768", alias="model")
    """Model name to use."""
    temperature: float = 0.7
    """What sampling temperature to use."""
    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
    """Holds any model parameters valid for `create` call not explicitly specified."""
    groq_api_key: Optional[SecretStr] = Field(default=None, alias="api_key")
    """Automatically inferred from env var `groq_API_KEY` if not provided."""
    groq_api_base: Optional[str] = Field(default=None, alias="base_url")
    """Base URL path for API requests, leave blank if not using a proxy or service
        emulator."""
    # to support explicit proxy for Groq
    groq_proxy: Optional[str] = None
    request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
        default=None, alias="timeout"
    )
    """Timeout for requests to Groq completion API. Can be float, httpx.Timeout or
        None."""
    max_retries: int = 2
    """Maximum number of retries to make when generating."""
    streaming: bool = False
    """Whether to stream the results or not."""
    n: int = 1
    """Number of chat completions to generate for each prompt."""
    max_tokens: Optional[int] = None
    """Maximum number of tokens to generate."""
    default_headers: Union[Mapping[str, str], None] = None
    default_query: Union[Mapping[str, object], None] = None
    # Configure a custom httpx client. See the
    # [httpx documentation](https://www.python-httpx.org/api/#client) for more details.
    http_client: Union[Any, None] = None
    """Optional httpx.Client."""

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

        allow_population_by_field_name = True

    @root_validator(pre=True)
    def build_extra(cls, values: Dict[str, Any]) -> Dict[str, Any]:
        """Build extra kwargs from additional params that were passed in."""
        all_required_field_names = get_pydantic_field_names(cls)
        extra = values.get("model_kwargs", {})
        for field_name in list(values):
            if field_name in extra:
                raise ValueError(f"Found {field_name} supplied twice.")
            if field_name not in all_required_field_names:
                warnings.warn(
                    f"""WARNING! {field_name} is not default parameter.
                    {field_name} was transferred to model_kwargs.
                    Please confirm that {field_name} is what you intended."""
                )
                extra[field_name] = values.pop(field_name)

        invalid_model_kwargs = all_required_field_names.intersection(extra.keys())
        if invalid_model_kwargs:
            raise ValueError(
                f"Parameters {invalid_model_kwargs} should be specified explicitly. "
                f"Instead they were passed in as part of `model_kwargs` parameter."
            )

        values["model_kwargs"] = extra
        return values

    @root_validator()
    def validate_environment(cls, values: Dict) -> Dict:
        """Validate that api key and python package exists in environment."""
        if values["n"] < 1:
            raise ValueError("n must be at least 1.")
        if values["n"] > 1 and values["streaming"]:
            raise ValueError("n must be 1 when streaming.")

        if values["temperature"] == 0:
            values["temperature"] = 1e-8

        values["groq_api_key"] = convert_to_secret_str(
            get_from_dict_or_env(values, "groq_api_key", "GROQ_API_KEY")
        )
        values["groq_api_base"] = values["groq_api_base"] or os.getenv("GROQ_API_BASE")
        values["groq_proxy"] = values["groq_proxy"] = os.getenv("GROQ_PROXY")

        client_params = {
            "api_key": values["groq_api_key"].get_secret_value(),
            "base_url": values["groq_api_base"],
            "timeout": values["request_timeout"],
            "max_retries": values["max_retries"],
            "default_headers": values["default_headers"],
            "default_query": values["default_query"],
            "http_client": values["http_client"],
        }

        try:
            import groq

            if not values.get("client"):
                values["client"] = groq.Groq(**client_params).chat.completions
            if not values.get("async_client"):
                values["async_client"] = groq.AsyncGroq(
                    **client_params
                ).chat.completions
        except ImportError:
            raise ImportError(
                "Could not import groq python package. "
                "Please install it with `pip install groq`."
            )
        return values

    #
    # Serializable class method overrides
    #
    @property
    def lc_secrets(self) -> Dict[str, str]:
        return {"groq_api_key": "GROQ_API_KEY"}

    @classmethod
    def is_lc_serializable(cls) -> bool:
        """Return whether this model can be serialized by Langchain."""
        return True

    #
    # BaseChatModel method overrides
    #
    @property
    def _llm_type(self) -> str:
        """Return type of model."""
        return "groq-chat"

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        if self.streaming:
            stream_iter = self._stream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return generate_from_stream(stream_iter)
        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {
            **params,
            **kwargs,
        }
        response = self.client.create(messages=message_dicts, **params)
        return self._create_chat_result(response)

    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        if self.streaming:
            stream_iter = self._astream(
                messages, stop=stop, run_manager=run_manager, **kwargs
            )
            return await agenerate_from_stream(stream_iter)

        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {
            **params,
            **kwargs,
        }
        response = await self.async_client.create(messages=message_dicts, **params)
        return self._create_chat_result(response)

    def _stream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> Iterator[ChatGenerationChunk]:
        message_dicts, params = self._create_message_dicts(messages, stop)

        # groq api does not support streaming with tools yet
        if "tools" in kwargs:
            response = self.client.create(
                messages=message_dicts, **{**params, **kwargs}
            )
            chat_result = self._create_chat_result(response)
            generation = chat_result.generations[0]
            message = generation.message
            chunk_ = ChatGenerationChunk(
                message=AIMessageChunk(
                    content=message.content, additional_kwargs=message.additional_kwargs
                ),
                generation_info=generation.generation_info,
            )
            if run_manager:
                geninfo = chunk_.generation_info or {}
                run_manager.on_llm_new_token(
                    chunk_.text,
                    chunk=chunk_,
                    logprobs=geninfo.get("logprobs"),
                )
            yield chunk_
            return

        params = {**params, **kwargs, "stream": True}

        default_chunk_class = AIMessageChunk
        for chunk in self.client.create(messages=message_dicts, **params):
            if not isinstance(chunk, dict):
                chunk = chunk.dict()
            if len(chunk["choices"]) == 0:
                continue
            choice = chunk["choices"][0]
            chunk = _convert_delta_to_message_chunk(
                choice["delta"], default_chunk_class
            )
            generation_info = {}
            if finish_reason := choice.get("finish_reason"):
                generation_info["finish_reason"] = finish_reason
            logprobs = choice.get("logprobs")
            if logprobs:
                generation_info["logprobs"] = logprobs
            default_chunk_class = chunk.__class__
            chunk = ChatGenerationChunk(
                message=chunk, generation_info=generation_info or None
            )

            if run_manager:
                run_manager.on_llm_new_token(chunk.text, chunk=chunk, logprobs=logprobs)
            yield chunk

    async def _astream(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> AsyncIterator[ChatGenerationChunk]:
        message_dicts, params = self._create_message_dicts(messages, stop)

        # groq api does not support streaming with tools yet
        if "tools" in kwargs:
            response = await self.async_client.create(
                messages=message_dicts, **{**params, **kwargs}
            )
            chat_result = self._create_chat_result(response)
            generation = chat_result.generations[0]
            message = generation.message
            chunk_ = ChatGenerationChunk(
                message=AIMessageChunk(
                    content=message.content, additional_kwargs=message.additional_kwargs
                ),
                generation_info=generation.generation_info,
            )
            if run_manager:
                geninfo = chunk_.generation_info or {}
                await run_manager.on_llm_new_token(
                    chunk_.text,
                    chunk=chunk_,
                    logprobs=geninfo.get("logprobs"),
                )
            yield chunk_
            return

        params = {**params, **kwargs, "stream": True}

        default_chunk_class = AIMessageChunk
        async for chunk in await self.async_client.create(
            messages=message_dicts, **params
        ):
            if not isinstance(chunk, dict):
                chunk = chunk.dict()
            if len(chunk["choices"]) == 0:
                continue
            choice = chunk["choices"][0]
            chunk = _convert_delta_to_message_chunk(
                choice["delta"], default_chunk_class
            )
            generation_info = {}
            if finish_reason := choice.get("finish_reason"):
                generation_info["finish_reason"] = finish_reason
            logprobs = choice.get("logprobs")
            if logprobs:
                generation_info["logprobs"] = logprobs
            default_chunk_class = chunk.__class__
            chunk = ChatGenerationChunk(
                message=chunk, generation_info=generation_info or None
            )

            if run_manager:
                await run_manager.on_llm_new_token(
                    token=chunk.text, chunk=chunk, logprobs=logprobs
                )
            yield chunk

    #
    # Internal methods
    #
    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling Groq API."""
        params = {
            "model": self.model_name,
            "stream": self.streaming,
            "n": self.n,
            "temperature": self.temperature,
            **self.model_kwargs,
        }
        if self.max_tokens is not None:
            params["max_tokens"] = self.max_tokens
        return 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"))
            if "logprobs" in res:
                generation_info["logprobs"] = res["logprobs"]
            gen = ChatGeneration(
                message=message,
                generation_info=generation_info,
            )
            generations.append(gen)
        token_usage = response.get("usage", {})
        llm_output = {
            "token_usage": token_usage,
            "model_name": self.model_name,
            "system_fingerprint": response.get("system_fingerprint", ""),
        }
        return ChatResult(generations=generations, llm_output=llm_output)

    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:
            if "stop" in params:
                raise ValueError("`stop` found in both the input and default params.")
            params["stop"] = stop
        message_dicts = [_convert_message_to_dict(m) for m in messages]
        return message_dicts, params

    def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
        overall_token_usage: dict = {}
        system_fingerprint = None
        for output in llm_outputs:
            if output is None:
                # Happens in streaming
                continue
            token_usage = output["token_usage"]
            if token_usage is not None:
                for k, v in token_usage.items():
                    if k in overall_token_usage:
                        overall_token_usage[k] += v
                    else:
                        overall_token_usage[k] = v
            if system_fingerprint is None:
                system_fingerprint = output.get("system_fingerprint")
        combined = {"token_usage": overall_token_usage, "model_name": self.model_name}
        if system_fingerprint:
            combined["system_fingerprint"] = system_fingerprint
        return combined

    def bind_functions(
        self,
        functions: Sequence[Union[Dict[str, Any], Type[BaseModel], Callable, BaseTool]],
        function_call: Optional[
            Union[_FunctionCall, str, Literal["auto", "none"]]
        ] = None,
        **kwargs: Any,
    ) -> Runnable[LanguageModelInput, BaseMessage]:
        """Bind functions (and other objects) to this chat model.

        Model is compatible with OpenAI function-calling API.

        NOTE: Using bind_tools is recommended instead, as the `functions` and
            `function_call` request parameters are officially deprecated.

        Args:
            functions: A list of function definitions to bind to this chat model.
                Can be  a dictionary, pydantic model, or callable. Pydantic
                models and callables will be automatically converted to
                their schema dictionary representation.
            function_call: Which function to require the model to call.
                Must be the name of the single provided function or
                "auto" to automatically determine which function to call
                (if any).
            **kwargs: Any additional parameters to pass to the
                :class:`~langchain.runnable.Runnable` constructor.
        """

        formatted_functions = [convert_to_openai_function(fn) for fn in functions]
        if function_call is not None:
            function_call = (
                {"name": function_call}
                if isinstance(function_call, str)
                and function_call not in ("auto", "none")
                else function_call
            )
            if isinstance(function_call, dict) and len(formatted_functions) != 1:
                raise ValueError(
                    "When specifying `function_call`, you must provide exactly one "
                    "function."
                )
            if (
                isinstance(function_call, dict)
                and formatted_functions[0]["name"] != function_call["name"]
            ):
                raise ValueError(
                    f"Function call {function_call} was specified, but the only "
                    f"provided function was {formatted_functions[0]['name']}."
                )
            kwargs = {**kwargs, "function_call": function_call}
        return super().bind(
            functions=formatted_functions,
            **kwargs,
        )

    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, BaseMessage]:
        """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: Which tool to require the model to call.
                Must be the name of the single provided function,
                "auto" to automatically determine which function to call
                with the option to not call any function, "any" to enforce that some
                function is called, or a dict of the form:
                {"type": "function", "function": {"name": <<tool_name>>}}.
            **kwargs: Any additional parameters to pass to the
                :class:`~langchain.runnable.Runnable` constructor.
        """

        formatted_tools = [convert_to_openai_tool(tool) for tool in tools]
        if tool_choice is not None and tool_choice:
            if isinstance(tool_choice, str) and (
                tool_choice not in ("auto", "any", "none")
            ):
                tool_choice = {"type": "function", "function": {"name": tool_choice}}
            if isinstance(tool_choice, dict) and (len(formatted_tools) != 1):
                raise ValueError(
                    "When specifying `tool_choice`, you must provide exactly one "
                    f"tool. Received {len(formatted_tools)} tools."
                )
            if isinstance(tool_choice, dict) and (
                formatted_tools[0]["function"]["name"]
                != tool_choice["function"]["name"]
            ):
                raise ValueError(
                    f"Tool choice {tool_choice} was specified, but the only "
                    f"provided tool was {formatted_tools[0]['function']['name']}."
                )
            if isinstance(tool_choice, bool):
                if len(tools) > 1:
                    raise ValueError(
                        "tool_choice can only be True when there is one tool. Received "
                        f"{len(tools)} tools."
                    )
                tool_name = formatted_tools[0]["function"]["name"]
                tool_choice = {
                    "type": "function",
                    "function": {"name": tool_name},
                }

            kwargs["tool_choice"] = tool_choice
        return super().bind(tools=formatted_tools, **kwargs)

    @beta()
    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". If "function_calling" then the schema will be converted
                to a OpenAI function and the returned model will make use of the
                function-calling API. If "json_mode" then Groq's JSON mode will be
                used. Note that if using "json_mode" then you must include instructions
                for formatting the output into the desired schema into the model call.
            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_groq import ChatGroq
                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 = ChatGroq(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_groq import ChatGroq
                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 = ChatGroq(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_groq import ChatGroq
                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 = ChatGroq(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'}
                # }

        Example: JSON mode, Pydantic schema (method="json_mode", include_raw=True):
            .. code-block::

                from langchain_groq import ChatGroq
                from langchain_core.pydantic_v1 import BaseModel

                class AnswerWithJustification(BaseModel):
                    answer: str
                    justification: str

                llm = ChatGroq(temperature=0)
                structured_llm = llm.with_structured_output(
                    AnswerWithJustification,
                    method="json_mode",
                    include_raw=True
                )

                structured_llm.invoke(
                    "Answer the following question. "
                    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
                    "What's heavier a pound of bricks or a pound of feathers?"
                )
                # -> {
                #     'raw': AIMessage(content='{\n  "answer": "A pound of bricks is the same weight as a pound of feathers.",\n  "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The material being weighed does not affect the weight, only the volume or number of items being weighed."\n}', id='run-e5453bc5-5025-4833-95f9-4967bf6d5c4f-0'),
                #     'parsed': AnswerWithJustification(answer='A pound of bricks is the same weight as a pound of feathers.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The material being weighed does not affect the weight, only the volume or number of items being weighed.'),
                #     'parsing_error': None
                # }

        Example: JSON mode, no schema (schema=None, method="json_mode", include_raw=True):
            .. code-block::

                from langchain_groq import ChatGroq

                llm = ChatGroq(temperature=0)
                structured_llm = llm.with_structured_output(method="json_mode", include_raw=True)

                structured_llm.invoke(
                    "Answer the following question. "
                    "Make sure to return a JSON blob with keys 'answer' and 'justification'.\n\n"
                    "What's heavier a pound of bricks or a pound of feathers?"
                )
                # -> {
                #     'raw': AIMessage(content='{\n  "answer": "A pound of bricks is the same weight as a pound of feathers.",\n  "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The material doesn\'t change the weight, only the volume or space that the material takes up."\n}', id='run-a4abbdb6-c20e-456f-bfff-da906a7e76b5-0'),
                #     'parsed': {
                #         'answer': 'A pound of bricks is the same weight as a pound of feathers.',
                #         'justification': "Both a pound of bricks and a pound of feathers weigh one pound. The material doesn't change the weight, only the volume or space that the material takes up."},
                #     'parsing_error': None
                # }


        """  # 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."
                )
            llm = self.bind_tools([schema], tool_choice=True)
            if is_pydantic_schema:
                output_parser: OutputParserLike = PydanticToolsParser(
                    tools=[schema], first_tool_only=True
                )
            else:
                key_name = convert_to_openai_tool(schema)["function"]["name"]
                output_parser = JsonOutputKeyToolsParser(
                    key_name=key_name, first_tool_only=True
                )
        elif method == "json_mode":
            llm = self.bind(response_format={"type": "json_object"})
            output_parser = (
                PydanticOutputParser(pydantic_object=schema)
                if is_pydantic_schema
                else JsonOutputParser()
            )
        else:
            raise ValueError(
                f"Unrecognized method argument. Expected one of 'function_calling' or "
                f"'json_format'. 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


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


class _FunctionCall(TypedDict):
    name: str


#
# Type conversion helpers
#
def _convert_message_to_dict(message: BaseMessage) -> dict:
    """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, 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
        if "tool_calls" in message.additional_kwargs:
            message_dict["tool_calls"] = message.additional_kwargs["tool_calls"]
            # If tool calls only, content is None not empty string
            if message_dict["content"] == "":
                message_dict["content"] = None
    elif isinstance(message, SystemMessage):
        message_dict = {"role": "system", "content": message.content}
    elif isinstance(message, FunctionMessage):
        message_dict = {
            "role": "function",
            "content": message.content,
            "name": message.name,
        }
    elif isinstance(message, ToolMessage):
        message_dict = {
            "role": "tool",
            "content": message.content,
            "tool_call_id": message.tool_call_id,
        }
    else:
        raise TypeError(f"Got unknown type {message}")
    if "name" in message.additional_kwargs:
        message_dict["name"] = message.additional_kwargs["name"]
    return message_dict


def _convert_delta_to_message_chunk(
    _dict: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
    role = cast(str, _dict.get("role"))
    content = cast(str, _dict.get("content") or "")
    additional_kwargs: Dict = {}
    if _dict.get("function_call"):
        function_call = dict(_dict["function_call"])
        if "name" in function_call and function_call["name"] is None:
            function_call["name"] = ""
        additional_kwargs["function_call"] = function_call
    if _dict.get("tool_calls"):
        additional_kwargs["tool_calls"] = _dict["tool_calls"]

    if role == "user" or default_class == HumanMessageChunk:
        return HumanMessageChunk(content=content)
    elif role == "assistant" or default_class == AIMessageChunk:
        return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
    elif role == "system" or default_class == SystemMessageChunk:
        return SystemMessageChunk(content=content)
    elif role == "function" or default_class == FunctionMessageChunk:
        return FunctionMessageChunk(content=content, name=_dict["name"])
    elif role == "tool" or default_class == ToolMessageChunk:
        return ToolMessageChunk(content=content, tool_call_id=_dict["tool_call_id"])
    elif role or default_class == ChatMessageChunk:
        return ChatMessageChunk(content=content, role=role)
    else:
        return default_class(content=content)  # type: ignore


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

    Args:
        _dict: The dictionary.

    Returns:
        The LangChain message.
    """
    id_ = _dict.get("id")
    role = _dict.get("role")
    if role == "user":
        return HumanMessage(content=_dict.get("content", ""))
    elif role == "assistant":
        content = _dict.get("content", "") or ""
        additional_kwargs: Dict = {}
        if function_call := _dict.get("function_call"):
            additional_kwargs["function_call"] = dict(function_call)
        if tool_calls := _dict.get("tool_calls"):
            additional_kwargs["tool_calls"] = tool_calls
        return AIMessage(content=content, id=id_, additional_kwargs=additional_kwargs)
    elif role == "system":
        return SystemMessage(content=_dict.get("content", ""))
    elif role == "function":
        return FunctionMessage(content=_dict.get("content", ""), name=_dict.get("name"))
    elif role == "tool":
        additional_kwargs = {}
        if "name" in _dict:
            additional_kwargs["name"] = _dict["name"]
        return ToolMessage(
            content=_dict.get("content", ""),
            tool_call_id=_dict.get("tool_call_id"),
            additional_kwargs=additional_kwargs,
        )
    else:
        return ChatMessage(content=_dict.get("content", ""), role=role)