langchain/libs/partners/fireworks/langchain_fireworks/chat_models.py

"""Fireworks chat wrapper."""

from __future__ import annotations

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

from fireworks.client import AsyncFireworks, Fireworks  # type: ignore
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,
)
from langchain_core.utils.utils import build_extra_kwargs

logger = logging.getLogger(__name__)


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.
    """
    role = _dict.get("role")
    if role == "user":
        return HumanMessage(content=_dict.get("content", ""))
    elif role == "assistant":
        # Fix for azure
        # Also Fireworks returns None for tool invocations
        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, 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 or "")


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


class _FunctionCall(TypedDict):
    name: str


# This is basically a copy and replace for ChatFireworks, except
# - I needed to gut out tiktoken and some of the token estimation logic
# (not sure how important it is)
# - Environment variable is different
# we should refactor into some OpenAI-like class in the future
class ChatFireworks(BaseChatModel):
    """`Fireworks` Chat large language models API.

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

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

    Example:
        .. code-block:: python

            from langchain_fireworks.chat_models import ChatFireworks
            fireworks = ChatFireworks(
                model_name="accounts/fireworks/models/mixtral-8x7b-instruct")
    """

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

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

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

        return attributes

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

    client: Any = Field(default=None, exclude=True)  #: :meta private:
    async_client: Any = Field(default=None, exclude=True)  #: :meta private:
    model_name: str = Field(
        default="accounts/fireworks/models/mixtral-8x7b-instruct", alias="model"
    )
    """Model name to use."""
    temperature: float = 0.0
    """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."""
    fireworks_api_key: SecretStr = Field(default=None, alias="api_key")
    """Automatically inferred from env var `FIREWORKS_API_KEY` if not provided."""
    fireworks_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."""
    request_timeout: Union[float, Tuple[float, float], Any, None] = Field(
        default=None, alias="timeout"
    )
    """Timeout for requests to Fireworks completion API. Can be float, httpx.Timeout or 
        None."""
    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."""

    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", {})
        values["model_kwargs"] = build_extra_kwargs(
            extra, values, all_required_field_names
        )
        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.")

        values["fireworks_api_key"] = convert_to_secret_str(
            get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY")
        )
        values["fireworks_api_base"] = values["fireworks_api_base"] or os.getenv(
            "FIREWORKS_API_BASE"
        )
        client_params = {
            "api_key": (
                values["fireworks_api_key"].get_secret_value()
                if values["fireworks_api_key"]
                else None
            ),
            "base_url": values["fireworks_api_base"],
            "timeout": values["request_timeout"],
        }

        if not values.get("client"):
            values["client"] = Fireworks(**client_params).chat.completions
        if not values.get("async_client"):
            values["async_client"] = AsyncFireworks(**client_params).chat.completions
        return values

    @property
    def _default_params(self) -> Dict[str, Any]:
        """Get the default parameters for calling Fireworks 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 _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 _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)
        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

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = 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._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,
            **({"stream": stream} if stream is not None else {}),
            **kwargs,
        }
        response = self.client.create(messages=message_dicts, **params)
        return self._create_chat_result(response)

    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 _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)

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

        default_chunk_class = AIMessageChunk
        async for chunk in self.async_client.acreate(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

    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)

        message_dicts, params = self._create_message_dicts(messages, stop)
        params = {
            **params,
            **({"stream": stream} if stream is not None else {}),
            **kwargs,
        }
        response = await self.async_client.acreate(messages=message_dicts, **params)
        return self._create_chat_result(response)

    @property
    def _identifying_params(self) -> Dict[str, Any]:
        """Get the identifying parameters."""
        return {"model_name": self.model_name, **self._default_params}

    def _get_invocation_params(
        self, stop: Optional[List[str]] = None, **kwargs: Any
    ) -> Dict[str, Any]:
        """Get the parameters used to invoke the model."""
        return {
            "model": self.model_name,
            **super()._get_invocation_params(stop=stop),
            **self._default_params,
            **kwargs,
        }

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

    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.

        Assumes model is compatible with Fireworks function-calling API.

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

        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.

        Assumes model is compatible with Fireworks 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.
            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 Fireworks 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 Fireworks function and the returned model will make use of the
                function-calling API. If "json_mode" then Fireworks'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_fireworks import ChatFireworks
                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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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='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_fireworks import ChatFireworks
                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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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_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_fireworks import ChatFireworks
                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 = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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': '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.'
                # }

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

                from langchain_fireworks import ChatFireworks
                from langchain_core.pydantic_v1 import BaseModel

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

                llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
                #     'parsed': AnswerWithJustification(answer='They are both the same weight.', justification='Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'),
                #     'parsing_error': None
                # }

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

                from langchain_fireworks import ChatFireworks

                llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", 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": "They are both the same weight.",\n    "justification": "Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight." \n}'),
                #     'parsed': {
                #         'answer': 'They are both the same weight.',
                #         'justification': 'Both a pound of bricks and a pound of feathers weigh one pound. The difference lies in the volume and density of the materials, not the weight.'
                #     },
                #     '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)