import json 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.messages.tool import tool_call_chunk from langchain_core.output_parsers.base import OutputParserLike from langchain_core.output_parsers.openai_tools import ( JsonOutputKeyToolsParser, PydanticToolsParser, ) from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough from langchain_core.tools import BaseTool from langchain_core.utils import convert_to_secret_str, get_from_dict_or_env from langchain_core.utils.function_calling import convert_to_openai_tool from langchain_core.utils.pydantic import get_fields, is_basemodel_subclass from pydantic import ( BaseModel, ConfigDict, Field, SecretStr, model_validator, ) 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 len(message.tool_calls) != 0: tool_call = message.tool_calls[0] message_dict["function_call"] = { "name": tool_call["name"], "arguments": json.dumps(tool_call["args"], ensure_ascii=False), } # If function call only, content is None not empty string message_dict["content"] = None elif isinstance(message, (FunctionMessage, ToolMessage)): message_dict = { "role": "function", "content": _create_tool_content(message.content), "name": message.name or message.additional_kwargs.get("name"), } else: raise TypeError(f"Got unknown type {message}") return message_dict def _create_tool_content(content: Union[str, List[Union[str, Dict[Any, Any]]]]) -> str: """Convert tool content to dict scheme.""" if isinstance(content, str): try: if isinstance(json.loads(content), dict): return content else: return json.dumps({"tool_result": content}) except json.JSONDecodeError: return json.dumps({"tool_result": content}) else: return json.dumps({"tool_result": content}) 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") # DO NOT ADD ANY NUMERIC OBJECT TO `msg_additional_kwargs` AND `additional_kwargs` # ALONG WITH THEIRS SUB-CONTAINERS !!! # OR IT WILL RAISE A DEADLY EXCEPTION FROM `merge_dict` # 不要往 `msg_additional_kwargs` 和 `additional_kwargs` 里面加任何数值类对象！ # 子容器也不行！ # 不然 `merge_dict` 会报错导致代码无法运行 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", []), ) 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()), } ] ret = AIMessage( content=content, additional_kwargs=msg_additional_kwargs, ) if usage := additional_kwargs.get("usage", None): ret.usage_metadata = UsageMetadata( input_tokens=usage.get("prompt_tokens", 0), output_tokens=usage.get("completion_tokens", 0), total_tokens=usage.get("total_tokens", 0), ) return ret 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(messages) .. 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 stream = chat.stream(messages) 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 pydantic import BaseModel, Field class GetWeather(BaseModel): '''Get the current weather in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) class GetPopulation(BaseModel): '''Get the current population in a given location''' location: str = Field( ..., description="The city and state, e.g. San Francisco, CA" ) chat_with_tools = 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 pydantic import BaseModel, Field class Joke(BaseModel): '''Joke to tell user.''' setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") rating: Optional[int] = Field(description="How funny the joke is, from 1 to 10") structured_chat = 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 = None #: :meta private: # It could be empty due to the use of Console API # And they're not list here qianfan_ak: Optional[SecretStr] = Field(default=None, 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: Optional[str] = Field(default=None) """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 set by `qianfan` SDK, not here """ endpoint: Optional[str] = None """Endpoint of the Qianfan LLM, required if custom model used.""" model_config = ConfigDict( populate_by_name=True, ) @model_validator(mode="before") @classmethod def validate_environment(cls, values: Dict) -> Any: values["qianfan_ak"] = convert_to_secret_str( get_from_dict_or_env( values, ["qianfan_ak", "api_key"], "QIANFAN_AK", default="" ) ) values["qianfan_sk"] = convert_to_secret_str( get_from_dict_or_env( values, ["qianfan_sk", "secret_key"], "QIANFAN_SK", default="" ) ) default_values = { name: field.default for name, field in get_fields(cls).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 = {} usage_metadata: Optional[UsageMetadata] = None 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 if isinstance(chunk.message, AIMessageChunk): usage_metadata = chunk.message.usage_metadata lc_msg = AIMessage( content=completion, additional_kwargs={}, usage_metadata=usage_metadata, ) gen = ChatGeneration( message=lc_msg, generation_info=dict(finish_reason="stop"), ) return ChatResult( generations=[gen], llm_output={ "token_usage": usage_metadata or {}, "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 = {} usage_metadata: Optional[UsageMetadata] = None 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 if isinstance(chunk.message, AIMessageChunk): usage_metadata = chunk.message.usage_metadata lc_msg = AIMessage( content=completion, additional_kwargs={}, usage_metadata=usage_metadata, ) gen = ChatGeneration( message=lc_msg, generation_info=dict(finish_reason="stop"), ) return ChatResult( generations=[gen], llm_output={ "token_usage": usage_metadata or {}, "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, tool_call_chunks=[ tool_call_chunk( name=tc["name"], args=json.dumps(tc["args"]), id=tc["id"], index=None, ) for tc in msg.tool_calls ], ), 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, tool_call_chunks=[ tool_call_chunk( name=tc["name"], args=json.dumps(tc["args"]), id=tc["id"], index=None, ) for tc in msg.tool_calls ], ), 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 pydantic 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 pydantic 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 pydantic import BaseModel from langchain_core.utils.function_calling import convert_to_openai_tool class AnswerWithJustification(BaseModel): '''An answer to the user question along with justification for the answer.''' answer: str justification: str dict_schema = convert_to_openai_tool(AnswerWithJustification) llm = 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 is_basemodel_subclass(schema) 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