"""Fireworks chat wrapper."""
from __future__ import annotations
import json
import logging
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 deprecated
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LanguageModelInput
from langchain_core.language_models.chat_models import (
BaseChatModel,
LangSmithParams,
agenerate_from_stream,
generate_from_stream,
)
from langchain_core.messages import (
AIMessage,
AIMessageChunk,
BaseMessage,
BaseMessageChunk,
ChatMessage,
ChatMessageChunk,
FunctionMessage,
FunctionMessageChunk,
HumanMessage,
HumanMessageChunk,
InvalidToolCall,
SystemMessage,
SystemMessageChunk,
ToolCall,
ToolMessage,
ToolMessageChunk,
)
from langchain_core.messages.tool import (
ToolCallChunk,
)
from langchain_core.messages.tool import (
tool_call_chunk as create_tool_call_chunk,
)
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,
make_invalid_tool_call,
parse_tool_call,
)
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import Runnable, RunnableMap, RunnablePassthrough
from langchain_core.tools import BaseTool
from langchain_core.utils import (
get_pydantic_field_names,
)
from langchain_core.utils.function_calling import (
convert_to_openai_function,
convert_to_openai_tool,
)
from langchain_core.utils.pydantic import is_basemodel_subclass
from langchain_core.utils.utils import _build_model_kwargs, from_env, secret_from_env
from pydantic import (
BaseModel,
ConfigDict,
Field,
SecretStr,
model_validator,
)
from typing_extensions import Self
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)
tool_calls = []
invalid_tool_calls = []
if raw_tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
for raw_tool_call in raw_tool_calls:
try:
tool_calls.append(parse_tool_call(raw_tool_call, return_id=True))
except Exception as e:
invalid_tool_calls.append(
dict(make_invalid_tool_call(raw_tool_call, str(e)))
)
return AIMessage(
content=content,
additional_kwargs=additional_kwargs,
tool_calls=tool_calls,
invalid_tool_calls=invalid_tool_calls,
)
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 message.tool_calls or message.invalid_tool_calls:
message_dict["tool_calls"] = [
_lc_tool_call_to_fireworks_tool_call(tc) for tc in message.tool_calls
] + [
_lc_invalid_tool_call_to_fireworks_tool_call(tc)
for tc in message.invalid_tool_calls
]
elif "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 "tool_calls" in message_dict and message_dict["content"] == "":
message_dict["content"] = None
else:
pass
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_chunk_to_message_chunk(
chunk: Mapping[str, Any], default_class: Type[BaseMessageChunk]
) -> BaseMessageChunk:
choice = chunk["choices"][0]
_dict = choice["delta"]
role = cast(str, _dict.get("role"))
content = cast(str, _dict.get("content") or "")
additional_kwargs: Dict = {}
tool_call_chunks: List[ToolCallChunk] = []
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 raw_tool_calls := _dict.get("tool_calls"):
additional_kwargs["tool_calls"] = raw_tool_calls
for rtc in raw_tool_calls:
try:
tool_call_chunks.append(
create_tool_call_chunk(
name=rtc["function"].get("name"),
args=rtc["function"].get("arguments"),
id=rtc.get("id"),
index=rtc.get("index"),
)
)
except KeyError:
pass
if role == "user" or default_class == HumanMessageChunk:
return HumanMessageChunk(content=content)
elif role == "assistant" or default_class == AIMessageChunk:
if usage := chunk.get("usage"):
input_tokens = usage.get("prompt_tokens", 0)
output_tokens = usage.get("completion_tokens", 0)
usage_metadata = {
"input_tokens": input_tokens,
"output_tokens": output_tokens,
"total_tokens": usage.get("total_tokens", input_tokens + output_tokens),
}
else:
usage_metadata = None
return AIMessageChunk(
content=content,
additional_kwargs=additional_kwargs,
tool_call_chunks=tool_call_chunks,
usage_metadata=usage_metadata, # type: ignore[arg-type]
)
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
[docs]
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."""
stop: Optional[Union[str, List[str]]] = Field(default=None, alias="stop_sequences")
"""Default stop sequences."""
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(
alias="api_key",
default_factory=secret_from_env(
"FIREWORKS_API_KEY",
error_message=(
"You must specify an api key. "
"You can pass it an argument as `api_key=...` or "
"set the environment variable `FIREWORKS_API_KEY`."
),
),
)
"""Fireworks API key.
Automatically read from env variable `FIREWORKS_API_KEY` if not provided.
"""
fireworks_api_base: Optional[str] = Field(
alias="base_url", default_factory=from_env("FIREWORKS_API_BASE", default=None)
)
"""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."""
max_retries: Optional[int] = None
"""Maximum number of retries to make when generating."""
model_config = ConfigDict(
populate_by_name=True,
)
@model_validator(mode="before")
@classmethod
def build_extra(cls, values: Dict[str, Any]) -> Any:
"""Build extra kwargs from additional params that were passed in."""
all_required_field_names = get_pydantic_field_names(cls)
values = _build_model_kwargs(values, all_required_field_names)
return values
@model_validator(mode="after")
def validate_environment(self) -> Self:
"""Validate that api key and python package exists in environment."""
if self.n < 1:
raise ValueError("n must be at least 1.")
if self.n > 1 and self.streaming:
raise ValueError("n must be 1 when streaming.")
client_params = {
"api_key": (
self.fireworks_api_key.get_secret_value()
if self.fireworks_api_key
else None
),
"base_url": self.fireworks_api_base,
"timeout": self.request_timeout,
}
if not self.client:
self.client = Fireworks(**client_params).chat.completions
if not self.async_client:
self.async_client = AsyncFireworks(**client_params).chat.completions
if self.max_retries:
self.client._max_retries = self.max_retries
self.async_client._max_retries = self.max_retries
return self
@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,
"stop": self.stop,
**self.model_kwargs,
}
if self.max_tokens is not None:
params["max_tokens"] = self.max_tokens
return params
def _get_ls_params(
self, stop: Optional[List[str]] = None, **kwargs: Any
) -> LangSmithParams:
"""Get standard params for tracing."""
params = self._get_invocation_params(stop=stop, **kwargs)
ls_params = LangSmithParams(
ls_provider="fireworks",
ls_model_name=self.model_name,
ls_model_type="chat",
ls_temperature=params.get("temperature", self.temperature),
)
if ls_max_tokens := params.get("max_tokens", self.max_tokens):
ls_params["ls_max_tokens"] = ls_max_tokens
if ls_stop := stop or params.get("stop", None):
ls_params["ls_stop"] = ls_stop
return ls_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: Type[BaseMessageChunk] = AIMessageChunk
for chunk in self.client.create(messages=message_dicts, **params):
if not isinstance(chunk, dict):
chunk = chunk.model_dump()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
message_chunk = _convert_chunk_to_message_chunk(chunk, 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 = message_chunk.__class__
generation_chunk = ChatGenerationChunk(
message=message_chunk, generation_info=generation_info or None
)
if run_manager:
run_manager.on_llm_new_token(
generation_chunk.text, chunk=generation_chunk, logprobs=logprobs
)
yield generation_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:
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.model_dump()
token_usage = response.get("usage", {})
for res in response["choices"]:
message = _convert_dict_to_message(res["message"])
if token_usage and isinstance(message, AIMessage):
message.usage_metadata = {
"input_tokens": token_usage.get("prompt_tokens", 0),
"output_tokens": token_usage.get("completion_tokens", 0),
"total_tokens": token_usage.get("total_tokens", 0),
}
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)
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: Type[BaseMessageChunk] = AIMessageChunk
async for chunk in self.async_client.acreate(messages=message_dicts, **params):
if not isinstance(chunk, dict):
chunk = chunk.model_dump()
if len(chunk["choices"]) == 0:
continue
choice = chunk["choices"][0]
message_chunk = _convert_chunk_to_message_chunk(chunk, 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 = message_chunk.__class__
generation_chunk = ChatGenerationChunk(
message=message_chunk, generation_info=generation_info or None
)
if run_manager:
await run_manager.on_llm_new_token(
token=generation_chunk.text,
chunk=generation_chunk,
logprobs=logprobs,
)
yield generation_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"
[docs]
@deprecated(
since="0.2.1",
alternative="langchain_fireworks.chat_models.ChatFireworks.bind_tools",
removal="0.3.0",
)
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,
)
[docs]
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. Can be passed in as:
- an OpenAI function/tool schema,
- a JSON Schema,
- a TypedDict class (support added in 0.1.7),
- or a Pydantic class.
If ``schema`` is a Pydantic class then the model output will be a
Pydantic instance of that class, and the model-generated fields will be
validated by the Pydantic class. Otherwise the model output will be a
dict and will not be validated. See :meth:`langchain_core.utils.function_calling.convert_to_openai_tool`
for more on how to properly specify types and descriptions of
schema fields when specifying a Pydantic or TypedDict class.
.. versionchanged:: 0.1.7
Added support for TypedDict class.
method:
The method for steering model generation, either "function_calling"
or "json_mode". If "function_calling" then the schema will be converted
to an OpenAI function and the returned model will make use of the
function-calling API. If "json_mode" then OpenAI'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 same inputs as a :class:`langchain_core.language_models.chat.BaseChatModel`.
If ``include_raw`` is False and ``schema`` is a Pydantic class, Runnable outputs
an instance of ``schema`` (i.e., a Pydantic object).
Otherwise, if ``include_raw`` is False then Runnable outputs a dict.
If ``include_raw`` is True, then Runnable outputs a dict with keys:
- ``"raw"``: BaseMessage
- ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above.
- ``"parsing_error"``: Optional[BaseException]
Example: schema=Pydantic class, method="function_calling", include_raw=False:
.. code-block:: python
from typing import Optional
from langchain_fireworks import ChatFireworks
from pydantic import BaseModel, Field
class AnswerWithJustification(BaseModel):
'''An answer to the user question along with justification for the answer.'''
answer: str
# If we provide default values and/or descriptions for fields, these will be passed
# to the model. This is an important part of improving a model's ability to
# correctly return structured outputs.
justification: Optional[str] = Field(
default=None, description="A justification for the answer."
)
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: schema=Pydantic class, method="function_calling", include_raw=True:
.. code-block:: python
from langchain_fireworks import ChatFireworks
from pydantic 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: schema=TypedDict class, method="function_calling", include_raw=False:
.. code-block:: python
# IMPORTANT: If you are using Python <=3.8, you need to import Annotated
# from typing_extensions, not from typing.
from typing_extensions import Annotated, TypedDict
from langchain_fireworks import ChatFireworks
class AnswerWithJustification(TypedDict):
'''An answer to the user question along with justification for the answer.'''
answer: str
justification: Annotated[
Optional[str], None, "A justification for the answer."
]
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"
)
# -> {
# '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: schema=OpenAI function schema, method="function_calling", include_raw=False:
.. code-block:: python
from langchain_fireworks import ChatFireworks
oai_schema = {
'name': 'AnswerWithJustification',
'description': 'An answer to the user question along with justification for the answer.',
'parameters': {
'type': 'object',
'properties': {
'answer': {'type': 'string'},
'justification': {'description': 'A justification for the answer.', 'type': 'string'}
},
'required': ['answer']
}
}
llm = ChatFireworks(model="accounts/fireworks/models/firefunction-v1", temperature=0)
structured_llm = llm.with_structured_output(oai_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: schema=Pydantic class, method="json_mode", include_raw=True:
.. code-block::
from langchain_fireworks import ChatFireworks
from pydantic 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: schema=None, method="json_mode", include_raw=True:
.. code-block::
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."
)
tool_name = convert_to_openai_tool(schema)["function"]["name"]
llm = self.bind_tools([schema], tool_choice=tool_name)
if is_pydantic_schema:
output_parser: OutputParserLike = PydanticToolsParser(
tools=[schema], # type: ignore[list-item]
first_tool_only=True, # type: ignore[list-item]
)
else:
output_parser = JsonOutputKeyToolsParser(
key_name=tool_name, first_tool_only=True
)
elif method == "json_mode":
llm = self.bind(response_format={"type": "json_object"})
output_parser = (
PydanticOutputParser(pydantic_object=schema) # type: ignore[type-var, arg-type]
if is_pydantic_schema
else JsonOutputParser()
)
else:
raise ValueError(
f"Unrecognized method argument. Expected one of 'function_calling' or "
f"'json_mode'. 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 is_basemodel_subclass(obj)
def _lc_tool_call_to_fireworks_tool_call(tool_call: ToolCall) -> dict:
return {
"type": "function",
"id": tool_call["id"],
"function": {
"name": tool_call["name"],
"arguments": json.dumps(tool_call["args"]),
},
}
def _lc_invalid_tool_call_to_fireworks_tool_call(
invalid_tool_call: InvalidToolCall,
) -> dict:
return {
"type": "function",
"id": invalid_tool_call["id"],
"function": {
"name": invalid_tool_call["name"],
"arguments": invalid_tool_call["args"],
},
}
