mirror of
https://github.com/hwchase17/langchain
synced 2024-11-08 07:10:35 +00:00
25fbe356b4
This PR upgrades community to a recent version of mypy. It inserts type: ignore on all existing failures.
372 lines
12 KiB
Python
372 lines
12 KiB
Python
from typing import (
|
|
Any,
|
|
AsyncIterator,
|
|
Callable,
|
|
Dict,
|
|
Iterator,
|
|
List,
|
|
Optional,
|
|
Type,
|
|
Union,
|
|
)
|
|
|
|
from langchain_core._api.deprecation import deprecated
|
|
from langchain_core.callbacks import (
|
|
AsyncCallbackManagerForLLMRun,
|
|
CallbackManagerForLLMRun,
|
|
)
|
|
from langchain_core.language_models.chat_models import BaseChatModel
|
|
from langchain_core.language_models.llms import create_base_retry_decorator
|
|
from langchain_core.messages import (
|
|
AIMessage,
|
|
AIMessageChunk,
|
|
BaseMessage,
|
|
BaseMessageChunk,
|
|
ChatMessage,
|
|
ChatMessageChunk,
|
|
FunctionMessage,
|
|
FunctionMessageChunk,
|
|
HumanMessage,
|
|
HumanMessageChunk,
|
|
SystemMessage,
|
|
SystemMessageChunk,
|
|
)
|
|
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
|
|
from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
|
|
from langchain_core.utils import convert_to_secret_str
|
|
from langchain_core.utils.env import get_from_dict_or_env
|
|
|
|
from langchain_community.adapters.openai import convert_message_to_dict
|
|
|
|
|
|
def _convert_delta_to_message_chunk(
|
|
_dict: Any, default_class: Type[BaseMessageChunk]
|
|
) -> BaseMessageChunk:
|
|
"""Convert a delta response to a message chunk."""
|
|
role = _dict.role
|
|
content = _dict.content or ""
|
|
additional_kwargs: Dict = {}
|
|
|
|
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 or default_class == ChatMessageChunk:
|
|
return ChatMessageChunk(content=content, role=role)
|
|
else:
|
|
return default_class(content=content) # type: ignore[call-arg]
|
|
|
|
|
|
def convert_dict_to_message(_dict: Any) -> BaseMessage:
|
|
"""Convert a dict response to a message."""
|
|
role = _dict.role
|
|
content = _dict.content or ""
|
|
if role == "user":
|
|
return HumanMessage(content=content)
|
|
elif role == "assistant":
|
|
content = _dict.content
|
|
additional_kwargs: Dict = {}
|
|
return AIMessage(content=content, additional_kwargs=additional_kwargs)
|
|
elif role == "system":
|
|
return SystemMessage(content=content)
|
|
elif role == "function":
|
|
return FunctionMessage(content=content, name=_dict.name)
|
|
else:
|
|
return ChatMessage(content=content, role=role)
|
|
|
|
|
|
@deprecated(
|
|
since="0.0.26",
|
|
removal="0.3",
|
|
alternative_import="langchain_fireworks.ChatFireworks",
|
|
)
|
|
class ChatFireworks(BaseChatModel):
|
|
"""Fireworks Chat models."""
|
|
|
|
model: str = "accounts/fireworks/models/llama-v2-7b-chat"
|
|
model_kwargs: dict = Field(
|
|
default_factory=lambda: {
|
|
"temperature": 0.7,
|
|
"max_tokens": 512,
|
|
"top_p": 1,
|
|
}.copy()
|
|
)
|
|
fireworks_api_key: Optional[SecretStr] = None
|
|
max_retries: int = 20
|
|
use_retry: bool = True
|
|
|
|
@property
|
|
def lc_secrets(self) -> Dict[str, str]:
|
|
return {"fireworks_api_key": "FIREWORKS_API_KEY"}
|
|
|
|
@classmethod
|
|
def is_lc_serializable(cls) -> bool:
|
|
return True
|
|
|
|
@classmethod
|
|
def get_lc_namespace(cls) -> List[str]:
|
|
"""Get the namespace of the langchain object."""
|
|
return ["langchain", "chat_models", "fireworks"]
|
|
|
|
@root_validator()
|
|
def validate_environment(cls, values: Dict) -> Dict:
|
|
"""Validate that api key in environment."""
|
|
try:
|
|
import fireworks.client
|
|
except ImportError as e:
|
|
raise ImportError(
|
|
"Could not import fireworks-ai python package. "
|
|
"Please install it with `pip install fireworks-ai`."
|
|
) from e
|
|
fireworks_api_key = convert_to_secret_str(
|
|
get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY")
|
|
)
|
|
fireworks.client.api_key = fireworks_api_key.get_secret_value()
|
|
return values
|
|
|
|
@property
|
|
def _llm_type(self) -> str:
|
|
"""Return type of llm."""
|
|
return "fireworks-chat"
|
|
|
|
def _generate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
message_dicts = self._create_message_dicts(messages)
|
|
|
|
params = {
|
|
"model": self.model,
|
|
"messages": message_dicts,
|
|
**self.model_kwargs,
|
|
**kwargs,
|
|
}
|
|
response = completion_with_retry(
|
|
self,
|
|
self.use_retry,
|
|
run_manager=run_manager,
|
|
stop=stop,
|
|
**params,
|
|
)
|
|
return self._create_chat_result(response)
|
|
|
|
async def _agenerate(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> ChatResult:
|
|
message_dicts = self._create_message_dicts(messages)
|
|
params = {
|
|
"model": self.model,
|
|
"messages": message_dicts,
|
|
**self.model_kwargs,
|
|
**kwargs,
|
|
}
|
|
response = await acompletion_with_retry(
|
|
self, self.use_retry, run_manager=run_manager, stop=stop, **params
|
|
)
|
|
return self._create_chat_result(response)
|
|
|
|
def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
|
|
if llm_outputs[0] is None:
|
|
return {}
|
|
return llm_outputs[0]
|
|
|
|
def _create_chat_result(self, response: Any) -> ChatResult:
|
|
generations = []
|
|
for res in response.choices:
|
|
message = convert_dict_to_message(res.message)
|
|
gen = ChatGeneration(
|
|
message=message,
|
|
generation_info=dict(finish_reason=res.finish_reason),
|
|
)
|
|
generations.append(gen)
|
|
llm_output = {"model": self.model}
|
|
return ChatResult(generations=generations, llm_output=llm_output)
|
|
|
|
def _create_message_dicts(
|
|
self, messages: List[BaseMessage]
|
|
) -> List[Dict[str, Any]]:
|
|
message_dicts = [convert_message_to_dict(m) for m in messages]
|
|
return message_dicts
|
|
|
|
def _stream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Iterator[ChatGenerationChunk]:
|
|
message_dicts = self._create_message_dicts(messages)
|
|
default_chunk_class = AIMessageChunk
|
|
params = {
|
|
"model": self.model,
|
|
"messages": message_dicts,
|
|
"stream": True,
|
|
**self.model_kwargs,
|
|
**kwargs,
|
|
}
|
|
for chunk in completion_with_retry(
|
|
self, self.use_retry, run_manager=run_manager, stop=stop, **params
|
|
):
|
|
choice = chunk.choices[0]
|
|
chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class)
|
|
finish_reason = choice.finish_reason
|
|
generation_info = (
|
|
dict(finish_reason=finish_reason) if finish_reason is not None else None
|
|
)
|
|
default_chunk_class = chunk.__class__
|
|
cg_chunk = ChatGenerationChunk(
|
|
message=chunk, generation_info=generation_info
|
|
)
|
|
if run_manager:
|
|
run_manager.on_llm_new_token(cg_chunk.text, chunk=cg_chunk)
|
|
yield cg_chunk
|
|
|
|
async def _astream(
|
|
self,
|
|
messages: List[BaseMessage],
|
|
stop: Optional[List[str]] = None,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> AsyncIterator[ChatGenerationChunk]:
|
|
message_dicts = self._create_message_dicts(messages)
|
|
default_chunk_class = AIMessageChunk
|
|
params = {
|
|
"model": self.model,
|
|
"messages": message_dicts,
|
|
"stream": True,
|
|
**self.model_kwargs,
|
|
**kwargs,
|
|
}
|
|
async for chunk in await acompletion_with_retry_streaming(
|
|
self, self.use_retry, run_manager=run_manager, stop=stop, **params
|
|
):
|
|
choice = chunk.choices[0]
|
|
chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class)
|
|
finish_reason = choice.finish_reason
|
|
generation_info = (
|
|
dict(finish_reason=finish_reason) if finish_reason is not None else None
|
|
)
|
|
default_chunk_class = chunk.__class__
|
|
cg_chunk = ChatGenerationChunk(
|
|
message=chunk, generation_info=generation_info
|
|
)
|
|
if run_manager:
|
|
await run_manager.on_llm_new_token(token=chunk.text, chunk=cg_chunk)
|
|
yield cg_chunk
|
|
|
|
|
|
def conditional_decorator(
|
|
condition: bool, decorator: Callable[[Any], Any]
|
|
) -> Callable[[Any], Any]:
|
|
"""Define conditional decorator.
|
|
|
|
Args:
|
|
condition: The condition.
|
|
decorator: The decorator.
|
|
|
|
Returns:
|
|
The decorated function.
|
|
"""
|
|
|
|
def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
|
|
if condition:
|
|
return decorator(func)
|
|
return func
|
|
|
|
return actual_decorator
|
|
|
|
|
|
def completion_with_retry(
|
|
llm: ChatFireworks,
|
|
use_retry: bool,
|
|
*,
|
|
run_manager: Optional[CallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use tenacity to retry the completion call."""
|
|
import fireworks.client
|
|
|
|
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
|
|
|
|
@conditional_decorator(use_retry, retry_decorator)
|
|
def _completion_with_retry(**kwargs: Any) -> Any:
|
|
"""Use tenacity to retry the completion call."""
|
|
return fireworks.client.ChatCompletion.create(
|
|
**kwargs,
|
|
)
|
|
|
|
return _completion_with_retry(**kwargs)
|
|
|
|
|
|
async def acompletion_with_retry(
|
|
llm: ChatFireworks,
|
|
use_retry: bool,
|
|
*,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use tenacity to retry the async completion call."""
|
|
import fireworks.client
|
|
|
|
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
|
|
|
|
@conditional_decorator(use_retry, retry_decorator)
|
|
async def _completion_with_retry(**kwargs: Any) -> Any:
|
|
return await fireworks.client.ChatCompletion.acreate(
|
|
**kwargs,
|
|
)
|
|
|
|
return await _completion_with_retry(**kwargs)
|
|
|
|
|
|
async def acompletion_with_retry_streaming(
|
|
llm: ChatFireworks,
|
|
use_retry: bool,
|
|
*,
|
|
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
|
|
**kwargs: Any,
|
|
) -> Any:
|
|
"""Use tenacity to retry the completion call for streaming."""
|
|
import fireworks.client
|
|
|
|
retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
|
|
|
|
@conditional_decorator(use_retry, retry_decorator)
|
|
async def _completion_with_retry(**kwargs: Any) -> Any:
|
|
return fireworks.client.ChatCompletion.acreate(
|
|
**kwargs,
|
|
)
|
|
|
|
return await _completion_with_retry(**kwargs)
|
|
|
|
|
|
def _create_retry_decorator(
|
|
llm: ChatFireworks,
|
|
run_manager: Optional[
|
|
Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
|
|
] = None,
|
|
) -> Callable[[Any], Any]:
|
|
"""Define retry mechanism."""
|
|
import fireworks.client
|
|
|
|
errors = [
|
|
fireworks.client.error.RateLimitError,
|
|
fireworks.client.error.InternalServerError,
|
|
fireworks.client.error.BadGatewayError,
|
|
fireworks.client.error.ServiceUnavailableError,
|
|
]
|
|
return create_base_retry_decorator(
|
|
error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
|
|
)
|