mirror of
https://github.com/hwchase17/langchain.git
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b2fd41331e
Addded missed docstrings. Fixed inconsistency in docstrings. **Note** CC @efriis There were PR errors on `langchain_experimental/prompt_injection_identifier/hugging_face_identifier.py` But, I didn't touch this file in this PR! Can it be some cache problems? I fixed this error.
362 lines
12 KiB
Python
362 lines
12 KiB
Python
from typing import (
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Any,
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AsyncIterator,
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Callable,
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Dict,
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Iterator,
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List,
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Optional,
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Type,
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Union,
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)
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from langchain_core.callbacks import (
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AsyncCallbackManagerForLLMRun,
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CallbackManagerForLLMRun,
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)
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from langchain_core.language_models.chat_models import BaseChatModel
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from langchain_core.language_models.llms import create_base_retry_decorator
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from langchain_core.messages import (
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AIMessage,
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AIMessageChunk,
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BaseMessage,
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BaseMessageChunk,
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ChatMessage,
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ChatMessageChunk,
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FunctionMessage,
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FunctionMessageChunk,
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HumanMessage,
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HumanMessageChunk,
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SystemMessage,
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SystemMessageChunk,
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)
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from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
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from langchain_core.pydantic_v1 import Field, SecretStr, root_validator
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from langchain_core.utils import convert_to_secret_str
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from langchain_core.utils.env import get_from_dict_or_env
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from langchain_community.adapters.openai import convert_message_to_dict
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def _convert_delta_to_message_chunk(
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_dict: Any, default_class: Type[BaseMessageChunk]
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) -> BaseMessageChunk:
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"""Convert a delta response to a message chunk."""
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role = _dict.role
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content = _dict.content or ""
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additional_kwargs: Dict = {}
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if role == "user" or default_class == HumanMessageChunk:
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return HumanMessageChunk(content=content)
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elif role == "assistant" or default_class == AIMessageChunk:
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return AIMessageChunk(content=content, additional_kwargs=additional_kwargs)
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elif role == "system" or default_class == SystemMessageChunk:
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return SystemMessageChunk(content=content)
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elif role == "function" or default_class == FunctionMessageChunk:
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return FunctionMessageChunk(content=content, name=_dict.name)
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elif role or default_class == ChatMessageChunk:
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return ChatMessageChunk(content=content, role=role)
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else:
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return default_class(content=content)
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def convert_dict_to_message(_dict: Any) -> BaseMessage:
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"""Convert a dict response to a message."""
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role = _dict.role
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content = _dict.content or ""
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if role == "user":
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return HumanMessage(content=content)
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elif role == "assistant":
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content = _dict.content
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additional_kwargs: Dict = {}
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return AIMessage(content=content, additional_kwargs=additional_kwargs)
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elif role == "system":
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return SystemMessage(content=content)
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elif role == "function":
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return FunctionMessage(content=content, name=_dict.name)
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else:
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return ChatMessage(content=content, role=role)
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class ChatFireworks(BaseChatModel):
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"""Fireworks Chat models."""
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model: str = "accounts/fireworks/models/llama-v2-7b-chat"
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model_kwargs: dict = Field(
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default_factory=lambda: {
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"temperature": 0.7,
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"max_tokens": 512,
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"top_p": 1,
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}.copy()
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)
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fireworks_api_key: Optional[SecretStr] = None
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max_retries: int = 20
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use_retry: bool = True
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@property
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def lc_secrets(self) -> Dict[str, str]:
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return {"fireworks_api_key": "FIREWORKS_API_KEY"}
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@classmethod
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def is_lc_serializable(cls) -> bool:
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return True
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@classmethod
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def get_lc_namespace(cls) -> List[str]:
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"""Get the namespace of the langchain object."""
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return ["langchain", "chat_models", "fireworks"]
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@root_validator()
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def validate_environment(cls, values: Dict) -> Dict:
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"""Validate that api key in environment."""
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try:
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import fireworks.client
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except ImportError as e:
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raise ImportError(
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"Could not import fireworks-ai python package. "
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"Please install it with `pip install fireworks-ai`."
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) from e
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fireworks_api_key = convert_to_secret_str(
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get_from_dict_or_env(values, "fireworks_api_key", "FIREWORKS_API_KEY")
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)
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fireworks.client.api_key = fireworks_api_key.get_secret_value()
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return values
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@property
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def _llm_type(self) -> str:
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"""Return type of llm."""
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return "fireworks-chat"
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def _generate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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message_dicts = self._create_message_dicts(messages)
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params = {
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"model": self.model,
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"messages": message_dicts,
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**self.model_kwargs,
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**kwargs,
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}
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response = completion_with_retry(
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self,
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self.use_retry,
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run_manager=run_manager,
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stop=stop,
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**params,
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)
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return self._create_chat_result(response)
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async def _agenerate(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> ChatResult:
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message_dicts = self._create_message_dicts(messages)
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params = {
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"model": self.model,
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"messages": message_dicts,
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**self.model_kwargs,
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**kwargs,
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}
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response = await acompletion_with_retry(
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self, self.use_retry, run_manager=run_manager, stop=stop, **params
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)
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return self._create_chat_result(response)
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def _combine_llm_outputs(self, llm_outputs: List[Optional[dict]]) -> dict:
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if llm_outputs[0] is None:
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return {}
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return llm_outputs[0]
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def _create_chat_result(self, response: Any) -> ChatResult:
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generations = []
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for res in response.choices:
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message = convert_dict_to_message(res.message)
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gen = ChatGeneration(
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message=message,
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generation_info=dict(finish_reason=res.finish_reason),
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)
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generations.append(gen)
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llm_output = {"model": self.model}
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return ChatResult(generations=generations, llm_output=llm_output)
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def _create_message_dicts(
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self, messages: List[BaseMessage]
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) -> List[Dict[str, Any]]:
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message_dicts = [convert_message_to_dict(m) for m in messages]
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return message_dicts
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def _stream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Iterator[ChatGenerationChunk]:
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message_dicts = self._create_message_dicts(messages)
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default_chunk_class = AIMessageChunk
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params = {
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"model": self.model,
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"messages": message_dicts,
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"stream": True,
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**self.model_kwargs,
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**kwargs,
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}
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for chunk in completion_with_retry(
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self, self.use_retry, run_manager=run_manager, stop=stop, **params
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):
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choice = chunk.choices[0]
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chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class)
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finish_reason = choice.finish_reason
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generation_info = (
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dict(finish_reason=finish_reason) if finish_reason is not None else None
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)
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
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yield chunk
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if run_manager:
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run_manager.on_llm_new_token(chunk.text, chunk=chunk)
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async def _astream(
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self,
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messages: List[BaseMessage],
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stop: Optional[List[str]] = None,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> AsyncIterator[ChatGenerationChunk]:
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message_dicts = self._create_message_dicts(messages)
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default_chunk_class = AIMessageChunk
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params = {
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"model": self.model,
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"messages": message_dicts,
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"stream": True,
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**self.model_kwargs,
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**kwargs,
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}
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async for chunk in await acompletion_with_retry_streaming(
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self, self.use_retry, run_manager=run_manager, stop=stop, **params
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):
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choice = chunk.choices[0]
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chunk = _convert_delta_to_message_chunk(choice.delta, default_chunk_class)
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finish_reason = choice.finish_reason
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generation_info = (
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dict(finish_reason=finish_reason) if finish_reason is not None else None
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)
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default_chunk_class = chunk.__class__
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chunk = ChatGenerationChunk(message=chunk, generation_info=generation_info)
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yield chunk
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if run_manager:
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await run_manager.on_llm_new_token(token=chunk.text, chunk=chunk)
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def conditional_decorator(
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condition: bool, decorator: Callable[[Any], Any]
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) -> Callable[[Any], Any]:
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"""Define conditional decorator.
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Args:
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condition: The condition.
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decorator: The decorator.
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Returns:
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The decorated function.
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"""
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def actual_decorator(func: Callable[[Any], Any]) -> Callable[[Any], Any]:
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if condition:
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return decorator(func)
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return func
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return actual_decorator
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def completion_with_retry(
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llm: ChatFireworks,
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use_retry: bool,
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*,
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run_manager: Optional[CallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the completion call."""
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import fireworks.client
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@conditional_decorator(use_retry, retry_decorator)
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def _completion_with_retry(**kwargs: Any) -> Any:
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"""Use tenacity to retry the completion call."""
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return fireworks.client.ChatCompletion.create(
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**kwargs,
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)
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return _completion_with_retry(**kwargs)
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async def acompletion_with_retry(
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llm: ChatFireworks,
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use_retry: bool,
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*,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the async completion call."""
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import fireworks.client
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@conditional_decorator(use_retry, retry_decorator)
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async def _completion_with_retry(**kwargs: Any) -> Any:
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return await fireworks.client.ChatCompletion.acreate(
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**kwargs,
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)
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return await _completion_with_retry(**kwargs)
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async def acompletion_with_retry_streaming(
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llm: ChatFireworks,
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use_retry: bool,
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*,
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run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
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**kwargs: Any,
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) -> Any:
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"""Use tenacity to retry the completion call for streaming."""
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import fireworks.client
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retry_decorator = _create_retry_decorator(llm, run_manager=run_manager)
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@conditional_decorator(use_retry, retry_decorator)
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async def _completion_with_retry(**kwargs: Any) -> Any:
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return fireworks.client.ChatCompletion.acreate(
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**kwargs,
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)
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return await _completion_with_retry(**kwargs)
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def _create_retry_decorator(
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llm: ChatFireworks,
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run_manager: Optional[
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Union[AsyncCallbackManagerForLLMRun, CallbackManagerForLLMRun]
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] = None,
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) -> Callable[[Any], Any]:
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"""Define retry mechanism."""
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import fireworks.client
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errors = [
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fireworks.client.error.RateLimitError,
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fireworks.client.error.InternalServerError,
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fireworks.client.error.BadGatewayError,
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fireworks.client.error.ServiceUnavailableError,
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]
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return create_base_retry_decorator(
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error_types=errors, max_retries=llm.max_retries, run_manager=run_manager
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)
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