"""Generic Wrapper for chat LLMs, with sample implementations
for Llama-2-chat, Llama-2-instruct and Vicuna models.
"""
from typing import Any, List, Optional, cast
from langchain.schema import (
AIMessage,
BaseMessage,
ChatGeneration,
ChatResult,
HumanMessage,
LLMResult,
SystemMessage,
)
from langchain_core.callbacks.manager import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import LLM, BaseChatModel
DEFAULT_SYSTEM_PROMPT = """You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.
If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.""" # noqa: E501
[docs]
class ChatWrapper(BaseChatModel):
"""Wrapper for chat LLMs."""
llm: LLM
sys_beg: str
sys_end: str
ai_n_beg: str
ai_n_end: str
usr_n_beg: str
usr_n_end: str
usr_0_beg: Optional[str] = None
usr_0_end: Optional[str] = None
system_message: SystemMessage = SystemMessage(content=DEFAULT_SYSTEM_PROMPT)
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
llm_input = self._to_chat_prompt(messages)
llm_result = self.llm._generate(
prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
)
return self._to_chat_result(llm_result)
async def _agenerate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
llm_input = self._to_chat_prompt(messages)
llm_result = await self.llm._agenerate(
prompts=[llm_input], stop=stop, run_manager=run_manager, **kwargs
)
return self._to_chat_result(llm_result)
def _to_chat_prompt(
self,
messages: List[BaseMessage],
) -> str:
"""Convert a list of messages into a prompt format expected by wrapped LLM."""
if not messages:
raise ValueError("at least one HumanMessage must be provided")
if not isinstance(messages[0], SystemMessage):
messages = [self.system_message] + messages
if not isinstance(messages[1], HumanMessage):
raise ValueError(
"messages list must start with a SystemMessage or UserMessage"
)
if not isinstance(messages[-1], HumanMessage):
raise ValueError("last message must be a HumanMessage")
prompt_parts = []
if self.usr_0_beg is None:
self.usr_0_beg = self.usr_n_beg
if self.usr_0_end is None:
self.usr_0_end = self.usr_n_end
prompt_parts.append(
self.sys_beg + cast(str, messages[0].content) + self.sys_end
)
prompt_parts.append(
self.usr_0_beg + cast(str, messages[1].content) + self.usr_0_end
)
for ai_message, human_message in zip(messages[2::2], messages[3::2]):
if not isinstance(ai_message, AIMessage) or not isinstance(
human_message, HumanMessage
):
raise ValueError(
"messages must be alternating human- and ai-messages, "
"optionally prepended by a system message"
)
prompt_parts.append(
self.ai_n_beg + cast(str, ai_message.content) + self.ai_n_end
)
prompt_parts.append(
self.usr_n_beg + cast(str, human_message.content) + self.usr_n_end
)
return "".join(prompt_parts)
@staticmethod
def _to_chat_result(llm_result: LLMResult) -> ChatResult:
chat_generations = []
for g in llm_result.generations[0]:
chat_generation = ChatGeneration(
message=AIMessage(content=g.text), generation_info=g.generation_info
)
chat_generations.append(chat_generation)
return ChatResult(
generations=chat_generations, llm_output=llm_result.llm_output
)
[docs]
class Llama2Chat(ChatWrapper):
"""Wrapper for Llama-2-chat model."""
@property
def _llm_type(self) -> str:
return "llama-2-chat"
sys_beg: str = "<s>[INST] <<SYS>>\n"
sys_end: str = "\n<</SYS>>\n\n"
ai_n_beg: str = " "
ai_n_end: str = " </s>"
usr_n_beg: str = "<s>[INST] "
usr_n_end: str = " [/INST]"
usr_0_beg: str = ""
usr_0_end: str = " [/INST]"
[docs]
class Mixtral(ChatWrapper):
"""See https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1#instruction-format"""
@property
def _llm_type(self) -> str:
return "mixtral"
sys_beg: str = "<s>[INST] "
sys_end: str = "\n"
ai_n_beg: str = " "
ai_n_end: str = " </s>"
usr_n_beg: str = " [INST] "
usr_n_end: str = " [/INST]"
usr_0_beg: str = ""
usr_0_end: str = " [/INST]"
[docs]
class Orca(ChatWrapper):
"""Wrapper for Orca-style models."""
@property
def _llm_type(self) -> str:
return "orca-style"
sys_beg: str = "### System:\n"
sys_end: str = "\n\n"
ai_n_beg: str = "### Assistant:\n"
ai_n_end: str = "\n\n"
usr_n_beg: str = "### User:\n"
usr_n_end: str = "\n\n"
[docs]
class Vicuna(ChatWrapper):
"""Wrapper for Vicuna-style models."""
@property
def _llm_type(self) -> str:
return "vicuna-style"
sys_beg: str = ""
sys_end: str = " "
ai_n_beg: str = "ASSISTANT: "
ai_n_end: str = " </s>"
usr_n_beg: str = "USER: "
usr_n_end: str = " "
