Source code for langchain_experimental.chat_models.llm_wrapper

"""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 = " "