add llmchain_with_history.py as example
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imClumsyPanda
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from server.chat.chat_openai_chain.chat_openai_chain import BaseChatOpenAIChain
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__all__ = [
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"BaseChatOpenAIChain"
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]
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from __future__ import annotations
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from abc import ABC
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from typing import Any, Dict, List, Optional
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from langchain.chains.base import Chain
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from langchain.schema import (
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BaseMessage,
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AIMessage,
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BaseMessage,
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ChatMessage,
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HumanMessage,
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SystemMessage,
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LLMResult
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)
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from langchain.chat_models import ChatOpenAI
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from langchain.callbacks.manager import (
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AsyncCallbackManagerForChainRun,
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CallbackManagerForChainRun,
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Callbacks,
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)
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from server.model.chat_openai_chain import OpenAiChatMsgDto, OpenAiMessageDto, BaseMessageDto
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def _convert_dict_to_message(_dict: dict) -> BaseMessage:
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role = _dict["role"]
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if role == "user":
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return HumanMessage(content=_dict["content"])
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elif role == "assistant":
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return AIMessage(content=_dict["content"])
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elif role == "system":
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return SystemMessage(content=_dict["content"])
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else:
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return ChatMessage(content=_dict["content"], role=role)
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def convert_message_processors(message_data: List[OpenAiMessageDto]) -> List[BaseMessage]:
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"""
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前端消息传输对象DTO转换为chat消息传输对象DTO
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:param message_data:
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:return:
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"""
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messages = []
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for message_datum in message_data:
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messages.append(message_datum.dict())
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return _convert_dict_to_message(messages)
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class BaseChatOpenAIChain(Chain, ABC):
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chat: ChatOpenAI
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message_dto_key: str = "message_dto" #: :meta private:
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output_key: str = "text" #: :meta private:
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@classmethod
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def from_chain(
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cls,
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model_name: str,
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streaming: Optional[bool],
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verbose: Optional[bool],
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callbacks: Optional[Callbacks],
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openai_api_key: Optional[str],
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# openai_api_base: Optional[str],
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**kwargs: Any,
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) -> BaseChatOpenAIChain:
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chat = ChatOpenAI(
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streaming=streaming,
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verbose=verbose,
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callbacks=callbacks,
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openai_api_key=openai_api_key,
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# openai_api_base=openai_api_base,
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model_name=model_name
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)
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return cls(chat=chat, **kwargs)
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@property
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def _chain_type(self) -> str:
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return "BaseChatOpenAIChain"
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@property
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def input_keys(self) -> List[str]:
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return [self.message_dto_key]
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@property
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def output_keys(self) -> List[str]:
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return [self.output_key]
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def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
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"""Create outputs from response."""
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return [
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# Get the text of the top generated string.
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{self.output_key: generation[0].text}
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for generation in response.generations
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]
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def _call(
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self,
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inputs: Dict[str, OpenAiChatMsgDto],
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run_manager: Optional[CallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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msg_dto = inputs[self.message_dto_key]
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openai_messages_dto = convert_message_processors(msg_dto.messages)
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_text = "openai_messages_dto after formatting:\n" + str(openai_messages_dto)
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if run_manager:
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run_manager.on_text(_text, end="\n", verbose=self.verbose)
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response = self.chat(messages=openai_messages_dto, stop=msg_dto.stop)
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return self.create_outputs(response)[0]
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async def _acall(
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self,
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inputs: Dict[str, OpenAiChatMsgDto],
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run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
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) -> Dict[str, str]:
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msg_dto = inputs[self.message_dto_key]
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openai_messages_dto = convert_message_processors(msg_dto.messages)
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_text = "openai_messages_dto after formatting:\n" + str(openai_messages_dto)
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if run_manager:
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run_manager.on_text(_text, end="\n", verbose=self.verbose)
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response = await self.chat(messages=openai_messages_dto, stop=msg_dto.stop)
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return self.create_outputs(response)[0]
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if __name__ == "__main__":
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from langchain.callbacks import AsyncIteratorCallbackHandler
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import json
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# Convert instances of the classes to dictionaries
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message_dto1 = OpenAiMessageDto(type="human", data=BaseMessageDto(content="Hello!"))
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message_dto2 = OpenAiMessageDto(type="system", data=BaseMessageDto(content="hi!"))
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chat_msg_dto = OpenAiChatMsgDto(model_name="gpt-3.5-turbo", messages=[message_dto1, message_dto2])
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chat_msg_json = json.dumps(chat_msg_dto.dict(), indent=2)
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print("OpenAiChatMsgDto JSON:")
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print(chat_msg_json)
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callback = AsyncIteratorCallbackHandler()
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chains = BaseChatOpenAIChain.from_chain(
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streaming=chat_msg_dto.stream,
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verbose=True,
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callbacks=[callback],
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openai_api_key="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",
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model_name=chat_msg_dto.model_name
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)
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out = chains({"message_dto": chat_msg_dto})
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print(out)
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@ -0,0 +1,29 @@
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from langchain.chat_models import ChatOpenAI
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from configs.model_config import llm_model_dict, LLM_MODEL
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from langchain import LLMChain
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from langchain.prompts.chat import (
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ChatPromptTemplate,
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HumanMessagePromptTemplate,
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)
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model = ChatOpenAI(
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streaming=True,
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verbose=True,
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# callbacks=[callback],
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openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
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openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
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model_name=LLM_MODEL
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)
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human_prompt = "{input}"
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human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)
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chat_prompt = ChatPromptTemplate.from_messages(
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[("human", "我们来玩成语接龙，我先来，生龙活虎"),
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("ai", "虎头虎脑"),
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("human", "{input}")])
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chain = LLMChain(prompt=chat_prompt, llm=model, verbose=True)
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print(chain({"input": "恼羞成怒"}))
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@ -1,44 +0,0 @@
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"""
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前端消息传输结构
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"""
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from pydantic import BaseModel, Field
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from typing import Any, Dict, List, Optional
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class BaseMessageDto(BaseModel):
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"""Message Dto."""
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content: str
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class OpenAiMessageDto(BaseModel):
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"""
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see @Link{langchain.schema._message_from_dict}
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"""
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type: Optional[str] = Field(
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default="user"
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)
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data: BaseMessageDto
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class OpenAiChatMsgDto(BaseModel):
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model_name: str
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messages: List[OpenAiMessageDto]
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temperature: Optional[float] = Field(
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default=0.7
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)
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max_tokens: Optional[int] = Field(
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default=512
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)
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stop: List[str] = Field(
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default=[]
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)
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stream: Optional[bool] = Field(
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default=False
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)
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presence_penalty: Optional[int] = Field(
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default=0
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)
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frequency_penalty: Optional[int] = Field(
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default=0
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)
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