diff --git a/server/chat/chat_openai_chain/__init__.py b/server/chat/chat_openai_chain/__init__.py
deleted file mode 100644
index d53d695..0000000
--- a/server/chat/chat_openai_chain/__init__.py
+++ /dev/null
@@ -1,5 +0,0 @@
-from server.chat.chat_openai_chain.chat_openai_chain import BaseChatOpenAIChain
-
-__all__ = [
-    "BaseChatOpenAIChain"
-]
diff --git a/server/chat/chat_openai_chain/chat_openai_chain.py b/server/chat/chat_openai_chain/chat_openai_chain.py
deleted file mode 100644
index 7757f98..0000000
--- a/server/chat/chat_openai_chain/chat_openai_chain.py
+++ /dev/null
@@ -1,152 +0,0 @@
-from __future__ import annotations
-from abc import ABC
-from typing import Any, Dict, List, Optional
-from langchain.chains.base import Chain
-from langchain.schema import (
-    BaseMessage,
-    AIMessage,
-    BaseMessage,
-    ChatMessage,
-    HumanMessage,
-    SystemMessage,
-    LLMResult
-)
-from langchain.chat_models import ChatOpenAI
-from langchain.callbacks.manager import (
-    AsyncCallbackManagerForChainRun,
-    CallbackManagerForChainRun,
-    Callbacks,
-)
-from server.model.chat_openai_chain import OpenAiChatMsgDto, OpenAiMessageDto, BaseMessageDto
-
-
-def _convert_dict_to_message(_dict: dict) -> BaseMessage:
-    role = _dict["role"]
-    if role == "user":
-        return HumanMessage(content=_dict["content"])
-    elif role == "assistant":
-        return AIMessage(content=_dict["content"])
-    elif role == "system":
-        return SystemMessage(content=_dict["content"])
-    else:
-        return ChatMessage(content=_dict["content"], role=role)
-
-
-def convert_message_processors(message_data: List[OpenAiMessageDto]) -> List[BaseMessage]:
-    """
-    前端消息传输对象DTO转换为chat消息传输对象DTO
-    :param message_data:
-    :return:
-    """
-    messages = []
-    for message_datum in message_data:
-        messages.append(message_datum.dict())
-    return _convert_dict_to_message(messages)
-
-
-class BaseChatOpenAIChain(Chain, ABC):
-    chat: ChatOpenAI
-    message_dto_key: str = "message_dto"  #: :meta private:
-    output_key: str = "text"  #: :meta private:
-
-    @classmethod
-    def from_chain(
-            cls,
-            model_name: str,
-            streaming: Optional[bool],
-            verbose: Optional[bool],
-            callbacks: Optional[Callbacks],
-            openai_api_key: Optional[str],
-            # openai_api_base: Optional[str],
-            **kwargs: Any,
-    ) -> BaseChatOpenAIChain:
-        chat = ChatOpenAI(
-            streaming=streaming,
-            verbose=verbose,
-            callbacks=callbacks,
-            openai_api_key=openai_api_key,
-            # openai_api_base=openai_api_base,
-            model_name=model_name
-        )
-        return cls(chat=chat, **kwargs)
-
-    @property
-    def _chain_type(self) -> str:
-        return "BaseChatOpenAIChain"
-
-    @property
-    def input_keys(self) -> List[str]:
-        return [self.message_dto_key]
-
-    @property
-    def output_keys(self) -> List[str]:
-        return [self.output_key]
-
-    def create_outputs(self, response: LLMResult) -> List[Dict[str, str]]:
-        """Create outputs from response."""
-        return [
-            # Get the text of the top generated string.
-            {self.output_key: generation[0].text}
-            for generation in response.generations
-        ]
-
-    def _call(
-            self,
-            inputs: Dict[str, OpenAiChatMsgDto],
-            run_manager: Optional[CallbackManagerForChainRun] = None,
-    ) -> Dict[str, str]:
-        msg_dto = inputs[self.message_dto_key]
-        openai_messages_dto = convert_message_processors(msg_dto.messages)
-
-        _text = "openai_messages_dto after formatting:\n" + str(openai_messages_dto)
-        if run_manager:
-            run_manager.on_text(_text, end="\n", verbose=self.verbose)
-
-        response = self.chat(messages=openai_messages_dto, stop=msg_dto.stop)
-        return self.create_outputs(response)[0]
-
-    async def _acall(
-            self,
-            inputs: Dict[str, OpenAiChatMsgDto],
-            run_manager: Optional[AsyncCallbackManagerForChainRun] = None,
-    ) -> Dict[str, str]:
-        msg_dto = inputs[self.message_dto_key]
-
-        openai_messages_dto = convert_message_processors(msg_dto.messages)
-
-        _text = "openai_messages_dto after formatting:\n" + str(openai_messages_dto)
-        if run_manager:
-            run_manager.on_text(_text, end="\n", verbose=self.verbose)
-
-        response = await self.chat(messages=openai_messages_dto, stop=msg_dto.stop)
-        return self.create_outputs(response)[0]
-
-
-if __name__ == "__main__":
-    from langchain.callbacks import AsyncIteratorCallbackHandler
-    import json
-
-    # Convert instances of the classes to dictionaries
-    message_dto1 = OpenAiMessageDto(type="human", data=BaseMessageDto(content="Hello!"))
-    message_dto2 = OpenAiMessageDto(type="system", data=BaseMessageDto(content="hi!"))
-    chat_msg_dto = OpenAiChatMsgDto(model_name="gpt-3.5-turbo", messages=[message_dto1, message_dto2])
-
-    chat_msg_json = json.dumps(chat_msg_dto.dict(), indent=2)
-
-    print("OpenAiChatMsgDto JSON:")
-    print(chat_msg_json)
-
-    callback = AsyncIteratorCallbackHandler()
-
-    chains = BaseChatOpenAIChain.from_chain(
-        streaming=chat_msg_dto.stream,
-        verbose=True,
-        callbacks=[callback],
-        openai_api_key="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",
-        model_name=chat_msg_dto.model_name
-
-    )
-
-    out = chains({"message_dto": chat_msg_dto})
-
-    print(out)
diff --git a/server/chat/llmchain_with_history.py b/server/chat/llmchain_with_history.py
new file mode 100644
index 0000000..3d36042
--- /dev/null
+++ b/server/chat/llmchain_with_history.py
@@ -0,0 +1,29 @@
+from langchain.chat_models import ChatOpenAI
+from configs.model_config import llm_model_dict, LLM_MODEL
+from langchain import LLMChain
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+)
+
+model = ChatOpenAI(
+    streaming=True,
+    verbose=True,
+    # callbacks=[callback],
+    openai_api_key=llm_model_dict[LLM_MODEL]["api_key"],
+    openai_api_base=llm_model_dict[LLM_MODEL]["api_base_url"],
+    model_name=LLM_MODEL
+)
+
+
+human_prompt = "{input}"
+human_message_template = HumanMessagePromptTemplate.from_template(human_prompt)
+
+chat_prompt = ChatPromptTemplate.from_messages(
+    [("human", "我们来玩成语接龙，我先来，生龙活虎"),
+     ("ai", "虎头虎脑"),
+     ("human", "{input}")])
+
+
+chain = LLMChain(prompt=chat_prompt, llm=model, verbose=True)
+print(chain({"input": "恼羞成怒"}))
\ No newline at end of file
diff --git a/server/model/chat_openai_chain.py b/server/model/chat_openai_chain.py
deleted file mode 100644
index f4a0f55..0000000
--- a/server/model/chat_openai_chain.py
+++ /dev/null
@@ -1,44 +0,0 @@
-"""
-前端消息传输结构
-"""
-from pydantic import BaseModel, Field
-from typing import Any, Dict, List, Optional
-
-
-class BaseMessageDto(BaseModel):
-    """Message Dto."""
-
-    content: str
-
-
-class OpenAiMessageDto(BaseModel):
-    """
-    see @Link{langchain.schema._message_from_dict}
-    """
-    type: Optional[str] = Field(
-        default="user"
-    )
-    data: BaseMessageDto
-
-
-class OpenAiChatMsgDto(BaseModel):
-    model_name: str
-    messages: List[OpenAiMessageDto]
-    temperature: Optional[float] = Field(
-        default=0.7
-    )
-    max_tokens: Optional[int] = Field(
-        default=512
-    )
-    stop: List[str] = Field(
-        default=[]
-    )
-    stream: Optional[bool] = Field(
-        default=False
-    )
-    presence_penalty: Optional[int] = Field(
-        default=0
-    )
-    frequency_penalty: Optional[int] = Field(
-        default=0
-    )