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Simple

SimpleChatEngine #

Bases: BaseChatEngine

简单的聊天引擎。

与LLM进行对话。 这不使用知识库。

Source code in llama_index/core/chat_engine/simple.py
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class SimpleChatEngine(BaseChatEngine):
    """简单的聊天引擎。

与LLM进行对话。
这不使用知识库。"""

    def __init__(
        self,
        llm: LLM,
        memory: BaseMemory,
        prefix_messages: List[ChatMessage],
        callback_manager: Optional[CallbackManager] = None,
    ) -> None:
        self._llm = llm
        self._memory = memory
        self._prefix_messages = prefix_messages
        self.callback_manager = callback_manager or CallbackManager([])

    @classmethod
    def from_defaults(
        cls,
        chat_history: Optional[List[ChatMessage]] = None,
        memory: Optional[BaseMemory] = None,
        memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
        system_prompt: Optional[str] = None,
        prefix_messages: Optional[List[ChatMessage]] = None,
        llm: Optional[LLM] = None,
        # deprecated
        service_context: Optional[ServiceContext] = None,
        **kwargs: Any,
    ) -> "SimpleChatEngine":
        """使用默认参数初始化SimpleChatEngine。"""
        llm = llm or llm_from_settings_or_context(Settings, service_context)

        chat_history = chat_history or []
        memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm)

        if system_prompt is not None:
            if prefix_messages is not None:
                raise ValueError(
                    "Cannot specify both system_prompt and prefix_messages"
                )
            prefix_messages = [
                ChatMessage(content=system_prompt, role=llm.metadata.system_role)
            ]

        prefix_messages = prefix_messages or []

        return cls(
            llm=llm,
            memory=memory,
            prefix_messages=prefix_messages,
            callback_manager=callback_manager_from_settings_or_context(
                Settings, service_context
            ),
        )

    @trace_method("chat")
    def chat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> AgentChatResponse:
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        initial_token_count = len(
            self._memory.tokenizer_fn(
                " ".join([(m.content or "") for m in self._prefix_messages])
            )
        )
        all_messages = self._prefix_messages + self._memory.get(
            initial_token_count=initial_token_count
        )

        chat_response = self._llm.chat(all_messages)
        ai_message = chat_response.message
        self._memory.put(ai_message)

        return AgentChatResponse(response=str(chat_response.message.content))

    @trace_method("chat")
    def stream_chat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> StreamingAgentChatResponse:
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        initial_token_count = len(
            self._memory.tokenizer_fn(
                " ".join([(m.content or "") for m in self._prefix_messages])
            )
        )
        all_messages = self._prefix_messages + self._memory.get(
            initial_token_count=initial_token_count
        )

        chat_response = StreamingAgentChatResponse(
            chat_stream=self._llm.stream_chat(all_messages)
        )
        thread = Thread(
            target=chat_response.write_response_to_history, args=(self._memory,)
        )
        thread.start()

        return chat_response

    @trace_method("chat")
    async def achat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> AgentChatResponse:
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        initial_token_count = len(
            self._memory.tokenizer_fn(
                " ".join([(m.content or "") for m in self._prefix_messages])
            )
        )
        all_messages = self._prefix_messages + self._memory.get(
            initial_token_count=initial_token_count
        )

        chat_response = await self._llm.achat(all_messages)
        ai_message = chat_response.message
        self._memory.put(ai_message)

        return AgentChatResponse(response=str(chat_response.message.content))

    @trace_method("chat")
    async def astream_chat(
        self, message: str, chat_history: Optional[List[ChatMessage]] = None
    ) -> StreamingAgentChatResponse:
        if chat_history is not None:
            self._memory.set(chat_history)
        self._memory.put(ChatMessage(content=message, role="user"))
        initial_token_count = len(
            self._memory.tokenizer_fn(
                " ".join([(m.content or "") for m in self._prefix_messages])
            )
        )
        all_messages = self._prefix_messages + self._memory.get(
            initial_token_count=initial_token_count
        )

        chat_response = StreamingAgentChatResponse(
            achat_stream=await self._llm.astream_chat(all_messages)
        )
        asyncio.create_task(chat_response.awrite_response_to_history(self._memory))

        return chat_response

    def reset(self) -> None:
        self._memory.reset()

    @property
    def chat_history(self) -> List[ChatMessage]:
        """获取聊天记录。"""
        return self._memory.get_all()

chat_history property #

chat_history: List[ChatMessage]

获取聊天记录。

from_defaults classmethod #

from_defaults(
    chat_history: Optional[List[ChatMessage]] = None,
    memory: Optional[BaseMemory] = None,
    memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
    system_prompt: Optional[str] = None,
    prefix_messages: Optional[List[ChatMessage]] = None,
    llm: Optional[LLM] = None,
    service_context: Optional[ServiceContext] = None,
    **kwargs: Any
) -> SimpleChatEngine

使用默认参数初始化SimpleChatEngine。

Source code in llama_index/core/chat_engine/simple.py
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@classmethod
def from_defaults(
    cls,
    chat_history: Optional[List[ChatMessage]] = None,
    memory: Optional[BaseMemory] = None,
    memory_cls: Type[BaseMemory] = ChatMemoryBuffer,
    system_prompt: Optional[str] = None,
    prefix_messages: Optional[List[ChatMessage]] = None,
    llm: Optional[LLM] = None,
    # deprecated
    service_context: Optional[ServiceContext] = None,
    **kwargs: Any,
) -> "SimpleChatEngine":
    """使用默认参数初始化SimpleChatEngine。"""
    llm = llm or llm_from_settings_or_context(Settings, service_context)

    chat_history = chat_history or []
    memory = memory or memory_cls.from_defaults(chat_history=chat_history, llm=llm)

    if system_prompt is not None:
        if prefix_messages is not None:
            raise ValueError(
                "Cannot specify both system_prompt and prefix_messages"
            )
        prefix_messages = [
            ChatMessage(content=system_prompt, role=llm.metadata.system_role)
        ]

    prefix_messages = prefix_messages or []

    return cls(
        llm=llm,
        memory=memory,
        prefix_messages=prefix_messages,
        callback_manager=callback_manager_from_settings_or_context(
            Settings, service_context
        ),
    )