langchain_community.chat_models.minimax — 🦜🔗 LangChain 0.2.0

"""封装了Minimax聊天模型。"""
import logging
from typing import Any, Dict, List, Optional, cast

from langchain_core.callbacks import (
    AsyncCallbackManagerForLLMRun,
    CallbackManagerForLLMRun,
)
from langchain_core.language_models.chat_models import BaseChatModel
from langchain_core.messages import (
    AIMessage,
    BaseMessage,
    HumanMessage,
)
from langchain_core.outputs import ChatGeneration, ChatResult

from langchain_community.llms.minimax import MinimaxCommon
from langchain_community.llms.utils import enforce_stop_tokens

logger = logging.getLogger(__name__)


def _parse_message(msg_type: str, text: str) -> Dict:
    return {"sender_type": msg_type, "text": text}


def _parse_chat_history(history: List[BaseMessage]) -> List:
    """将一系列消息解析为历史记录。"""
    chat_history = []
    for message in history:
        content = cast(str, message.content)
        if isinstance(message, HumanMessage):
            chat_history.append(_parse_message("USER", content))
        if isinstance(message, AIMessage):
            chat_history.append(_parse_message("BOT", content))
    return chat_history


[docs]class MiniMaxChat(MinimaxCommon, BaseChatModel):
    """MiniMax大型语言模型。

    要使用，您应该设置环境变量``MINIMAX_GROUP_ID``和``MINIMAX_API_KEY``，并使用您的API令牌，或将其作为命名参数传递给构造函数。

    示例:
        .. code-block:: python

            from langchain_community.chat_models import MiniMaxChat
            llm = MiniMaxChat(model_name="abab5-chat")"""

    def _generate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[CallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        """生成对话中的下一轮内容。
参数：
    messages: 作为消息列表的对话历史。Code chat不支持上下文。
    stop: 停止词列表（可选）。
    run_manager: LLM运行的CallbackManager，目前未使用。

返回：
    包含模型生成输出的ChatResult。

引发：
    ValueError: 如果列表中的最后一条消息不是来自人类。
"""
        if not messages:
            raise ValueError(
                "You should provide at least one message to start the chat!"
            )
        history = _parse_chat_history(messages)
        payload = self._default_params
        payload["messages"] = history
        text = self._client.post(payload)

        # This is required since the stop are not enforced by the model parameters
        text = text if stop is None else enforce_stop_tokens(text, stop)
        return ChatResult(generations=[ChatGeneration(message=AIMessage(text))])  # type: ignore[misc]

    async def _agenerate(
        self,
        messages: List[BaseMessage],
        stop: Optional[List[str]] = None,
        run_manager: Optional[AsyncCallbackManagerForLLMRun] = None,
        **kwargs: Any,
    ) -> ChatResult:
        raise NotImplementedError(
            """Minimax AI doesn't support async requests at the moment."""
        )
Source code for langchain_community.chat_models.minimax
