langchain/docs/versioned_docs/version-0.2.x/integrations/chat/minimax.ipynb

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   "source": [
    "---\n",
    "sidebar_label: MiniMax\n",
    "---"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# MiniMaxChat\n",
    "\n",
    "[Minimax](https://api.minimax.chat) is a Chinese startup that provides LLM service for companies and individuals.\n",
    "\n",
    "This example goes over how to use LangChain to interact with MiniMax Inference for Chat."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "os.environ[\"MINIMAX_GROUP_ID\"] = \"MINIMAX_GROUP_ID\"\n",
    "os.environ[\"MINIMAX_API_KEY\"] = \"MINIMAX_API_KEY\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain_community.chat_models import MiniMaxChat\n",
    "from langchain_core.messages import HumanMessage"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chat = MiniMaxChat()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "chat(\n",
    "    [\n",
    "        HumanMessage(\n",
    "            content=\"Translate this sentence from English to French. I love programming.\"\n",
    "        )\n",
    "    ]\n",
    ")"
   ]
  }
 ],
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