langchain/docs/extras/modules/callbacks/custom_callbacks.ipynb

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    "# Custom callback handlers\n",
    "\n",
    "You can create a custom handler to set on the object as well. In the example below, we'll implement streaming with a custom handler."
   ]
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      "My custom handler, token: \n",
      "My custom handler, token: Why\n",
      "My custom handler, token:  don\n",
      "My custom handler, token: 't\n",
      "My custom handler, token:  scientists\n",
      "My custom handler, token:  trust\n",
      "My custom handler, token:  atoms\n",
      "My custom handler, token: ?\n",
      "My custom handler, token:  \n",
      "\n",
      "\n",
      "My custom handler, token: Because\n",
      "My custom handler, token:  they\n",
      "My custom handler, token:  make\n",
      "My custom handler, token:  up\n",
      "My custom handler, token:  everything\n",
      "My custom handler, token: .\n",
      "My custom handler, token: \n"
     ]
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     "data": {
      "text/plain": [
       "AIMessage(content=\"Why don't scientists trust atoms? \\n\\nBecause they make up everything.\", additional_kwargs={}, example=False)"
      ]
     },
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   "source": [
    "from langchain.callbacks.base import BaseCallbackHandler\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.schema import HumanMessage\n",
    "\n",
    "\n",
    "class MyCustomHandler(BaseCallbackHandler):\n",
    "    def on_llm_new_token(self, token: str, **kwargs) -> None:\n",
    "        print(f\"My custom handler, token: {token}\")\n",
    "\n",
    "\n",
    "# To enable streaming, we pass in `streaming=True` to the ChatModel constructor\n",
    "# Additionally, we pass in a list with our custom handler\n",
    "chat = ChatOpenAI(max_tokens=25, streaming=True, callbacks=[MyCustomHandler()])\n",
    "\n",
    "chat([HumanMessage(content=\"Tell me a joke\")])"
   ]
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