langchain/docs/extras/modules/model_io/models/llms/integrations/modal.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "# Modal\n",
    "\n",
    "The [Modal Python Library](https://modal.com/docs/guide) provides convenient, on-demand access to serverless cloud compute from Python scripts on your local computer. \n",
    "The `Modal` itself does not provide any LLMs but only the infrastructure.\n",
    "\n",
    "This example goes over how to use LangChain to interact with `Modal`.\n",
    "\n",
    "[Here](https://modal.com/docs/guide/ex/potus_speech_qanda) is another example how to use LangChain to interact with `Modal`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "!pip install modal-client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[?25lLaunching login page in your browser window\u001b[33m...\u001b[0m\n",
      "\u001b[2KIf this is not showing up, please copy this URL into your web browser manually:\n",
      "\u001b[2Km⠙\u001b[0m Waiting for authentication in the web browser...\n",
      "\u001b]8;id=417802;https://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\u001b\\\u001b[4;94mhttps://modal.com/token-flow/tf-ptEuGecm7T1T5YQe42kwM1\u001b[0m\u001b]8;;\u001b\\\n",
      "\n",
      "\u001b[2K\u001b[32m⠙\u001b[0m Waiting for authentication in the web browser...\n",
      "\u001b[1A\u001b[2K^C\n",
      "\n",
      "\u001b[31mAborted.\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "# register and get a new token\n",
    "\n",
    "!modal token new"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Follow [these instructions](https://modal.com/docs/guide/secrets) to deal with secrets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import Modal\n",
    "from langchain import PromptTemplate, LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Question: {question}\n",
    "\n",
    "Answer: Let's think step by step.\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = Modal(endpoint_url=\"YOUR_ENDPOINT_URL\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "question = \"What NFL team won the Super Bowl in the year Justin Beiber was born?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  }
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