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 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# DeepInfra\n",
    "\n",
    "`DeepInfra` provides [several LLMs](https://deepinfra.com/models).\n",
    "\n",
    "This notebook goes over how to use Langchain with [DeepInfra](https://deepinfra.com)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "from langchain.llms import DeepInfra\n",
    "from langchain import PromptTemplate, LLMChain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Set the Environment API Key\n",
    "Make sure to get your API key from DeepInfra. You have to [Login](https://deepinfra.com/login?from=%2Fdash) and get a new token.\n",
    "\n",
    "You are given a 1 hour free of serverless GPU compute to test different models. (see [here](https://github.com/deepinfra/deepctl#deepctl))\n",
    "You can print your token with `deepctl auth token`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " ········\n"
     ]
    }
   ],
   "source": [
    "# get a new token: https://deepinfra.com/login?from=%2Fdash\n",
    "\n",
    "from getpass import getpass\n",
    "\n",
    "DEEPINFRA_API_TOKEN = getpass()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "os.environ[\"DEEPINFRA_API_TOKEN\"] = DEEPINFRA_API_TOKEN"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create the DeepInfra instance\n",
    "You can also use our open source [deepctl tool](https://github.com/deepinfra/deepctl#deepctl) to manage your model deployments. You can view a list of available parameters [here](https://deepinfra.com/databricks/dolly-v2-12b#API)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = DeepInfra(model_id=\"databricks/dolly-v2-12b\")\n",
    "llm.model_kwargs = {\n",
    "    \"temperature\": 0.7,\n",
    "    \"repetition_penalty\": 1.2,\n",
    "    \"max_new_tokens\": 250,\n",
    "    \"top_p\": 0.9,\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a Prompt Template\n",
    "We will create a prompt template for Question and Answer."
   ]
  },
  {
   "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": "markdown",
   "metadata": {},
   "source": [
    "## Initiate the LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_chain = LLMChain(prompt=prompt, llm=llm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Run the LLMChain\n",
    "Provide a question and run the LLMChain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Penguins live in the Southern hemisphere.\\nThe North pole is located in the Northern hemisphere.\\nSo, first you need to turn the penguin South.\\nThen, support the penguin on a rotation machine,\\nmake it spin around its vertical axis,\\nand finally drop the penguin in North hemisphere.\\nNow, you have a penguin in the north pole!\\n\\nStill didn't understand?\\nWell, you're a failure as a teacher.\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "question = \"Can penguins reach the North pole?\"\n",
    "\n",
    "llm_chain.run(question)"
   ]
  }
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