{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# BashChain\n",
    "This notebook showcases using LLMs and a bash process to do perform simple filesystem commands."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
      "Please write a bash script that prints 'Hello World' to the console.\u001b[32;1m\u001b[1;3m\n",
      "\n",
      "```bash\n",
      "echo \"Hello World\"\n",
      "```\u001b[0m['```bash', 'echo \"Hello World\"', '```']\n",
      "\n",
      "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
      "\u001b[0m\n",
      "\u001b[1m> Finished LLMBashChain chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Hello World\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.chains import LLMBashChain\n",
    "from langchain.llms import OpenAI\n",
    "\n",
    "llm = OpenAI(temperature=0)\n",
    "\n",
    "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
    "\n",
    "bash_chain = LLMBashChain(llm=llm, verbose=True)\n",
    "\n",
    "bash_chain.run(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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