langchain/cookbook/llm_bash.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bash chain\n",
    "This notebook showcases using LLMs and a bash process to 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\n",
      "Code: \u001b[33;1m\u001b[1;3m['echo \"Hello World\"']\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
      "\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Hello World\\n'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.llms import OpenAI\n",
    "from langchain_experimental.llm_bash.base import LLMBashChain\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.from_llm(llm, verbose=True)\n",
    "\n",
    "bash_chain.run(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customize Prompt\n",
    "You can also customize the prompt that is used. Here is an example prompting to avoid using the 'echo' utility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts.prompt import PromptTemplate\n",
    "from langchain_experimental.llm_bash.prompt import BashOutputParser\n",
    "\n",
    "_PROMPT_TEMPLATE = \"\"\"If someone asks you to perform a task, your job is to come up with a series of bash commands that will perform the task. There is no need to put \"#!/bin/bash\" in your answer. Make sure to reason step by step, using this format:\n",
    "Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
    "I need to take the following actions:\n",
    "- List all files in the directory\n",
    "- Create a new directory\n",
    "- Copy the files from the first directory into the second directory\n",
    "```bash\n",
    "ls\n",
    "mkdir myNewDirectory\n",
    "cp -r target/* myNewDirectory\n",
    "```\n",
    "\n",
    "Do not use 'echo' when writing the script.\n",
    "\n",
    "That is the format. Begin!\n",
    "Question: {question}\"\"\"\n",
    "\n",
    "PROMPT = PromptTemplate(\n",
    "    input_variables=[\"question\"],\n",
    "    template=_PROMPT_TEMPLATE,\n",
    "    output_parser=BashOutputParser(),\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
      "printf \"Hello World\\n\"\n",
      "```\u001b[0m\n",
      "Code: \u001b[33;1m\u001b[1;3m['printf \"Hello World\\\\n\"']\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3mHello World\n",
      "\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Hello World\\n'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
    "\n",
    "text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
    "\n",
    "bash_chain.run(text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Persistent Terminal\n",
    "\n",
    "By default, the chain will run in a separate subprocess each time it is called. This behavior can be changed by instantiating with a persistent bash process."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
      "List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
      "\n",
      "```bash\n",
      "ls\n",
      "cd ..\n",
      "```\u001b[0m\n",
      "Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3mcpal.ipynb  llm_bash.ipynb  llm_symbolic_math.ipynb\n",
      "index.mdx   llm_math.ipynb  pal.ipynb\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'cpal.ipynb  llm_bash.ipynb  llm_symbolic_math.ipynb\\r\\nindex.mdx   llm_math.ipynb  pal.ipynb'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_experimental.llm_bash.bash import BashProcess\n",
    "\n",
    "persistent_process = BashProcess(persistent=True)\n",
    "bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
    "\n",
    "text = \"List the current directory then move up a level.\"\n",
    "\n",
    "bash_chain.run(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
      "List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
      "\n",
      "```bash\n",
      "ls\n",
      "cd ..\n",
      "```\u001b[0m\n",
      "Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m_category_.yml\tdata_generation.ipynb\t\t   self_check\n",
      "agents\t\tgraph\n",
      "code_writing\tlearned_prompt_optimization.ipynb\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'_category_.yml\\tdata_generation.ipynb\\t\\t   self_check\\r\\nagents\\t\\tgraph\\r\\ncode_writing\\tlearned_prompt_optimization.ipynb'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Run the same command again and see that the state is maintained between calls\n",
    "bash_chain.run(text)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}