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docs/extras/use_cases/code_writing/llm_bash.ipynb
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docs/extras/use_cases/code_writing/llm_bash.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Bash chain\n",
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"This notebook showcases using LLMs and a bash process to perform simple filesystem commands."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
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"Please write a bash script that prints 'Hello World' to the console.\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"```bash\n",
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"echo \"Hello World\"\n",
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"```\u001b[0m\n",
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"Code: \u001b[33;1m\u001b[1;3m['echo \"Hello World\"']\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3mHello World\n",
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"\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Hello World\\n'"
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]
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},
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"execution_count": 9,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.chains import LLMBashChain\n",
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"from langchain.llms import OpenAI\n",
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"\n",
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"llm = OpenAI(temperature=0)\n",
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"\n",
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"text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
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"\n",
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"bash_chain = LLMBashChain.from_llm(llm, verbose=True)\n",
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"\n",
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"bash_chain.run(text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Customize Prompt\n",
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"You can also customize the prompt that is used. Here is an example prompting to avoid using the 'echo' utility"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain.prompts.prompt import PromptTemplate\n",
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"from langchain.chains.llm_bash.prompt import BashOutputParser\n",
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"\n",
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"_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",
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"Question: \"copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'\"\n",
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"I need to take the following actions:\n",
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"- List all files in the directory\n",
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"- Create a new directory\n",
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"- Copy the files from the first directory into the second directory\n",
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"```bash\n",
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"ls\n",
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"mkdir myNewDirectory\n",
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"cp -r target/* myNewDirectory\n",
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"```\n",
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"\n",
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"Do not use 'echo' when writing the script.\n",
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"\n",
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"That is the format. Begin!\n",
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"Question: {question}\"\"\"\n",
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"\n",
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"PROMPT = PromptTemplate(\n",
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" input_variables=[\"question\"],\n",
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" template=_PROMPT_TEMPLATE,\n",
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" output_parser=BashOutputParser(),\n",
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")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
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"Please write a bash script that prints 'Hello World' to the console.\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"```bash\n",
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"printf \"Hello World\\n\"\n",
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"```\u001b[0m\n",
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"Code: \u001b[33;1m\u001b[1;3m['printf \"Hello World\\\\n\"']\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3mHello World\n",
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"\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'Hello World\\n'"
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]
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},
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"execution_count": 11,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
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"\n",
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"text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
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"\n",
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"bash_chain.run(text)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Persistent Terminal\n",
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"\n",
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"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."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
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"List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"```bash\n",
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"ls\n",
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"cd ..\n",
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"```\u001b[0m\n",
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"Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3mapi.html\t\t\tllm_summarization_checker.html\n",
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"constitutional_chain.html\tmoderation.html\n",
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"llm_bash.html\t\t\topenai_openapi.yaml\n",
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"llm_checker.html\t\topenapi.html\n",
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"llm_math.html\t\t\tpal.html\n",
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"llm_requests.html\t\tsqlite.html\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'api.html\\t\\t\\tllm_summarization_checker.html\\r\\nconstitutional_chain.html\\tmoderation.html\\r\\nllm_bash.html\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.html\\t\\topenapi.html\\r\\nllm_math.html\\t\\t\\tpal.html\\r\\nllm_requests.html\\t\\tsqlite.html'"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain.utilities.bash import BashProcess\n",
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"\n",
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"\n",
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"persistent_process = BashProcess(persistent=True)\n",
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"bash_chain = LLMBashChain.from_llm(llm, bash_process=persistent_process, verbose=True)\n",
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"\n",
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"text = \"List the current directory then move up a level.\"\n",
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"\n",
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"bash_chain.run(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 13,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"\n",
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"\u001b[1m> Entering new LLMBashChain chain...\u001b[0m\n",
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"List the current directory then move up a level.\u001b[32;1m\u001b[1;3m\n",
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"\n",
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"```bash\n",
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"ls\n",
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"cd ..\n",
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"```\u001b[0m\n",
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"Code: \u001b[33;1m\u001b[1;3m['ls', 'cd ..']\u001b[0m\n",
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"Answer: \u001b[33;1m\u001b[1;3mexamples\t\tgetting_started.html\tindex_examples\n",
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"generic\t\t\thow_to_guides.rst\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"'examples\\t\\tgetting_started.html\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
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]
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},
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"execution_count": 13,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"# Run the same command again and see that the state is maintained between calls\n",
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"bash_chain.run(text)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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