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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
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"# BashChain\n",
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"This notebook showcases using LLMs and a bash process to perform simple filesystem commands."
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]
},
{
"cell_type": "code",
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"execution_count": 9,
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"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",
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"```\u001b[0m\n",
"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",
"\u001b[0m\n",
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"\u001b[1m> Finished chain.\u001b[0m\n"
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]
},
{
"data": {
"text/plain": [
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"'Hello World\\n'"
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]
},
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"execution_count": 9,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"from langchain.chains import LLMBashChain\n",
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"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",
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"bash_chain = LLMBashChain.from_llm(llm, verbose=True)\n",
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"\n",
"bash_chain.run(text)"
]
},
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{
"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"
]
},
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{
"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
"outputs": [],
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"source": [
"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",
"_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",
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"PROMPT = PromptTemplate(input_variables=[\"question\"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser())"
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]
},
{
"cell_type": "code",
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"execution_count": 11,
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"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",
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"```\u001b[0m\n",
"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",
"\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'Hello World\\n'"
]
},
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"execution_count": 11,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
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"bash_chain = LLMBashChain.from_llm(llm, prompt=PROMPT, verbose=True)\n",
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"\n",
"text = \"Please write a bash script that prints 'Hello World' to the console.\"\n",
"\n",
"bash_chain.run(text)"
]
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},
{
"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",
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"execution_count": 12,
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"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;3mapi.ipynb\t\t\tllm_summarization_checker.ipynb\n",
"constitutional_chain.ipynb\tmoderation.ipynb\n",
"llm_bash.ipynb\t\t\topenai_openapi.yaml\n",
"llm_checker.ipynb\t\topenapi.ipynb\n",
"llm_math.ipynb\t\t\tpal.ipynb\n",
"llm_requests.ipynb\t\tsqlite.ipynb\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'api.ipynb\\t\\t\\tllm_summarization_checker.ipynb\\r\\nconstitutional_chain.ipynb\\tmoderation.ipynb\\r\\nllm_bash.ipynb\\t\\t\\topenai_openapi.yaml\\r\\nllm_checker.ipynb\\t\\topenapi.ipynb\\r\\nllm_math.ipynb\\t\\t\\tpal.ipynb\\r\\nllm_requests.ipynb\\t\\tsqlite.ipynb'"
]
},
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"execution_count": 12,
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"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from langchain.utilities.bash import BashProcess\n",
"\n",
"\n",
"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",
"text = \"List the current directory then move up a level.\"\n",
"\n",
"bash_chain.run(text)"
]
},
{
"cell_type": "code",
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"execution_count": 13,
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"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;3mexamples\t\tgetting_started.ipynb\tindex_examples\n",
"generic\t\t\thow_to_guides.rst\u001b[0m\n",
"\u001b[1m> Finished chain.\u001b[0m\n"
]
},
{
"data": {
"text/plain": [
"'examples\\t\\tgetting_started.ipynb\\tindex_examples\\r\\ngeneric\\t\\t\\thow_to_guides.rst'"
]
},
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"execution_count": 13,
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"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)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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}
],
"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",
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"version": "3.9.1"
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}
},
"nbformat": 4,
"nbformat_minor": 4
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}