Source code for langchain_experimental.llm_bash.prompt

# flake8: noqa
from __future__ import annotations

import re
from typing import List

from langchain.prompts.prompt import PromptTemplate
from langchain.schema import BaseOutputParser, OutputParserException

_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:

Question: "copy the files in the directory named 'target' into a new directory at the same level as target called 'myNewDirectory'"

I need to take the following actions:
- List all files in the directory
- Create a new directory
- Copy the files from the first directory into the second directory
```bash
ls
mkdir myNewDirectory
cp -r target/* myNewDirectory
```

That is the format. Begin!

Question: {question}"""


[docs]class BashOutputParser(BaseOutputParser): """Parser for bash output."""
[docs] def parse(self, text: str) -> List[str]: if "```bash" in text: return self.get_code_blocks(text) else: raise OutputParserException( f"Failed to parse bash output. Got: {text}", )
[docs] @staticmethod def get_code_blocks(t: str) -> List[str]: """Get multiple code blocks from the LLM result.""" code_blocks: List[str] = [] # Bash markdown code blocks pattern = re.compile(r"```bash(.*?)(?:\n\s*)```", re.DOTALL) for match in pattern.finditer(t): matched = match.group(1).strip() if matched: code_blocks.extend( [line for line in matched.split("\n") if line.strip()] ) return code_blocks
@property def _type(self) -> str: return "bash"
PROMPT = PromptTemplate( input_variables=["question"], template=_PROMPT_TEMPLATE, output_parser=BashOutputParser(), )