Source code for langchain_experimental.utilities.python
import functools
import logging
import multiprocessing
import re
import sys
from io import StringIO
from typing import Dict, Optional
from pydantic import BaseModel, Field
logger = logging.getLogger(__name__)
@functools.lru_cache(maxsize=None)
def warn_once() -> None:
"""Warn once about the dangers of PythonREPL."""
logger.warning("Python REPL can execute arbitrary code. Use with caution.")
[docs]
class PythonREPL(BaseModel):
"""Simulates a standalone Python REPL."""
globals: Optional[Dict] = Field(default_factory=dict, alias="_globals")
locals: Optional[Dict] = Field(default_factory=dict, alias="_locals")
[docs]
@classmethod
def worker(
cls,
command: str,
globals: Optional[Dict],
locals: Optional[Dict],
queue: multiprocessing.Queue,
) -> None:
old_stdout = sys.stdout
sys.stdout = mystdout = StringIO()
try:
cleaned_command = cls.sanitize_input(command)
exec(cleaned_command, globals, locals)
sys.stdout = old_stdout
queue.put(mystdout.getvalue())
except Exception as e:
sys.stdout = old_stdout
queue.put(repr(e))
[docs]
def run(self, command: str, timeout: Optional[int] = None) -> str:
"""Run command with own globals/locals and returns anything printed.
Timeout after the specified number of seconds."""
# Warn against dangers of PythonREPL
warn_once()
queue: multiprocessing.Queue = multiprocessing.Queue()
# Only use multiprocessing if we are enforcing a timeout
if timeout is not None:
# create a Process
p = multiprocessing.Process(
target=self.worker, args=(command, self.globals, self.locals, queue)
)
# start it
p.start()
# wait for the process to finish or kill it after timeout seconds
p.join(timeout)
if p.is_alive():
p.terminate()
return "Execution timed out"
else:
self.worker(command, self.globals, self.locals, queue)
# get the result from the worker function
return queue.get()