langchain/libs/experimental/langchain_experimental/pal_chain/base.py
Kwanghoon Choi fbb82608cd
Fixed a bug in reporting Python code validation (#11522)
- **Description:** fixed a bug in pal-chain when it reports Python
    code validation errors. When node.func does not have any ids, the
    original code tried to print node.func.id in raising ValueError.
- **Issue:** n/a,
- **Dependencies:** no dependencies,
- **Tag maintainer:** @hazzel-cn, @eyurtsev
- **Twitter handle:** @lazyswamp

---------

Co-authored-by: Bagatur <baskaryan@gmail.com>
2023-10-11 14:34:28 -07:00

307 lines
12 KiB
Python

"""Implements Program-Aided Language Models.
This module implements the Program-Aided Language Models (PAL) for generating code
solutions. PAL is a technique described in the paper "Program-Aided Language Models"
(https://arxiv.org/pdf/2211.10435.pdf).
"""
from __future__ import annotations
import ast
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.schema.language_model import BaseLanguageModel
from langchain.utilities import PythonREPL
from langchain_experimental.pal_chain.colored_object_prompt import COLORED_OBJECT_PROMPT
from langchain_experimental.pal_chain.math_prompt import MATH_PROMPT
from langchain_experimental.pydantic_v1 import Extra, Field
COMMAND_EXECUTION_FUNCTIONS = ["system", "exec", "execfile", "eval", "__import__"]
class PALValidation:
SOLUTION_EXPRESSION_TYPE_FUNCTION = ast.FunctionDef
SOLUTION_EXPRESSION_TYPE_VARIABLE = ast.Name
def __init__(
self,
solution_expression_name: Optional[str] = None,
solution_expression_type: Optional[type] = None,
allow_imports: bool = False,
allow_command_exec: bool = False,
):
"""Initialize a PALValidation instance.
Args:
solution_expression_name (str): Name of the expected solution expression.
If passed, solution_expression_type must be passed as well.
solution_expression_type (str): AST type of the expected solution
expression. If passed, solution_expression_name must be passed as well.
Must be one of PALValidation.SOLUTION_EXPRESSION_TYPE_FUNCTION,
PALValidation.SOLUTION_EXPRESSION_TYPE_VARIABLE.
allow_imports (bool): Allow import statements.
allow_command_exec (bool): Allow using known command execution functions.
"""
self.solution_expression_name = solution_expression_name
self.solution_expression_type = solution_expression_type
if solution_expression_name is not None:
if not isinstance(self.solution_expression_name, str):
raise ValueError(
f"Expected solution_expression_name to be str, "
f"instead found {type(self.solution_expression_name)}"
)
if solution_expression_type is not None:
if (
self.solution_expression_type
is not self.SOLUTION_EXPRESSION_TYPE_FUNCTION
and self.solution_expression_type
is not self.SOLUTION_EXPRESSION_TYPE_VARIABLE
):
raise ValueError(
f"Expected solution_expression_type to be one of "
f"({self.SOLUTION_EXPRESSION_TYPE_FUNCTION},"
f"{self.SOLUTION_EXPRESSION_TYPE_VARIABLE}),"
f"instead found {self.solution_expression_type}"
)
if solution_expression_name is not None and solution_expression_type is None:
raise TypeError(
"solution_expression_name "
"requires solution_expression_type to be passed as well"
)
if solution_expression_name is None and solution_expression_type is not None:
raise TypeError(
"solution_expression_type "
"requires solution_expression_name to be passed as well"
)
self.allow_imports = allow_imports
self.allow_command_exec = allow_command_exec
class PALChain(Chain):
"""Implements Program-Aided Language Models (PAL).
This class implements the Program-Aided Language Models (PAL) for generating code
solutions. PAL is a technique described in the paper "Program-Aided Language Models"
(https://arxiv.org/pdf/2211.10435.pdf).
*Security note*: This class implements an AI technique that generates and evaluates
Python code, which can be dangerous and requires a specially sandboxed
environment to be safely used. While this class implements some basic guardrails
by limiting available locals/globals and by parsing and inspecting
the generated Python AST using `PALValidation`, those guardrails will not
deter sophisticated attackers and are not a replacement for a proper sandbox.
Do not use this class on untrusted inputs, with elevated permissions,
or without consulting your security team about proper sandboxing!
"""
llm_chain: LLMChain
stop: str = "\n\n"
"""Stop token to use when generating code."""
get_answer_expr: str = "print(solution())"
"""Expression to use to get the answer from the generated code."""
python_globals: Optional[Dict[str, Any]] = None
"""Python globals and locals to use when executing the generated code."""
python_locals: Optional[Dict[str, Any]] = None
"""Python globals and locals to use when executing the generated code."""
output_key: str = "result" #: :meta private:
return_intermediate_steps: bool = False
"""Whether to return intermediate steps in the generated code."""
code_validations: PALValidation = Field(default_factory=PALValidation)
"""Validations to perform on the generated code."""
timeout: Optional[int] = 10
"""Timeout in seconds for the generated code to execute."""
class Config:
"""Configuration for this pydantic object."""
extra = Extra.forbid
arbitrary_types_allowed = True
@property
def input_keys(self) -> List[str]:
"""Return the singular input key.
:meta private:
"""
return self.llm_chain.prompt.input_variables
@property
def output_keys(self) -> List[str]:
"""Return the singular output key.
:meta private:
"""
if not self.return_intermediate_steps:
return [self.output_key]
else:
return [self.output_key, "intermediate_steps"]
def _call(
self,
inputs: Dict[str, Any],
run_manager: Optional[CallbackManagerForChainRun] = None,
) -> Dict[str, str]:
_run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager()
code = self.llm_chain.predict(
stop=[self.stop], callbacks=_run_manager.get_child(), **inputs
)
_run_manager.on_text(code, color="green", end="\n", verbose=self.verbose)
PALChain.validate_code(code, self.code_validations)
repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
res = repl.run(code + f"\n{self.get_answer_expr}", timeout=self.timeout)
output = {self.output_key: res.strip()}
if self.return_intermediate_steps:
output["intermediate_steps"] = code
return output
@classmethod
def validate_code(cls, code: str, code_validations: PALValidation) -> None:
try:
code_tree = ast.parse(code)
except (SyntaxError, UnicodeDecodeError):
raise ValueError(f"Generated code is not valid python code: {code}")
except TypeError:
raise ValueError(
f"Generated code is expected to be a string, "
f"instead found {type(code)}"
)
except OverflowError:
raise ValueError(
f"Generated code too long / complex to be parsed by ast: {code}"
)
found_solution_expr = False
if code_validations.solution_expression_name is None:
# Skip validation if no solution_expression_name was given
found_solution_expr = True
has_imports = False
top_level_nodes = list(ast.iter_child_nodes(code_tree))
for node in top_level_nodes:
if (
code_validations.solution_expression_name is not None
and code_validations.solution_expression_type is not None
):
# Check root nodes (like func def)
if (
isinstance(node, code_validations.solution_expression_type)
and hasattr(node, "name")
and node.name == code_validations.solution_expression_name
):
found_solution_expr = True
# Check assigned nodes (like answer variable)
if isinstance(node, ast.Assign):
for target_node in node.targets:
if (
isinstance(
target_node, code_validations.solution_expression_type
)
and hasattr(target_node, "id")
and target_node.id
== code_validations.solution_expression_name
):
found_solution_expr = True
if isinstance(node, ast.Import) or isinstance(node, ast.ImportFrom):
has_imports = True
if not found_solution_expr:
raise ValueError(
f"Generated code is missing the solution expression: "
f"{code_validations.solution_expression_name} of type: "
f"{code_validations.solution_expression_type}"
)
if not code_validations.allow_imports and has_imports:
raise ValueError(f"Generated code has disallowed imports: {code}")
if (
not code_validations.allow_command_exec
or not code_validations.allow_imports
):
for node in ast.walk(code_tree):
if (not code_validations.allow_command_exec) and isinstance(
node, ast.Call
):
if (
hasattr(node.func, "id")
and node.func.id in COMMAND_EXECUTION_FUNCTIONS
):
raise ValueError(
f"Found illegal command execution function "
f"{node.func.id} in code {code}"
)
if (
isinstance(node.func, ast.Attribute)
and node.func.attr in COMMAND_EXECUTION_FUNCTIONS
):
raise ValueError(
f"Found illegal command execution function "
f"{node.func.attr} in code {code}"
)
if (not code_validations.allow_imports) and (
isinstance(node, ast.Import) or isinstance(node, ast.ImportFrom)
):
raise ValueError(f"Generated code has disallowed imports: {code}")
@classmethod
def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
"""Load PAL from math prompt.
Args:
llm (BaseLanguageModel): The language model to use for generating code.
Returns:
PALChain: An instance of PALChain.
"""
llm_chain = LLMChain(llm=llm, prompt=MATH_PROMPT)
code_validations = PALValidation(
solution_expression_name="solution",
solution_expression_type=PALValidation.SOLUTION_EXPRESSION_TYPE_FUNCTION,
)
return cls(
llm_chain=llm_chain,
stop="\n\n",
get_answer_expr="print(solution())",
code_validations=code_validations,
**kwargs,
)
@classmethod
def from_colored_object_prompt(
cls, llm: BaseLanguageModel, **kwargs: Any
) -> PALChain:
"""Load PAL from colored object prompt.
Args:
llm (BaseLanguageModel): The language model to use for generating code.
Returns:
PALChain: An instance of PALChain.
"""
llm_chain = LLMChain(llm=llm, prompt=COLORED_OBJECT_PROMPT)
code_validations = PALValidation(
solution_expression_name="answer",
solution_expression_type=PALValidation.SOLUTION_EXPRESSION_TYPE_VARIABLE,
)
return cls(
llm_chain=llm_chain,
stop="\n\n\n",
get_answer_expr="print(answer)",
code_validations=code_validations,
**kwargs,
)
@property
def _chain_type(self) -> str:
return "pal_chain"