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"""Implements Program-Aided Language Models.
As in https://arxiv.org/pdf/2211.10435.pdf.
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
from pydantic import BaseModel, Extra
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
from langchain.chains.pal.math_prompt import MATH_PROMPT
from langchain.prompts.base import BasePromptTemplate
from langchain.python import PythonREPL
from langchain.schema import BaseLanguageModel
class PALChain(Chain, BaseModel):
"""Implements Program-Aided Language Models."""
llm: BaseLanguageModel
prompt: BasePromptTemplate
stop: str = "\n\n"
get_answer_expr: str = "print(solution())"
python_globals: Optional[Dict[str, Any]] = None
python_locals: Optional[Dict[str, Any]] = None
output_key: str = "result" #: :meta private:
return_intermediate_steps: bool = False
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.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, str]) -> Dict[str, str]:
llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
code = llm_chain.predict(stop=[self.stop], **inputs)
self.callback_manager.on_text(
code, color="green", end="\n", verbose=self.verbose
)
repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
res = repl.run(code + f"\n{self.get_answer_expr}")
output = {self.output_key: res.strip()}
if self.return_intermediate_steps:
output["intermediate_steps"] = code
return output
@classmethod
def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
"""Load PAL from math prompt."""
return cls(
llm=llm,
prompt=MATH_PROMPT,
stop="\n\n",
get_answer_expr="print(solution())",
**kwargs,
)
@classmethod
def from_colored_object_prompt(
cls, llm: BaseLanguageModel, **kwargs: Any
) -> PALChain:
"""Load PAL from colored object prompt."""
return cls(
llm=llm,
prompt=COLORED_OBJECT_PROMPT,
stop="\n\n\n",
get_answer_expr="print(answer)",
**kwargs,
)
@property
def _chain_type(self) -> str:
return "pal_chain"
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