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97 lines
2.9 KiB
Python
97 lines
2.9 KiB
Python
"""Implements Program-Aided Language Models.
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As in https://arxiv.org/pdf/2211.10435.pdf.
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"""
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from __future__ import annotations
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from typing import Any, Dict, List, Optional
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from pydantic import Extra
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from langchain.chains.base import Chain
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from langchain.chains.llm import LLMChain
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from langchain.chains.pal.colored_object_prompt import COLORED_OBJECT_PROMPT
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from langchain.chains.pal.math_prompt import MATH_PROMPT
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from langchain.prompts.base import BasePromptTemplate
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from langchain.python import PythonREPL
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from langchain.schema import BaseLanguageModel
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class PALChain(Chain):
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"""Implements Program-Aided Language Models."""
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llm: BaseLanguageModel
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prompt: BasePromptTemplate
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stop: str = "\n\n"
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get_answer_expr: str = "print(solution())"
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python_globals: Optional[Dict[str, Any]] = None
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python_locals: Optional[Dict[str, Any]] = None
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output_key: str = "result" #: :meta private:
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return_intermediate_steps: bool = False
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class Config:
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"""Configuration for this pydantic object."""
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extra = Extra.forbid
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arbitrary_types_allowed = True
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@property
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def input_keys(self) -> List[str]:
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"""Return the singular input key.
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:meta private:
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"""
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return self.prompt.input_variables
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@property
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def output_keys(self) -> List[str]:
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"""Return the singular output key.
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:meta private:
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"""
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if not self.return_intermediate_steps:
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return [self.output_key]
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else:
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return [self.output_key, "intermediate_steps"]
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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llm_chain = LLMChain(llm=self.llm, prompt=self.prompt)
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code = llm_chain.predict(stop=[self.stop], **inputs)
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self.callback_manager.on_text(
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code, color="green", end="\n", verbose=self.verbose
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)
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repl = PythonREPL(_globals=self.python_globals, _locals=self.python_locals)
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res = repl.run(code + f"\n{self.get_answer_expr}")
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output = {self.output_key: res.strip()}
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if self.return_intermediate_steps:
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output["intermediate_steps"] = code
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return output
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@classmethod
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def from_math_prompt(cls, llm: BaseLanguageModel, **kwargs: Any) -> PALChain:
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"""Load PAL from math prompt."""
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return cls(
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llm=llm,
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prompt=MATH_PROMPT,
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stop="\n\n",
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get_answer_expr="print(solution())",
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**kwargs,
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)
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@classmethod
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def from_colored_object_prompt(
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cls, llm: BaseLanguageModel, **kwargs: Any
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) -> PALChain:
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"""Load PAL from colored object prompt."""
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return cls(
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llm=llm,
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prompt=COLORED_OBJECT_PROMPT,
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stop="\n\n\n",
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get_answer_expr="print(answer)",
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**kwargs,
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)
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@property
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def _chain_type(self) -> str:
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return "pal_chain"
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