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72 lines
2.1 KiB
Python
72 lines
2.1 KiB
Python
"""Chain that interprets a prompt and executes python code to do math."""
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from typing import Dict, List
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from pydantic import BaseModel, 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.llm_math.prompt import PROMPT
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from langchain.input import print_text
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from langchain.llms.base import BaseLLM
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from langchain.python import PythonREPL
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class LLMMathChain(Chain, BaseModel):
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"""Chain that interprets a prompt and executes python code to do math.
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Example:
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.. code-block:: python
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from langchain import LLMMathChain, OpenAI
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llm_math = LLMMathChain(llm=OpenAI())
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"""
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llm: BaseLLM
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"""LLM wrapper to use."""
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input_key: str = "question" #: :meta private:
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output_key: str = "answer" #: :meta private:
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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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"""Expect input key.
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:meta private:
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"""
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return [self.input_key]
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@property
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def output_keys(self) -> List[str]:
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"""Expect output key.
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:meta private:
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"""
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return [self.output_key]
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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llm_executor = LLMChain(prompt=PROMPT, llm=self.llm)
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python_executor = PythonREPL()
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if self.verbose:
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print_text(inputs[self.input_key])
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t = llm_executor.predict(question=inputs[self.input_key], stop=["```output"])
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if self.verbose:
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print_text(t, color="green")
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t = t.strip()
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if t.startswith("```python"):
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code = t[9:-4]
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output = python_executor.run(code)
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if self.verbose:
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print_text("\nAnswer: ")
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print_text(output, color="yellow")
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answer = "Answer: " + output
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elif t.startswith("Answer:"):
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answer = t
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else:
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raise ValueError(f"unknown format from LLM: {t}")
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return {self.output_key: answer}
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