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58 lines
1.8 KiB
Python
58 lines
1.8 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.chains.python import PythonChain
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from langchain.llms.base import LLM
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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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llm: LLM
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verbose: bool = False
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input_key: str = "question"
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output_key: str = "answer"
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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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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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return [self.output_key]
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def _run(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 = PythonChain()
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question = inputs[self.input_key]
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t = llm_executor.predict(question=question, stop=["```output"]).strip()
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if t.startswith("```python"):
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code = t[9:-4]
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if self.verbose:
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print("[DEBUG] evaluating code")
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print(code)
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output = python_executor.run(code)
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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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def run(self, question: str) -> str:
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"""More user-friendly interface for interfacing with LLM math."""
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return self({self.input_key: question})[self.output_key]
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