Source code for langchain_experimental.llm_symbolic_math.base

"""Chain that interprets a prompt and executes python code to do symbolic math."""
from __future__ import annotations

import re
from typing import Any, Dict, List, Optional

from langchain.base_language import BaseLanguageModel
from langchain.chains.base import Chain
from langchain.chains.llm import LLMChain
from langchain_core.callbacks.manager import (
    AsyncCallbackManagerForChainRun,
    CallbackManagerForChainRun,
)
from langchain_core.prompts.base import BasePromptTemplate

from langchain_experimental.llm_symbolic_math.prompt import PROMPT
from langchain_experimental.pydantic_v1 import Extra


[docs]class LLMSymbolicMathChain(Chain): """Chain that interprets a prompt and executes python code to do symbolic math. It is based on the sympy library and can be used to evaluate mathematical expressions. See https://www.sympy.org/ for more information. Example: .. code-block:: python from langchain.chains import LLMSymbolicMathChain from langchain_community.llms import OpenAI llm_symbolic_math = LLMSymbolicMathChain.from_llm(OpenAI()) """ llm_chain: LLMChain input_key: str = "question" #: :meta private: output_key: str = "answer" #: :meta private: class Config: """Configuration for this pydantic object.""" extra = Extra.forbid arbitrary_types_allowed = True @property def input_keys(self) -> List[str]: """Expect input key. :meta private: """ return [self.input_key] @property def output_keys(self) -> List[str]: """Expect output key. :meta private: """ return [self.output_key] def _evaluate_expression(self, expression: str) -> str: try: import sympy except ImportError as e: raise ImportError( "Unable to import sympy, please install it with `pip install sympy`." ) from e try: output = str(sympy.sympify(expression, evaluate=True)) except Exception as e: raise ValueError( f'LLMSymbolicMathChain._evaluate("{expression}") raised error: {e}.' " Please try again with a valid numerical expression" ) # Remove any leading and trailing brackets from the output return re.sub(r"^\[|\]$", "", output) def _process_llm_result( self, llm_output: str, run_manager: CallbackManagerForChainRun ) -> Dict[str, str]: run_manager.on_text(llm_output, color="green", verbose=self.verbose) llm_output = llm_output.strip() text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL) if text_match: expression = text_match.group(1) output = self._evaluate_expression(expression) run_manager.on_text("\nAnswer: ", verbose=self.verbose) run_manager.on_text(output, color="yellow", verbose=self.verbose) answer = "Answer: " + output elif llm_output.startswith("Answer:"): answer = llm_output elif "Answer:" in llm_output: answer = "Answer: " + llm_output.split("Answer:")[-1] else: raise ValueError(f"unknown format from LLM: {llm_output}") return {self.output_key: answer} async def _aprocess_llm_result( self, llm_output: str, run_manager: AsyncCallbackManagerForChainRun, ) -> Dict[str, str]: await run_manager.on_text(llm_output, color="green", verbose=self.verbose) llm_output = llm_output.strip() text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL) if text_match: expression = text_match.group(1) output = self._evaluate_expression(expression) await run_manager.on_text("\nAnswer: ", verbose=self.verbose) await run_manager.on_text(output, color="yellow", verbose=self.verbose) answer = "Answer: " + output elif llm_output.startswith("Answer:"): answer = llm_output elif "Answer:" in llm_output: answer = "Answer: " + llm_output.split("Answer:")[-1] else: raise ValueError(f"unknown format from LLM: {llm_output}") return {self.output_key: answer} def _call( self, inputs: Dict[str, str], run_manager: Optional[CallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or CallbackManagerForChainRun.get_noop_manager() _run_manager.on_text(inputs[self.input_key]) llm_output = self.llm_chain.predict( question=inputs[self.input_key], stop=["```output"], callbacks=_run_manager.get_child(), ) return self._process_llm_result(llm_output, _run_manager) async def _acall( self, inputs: Dict[str, str], run_manager: Optional[AsyncCallbackManagerForChainRun] = None, ) -> Dict[str, str]: _run_manager = run_manager or AsyncCallbackManagerForChainRun.get_noop_manager() await _run_manager.on_text(inputs[self.input_key]) llm_output = await self.llm_chain.apredict( question=inputs[self.input_key], stop=["```output"], callbacks=_run_manager.get_child(), ) return await self._aprocess_llm_result(llm_output, _run_manager) @property def _chain_type(self) -> str: return "llm_symbolic_math_chain"
[docs] @classmethod def from_llm( cls, llm: BaseLanguageModel, prompt: BasePromptTemplate = PROMPT, **kwargs: Any, ) -> LLMSymbolicMathChain: llm_chain = LLMChain(llm=llm, prompt=prompt) return cls(llm_chain=llm_chain, **kwargs)