"""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
[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:
arbitrary_types_allowed = True
extra = "forbid"
@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)
