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151 lines
5.5 KiB
Python
151 lines
5.5 KiB
Python
"""Chain that interprets a prompt and executes python code to do math."""
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import math
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import re
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from typing import Dict, List
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import numexpr
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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.llm_math.prompt import PROMPT
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from langchain.prompts.base import BasePromptTemplate
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from langchain.schema import BaseLanguageModel
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from langchain.utilities import PythonREPL
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class LLMMathChain(Chain):
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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: BaseLanguageModel
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"""LLM wrapper to use."""
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prompt: BasePromptTemplate = PROMPT
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"""Prompt to use to translate to python if neccessary."""
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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 _evaluate_expression(self, expression: str) -> str:
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try:
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local_dict = {"pi": math.pi, "e": math.e}
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output = str(
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numexpr.evaluate(
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expression.strip(),
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global_dict={}, # restrict access to globals
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local_dict=local_dict, # add common mathematical functions
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)
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)
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except Exception as e:
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raise ValueError(f"{e}. Please try again with a valid numerical expression")
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# Remove any leading and trailing brackets from the output
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return re.sub(r"^\[|\]$", "", output)
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def _process_llm_result(self, llm_output: str) -> Dict[str, str]:
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self.callback_manager.on_text(llm_output, color="green", verbose=self.verbose)
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llm_output = llm_output.strip()
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text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
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if text_match:
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expression = text_match.group(1)
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output = self._evaluate_expression(expression)
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self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
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self.callback_manager.on_text(output, color="yellow", verbose=self.verbose)
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answer = "Answer: " + output
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elif llm_output.startswith("Answer:"):
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answer = llm_output
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elif "Answer:" in llm_output:
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answer = "Answer: " + llm_output.split("Answer:")[-1]
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else:
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raise ValueError(f"unknown format from LLM: {llm_output}")
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return {self.output_key: answer}
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async def _aprocess_llm_result(self, llm_output: str) -> Dict[str, str]:
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if self.callback_manager.is_async:
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await self.callback_manager.on_text(
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llm_output, color="green", verbose=self.verbose
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)
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else:
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self.callback_manager.on_text(
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llm_output, color="green", verbose=self.verbose
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)
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llm_output = llm_output.strip()
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text_match = re.search(r"^```text(.*?)```", llm_output, re.DOTALL)
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if text_match:
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expression = text_match.group(1)
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output = self._evaluate_expression(expression)
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if self.callback_manager.is_async:
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await self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
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await self.callback_manager.on_text(
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output, color="yellow", verbose=self.verbose
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)
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else:
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await self.callback_manager.on_text("\nAnswer: ", verbose=self.verbose)
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await self.callback_manager.on_text(
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output, color="yellow", verbose=self.verbose
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)
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answer = "Answer: " + output
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elif llm_output.startswith("Answer:"):
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answer = llm_output
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elif "Answer:" in llm_output:
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answer = "Answer: " + llm_output.split("Answer:")[-1]
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else:
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raise ValueError(f"unknown format from LLM: {llm_output}")
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return {self.output_key: answer}
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def _call(self, inputs: Dict[str, str]) -> Dict[str, str]:
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llm_executor = LLMChain(
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prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
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)
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self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
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llm_output = llm_executor.predict(
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question=inputs[self.input_key], stop=["```output"]
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)
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return self._process_llm_result(llm_output)
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async def _acall(self, inputs: Dict[str, str]) -> Dict[str, str]:
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llm_executor = LLMChain(
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prompt=self.prompt, llm=self.llm, callback_manager=self.callback_manager
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)
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if self.callback_manager.is_async:
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await self.callback_manager.on_text(
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inputs[self.input_key], verbose=self.verbose
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)
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else:
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self.callback_manager.on_text(inputs[self.input_key], verbose=self.verbose)
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llm_output = await llm_executor.apredict(
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question=inputs[self.input_key], stop=["```output"]
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)
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return await self._aprocess_llm_result(llm_output)
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@property
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def _chain_type(self) -> str:
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return "llm_math_chain"
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