{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain import OpenAI, LLMMathChain, LLMChain\n",
    "from langchain.prompts import PromptTemplate\n",
    "\n",
    "llm = OpenAI(temperature=0)\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "    input_variables = [\"question\"],\n",
    "    template = \"{question}\"\n",
    ")\n",
    "llmChain = LLMChain(llm=llm, prompt=prompt)\n",
    "llm_math = LLMMathChain.from_llm(llm, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n2的4次方等于16'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# llmChain.run(question=\"2的0.1次方\")\n",
    "llmChain.run(question=\"2的4次方\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
      "2的4次方\u001b[32;1m\u001b[1;3m\n",
      "```text\n",
      "2**4\n",
      "```\n",
      "...numexpr.evaluate(\"2**4\")...\n",
      "\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m16\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Answer: 16'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# llm_math.run(\"2的0.1次方\")\n",
    "llm_math.run(\"2的4次方\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
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