langchain/docs/modules/chains/examples/llm_math.ipynb

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   "cell_type": "markdown",
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   "source": [
    "# LLM Math\n",
    "\n",
    "This notebook showcases using LLMs and Python REPLs to do complex word math problems."
   ]
  },
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      "\n",
      "\n",
      "\u001b[1m> Entering new LLMMathChain chain...\u001b[0m\n",
      "What is 13 raised to the .3432 power?\u001b[32;1m\u001b[1;3m\n",
      "```text\n",
      "13 ** .3432\n",
      "```\n",
      "...numexpr.evaluate(\"13 ** .3432\")...\n",
      "\u001b[0m\n",
      "Answer: \u001b[33;1m\u001b[1;3m2.4116004626599237\u001b[0m\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
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    {
     "data": {
      "text/plain": [
       "'Answer: 2.4116004626599237'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain import OpenAI, LLMMathChain\n",
    "\n",
    "llm = OpenAI(temperature=0)\n",
    "llm_math = LLMMathChain.from_llm(llm, verbose=True)\n",
    "\n",
    "llm_math.run(\"What is 13 raised to the .3432 power?\")"
   ]
  },
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   "execution_count": null,
   "id": "e978bb8e",
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   "outputs": [],
   "source": []
  }
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