langchain/docs/modules/agents/examples/multi_input_tool.ipynb

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    "# Multi Input Tools\n",
    "\n",
    "This notebook shows how to use a tool that requires multiple inputs with an agent.\n",
    "\n",
    "The difficulty in doing so comes from the fact that an agent decides it's next step from a language model, which outputs a string. So if that step requires multiple inputs, they need to be parsed from that. Therefor, the currently supported way to do this is write a smaller wrapper function that parses that a string into multiple inputs.\n",
    "\n",
    "For a concrete example, let's work on giving an agent access to a multiplication function, which takes as input two integers. In order to use this, we will tell the agent to generate the \"Action Input\" as a comma separated list of length two. We will then write a thin wrapper that takes a string, splits it into two around a comma, and passes both parsed sides as integers to the multiplication function."
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   "source": [
    "from langchain.llms import OpenAI\n",
    "from langchain.agents import initialize_agent, Tool"
   ]
  },
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   "source": [
    "Here is the multiplication function, as well as a wrapper to parse a string as input."
   ]
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    "def multiplier(a, b):\n",
    "    return a * b\n",
    "\n",
    "def parsing_multiplier(string):\n",
    "    a, b = string.split(\",\")\n",
    "    return multiplier(int(a), int(b))"
   ]
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    "llm = OpenAI(temperature=0)\n",
    "tools = [\n",
    "    Tool(\n",
    "        name = \"Multiplier\",\n",
    "        func=parsing_multiplier,\n",
    "        description=\"useful for when you need to multiply two numbers together. The input to this tool should be a comma separated list of numbers of length two, representing the two numbers you want to multiply together. For example, `1,2` would be the input if you wanted to multiply 1 by 2.\"\n",
    "    )\n",
    "]\n",
    "mrkl = initialize_agent(tools, llm, agent=\"zero-shot-react-description\", verbose=True)"
   ]
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      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3m I need to multiply two numbers\n",
      "Action: Multiplier\n",
      "Action Input: 3,4\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m12\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3m I now know the final answer\n",
      "Final Answer: 3 times 4 is 12\u001b[0m\n",
      "\u001b[1m> Finished AgentExecutor chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'3 times 4 is 12'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "mrkl.run(\"What is 3 times 4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ea340c0",
   "metadata": {},
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