{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import things that are needed generically\n",
    "from langchain import LLMMathChain, SerpAPIWrapper\n",
    "from langchain.agents import AgentType, initialize_agent\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.tools import BaseTool, StructuredTool, Tool, tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the tool configs that are needed.\n",
    "search = SerpAPIWrapper()\n",
    "llm_math_chain = LLMMathChain(llm=llm, verbose=True)\n",
    "tools = [\n",
    "    Tool.from_function(\n",
    "        func=search.run,\n",
    "        name=\"Search\",\n",
    "        description=\"useful for when you need to answer questions about current events\"\n",
    "        # coroutine= ... <- you can specify an async method if desired as well\n",
    "    ),\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pydantic import BaseModel, Field\n",
    "\n",
    "\n",
    "class CalculatorInput(BaseModel):\n",
    "    question: str = Field()\n",
    "\n",
    "\n",
    "tools.append(\n",
    "    Tool.from_function(\n",
    "        func=llm_math_chain.run,\n",
    "        name=\"Calculator\",\n",
    "        description=\"useful for when you need to answer questions about math\",\n",
    "        args_schema=CalculatorInput\n",
    "        # coroutine= ... <- you can specify an async method if desired as well\n",
    "    )\n",
    ")\n",
    "\n"
   ]
  }
 ],
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