{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Human as a tool\n",
    "\n",
    "Human are AGI so they can certainly be used as a tool to help out AI agent \n",
    "when it is confused."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import sys\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.llms import OpenAI\n",
    "from langchain.agents import load_tools, initialize_agent\n",
    "from langchain.agents import AgentType\n",
    "\n",
    "llm = ChatOpenAI(temperature=0.0)\n",
    "math_llm = OpenAI(temperature=0.0)\n",
    "tools = load_tools(\n",
    "    [\"human\", \"llm-math\"], \n",
    "    llm=math_llm,\n",
    ")\n",
    "\n",
    "agent_chain = initialize_agent(\n",
    "    tools,\n",
    "    llm,\n",
    "    agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION,\n",
    "    verbose=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the above code you can see the tool takes input directly from command line.\n",
    "You can customize `prompt_func` and `input_func` according to your need."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI don't know Eric Zhu, so I should ask a human for guidance.\n",
      "Action: Human\n",
      "Action Input: \"Do you know when Eric Zhu's birthday is?\"\u001b[0m\n",
      "\n",
      "Do you know when Eric Zhu's birthday is?\n",
      "last week\n",
      "\n",
      "Observation: \u001b[36;1m\u001b[1;3mlast week\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mThat's not very helpful. I should ask for more information.\n",
      "Action: Human\n",
      "Action Input: \"Do you know the specific date of Eric Zhu's birthday?\"\u001b[0m\n",
      "\n",
      "Do you know the specific date of Eric Zhu's birthday?\n",
      "august 1st\n",
      "\n",
      "Observation: \u001b[36;1m\u001b[1;3maugust 1st\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mNow that I have the date, I can check if it's a leap year or not.\n",
      "Action: Calculator\n",
      "Action Input: \"Is 2021 a leap year?\"\u001b[0m\n",
      "Observation: \u001b[33;1m\u001b[1;3mAnswer: False\n",
      "\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI have all the information I need to answer the original question.\n",
      "Final Answer: Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Eric Zhu's birthday is on August 1st and it is not a leap year in 2021.\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "agent_chain.run(\"What is Eric Zhu's birthday?\")\n",
    "# Answer with \"last week\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}