21-Agents.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JSJeong-me/GPT-Insights/blob/main/21-Agents.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52824b89-532a-4e54-87e9-1410813cd39e",
      "metadata": {
        "id": "52824b89-532a-4e54-87e9-1410813cd39e"
      },
      "source": [
        "# LangChain: Agents\n",
        "\n",
        "### 여기 먼저 https://python.langchain.com/docs/modules/agents/\n",
        "\n",
        "## Outline:\n",
        "\n",
        "* Using built in LangChain tools: DuckDuckGo search and Wikipedia\n",
        "* Defining your own tools"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain==0.0.179\n",
        "!pip install python-dotenv"
      ],
      "metadata": {
        "id": "-I5pNAW27_59"
      },
      "id": "-I5pNAW27_59",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!echo \"OPENAI_API_KEY=sk-\" >> .env\n",
        "!source /content/.env"
      ],
      "metadata": {
        "id": "FRs0HlDkPoXG"
      },
      "id": "FRs0HlDkPoXG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7ed03ed-1322-49e3-b2a2-33e94fb592ef",
      "metadata": {
        "height": 148,
        "tags": [],
        "id": "b7ed03ed-1322-49e3-b2a2-33e94fb592ef"
      },
      "outputs": [],
      "source": [
        "import os\n",
        "\n",
        "from dotenv import load_dotenv, find_dotenv\n",
        "_ = load_dotenv(find_dotenv()) # read local .env file\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "36fd0194-9f3d-4468-9390-f23df83e83c4",
      "metadata": {
        "id": "36fd0194-9f3d-4468-9390-f23df83e83c4"
      },
      "source": [
        "참고: LLM이 항상 동일한 결과를 생성하는 것은 아닙니다. 노트북에서 코드를 실행하면 약간 다른 답변을 얻을 수 있습니다."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b7269176-8094-40ce-bedc-4c6421b16e98",
      "metadata": {
        "height": 250,
        "id": "b7269176-8094-40ce-bedc-4c6421b16e98"
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      "source": [
        "# account for deprecation of LLM model\n",
        "import datetime\n",
        "# Get the current date\n",
        "current_date = datetime.datetime.now().date()\n",
        "\n",
        "# Define the date after which the model should be set to \"gpt-3.5-turbo\"\n",
        "target_date = datetime.date(2024, 6, 12)\n",
        "\n",
        "# Set the model variable based on the current date\n",
        "if current_date > target_date:\n",
        "    llm_model = \"gpt-3.5-turbo\"\n",
        "else:\n",
        "    llm_model = \"gpt-3.5-turbo-0301\""
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0e3b9ad2",
      "metadata": {
        "id": "0e3b9ad2"
      },
      "source": [
        "## Built-in LangChain tools"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "974acf8e-8f88-42de-88f8-40a82cb58e8b",
      "metadata": {
        "height": 29,
        "tags": [],
        "id": "974acf8e-8f88-42de-88f8-40a82cb58e8b"
      },
      "outputs": [],
      "source": [
        "!pip install -U wikipedia"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install openai==0.27.7"
      ],
      "metadata": {
        "id": "hJ-rUTQY8t5n"
      },
      "id": "hJ-rUTQY8t5n",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b93d0e8",
      "metadata": {
        "height": 131,
        "tags": [],
        "id": "7b93d0e8"
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      "outputs": [],
      "source": [
        "from langchain.agents.agent_toolkits import create_python_agent\n",
        "from langchain.agents import load_tools, initialize_agent\n",
        "from langchain.agents import AgentType\n",
        "from langchain.tools.python.tool import PythonREPLTool\n",
        "from langchain.python import PythonREPL\n",
        "from langchain.chat_models import ChatOpenAI"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7aadd5c1",
      "metadata": {
        "height": 46,
        "tags": [],
        "id": "7aadd5c1"
      },
      "outputs": [],
      "source": [
        "llm = ChatOpenAI(temperature=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "394924ac",
      "metadata": {
        "height": 46,
        "tags": [],
        "id": "394924ac"
      },
      "outputs": [],
      "source": [
        "tools = load_tools([\"wikipedia\", \"llm-math\" ], llm=llm)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "02cd4956",
      "metadata": {
        "height": 131,
        "tags": [],
        "id": "02cd4956"
      },
      "outputs": [],
      "source": [
        "agent= initialize_agent(\n",
        "    tools,\n",
        "    llm,\n",
        "    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
        "    handle_parsing_errors=True,\n",
        "    verbose = True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agent.run(\"newjeans\")"
      ],
      "metadata": {
        "id": "iskwS4CIkRdI"
      },
      "id": "iskwS4CIkRdI",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "44601fe4-5f24-4ff2-a5df-a7e194a5b4f8",
      "metadata": {
        "height": 29,
        "tags": [],
        "id": "44601fe4-5f24-4ff2-a5df-a7e194a5b4f8"
      },
      "outputs": [],
      "source": [
        "agent(\"What is the 25% of 300?\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agent(\"New Jeans 맴버들의 나이 합은?\")"
      ],
      "metadata": {
        "id": "4bzHmVaa86qN"
      },
      "id": "4bzHmVaa86qN",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "agent(\"Can you please provide me stock price of Tesla?\")"
      ],
      "metadata": {
        "id": "PqmjRwk2wZep"
      },
      "id": "PqmjRwk2wZep",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "10ee3df6-ded9-4c96-ba47-a6cf636c1b9b",
      "metadata": {
        "id": "10ee3df6-ded9-4c96-ba47-a6cf636c1b9b"
      },
      "source": [
        "## Wikipedia example"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "441df699-0a52-4812-a0a7-4605204f0aa8",
      "metadata": {
        "height": 97,
        "tags": [],
        "id": "441df699-0a52-4812-a0a7-4605204f0aa8"
      },
      "outputs": [],
      "source": [
        "question = \"Tom M. Mitchell is an American computer scientist \\\n",
        "and the Founders University Professor at Carnegie Mellon University (CMU)\\\n",
        "what book did he write?\"\n",
        "result = agent(question)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "90299a00-eb79-4755-bb4b-bf61d88c7fed",
      "metadata": {
        "id": "90299a00-eb79-4755-bb4b-bf61d88c7fed"
      },
      "source": [
        "## Python Agent"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "c7617576-d855-4ede-9c1a-be742f1504cf",
      "metadata": {
        "height": 97,
        "tags": [],
        "id": "c7617576-d855-4ede-9c1a-be742f1504cf"
      },
      "outputs": [],
      "source": [
        "agent = create_python_agent(\n",
        "    llm,\n",
        "    tool=PythonREPLTool(),\n",
        "    verbose=True\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d05e7dc6-f07e-43df-a2f6-9a0816291861",
      "metadata": {
        "height": 148,
        "tags": [],
        "id": "d05e7dc6-f07e-43df-a2f6-9a0816291861"
      },
      "outputs": [],
      "source": [
        "customer_list = [[\"Harrison\", \"Chase\"],\n",
        "                 [\"Lang\", \"Chain\"],\n",
        "                 [\"Dolly\", \"Too\"],\n",
        "                 [\"Elle\", \"Elem\"],\n",
        "                 [\"Geoff\",\"Fusion\"],\n",
        "                 [\"Trance\",\"Former\"],\n",
        "                 [\"Jen\",\"Ayai\"]\n",
        "                ]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f887f8f9-216e-489a-945a-f38fbccbe5c4",
      "metadata": {
        "height": 63,
        "tags": [],
        "id": "f887f8f9-216e-489a-945a-f38fbccbe5c4"
      },
      "outputs": [],
      "source": [
        "agent.run(f\"\"\"Sort these customers by \\\n",
        "last name and then first name \\\n",
        "and print the output: {customer_list}\"\"\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "agent.run(f\"\"\"Using the CodeInterpreter and Yahoo yfinance \\\n",
        "          get the price of today \\\n",
        "           stock price of Tesla?\"\"\")"
      ],
      "metadata": {
        "id": "u5QqTG_M9Te8"
      },
      "id": "u5QqTG_M9Te8",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Mdsmvqnb9KL4"
      },
      "id": "Mdsmvqnb9KL4",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "74466cec-2d98-438b-805f-33e31fdc3035",
      "metadata": {
        "id": "74466cec-2d98-438b-805f-33e31fdc3035"
      },
      "source": [
        "#### View detailed outputs of the chains"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12548579-810b-416e-afb5-178296685c2c",
      "metadata": {
        "height": 114,
        "tags": [],
        "id": "12548579-810b-416e-afb5-178296685c2c"
      },
      "outputs": [],
      "source": [
        "import langchain\n",
        "langchain.debug=True\n",
        "agent.run(f\"\"\"Sort these customers by \\\n",
        "last name and then first name \\\n",
        "and print the output: {customer_list}\"\"\")\n",
        "langchain.debug=False"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2d2a4685",
      "metadata": {
        "id": "2d2a4685"
      },
      "source": [
        "## Define your own tool"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "70b32cfe",
      "metadata": {
        "height": 29,
        "tags": [],
        "id": "70b32cfe"
      },
      "outputs": [],
      "source": [
        "!pip install DateTime"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "73f8b595",
      "metadata": {
        "height": 46,
        "tags": [],
        "id": "73f8b595"
      },
      "outputs": [],
      "source": [
        "from langchain.agents import tool\n",
        "from datetime import date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "61c64268",
      "metadata": {
        "height": 182,
        "tags": [],
        "id": "61c64268"
      },
      "outputs": [],
      "source": [
        "@tool\n",
        "def time(text: str) -> str:\n",
        "    \"\"\"Returns todays date, use this for any \\\n",
        "    questions related to knowing todays date. \\\n",
        "    The input should always be an empty string, \\\n",
        "    and this function will always return todays \\\n",
        "    date - any date mathmatics should occur \\\n",
        "    outside this function.\"\"\"\n",
        "    return str(date.today())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0d2fd9ee",
      "metadata": {
        "height": 131,
        "tags": [],
        "id": "0d2fd9ee"
      },
      "outputs": [],
      "source": [
        "agent= initialize_agent(\n",
        "    tools + [time],\n",
        "    llm,\n",
        "    agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION,\n",
        "    handle_parsing_errors=True,\n",
        "    verbose = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f9f30e7c-77f1-43fc-a966-140f05160378",
      "metadata": {
        "id": "f9f30e7c-77f1-43fc-a966-140f05160378"
      },
      "source": [
        "**Note**:\n",
        "\n",
        "The agent will sometimes come to the wrong conclusion (agents are a work in progress!).\n",
        "\n",
        "If it does, please try running it again."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1fcd13cb",
      "metadata": {
        "height": 80,
        "tags": [],
        "id": "1fcd13cb"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "    result = agent(\"whats the date today?\")\n",
        "except:\n",
        "    print(\"exception on external access\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fe0d694e-d6ca-49db-8ea7-a48339af1542",
      "metadata": {
        "id": "fe0d694e-d6ca-49db-8ea7-a48339af1542"
      },
      "source": [
        "Reminder: Download your notebook to you local computer to save your work."
      ]
    }
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