langchain_06_Agents.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "52824b89-532a-4e54-87e9-1410813cd39e",
   "metadata": {},
   "source": [
    "# LangChain: Agents\n",
    "\n",
    "## Outline:\n",
    "\n",
    "* Using built in LangChain tools: DuckDuckGo search and Wikipedia\n",
    "* Defining your own tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b7ed03ed-1322-49e3-b2a2-33e94fb592ef",
   "metadata": {
    "height": 132,
    "tags": []
   },
   "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": {},
   "source": [
    "Note: LLM's do not always produce the same results. When executing the code in your notebook, you may get slightly different answers that those in the video."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b7269176-8094-40ce-bedc-4c6421b16e98",
   "metadata": {
    "height": 234
   },
   "outputs": [],
   "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": {},
   "source": [
    "## Built-in LangChain tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "974acf8e-8f88-42de-88f8-40a82cb58e8b",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install -U wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "7b93d0e8",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "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": 5,
   "id": "7aadd5c1",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0, model=llm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "394924ac",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "tools = load_tools([\"llm-math\",\"wikipedia\"], llm=llm)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "02cd4956",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "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",
   "execution_count": 12,
   "id": "44601fe4-5f24-4ff2-a5df-a7e194a5b4f8",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI can use Python to calculate this by multiplying 0.25 and 300.\n",
      "Action: Python REPL\n",
      "Action Input: `0.25 * 300`\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mThe answer is 75.\n",
      "Final Answer: 75\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'input': 'What is the 25% of 300?', 'output': '75'}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent(\"What is the 25% of 300?\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10ee3df6-ded9-4c96-ba47-a6cf636c1b9b",
   "metadata": {},
   "source": [
    "## Wikipedia example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "441df699-0a52-4812-a0a7-4605204f0aa8",
   "metadata": {
    "height": 81,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI'm not sure, but I can try searching for his books.\n",
      "Action: Python REPL\n",
      "Action Input: search(\"Tom M. Mitchell books\")\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mNameError(\"name 'search' is not defined\")\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mOops, I can't just search like that. I'll try using a search engine API.\n",
      "Action: Python REPL\n",
      "Action Input: pip install googlesearch-python\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mSyntaxError('invalid syntax', ('<string>', 1, 5, 'pip install googlesearch-python\\n'))\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to exit the Python REPL and install the package using the command line.\n",
      "Action: Exit Python REPL\n",
      "Action Input: N/A\u001b[0m\n",
      "Observation: Exit Python REPL is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI'll open up a command prompt and install the package there.\n",
      "Action: Command Prompt\n",
      "Action Input: pip install googlesearch-python\u001b[0m\n",
      "Observation: Command Prompt is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI'll use a terminal to install the package.\n",
      "Action: Terminal\n",
      "Action Input: pip install googlesearch-python\u001b[0m\n",
      "Observation: Terminal is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI'll use a Jupyter Notebook to install the package and search for Tom M. Mitchell's books.\n",
      "Action: Jupyter Notebook\n",
      "Action Input: \n",
      "```\n",
      "!pip install googlesearch-python\n",
      "from googlesearch import search\n",
      "\n",
      "query = \"Tom M. Mitchell books\"\n",
      "for j in search(query, num=5):\n",
      "    print(j)\n",
      "```\u001b[0m\n",
      "Observation: Jupyter Notebook is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI'll use a web browser to search for Tom M. Mitchell's books.\n",
      "Action: Web Browser\n",
      "Action Input: \"Tom M. Mitchell books\"\u001b[0m\n",
      "Observation: Web Browser is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI can try using a search engine directly to find the answer.\n",
      "Action: Web Browser\n",
      "Action Input: \"Tom M. Mitchell books\"\u001b[0m\n",
      "Observation: Web Browser is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI can try using a library to scrape information from a website.\n",
      "Action: Python REPL\n",
      "Action Input: \n",
      "```\n",
      "!pip install beautifulsoup4\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://en.wikipedia.org/wiki/Tom_M._Mitchell\"\n",
      "page = requests.get(url)\n",
      "soup = BeautifulSoup(page.content, 'html.parser')\n",
      "\n",
      "book_list = soup.find_all('div', class_='div-col columns column-width')\n",
      "for book in book_list:\n",
      "    if \"Machine Learning\" in book.text:\n",
      "        print(book.text)\n",
      "```\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3mSyntaxError('invalid syntax', ('<string>', 1, 1, '!pip install beautifulsoup4\\n'))\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI need to exit the Python REPL and install the package using the command line.\n",
      "Action: Exit Python REPL\n",
      "Action Input: N/A\u001b[0m\n",
      "Observation: Exit Python REPL is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI'll use a terminal to install the package and search for Tom M. Mitchell's books.\n",
      "Action: Terminal\n",
      "Action Input: pip install beautifulsoup4\u001b[0m\n",
      "Observation: Terminal is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI'll use a Jupyter Notebook to install the package and search for Tom M. Mitchell's books.\n",
      "Action: Jupyter Notebook\n",
      "Action Input: \n",
      "```\n",
      "!pip install beautifulsoup4\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://en.wikipedia.org/wiki/Tom_M._Mitchell\"\n",
      "page = requests.get(url)\n",
      "soup = BeautifulSoup(page.content, 'html.parser')\n",
      "\n",
      "book_list = soup.find_all('div', class_='div-col columns column-width')\n",
      "for book in book_list:\n",
      "    if \"Machine Learning\" in book.text:\n",
      "        print(book.text)\n",
      "```\u001b[0m\n",
      "Observation: Jupyter Notebook is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI can try using a text editor to write a Python script that will search for Tom M. Mitchell's books.\n",
      "Action: Text Editor\n",
      "Action Input: \n",
      "```\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://en.wikipedia.org/wiki/Tom_M._Mitchell\"\n",
      "page = requests.get(url)\n",
      "soup = BeautifulSoup(page.content, 'html.parser')\n",
      "\n",
      "book_list = soup.find_all('div', class_='div-col columns column-width')\n",
      "for book in book_list:\n",
      "    if \"Machine Learning\" in book.text:\n",
      "        print(book.text)\n",
      "```\u001b[0m\n",
      "Observation: Text Editor is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI can try using a code editor like VS Code to write a Python script that will search for Tom M. Mitchell's books.\n",
      "Action: VS Code\n",
      "Action Input: \n",
      "```\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://en.wikipedia.org/wiki/Tom_M._Mitchell\"\n",
      "page = requests.get(url)\n",
      "soup = BeautifulSoup(page.content, 'html.parser')\n",
      "\n",
      "book_list = soup.find_all('div', class_='div-col columns column-width')\n",
      "for book in book_list:\n",
      "    if \"Machine Learning\" in book.text:\n",
      "        print(book.text)\n",
      "```\u001b[0m\n",
      "Observation: VS Code is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3mI can try using an online Python IDE to write a Python script that will search for Tom M. Mitchell's books.\n",
      "Action: Online Python IDE\n",
      "Action Input: \n",
      "```\n",
      "import requests\n",
      "from bs4 import BeautifulSoup\n",
      "\n",
      "url = \"https://en.wikipedia.org/wiki/Tom_M._Mitchell\"\n",
      "page = requests.get(url)\n",
      "soup = BeautifulSoup(page.content, 'html.parser')\n",
      "\n",
      "book_list = soup.find_all('div', class_='div-col columns column-width')\n",
      "for book in book_list:\n",
      "    if \"Machine Learning\" in book.text:\n",
      "        print(book.text)\n",
      "```\u001b[0m\n",
      "Observation: Online Python IDE is not a valid tool, try another one.\n",
      "Thought:\u001b[32;1m\u001b[1;3m\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ],
   "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": {},
   "source": [
    "## Python Agent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c7617576-d855-4ede-9c1a-be742f1504cf",
   "metadata": {
    "height": 98,
    "tags": []
   },
   "outputs": [],
   "source": [
    "agent = create_python_agent(\n",
    "    llm,\n",
    "    tool=PythonREPLTool(),\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d05e7dc6-f07e-43df-a2f6-9a0816291861",
   "metadata": {
    "height": 149,
    "tags": []
   },
   "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": 17,
   "id": "f887f8f9-216e-489a-945a-f38fbccbe5c4",
   "metadata": {
    "height": 64,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mI can use the sorted() function to sort the list of customers by last name and then first name. I will need to provide a key function to sorted() that returns a tuple of the last name and first name in that order.\n",
      "Action: Python REPL\n",
      "Action Input:\n",
      "```\n",
      "customers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\n",
      "sorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\n",
      "for customer in sorted_customers:\n",
      "    print(customer)\n",
      "```\u001b[0m\n",
      "Observation: \u001b[36;1m\u001b[1;3m['Jen', 'Ayai']\n",
      "['Lang', 'Chain']\n",
      "['Harrison', 'Chase']\n",
      "['Elle', 'Elem']\n",
      "['Trance', 'Former']\n",
      "['Geoff', 'Fusion']\n",
      "['Dolly', 'Too']\n",
      "\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mThe customers have been sorted by last name and then first name.\n",
      "Final Answer: [['Jen', 'Ayai'], ['Lang', 'Chain'], ['Harrison', 'Chase'], ['Elle', 'Elem'], ['Trance', 'Former'], ['Geoff', 'Fusion'], ['Dolly', 'Too']]\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"[['Jen', 'Ayai'], ['Lang', 'Chain'], ['Harrison', 'Chase'], ['Elle', 'Elem'], ['Trance', 'Former'], ['Geoff', 'Fusion'], ['Dolly', 'Too']]\""
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent.run(f\"\"\"Sort these customers by \\\n",
    "last name and then first name \\\n",
    "and print the output: {customer_list}\"\"\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74466cec-2d98-438b-805f-33e31fdc3035",
   "metadata": {},
   "source": [
    "#### View detailed outputs of the chains"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "12548579-810b-416e-afb5-178296685c2c",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"input\": \"Sort these customers by last name and then first name and print the output: [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"input\": \"Sort these customers by last name and then first name and print the output: [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\",\n",
      "  \"agent_scratchpad\": \"\",\n",
      "  \"stop\": [\n",
      "    \"\\nObservation:\",\n",
      "    \"\\n\\tObservation:\"\n",
      "  ]\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"Human: You are an agent designed to write and execute python code to answer questions.\\nYou have access to a python REPL, which you can use to execute python code.\\nIf you get an error, debug your code and try again.\\nOnly use the output of your code to answer the question. \\nYou might know the answer without running any code, but you should still run the code to get the answer.\\nIf it does not seem like you can write code to answer the question, just return \\\"I don't know\\\" as the answer.\\n\\n\\nPython REPL: A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Python REPL]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Sort these customers by last name and then first name and print the output: [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nThought:\"\n",
      "  ]\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:chain:LLMChain > 3:llm:ChatOpenAI] [1.96s] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"I can use the sorted() function to sort the list of customers by last name and then first name. I will need to provide a key function to sorted() that returns a tuple of the last name and first name in that order.\\nAction: Python REPL\\nAction Input:\\n```\\ncustomers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nsorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\\nfor customer in sorted_customers:\\n    print(customer)\\n```\",\n",
      "        \"generation_info\": null,\n",
      "        \"message\": {\n",
      "          \"content\": \"I can use the sorted() function to sort the list of customers by last name and then first name. I will need to provide a key function to sorted() that returns a tuple of the last name and first name in that order.\\nAction: Python REPL\\nAction Input:\\n```\\ncustomers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nsorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\\nfor customer in sorted_customers:\\n    print(customer)\\n```\",\n",
      "          \"additional_kwargs\": {},\n",
      "          \"example\": false\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": {\n",
      "    \"token_usage\": {\n",
      "      \"prompt_tokens\": 327,\n",
      "      \"completion_tokens\": 144,\n",
      "      \"total_tokens\": 471\n",
      "    },\n",
      "    \"model_name\": \"gpt-3.5-turbo-0301\"\n",
      "  }\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 2:chain:LLMChain] [1.96s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"I can use the sorted() function to sort the list of customers by last name and then first name. I will need to provide a key function to sorted() that returns a tuple of the last name and first name in that order.\\nAction: Python REPL\\nAction Input:\\n```\\ncustomers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nsorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\\nfor customer in sorted_customers:\\n    print(customer)\\n```\"\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[tool/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:tool:Python REPL] Entering Tool run with input:\n",
      "\u001b[0m\"```\n",
      "customers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\n",
      "sorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\n",
      "for customer in sorted_customers:\n",
      "    print(customer)\n",
      "```\"\n",
      "\u001b[36;1m\u001b[1;3m[tool/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 4:tool:Python REPL] [0.27799999999999997ms] Exiting Tool run with output:\n",
      "\u001b[0m\"['Jen', 'Ayai']\n",
      "['Lang', 'Chain']\n",
      "['Harrison', 'Chase']\n",
      "['Elle', 'Elem']\n",
      "['Trance', 'Former']\n",
      "['Geoff', 'Fusion']\n",
      "['Dolly', 'Too']\"\n",
      "\u001b[32;1m\u001b[1;3m[chain/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:chain:LLMChain] Entering Chain run with input:\n",
      "\u001b[0m{\n",
      "  \"input\": \"Sort these customers by last name and then first name and print the output: [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\",\n",
      "  \"agent_scratchpad\": \"I can use the sorted() function to sort the list of customers by last name and then first name. I will need to provide a key function to sorted() that returns a tuple of the last name and first name in that order.\\nAction: Python REPL\\nAction Input:\\n```\\ncustomers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nsorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\\nfor customer in sorted_customers:\\n    print(customer)\\n```\\nObservation: ['Jen', 'Ayai']\\n['Lang', 'Chain']\\n['Harrison', 'Chase']\\n['Elle', 'Elem']\\n['Trance', 'Former']\\n['Geoff', 'Fusion']\\n['Dolly', 'Too']\\n\\nThought:\",\n",
      "  \"stop\": [\n",
      "    \"\\nObservation:\",\n",
      "    \"\\n\\tObservation:\"\n",
      "  ]\n",
      "}\n",
      "\u001b[32;1m\u001b[1;3m[llm/start]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:chain:LLMChain > 6:llm:ChatOpenAI] Entering LLM run with input:\n",
      "\u001b[0m{\n",
      "  \"prompts\": [\n",
      "    \"Human: You are an agent designed to write and execute python code to answer questions.\\nYou have access to a python REPL, which you can use to execute python code.\\nIf you get an error, debug your code and try again.\\nOnly use the output of your code to answer the question. \\nYou might know the answer without running any code, but you should still run the code to get the answer.\\nIf it does not seem like you can write code to answer the question, just return \\\"I don't know\\\" as the answer.\\n\\n\\nPython REPL: A Python shell. Use this to execute python commands. Input should be a valid python command. If you want to see the output of a value, you should print it out with `print(...)`.\\n\\nUse the following format:\\n\\nQuestion: the input question you must answer\\nThought: you should always think about what to do\\nAction: the action to take, should be one of [Python REPL]\\nAction Input: the input to the action\\nObservation: the result of the action\\n... (this Thought/Action/Action Input/Observation can repeat N times)\\nThought: I now know the final answer\\nFinal Answer: the final answer to the original input question\\n\\nBegin!\\n\\nQuestion: Sort these customers by last name and then first name and print the output: [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nThought:I can use the sorted() function to sort the list of customers by last name and then first name. I will need to provide a key function to sorted() that returns a tuple of the last name and first name in that order.\\nAction: Python REPL\\nAction Input:\\n```\\ncustomers = [['Harrison', 'Chase'], ['Lang', 'Chain'], ['Dolly', 'Too'], ['Elle', 'Elem'], ['Geoff', 'Fusion'], ['Trance', 'Former'], ['Jen', 'Ayai']]\\nsorted_customers = sorted(customers, key=lambda x: (x[1], x[0]))\\nfor customer in sorted_customers:\\n    print(customer)\\n```\\nObservation: ['Jen', 'Ayai']\\n['Lang', 'Chain']\\n['Harrison', 'Chase']\\n['Elle', 'Elem']\\n['Trance', 'Former']\\n['Geoff', 'Fusion']\\n['Dolly', 'Too']\\n\\nThought:\"\n",
      "  ]\n",
      "}\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[36;1m\u001b[1;3m[llm/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:chain:LLMChain > 6:llm:ChatOpenAI] [992.02ms] Exiting LLM run with output:\n",
      "\u001b[0m{\n",
      "  \"generations\": [\n",
      "    [\n",
      "      {\n",
      "        \"text\": \"The customers have been sorted by last name and then first name.\\nFinal Answer: [['Jen', 'Ayai'], ['Lang', 'Chain'], ['Harrison', 'Chase'], ['Elle', 'Elem'], ['Trance', 'Former'], ['Geoff', 'Fusion'], ['Dolly', 'Too']]\",\n",
      "        \"generation_info\": null,\n",
      "        \"message\": {\n",
      "          \"content\": \"The customers have been sorted by last name and then first name.\\nFinal Answer: [['Jen', 'Ayai'], ['Lang', 'Chain'], ['Harrison', 'Chase'], ['Elle', 'Elem'], ['Trance', 'Former'], ['Geoff', 'Fusion'], ['Dolly', 'Too']]\",\n",
      "          \"additional_kwargs\": {},\n",
      "          \"example\": false\n",
      "        }\n",
      "      }\n",
      "    ]\n",
      "  ],\n",
      "  \"llm_output\": {\n",
      "    \"token_usage\": {\n",
      "      \"prompt_tokens\": 526,\n",
      "      \"completion_tokens\": 67,\n",
      "      \"total_tokens\": 593\n",
      "    },\n",
      "    \"model_name\": \"gpt-3.5-turbo-0301\"\n",
      "  }\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor > 5:chain:LLMChain] [992.402ms] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"text\": \"The customers have been sorted by last name and then first name.\\nFinal Answer: [['Jen', 'Ayai'], ['Lang', 'Chain'], ['Harrison', 'Chase'], ['Elle', 'Elem'], ['Trance', 'Former'], ['Geoff', 'Fusion'], ['Dolly', 'Too']]\"\n",
      "}\n",
      "\u001b[36;1m\u001b[1;3m[chain/end]\u001b[0m \u001b[1m[1:chain:AgentExecutor] [2.96s] Exiting Chain run with output:\n",
      "\u001b[0m{\n",
      "  \"output\": \"[['Jen', 'Ayai'], ['Lang', 'Chain'], ['Harrison', 'Chase'], ['Elle', 'Elem'], ['Trance', 'Former'], ['Geoff', 'Fusion'], ['Dolly', 'Too']]\"\n",
      "}\n"
     ]
    }
   ],
   "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": {},
   "source": [
    "## Define your own tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "70b32cfe",
   "metadata": {
    "height": 30,
    "tags": []
   },
   "outputs": [],
   "source": [
    "#!pip install DateTime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "73f8b595",
   "metadata": {
    "height": 47,
    "tags": []
   },
   "outputs": [],
   "source": [
    "from langchain.agents import tool\n",
    "from datetime import date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "61c64268",
   "metadata": {
    "height": 166,
    "tags": []
   },
   "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": 22,
   "id": "0d2fd9ee",
   "metadata": {
    "height": 115,
    "tags": []
   },
   "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": {},
   "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": 23,
   "id": "1fcd13cb",
   "metadata": {
    "height": 81,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new AgentExecutor chain...\u001b[0m\n",
      "\u001b[32;1m\u001b[1;3mThought: I need to use the `time` tool to get today's date.\n",
      "Action:\n",
      "```\n",
      "{\n",
      "  \"action\": \"time\",\n",
      "  \"action_input\": \"\"\n",
      "}\n",
      "```\n",
      "\u001b[0m\n",
      "Observation: \u001b[38;5;200m\u001b[1;3m2024-05-31\u001b[0m\n",
      "Thought:\u001b[32;1m\u001b[1;3mI have successfully retrieved today's date using the `time` tool.\n",
      "Final Answer: Today's date is 2024-05-31.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    }
   ],
   "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": {},
   "source": [
    "Reminder: Download your notebook to you local computer to save your work."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "178403f1",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da13800c",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d07a599",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67306deb",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccc16102",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5fc99309",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b26537f",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84683fd1",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac295066",
   "metadata": {
    "height": 30
   },
   "outputs": [],
   "source": []
  }
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