10_Agents.ipynb

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    "# **Agents** in LangChain\n",
    "\n",
    "## Outline:\n",
    "\n",
    "* Using built in LangChain tools: DuckDuckGo search and Wikipedia\n",
    "* Defining your own tools"
   ]
  },
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   "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\")"
   ]
  },
  {
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   "cell_type": "markdown",
   "id": "0e3b9ad2",
   "metadata": {},
   "source": [
    "## Built-in LangChain tools"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "974acf8e-8f88-42de-88f8-40a82cb58e8b",
   "metadata": {
    "height": 30,
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   },
   "outputs": [],
   "source": [
    "#!pip install -U wikipedia"
   ]
  },
  {
   "cell_type": "code",
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   "id": "7b93d0e8",
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   "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",
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   "source": [
    "llm = ChatOpenAI(temperature=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "394924ac",
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   "outputs": [],
   "source": [
    "tools = load_tools([\"llm-math\",\"wikipedia\"], llm=llm)"
   ]
  },
  {
   "cell_type": "code",
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   "id": "02cd4956",
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   "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",
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   "id": "44601fe4-5f24-4ff2-a5df-a7e194a5b4f8",
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   "outputs": [],
   "source": [
    "agent(\"What is the 25% of 300?\")"
   ]
  },
  {
   "cell_type": "code",
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   "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) "
   ]
  },
  {
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   "metadata": {},
   "source": [
    "## Python Agent"
   ]
  },
  {
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   "source": [
    "agent = create_python_agent(\n",
    "    llm,\n",
    "    tool=PythonREPLTool(),\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
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   "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",
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   "source": [
    "agent.run(f\"\"\"Sort these customers by \\\n",
    "last name and then first name \\\n",
    "and print the output: {customer_list}\"\"\") "
   ]
  },
  {
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   "id": "74466cec-2d98-438b-805f-33e31fdc3035",
   "metadata": {},
   "source": [
    "#### View detailed outputs of the chains"
   ]
  },
  {
   "cell_type": "code",
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    "height": 115
   },
   "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"
   ]
  },
  {
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   "cell_type": "markdown",
   "id": "2d2a4685",
   "metadata": {},
   "source": [
    "## Define your own tool"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "70b32cfe",
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   "source": [
    "#!pip install DateTime"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "from langchain.agents import tool\n",
    "from datetime import date"
   ]
  },
  {
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   "id": "61c64268",
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   "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",
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   "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)"
   ]
  },
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   "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."
   ]
  },
  {
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   "id": "1fcd13cb",
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   },
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   "source": [
    "try:\n",
    "    result = agent(\"whats the date today?\") \n",
    "except: \n",
    "    print(\"exception on external access\")"
   ]
  },
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