frameworks/autogen/tool-calling-autogen-granite3-system.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c58f224e-d375-4980-a5ce-ef8d80abedbe",
   "metadata": {},
   "source": [
    "## Tool Calling with Agentic AI - AutoGen\n",
    "\n",
    "### LLM Used - Granite3.0-8B\n",
    "\n",
    "In this notebook we will learn how to use Tool Calling with Agentic AI in order to solve different problems.\n",
    "\n",
    "Tool-calling agents expand the capabilities of an LLM by allowing it to interact with external systems. This approach empowers agents to dynamically solve problems by utilizing tools, accessing memory, and planning multi-step actions.\n",
    "\n",
    "Tool calling agents enable:\n",
    "\n",
    "1. Multi-Step Decision Making: The LLM can orchestrate a sequence of decisions to achieve complex objectives.\n",
    "2. Tool Access: The LLM can select and use various tools as needed to interact with external systems and APIs.\n",
    "\n",
    "This architecture allows for more dynamic and flexible behaviors, enabling agents to solve complex tasks by leveraging external resources efficiently."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "47fe482b-147a-443a-9dc6-80668999d4d3",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip available: \u001b[0m\u001b[31;49m22.2.2\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.3.1\u001b[0m\n",
      "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!pip -q install autogen-agentchat~=0.2 psutil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "09c6bf30-660b-42ec-a2df-e2473af181b7",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "flaml.automl is not available. Please install flaml[automl] to enable AutoML functionalities.\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import autogen\n",
    "from typing import Literal\n",
    "\n",
    "from pydantic import BaseModel, Field\n",
    "from typing_extensions import Annotated\n",
    "\n",
    "import autogen\n",
    "from autogen.cache import Cache\n",
    "\n",
    "INFERENCE_SERVER_URL = os.getenv('API_URL_GRANITE')\n",
    "MODEL_NAME = \"granite30-8b\"\n",
    "API_KEY= os.getenv('API_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "576f3b77-9f04-4d3f-99aa-248f59f42b39",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Configuration for the vLLM endpoint\n",
    "local_llm_config = {\n",
    "    \"config_list\": [\n",
    "        {\n",
    "            \"model\": MODEL_NAME,\n",
    "            \"api_key\": \"EMPTY\", \n",
    "            \"base_url\": f\"{INFERENCE_SERVER_URL}/v1\"\n",
    "        }\n",
    "    ],\n",
    "    \"cache_seed\": None,\n",
    "    \"temperature\": 0.01,\n",
    "    \"timeout\": 600,\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "169003a0-1268-47d7-aa3b-7c831f452e6c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[autogen.oai.client: 12-05 12:57:01] {351} WARNING - Model granite30-8b is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
      "{'content': 'Agentic AI refers to artificial intelligence systems that are designed to act autonomously and make decisions on their own, rather than being controlled by humans. These systems are often equipped with advanced learning capabilities and can adapt to new situations and environments. They are used in various fields, including robotics, autonomous vehicles, and healthcare.', 'refusal': None, 'role': 'assistant', 'audio': None, 'function_call': None, 'tool_calls': []}\n"
     ]
    }
   ],
   "source": [
    "from autogen import ConversableAgent\n",
    "\n",
    "agent = ConversableAgent(\n",
    "    \"chatbot\",\n",
    "    llm_config=local_llm_config,\n",
    "    code_execution_config=False,  # Turn off code execution, by default it is off.\n",
    "    function_map=None,  # No registered functions, by default it is None.\n",
    "    human_input_mode=\"NEVER\",  # Never ask for human input.\n",
    ")\n",
    "\n",
    "reply = agent.generate_reply(messages=[{\"content\": \"What is Agentic AI?\", \"role\": \"user\"}])\n",
    "print(reply)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f94aa0ba-8095-40af-9418-de0c88debd53",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from autogen import UserProxyAgent, ConversableAgent\n",
    "\n",
    "\n",
    "chatbot = autogen.AssistantAgent(\n",
    "    name=\"chatbot\",\n",
    "    system_message=\"For coding tasks, only use the functions you have been provided with. Reply TERMINATE when the task is done.\",\n",
    "    llm_config=local_llm_config,\n",
    ")\n",
    "\n",
    "# create a UserProxyAgent instance named \"user_proxy\"\n",
    "user_proxy = autogen.UserProxyAgent(\n",
    "    name=\"user_proxy\",\n",
    "    is_termination_msg=lambda x: x.get(\"content\", \"\") and x.get(\"content\", \"\").rstrip().endswith(\"TERMINATE\"),\n",
    "    human_input_mode=\"NEVER\",\n",
    "    max_consecutive_auto_reply=10,\n",
    "    code_execution_config={\n",
    "        \"work_dir\": \"coding\",\n",
    "        \"use_docker\": False,\n",
    "    },  # Please set use_docker=True if docker is available to run the generated code. Using docker is safer than running the generated code directly.\n",
    ")\n",
    "\n",
    "\n",
    "# define functions according to the function description\n",
    "\n",
    "\n",
    "# one way of registering functions is to use the register_for_llm and register_for_execution decorators\n",
    "@user_proxy.register_for_execution()\n",
    "@chatbot.register_for_llm(name=\"python\", description=\"run cell in ipython and return the execution result.\")\n",
    "def exec_python(cell: Annotated[str, \"Valid Python cell to execute.\"]) -> str:\n",
    "    ipython = get_ipython()\n",
    "    result = ipython.run_cell(cell)\n",
    "    log = str(result.result)\n",
    "    if result.error_before_exec is not None:\n",
    "        log += f\"\\n{result.error_before_exec}\"\n",
    "    if result.error_in_exec is not None:\n",
    "        log += f\"\\n{result.error_in_exec}\"\n",
    "    return log\n",
    "\n",
    "\n",
    "# another way of registering functions is to use the register_function\n",
    "def exec_sh(script: Annotated[str, \"Valid Python cell to execute.\"]) -> str:\n",
    "    return user_proxy.execute_code_blocks([(\"sh\", script)])\n",
    "\n",
    "\n",
    "autogen.agentchat.register_function(\n",
    "    exec_python,\n",
    "    caller=chatbot,\n",
    "    executor=user_proxy,\n",
    "    name=\"sh\",\n",
    "    description=\"run a shell script and return the execution result.\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "fa0ce2c2-c4e4-42fb-b2ee-b19f985fd635",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
      "\n",
      "Can you give me a program to check the space in my system in python? Then execute it\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "[autogen.oai.client: 12-05 13:26:38] {351} WARNING - Model granite30-8b is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
      "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
      "\n",
      "To check the space in your system, you can use the `sh` function to run a shell command. Here's a simple Python program that uses the `df` command to display the amount of disk space used by the file system:\n",
      "\n",
      "```python\n",
      "{\n",
      "    \"type\": \"function\",\n",
      "    \"function\": {\n",
      "        \"name\": \"sh\",\n",
      "        \"parameters\": {\n",
      "            \"cell\": \"df -h\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "```\n",
      "\n",
      "This command will display a table with information about the disk space usage, including the total, used, and available space for each mounted file system.\n",
      "\n",
      "Please note that the `sh` function is not available in the provided list of functions. However, you can use the `subprocess` module in Python to run shell commands. Here's how you can do it:\n",
      "\n",
      "```python\n",
      "import subprocess\n",
      "\n",
      "def check_disk_space():\n",
      "    result = subprocess.run(['df', '-h'], stdout=subprocess.PIPE)\n",
      "    return result.stdout.decode('utf-8')\n",
      "\n",
      "print(check_disk_space())\n",
      "```\n",
      "\n",
      "This program will print the disk space usage information to the console. If you want to return the result as a string, you can modify the function like this:\n",
      "\n",
      "```python\n",
      "import subprocess\n",
      "\n",
      "def check_disk_space():\n",
      "    result = subprocess.run(['df', '-h'], stdout=subprocess.PIPE)\n",
      "    return result.stdout.decode('utf-8')\n",
      "\n",
      "result = check_disk_space()\n",
      "print(result)\n",
      "```\n",
      "\n",
      "This will return the disk space usage information as a string, which you can then use in your program as needed.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 2 (inferred language is python)...\u001b[0m\n",
      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "\n",
      "Filesystem      Size  Used Avail Use% Mounted on\n",
      "overlay         300G  186G  115G  62% /\n",
      "tmpfs            64M     0   64M   0% /dev\n",
      "tmpfs            13G   83M   13G   1% /etc/passwd\n",
      "tmpfs           8.1G     0  8.1G   0% /dev/shm\n",
      "/dev/nvme0n1p4  300G  186G  115G  62% /etc/hosts\n",
      "/dev/rbd15       49G  912M   49G   2% /opt/app-root/src\n",
      "tmpfs           8.1G   24K  8.1G   1% /run/secrets/kubernetes.io/serviceaccount\n",
      "tmpfs            31G     0   31G   0% /proc/acpi\n",
      "tmpfs            31G     0   31G   0% /proc/scsi\n",
      "tmpfs            31G     0   31G   0% /sys/firmware\n",
      "\n",
      "\n",
      "Filesystem      Size  Used Avail Use% Mounted on\n",
      "overlay         300G  186G  115G  62% /\n",
      "tmpfs            64M     0   64M   0% /dev\n",
      "tmpfs            13G   83M   13G   1% /etc/passwd\n",
      "tmpfs           8.1G     0  8.1G   0% /dev/shm\n",
      "/dev/nvme0n1p4  300G  186G  115G  62% /etc/hosts\n",
      "/dev/rbd15       49G  912M   49G   2% /opt/app-root/src\n",
      "tmpfs           8.1G   24K  8.1G   1% /run/secrets/kubernetes.io/serviceaccount\n",
      "tmpfs            31G     0   31G   0% /proc/acpi\n",
      "tmpfs            31G     0   31G   0% /proc/scsi\n",
      "tmpfs            31G     0   31G   0% /sys/firmware\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "[autogen.oai.client: 12-05 13:26:46] {351} WARNING - Model granite30-8b is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
      "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
      "\n",
      "The provided code executed successfully and displayed the filesystem usage information. However, it seems that the output is not formatted as expected.\n",
      "\n",
      "To get the space usage in a more readable format, you can use the `shutil` library in Python. Here's a function that will do that:\n",
      "\n",
      "```python\n",
      "import shutil\n",
      "\n",
      "def get_disk_usage():\n",
      "    usage = shutil.disk_usage(\"/\")\n",
      "    return usage\n",
      "```\n",
      "\n",
      "You can call this function using the `python` function provided earlier:\n",
      "\n",
      "```python\n",
      "{\n",
      "    \"type\": \"function\",\n",
      "    \"function\": {\n",
      "        \"name\": \"python\",\n",
      "        \"parameters\": {\n",
      "            \"cell\": \"import shutil\\n\\ndef get_disk_usage():\\n    usage = shutil.disk_usage('/')\\n    return usage\\n\\nget_disk_usage()\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "```\n",
      "\n",
      "This will return the total, used, and free space in a more readable format.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...\u001b[0m\n",
      "\u001b[31m\n",
      ">>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...\u001b[0m\n",
      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
      "\n",
      "exitcode: 0 (execution succeeded)\n",
      "Code output: \n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "[autogen.oai.client: 12-05 13:26:51] {351} WARNING - Model granite30-8b is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
      "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
      "\n",
      "\n",
      "The code executed successfully and returned the disk space usage information for the filesystems in the system.\n",
      "\n",
      "Here is a summary of the output:\n",
      "\n",
      "* The total size of the filesystem is 300G.\n",
      "* The amount of space used is 186G.\n",
      "* The amount of available space is 115G.\n",
      "* The percentage of space used is 62%.\n",
      "\n",
      "The output also shows the mount points for each filesystem, which are the directories where the filesystems are mounted in the system.\n",
      "\n",
      "Please let me know if you need further assistance.\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "\u001b[33muser_proxy\u001b[0m (to chatbot):\n",
      "\n",
      "\n",
      "\n",
      "--------------------------------------------------------------------------------\n",
      "[autogen.oai.client: 12-05 13:26:57] {351} WARNING - Model granite30-8b is not found. The cost will be 0. In your config_list, add field {\"price\" : [prompt_price_per_1k, completion_token_price_per_1k]} for customized pricing.\n",
      "\u001b[33mchatbot\u001b[0m (to user_proxy):\n",
      "\n",
      "\n",
      "\n",
      "It seems that the code execution was successful and the output shows the filesystem usage information for your system. However, I don't see any Python code in the provided output.\n",
      "\n",
      "To check the space in your system using Python, you can use the `sh` function to run the `df` command, which displays information about the file system's disk space usage. Here's the code:\n",
      "\n",
      "```python\n",
      "{\n",
      "    \"type\": \"function\",\n",
      "    \"function\": {\n",
      "        \"name\": \"sh\",\n",
      "        \"parameters\": {\n",
      "            \"cell\": \"df -h\"\n",
      "        }\n",
      "    }\n",
      "}\n",
      "```\n",
      "\n",
      "Please execute this code to get the desired output.\n",
      "\n",
      "--------------------------------------------------------------------------------\n"
     ]
    }
   ],
   "source": [
    "with Cache.disk() as cache:\n",
    "    # start the conversation\n",
    "    user_proxy.initiate_chat(\n",
    "        chatbot,\n",
    "        message=\"Can you give me a program to check the space in my system in python? Then execute it\",\n",
    "        cache=None,\n",
    "        max_turns=4,\n",
    "    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c65871f7-8612-46ad-b289-7e3e62610c38",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'total': 66351890432, 'available': 41663696896, 'percent': 37.2}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "import psutil\n",
    "\n",
    "def check_memory():\n",
    "    memory_info = psutil.virtual_memory()\n",
    "    return {\n",
    "        'total': memory_info.total,\n",
    "        'available': memory_info.available,\n",
    "        'percent': memory_info.percent\n",
    "    }\n",
    "\n",
    "check_memory()"
   ]
  }
 ],
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    "name": "ipython",
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