{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tool Calling\n",
    "\n",
    "Before you begin, please ensure Llama Stack is installed and set up by following the [Getting Started Guide](https://llama-stack.readthedocs.io/en/latest/getting_started/index.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this section, we'll explore how to enhance your applications with tool calling capabilities. We'll cover:\n",
    "1. Setting up and using the Brave Search API\n",
    "2. Creating custom tools\n",
    "3. Configuring tool prompts and safety settings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up your connection parameters:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "HOST = \"localhost\"  # Replace with your host\n",
    "PORT = 5000        # Replace with your port"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: llama_stack_client in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (0.0.41)\n",
      "Collecting llama_stack_client\n",
      "  Downloading llama_stack_client-0.0.49-py3-none-any.whl.metadata (13 kB)\n",
      "Requirement already satisfied: anyio<5,>=3.5.0 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (4.6.2)\n",
      "Requirement already satisfied: distro<2,>=1.7.0 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (1.9.0)\n",
      "Requirement already satisfied: httpx<1,>=0.23.0 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (0.27.0)\n",
      "Requirement already satisfied: pydantic<3,>=1.9.0 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (2.9.2)\n",
      "Requirement already satisfied: sniffio in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (1.3.0)\n",
      "Requirement already satisfied: tabulate>=0.9.0 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (0.9.0)\n",
      "Requirement already satisfied: typing-extensions<5,>=4.7 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from llama_stack_client) (4.11.0)\n",
      "Requirement already satisfied: idna>=2.8 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from anyio<5,>=3.5.0->llama_stack_client) (3.10)\n",
      "Requirement already satisfied: exceptiongroup>=1.0.2 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from anyio<5,>=3.5.0->llama_stack_client) (1.2.0)\n",
      "Requirement already satisfied: certifi in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from httpx<1,>=0.23.0->llama_stack_client) (2024.8.30)\n",
      "Requirement already satisfied: httpcore==1.* in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from httpx<1,>=0.23.0->llama_stack_client) (1.0.2)\n",
      "Requirement already satisfied: h11<0.15,>=0.13 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from httpcore==1.*->httpx<1,>=0.23.0->llama_stack_client) (0.14.0)\n",
      "Requirement already satisfied: annotated-types>=0.6.0 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from pydantic<3,>=1.9.0->llama_stack_client) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.4 in /home/justinai/.conda/envs/agentstore/lib/python3.10/site-packages (from pydantic<3,>=1.9.0->llama_stack_client) (2.23.4)\n",
      "Downloading llama_stack_client-0.0.49-py3-none-any.whl (280 kB)\n",
      "Installing collected packages: llama_stack_client\n",
      "  Attempting uninstall: llama_stack_client\n",
      "    Found existing installation: llama_stack_client 0.0.41\n",
      "    Uninstalling llama_stack_client-0.0.41:\n",
      "      Successfully uninstalled llama_stack_client-0.0.41\n",
      "Successfully installed llama_stack_client-0.0.49\n"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade llama_stack_client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import asyncio\n",
    "import os\n",
    "from typing import Dict, List, Optional\n",
    "from dotenv import load_dotenv\n",
    "\n",
    "from llama_stack_client import LlamaStackClient\n",
    "from llama_stack_client.lib.agents.agent import Agent\n",
    "from llama_stack_client.lib.agents.event_logger import EventLogger\n",
    "from llama_stack_client.types.agent_create_params import (\n",
    "    AgentConfig,\n",
    "    AgentConfigToolSearchToolDefinition,\n",
    ")\n",
    "\n",
    "# Load environment variables\n",
    "load_dotenv()\n",
    "\n",
    "# Helper function to create an agent with tools\n",
    "async def create_tool_agent(\n",
    "    client: LlamaStackClient,\n",
    "    tools: List[Dict],\n",
    "    instructions: str = \"You are a helpful assistant\",\n",
    "    model: str = \"Llama3.2-11B-Vision-Instruct\",\n",
    ") -> Agent:\n",
    "    \"\"\"Create an agent with specified tools.\"\"\"\n",
    "    print(\"Using the following model: \", model)\n",
    "    agent_config = AgentConfig(\n",
    "        model=model,\n",
    "        instructions=instructions,\n",
    "        sampling_params={\n",
    "            \"strategy\": \"greedy\",\n",
    "            \"temperature\": 1.0,\n",
    "            \"top_p\": 0.9,\n",
    "        },\n",
    "        tools=tools,\n",
    "        tool_choice=\"auto\",\n",
    "        tool_prompt_format=\"json\",\n",
    "        enable_session_persistence=True,\n",
    "    )\n",
    "\n",
    "    return Agent(client, agent_config)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, create a `.env` file in your notebook directory with your Brave Search API key:\n",
    "\n",
    "```\n",
    "BRAVE_SEARCH_API_KEY=your_key_here\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using the following model:  Llama3.2-11B-Vision-Instruct\n",
      "\n",
      "Query: What are the latest developments in quantum computing?\n",
      "--------------------------------------------------\n",
      "\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[33mF\u001b[0m\u001b[33mIND\u001b[0m\u001b[33mINGS\u001b[0m\u001b[33m:\n",
      "\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m has\u001b[0m\u001b[33m made\u001b[0m\u001b[33m significant\u001b[0m\u001b[33m progress\u001b[0m\u001b[33m in\u001b[0m\u001b[33m recent\u001b[0m\u001b[33m years\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m several\u001b[0m\u001b[33m companies\u001b[0m\u001b[33m and\u001b[0m\u001b[33m research\u001b[0m\u001b[33m institutions\u001b[0m\u001b[33m making\u001b[0m\u001b[33m breakthrough\u001b[0m\u001b[33ms\u001b[0m\u001b[33m in\u001b[0m\u001b[33m the\u001b[0m\u001b[33m field\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Some\u001b[0m\u001b[33m of\u001b[0m\u001b[33m the\u001b[0m\u001b[33m latest\u001b[0m\u001b[33m developments\u001b[0m\u001b[33m include\u001b[0m\u001b[33m:\n",
      "\n",
      "\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m's\u001b[0m\u001b[33m S\u001b[0m\u001b[33myc\u001b[0m\u001b[33mam\u001b[0m\u001b[33more\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m achieved\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m supremacy\u001b[0m\u001b[33m in\u001b[0m\u001b[33m \u001b[0m\u001b[33m201\u001b[0m\u001b[33m9\u001b[0m\u001b[33m by\u001b[0m\u001b[33m performing\u001b[0m\u001b[33m a\u001b[0m\u001b[33m complex\u001b[0m\u001b[33m calculation\u001b[0m\u001b[33m that\u001b[0m\u001b[33m was\u001b[0m\u001b[33m beyond\u001b[0m\u001b[33m the\u001b[0m\u001b[33m capabilities\u001b[0m\u001b[33m of\u001b[0m\u001b[33m classical\u001b[0m\u001b[33m computers\u001b[0m\u001b[33m (\u001b[0m\u001b[33mSource\u001b[0m\u001b[33m:\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Sup\u001b[0m\u001b[33mrem\u001b[0m\u001b[33macy\u001b[0m\u001b[33m Using\u001b[0m\u001b[33m a\u001b[0m\u001b[33m \u001b[0m\u001b[33m53\u001b[0m\u001b[33m-Q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Computer\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m Researchers\u001b[0m\u001b[33m,\u001b[0m\u001b[33m ar\u001b[0m\u001b[33mX\u001b[0m\u001b[33miv\u001b[0m\u001b[33m:\u001b[0m\u001b[33m191\u001b[0m\u001b[33m0\u001b[0m\u001b[33m.\u001b[0m\u001b[33m113\u001b[0m\u001b[33m33\u001b[0m\u001b[33m).\n",
      "\u001b[0m\u001b[33m*\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m's\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Experience\u001b[0m\u001b[33m,\u001b[0m\u001b[33m a\u001b[0m\u001b[33m cloud\u001b[0m\u001b[33m-based\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computer\u001b[0m\u001b[33m that\u001b[0m\u001b[33m allows\u001b[0m\u001b[33m users\u001b[0m\u001b[33m to\u001b[0m\u001b[33m run\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m and\u001b[0m\u001b[33m experiments\u001b[0m\u001b[33m (\u001b[0m\u001b[33mSource\u001b[0m\u001b[33m:\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Experience\u001b[0m\u001b[33m,\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m Research\u001b[0m\u001b[33m).\n",
      "\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Rig\u001b[0m\u001b[33metti\u001b[0m\u001b[33m Computing\u001b[0m\u001b[33m's\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m cloud\u001b[0m\u001b[33m platform\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m provides\u001b[0m\u001b[33m access\u001b[0m\u001b[33m to\u001b[0m\u001b[33m a\u001b[0m\u001b[33m \u001b[0m\u001b[33m128\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processor\u001b[0m\u001b[33m (\u001b[0m\u001b[33mSource\u001b[0m\u001b[33m:\u001b[0m\u001b[33m Rig\u001b[0m\u001b[33metti\u001b[0m\u001b[33m Computing\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Rig\u001b[0m\u001b[33metti\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m).\n",
      "\u001b[0m\u001b[33m*\u001b[0m\u001b[33m Microsoft\u001b[0m\u001b[33m's\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Development\u001b[0m\u001b[33m Kit\u001b[0m\u001b[33m,\u001b[0m\u001b[33m a\u001b[0m\u001b[33m set\u001b[0m\u001b[33m of\u001b[0m\u001b[33m tools\u001b[0m\u001b[33m and\u001b[0m\u001b[33m software\u001b[0m\u001b[33m for\u001b[0m\u001b[33m developing\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m and\u001b[0m\u001b[33m applications\u001b[0m\u001b[33m (\u001b[0m\u001b[33mSource\u001b[0m\u001b[33m:\u001b[0m\u001b[33m Microsoft\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Development\u001b[0m\u001b[33m Kit\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Microsoft\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m).\n",
      "\u001b[0m\u001b[33m*\u001b[0m\u001b[33m The\u001b[0m\u001b[33m development\u001b[0m\u001b[33m of\u001b[0m\u001b[33m new\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m techniques\u001b[0m\u001b[33m,\u001b[0m\u001b[33m such\u001b[0m\u001b[33m as\u001b[0m\u001b[33m surface\u001b[0m\u001b[33m codes\u001b[0m\u001b[33m and\u001b[0m\u001b[33m top\u001b[0m\u001b[33mological\u001b[0m\u001b[33m codes\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m are\u001b[0m\u001b[33m essential\u001b[0m\u001b[33m for\u001b[0m\u001b[33m large\u001b[0m\u001b[33m-scale\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m (\u001b[0m\u001b[33mSource\u001b[0m\u001b[33m:\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Error\u001b[0m\u001b[33m Correction\u001b[0m\u001b[33m with\u001b[0m\u001b[33m Surface\u001b[0m\u001b[33m Codes\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Stephanie\u001b[0m\u001b[33m We\u001b[0m\u001b[33mh\u001b[0m\u001b[33mner\u001b[0m\u001b[33m et\u001b[0m\u001b[33m al\u001b[0m\u001b[33m.,\u001b[0m\u001b[33m ar\u001b[0m\u001b[33mX\u001b[0m\u001b[33miv\u001b[0m\u001b[33m:\u001b[0m\u001b[33m140\u001b[0m\u001b[33m1\u001b[0m\u001b[33m.\u001b[0m\u001b[33m408\u001b[0m\u001b[33m1\u001b[0m\u001b[33m).\n",
      "\n",
      "\u001b[0m\u001b[33mS\u001b[0m\u001b[33mOURCES\u001b[0m\u001b[33m:\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Sup\u001b[0m\u001b[33mrem\u001b[0m\u001b[33macy\u001b[0m\u001b[33m Using\u001b[0m\u001b[33m a\u001b[0m\u001b[33m \u001b[0m\u001b[33m53\u001b[0m\u001b[33m-Q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Computer\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m Researchers\u001b[0m\u001b[33m,\u001b[0m\u001b[33m ar\u001b[0m\u001b[33mX\u001b[0m\u001b[33miv\u001b[0m\u001b[33m:\u001b[0m\u001b[33m191\u001b[0m\u001b[33m0\u001b[0m\u001b[33m.\u001b[0m\u001b[33m113\u001b[0m\u001b[33m33\u001b[0m\u001b[33m\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Experience\u001b[0m\u001b[33m,\u001b[0m\u001b[33m IBM\u001b[0m\u001b[33m Research\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mwww\u001b[0m\u001b[33m.ibm\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m/\u001b[0m\u001b[33mquant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m-ex\u001b[0m\u001b[33mperience\u001b[0m\u001b[33m/)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m Rig\u001b[0m\u001b[33metti\u001b[0m\u001b[33m Computing\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Rig\u001b[0m\u001b[33metti\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mwww\u001b[0m\u001b[33m.r\u001b[0m\u001b[33mig\u001b[0m\u001b[33metti\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m/)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m Microsoft\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m Development\u001b[0m\u001b[33m Kit\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Microsoft\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mwww\u001b[0m\u001b[33m.microsoft\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m/en\u001b[0m\u001b[33m-us\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m/)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Error\u001b[0m\u001b[33m Correction\u001b[0m\u001b[33m with\u001b[0m\u001b[33m Surface\u001b[0m\u001b[33m Codes\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m by\u001b[0m\u001b[33m Stephanie\u001b[0m\u001b[33m We\u001b[0m\u001b[33mh\u001b[0m\u001b[33mner\u001b[0m\u001b[33m et\u001b[0m\u001b[33m al\u001b[0m\u001b[33m.,\u001b[0m\u001b[33m ar\u001b[0m\u001b[33mX\u001b[0m\u001b[33miv\u001b[0m\u001b[33m:\u001b[0m\u001b[33m140\u001b[0m\u001b[33m1\u001b[0m\u001b[33m.\u001b[0m\u001b[33m408\u001b[0m\u001b[33m1\u001b[0m\u001b[97m\u001b[0m\n",
      "\u001b[30m\u001b[0m"
     ]
    }
   ],
   "source": [
    "async def create_search_agent(client: LlamaStackClient) -> Agent:\n",
    "    \"\"\"Create an agent with Brave Search capability.\"\"\"\n",
    "    search_tool = AgentConfigToolSearchToolDefinition(\n",
    "        type=\"brave_search\",\n",
    "        engine=\"brave\",\n",
    "        api_key=\"dummy_value\"#os.getenv(\"BRAVE_SEARCH_API_KEY\"),\n",
    "    )\n",
    "    \n",
    "    models_response = client.models.list()\n",
    "    for model in models_response:\n",
    "        if model.identifier.endswith(\"Instruct\"):\n",
    "            model_name = model.llama_model\n",
    "    \n",
    "\n",
    "    return await create_tool_agent(\n",
    "        client=client,\n",
    "        tools=[search_tool],\n",
    "        model = model_name,\n",
    "        instructions=\"\"\"\n",
    "        You are a research assistant that can search the web.\n",
    "        Always cite your sources with URLs when providing information.\n",
    "        Format your responses as:\n",
    "\n",
    "        FINDINGS:\n",
    "        [Your summary here]\n",
    "\n",
    "        SOURCES:\n",
    "        - [Source title](URL)\n",
    "        \"\"\"\n",
    "    )\n",
    "\n",
    "# Example usage\n",
    "async def search_example():\n",
    "    client = LlamaStackClient(base_url=f\"http://{HOST}:{PORT}\")\n",
    "    agent = await create_search_agent(client)\n",
    "\n",
    "    # Create a session\n",
    "    session_id = agent.create_session(\"search-session\")\n",
    "\n",
    "    # Example queries\n",
    "    queries = [\n",
    "        \"What are the latest developments in quantum computing?\",\n",
    "        #\"Who won the most recent Super Bowl?\",\n",
    "    ]\n",
    "\n",
    "    for query in queries:\n",
    "        print(f\"\\nQuery: {query}\")\n",
    "        print(\"-\" * 50)\n",
    "\n",
    "        response = agent.create_turn(\n",
    "            messages=[{\"role\": \"user\", \"content\": query}],\n",
    "            session_id=session_id,\n",
    "        )\n",
    "\n",
    "        async for log in EventLogger().log(response):\n",
    "            log.print()\n",
    "\n",
    "# Run the example (in Jupyter, use asyncio.run())\n",
    "await search_example()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Custom Tool Creation\n",
    "\n",
    "Let's create a custom weather tool:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Query: What's the weather like in San Francisco?\n",
      "--------------------------------------------------\n",
      "\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[33m{\n",
      "\u001b[0m\u001b[33m   \u001b[0m\u001b[33m \"\u001b[0m\u001b[33mtype\u001b[0m\u001b[33m\":\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mfunction\u001b[0m\u001b[33m\",\n",
      "\u001b[0m\u001b[33m   \u001b[0m\u001b[33m \"\u001b[0m\u001b[33mname\u001b[0m\u001b[33m\":\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mget\u001b[0m\u001b[33m_weather\u001b[0m\u001b[33m\",\n",
      "\u001b[0m\u001b[33m   \u001b[0m\u001b[33m \"\u001b[0m\u001b[33mparameters\u001b[0m\u001b[33m\":\u001b[0m\u001b[33m {\n",
      "\u001b[0m\u001b[33m       \u001b[0m\u001b[33m \"\u001b[0m\u001b[33mlocation\u001b[0m\u001b[33m\":\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mSan\u001b[0m\u001b[33m Francisco\u001b[0m\u001b[33m\"\n",
      "\u001b[0m\u001b[33m   \u001b[0m\u001b[33m }\n",
      "\u001b[0m\u001b[33m}\u001b[0m\u001b[97m\u001b[0m\n",
      "tool_calls starting:  ToolCall(arguments={'location': 'San Francisco'}, call_id='f558e862-0465-48c5-9f65-f9160a59003b', tool_name='get_weather')\n",
      "Using Location: San Francisco\n",
      "   {\n",
      "            \"temperature\": 72.5,\n",
      "            \"conditions\": \"partly cloudy\",\n",
      "            \"humidity\": 65.0\n",
      "        }\n",
      "\n",
      "Query: Tell me the weather in Tokyo tomorrow\n",
      "--------------------------------------------------\n",
      "\u001b[30m\u001b[0m\u001b[33minference> \u001b[0m\u001b[36m\u001b[0m\u001b[36m{\"\u001b[0m\u001b[36mtype\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mfunction\u001b[0m\u001b[36m\",\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mname\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mget\u001b[0m\u001b[36m_weather\u001b[0m\u001b[36m\",\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mparameters\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m {\"\u001b[0m\u001b[36mlocation\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mTok\u001b[0m\u001b[36myo\u001b[0m\u001b[36m\",\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mdate\u001b[0m\u001b[36m\":\u001b[0m\u001b[36m \"\u001b[0m\u001b[36mtom\u001b[0m\u001b[36morrow\u001b[0m\u001b[36m\"}}\u001b[0m\u001b[97m\u001b[0m\n",
      "tool_calls starting:  ToolCall(arguments={'location': 'Tokyo', 'date': 'tomorrow'}, call_id='d2ee9a99-ee8f-4fcd-9cda-a1bfb52bcc72', tool_name='get_weather')\n",
      "Using Location: Tokyo\n",
      "Using date: tomorrow\n",
      "   {\n",
      "            \"temperature\": 72.5,\n",
      "            \"conditions\": \"partly cloudy\",\n",
      "            \"humidity\": 65.0\n",
      "        }\n"
     ]
    }
   ],
   "source": [
    "from typing import TypedDict, Optional, Dict, Any\n",
    "from datetime import datetime\n",
    "\n",
    "from llama_stack_client.types.tool_param_definition_param import ToolParamDefinitionParam\n",
    "from llama_stack_client.types import CompletionMessage\n",
    "class WeatherTool:\n",
    "    \"\"\"Example custom tool for weather information.\"\"\"\n",
    "\n",
    "    def get_name(self) -> str:\n",
    "        return \"get_weather\"\n",
    "\n",
    "    def get_description(self) -> str:\n",
    "        return \"Get weather information for a location\"\n",
    "\n",
    "    def get_params_definition(self) -> Dict[str, ToolParamDefinitionParam]:\n",
    "        return {\n",
    "            \"location\": ToolParamDefinitionParam(\n",
    "                param_type=\"str\",\n",
    "                description=\"City or location name\",\n",
    "                required=True\n",
    "            ),\n",
    "            \"date\": ToolParamDefinitionParam(\n",
    "                param_type=\"str\",\n",
    "                description=\"Optional date (YYYY-MM-DD)\",\n",
    "                required=False\n",
    "            )\n",
    "        }\n",
    "    async def run(self,messages):\n",
    "        for message in messages:\n",
    "            print(\"tool_calls starting: \", message.tool_calls[0])\n",
    "            return_dict = message.tool_calls[0].arguments\n",
    "            location = return_dict.get(\"location\",None)\n",
    "            date = return_dict.get(\"date\",None)\n",
    "            print(\"Using Location:\",location)\n",
    "            if date:\n",
    "                print(\"Using date:\",date)\n",
    "            return await self.run_impl(location,date)\n",
    "    \n",
    "    async def run_impl(self, location: str, date: Optional[str] = None) -> Dict[str, Any]:\n",
    "        \"\"\"Simulate getting weather data (replace with actual API call).\"\"\"\n",
    "        # Mock implementation\n",
    "        print(\"\"\"   {\n",
    "            \"temperature\": 72.5,\n",
    "            \"conditions\": \"partly cloudy\",\n",
    "            \"humidity\": 65.0\n",
    "        }\"\"\")\n",
    "        return [CompletionMessage(\n",
    "        content=\"\"\"{\n",
    "            \"temperature\": 72.5,\n",
    "            \"conditions\": \"partly cloudy\",\n",
    "            \"humidity\": 65.0\n",
    "        }\"\"\",\n",
    "        role='assistant',\n",
    "        stop_reason='end_of_message',\n",
    "        tool_calls=[],\n",
    "    )],\n",
    "        \n",
    "\n",
    "async def create_weather_agent(client: LlamaStackClient) -> Agent:\n",
    "    \"\"\"Create an agent with weather tool capability.\"\"\"\n",
    "    models_response = client.models.list()\n",
    "    for model in models_response:\n",
    "        if model.identifier.endswith(\"Instruct\"):\n",
    "            model_name = model.llama_model\n",
    "    agent_config = AgentConfig(\n",
    "        model=model_name,\n",
    "        instructions=\"\"\"\n",
    "        You are a weather assistant that can provide weather information.\n",
    "        Always specify the location clearly in your responses.\n",
    "        Include both temperature and conditions in your summaries.\n",
    "        \"\"\",\n",
    "        sampling_params={\n",
    "            \"strategy\": \"greedy\",\n",
    "            \"temperature\": 1.0,\n",
    "            \"top_p\": 0.9,\n",
    "        },\n",
    "        tools=[\n",
    "            {\n",
    "                \"function_name\": \"get_weather\",\n",
    "                \"description\": \"Get weather information for a location\",\n",
    "                \"parameters\": {\n",
    "                    \"location\": {\n",
    "                        \"param_type\": \"str\",\n",
    "                        \"description\": \"City or location name\",\n",
    "                        \"required\": True,\n",
    "                    },\n",
    "                    \"date\": {\n",
    "                        \"param_type\": \"str\",\n",
    "                        \"description\": \"Optional date (YYYY-MM-DD)\",\n",
    "                        \"required\": False,\n",
    "                    },\n",
    "                },\n",
    "                \"type\": \"function_call\",\n",
    "            }\n",
    "        ],\n",
    "        tool_choice=\"auto\",\n",
    "        tool_prompt_format=\"json\",\n",
    "        input_shields=[],\n",
    "        output_shields=[],\n",
    "        enable_session_persistence=True\n",
    "    )\n",
    "\n",
    "    # Create the agent with the tool\n",
    "    weather_tool = WeatherTool()\n",
    "    agent = Agent(\n",
    "        client=client,\n",
    "        agent_config=agent_config,\n",
    "        custom_tools=[weather_tool]\n",
    "    )\n",
    "\n",
    "    return agent\n",
    "\n",
    "# Example usage\n",
    "async def weather_example():\n",
    "    client = LlamaStackClient(base_url=f\"http://{HOST}:{PORT}\")\n",
    "    agent = await create_weather_agent(client)\n",
    "    session_id = agent.create_session(\"weather-session\")\n",
    "\n",
    "    queries = [\n",
    "        \"What's the weather like in San Francisco?\",\n",
    "        \"Tell me the weather in Tokyo tomorrow\",\n",
    "    ]\n",
    "\n",
    "    for query in queries:\n",
    "        print(f\"\\nQuery: {query}\")\n",
    "        print(\"-\" * 50)\n",
    "\n",
    "        response = agent.create_turn(\n",
    "            messages=[{\"role\": \"user\", \"content\": query}],\n",
    "            session_id=session_id,\n",
    "        )\n",
    "        \n",
    "        async for log in EventLogger().log(response):\n",
    "            log.print()\n",
    "\n",
    "# For Jupyter notebooks\n",
    "import nest_asyncio\n",
    "nest_asyncio.apply()\n",
    "\n",
    "# Run the example\n",
    "await weather_example()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Thanks for checking out this tutorial, hopefully you can now automate everything with Llama! :D\n",
    "\n",
    "Next up, we learn another hot topic of LLMs: Memory and Rag. Continue learning [here](./04_Memory101.ipynb)!"
   ]
  }
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