{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tool Calling"
   ]
  },
  {
   "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": "code",
   "execution_count": 22,
   "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 = \"Llama3.1-8B-Instruct\",\n",
    ") -> Agent:\n",
    "    \"\"\"Create an agent with specified tools.\"\"\"\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": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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[33mThe\u001b[0m\u001b[33m latest\u001b[0m\u001b[33m developments\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computing\u001b[0m\u001b[33m include\u001b[0m\u001b[33m advancements\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processors\u001b[0m\u001b[33m,\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m algorithms\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m.\u001b[0m\u001b[33m Researchers\u001b[0m\u001b[33m have\u001b[0m\u001b[33m made\u001b[0m\u001b[33m significant\u001b[0m\u001b[33m progress\u001b[0m\u001b[33m in\u001b[0m\u001b[33m developing\u001b[0m\u001b[33m more\u001b[0m\u001b[33m powerful\u001b[0m\u001b[33m and\u001b[0m\u001b[33m reliable\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m computers\u001b[0m\u001b[33m,\u001b[0m\u001b[33m with\u001b[0m\u001b[33m some\u001b[0m\u001b[33m companies\u001b[0m\u001b[33m already\u001b[0m\u001b[33m showcasing\u001b[0m\u001b[33m \u001b[0m\u001b[33m100\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m and\u001b[0m\u001b[33m \u001b[0m\u001b[33m127\u001b[0m\u001b[33m-q\u001b[0m\u001b[33mubit\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m processors\u001b[0m\u001b[33m (\u001b[0m\u001b[33mIBM\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m;\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m).\u001b[0m\u001b[33m These\u001b[0m\u001b[33m advancements\u001b[0m\u001b[33m have\u001b[0m\u001b[33m led\u001b[0m\u001b[33m to\u001b[0m\u001b[33m breakthrough\u001b[0m\u001b[33ms\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m simulations\u001b[0m\u001b[33m,\u001b[0m\u001b[33m machine\u001b[0m\u001b[33m learning\u001b[0m\u001b[33m,\u001b[0m\u001b[33m and\u001b[0m\u001b[33m optimization\u001b[0m\u001b[33m problems\u001b[0m\u001b[33m (\u001b[0m\u001b[33mB\u001b[0m\u001b[33mhart\u001b[0m\u001b[33mi\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m;\u001b[0m\u001b[33m Zhang\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m).\u001b[0m\u001b[33m Additionally\u001b[0m\u001b[33m,\u001b[0m\u001b[33m there\u001b[0m\u001b[33m have\u001b[0m\u001b[33m been\u001b[0m\u001b[33m significant\u001b[0m\u001b[33m improvements\u001b[0m\u001b[33m in\u001b[0m\u001b[33m quantum\u001b[0m\u001b[33m error\u001b[0m\u001b[33m correction\u001b[0m\u001b[33m,\u001b[0m\u001b[33m which\u001b[0m\u001b[33m is\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[33mG\u001b[0m\u001b[33mottes\u001b[0m\u001b[33mman\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\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 IBM\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m:\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Process\u001b[0m\u001b[33mors\u001b[0m\u001b[33m\"\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/com\u001b[0m\u001b[33mputer\u001b[0m\u001b[33m/)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m Google\u001b[0m\u001b[33m Quantum\u001b[0m\u001b[33m AI\u001b[0m\u001b[33m Lab\u001b[0m\u001b[33m:\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Process\u001b[0m\u001b[33mors\u001b[0m\u001b[33m\"\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mquant\u001b[0m\u001b[33mum\u001b[0m\u001b[33mai\u001b[0m\u001b[33m.google\u001b[0m\u001b[33m/al\u001b[0m\u001b[33mphabet\u001b[0m\u001b[33m/sub\u001b[0m\u001b[33m-page\u001b[0m\u001b[33m-\u001b[0m\u001b[33m1\u001b[0m\u001b[33m/)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m Bh\u001b[0m\u001b[33marti\u001b[0m\u001b[33m,\u001b[0m\u001b[33m K\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m).\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Computing\u001b[0m\u001b[33m:\u001b[0m\u001b[33m A\u001b[0m\u001b[33m Review\u001b[0m\u001b[33m of\u001b[0m\u001b[33m Recent\u001b[0m\u001b[33m Advances\u001b[0m\u001b[33m.\"\u001b[0m\u001b[33m Journal\u001b[0m\u001b[33m of\u001b[0m\u001b[33m Physics\u001b[0m\u001b[33m:\u001b[0m\u001b[33m Conference\u001b[0m\u001b[33m Series\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m218\u001b[0m\u001b[33m5\u001b[0m\u001b[33m(\u001b[0m\u001b[33m1\u001b[0m\u001b[33m),\u001b[0m\u001b[33m \u001b[0m\u001b[33m012\u001b[0m\u001b[33m001\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mi\u001b[0m\u001b[33mop\u001b[0m\u001b[33mscience\u001b[0m\u001b[33m.i\u001b[0m\u001b[33mop\u001b[0m\u001b[33m.org\u001b[0m\u001b[33m/article\u001b[0m\u001b[33m/\u001b[0m\u001b[33m10\u001b[0m\u001b[33m.\u001b[0m\u001b[33m108\u001b[0m\u001b[33m8\u001b[0m\u001b[33m/\u001b[0m\u001b[33m174\u001b[0m\u001b[33m2\u001b[0m\u001b[33m-\u001b[0m\u001b[33m659\u001b[0m\u001b[33m6\u001b[0m\u001b[33m/\u001b[0m\u001b[33m218\u001b[0m\u001b[33m5\u001b[0m\u001b[33m/\u001b[0m\u001b[33m1\u001b[0m\u001b[33m/\u001b[0m\u001b[33m012\u001b[0m\u001b[33m001\u001b[0m\u001b[33m)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m Zhang\u001b[0m\u001b[33m,\u001b[0m\u001b[33m Y\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\u001b[0m\u001b[33m).\u001b[0m\u001b[33m \"\u001b[0m\u001b[33mQuant\u001b[0m\u001b[33mum\u001b[0m\u001b[33m Algorithms\u001b[0m\u001b[33m for\u001b[0m\u001b[33m Machine\u001b[0m\u001b[33m Learning\u001b[0m\u001b[33m.\"\u001b[0m\u001b[33m Journal\u001b[0m\u001b[33m of\u001b[0m\u001b[33m Machine\u001b[0m\u001b[33m Learning\u001b[0m\u001b[33m Research\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m23\u001b[0m\u001b[33m,\u001b[0m\u001b[33m \u001b[0m\u001b[33m1\u001b[0m\u001b[33m-\u001b[0m\u001b[33m36\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mj\u001b[0m\u001b[33mml\u001b[0m\u001b[33mr\u001b[0m\u001b[33m.org\u001b[0m\u001b[33m/p\u001b[0m\u001b[33mapers\u001b[0m\u001b[33m/v\u001b[0m\u001b[33m23\u001b[0m\u001b[33m/\u001b[0m\u001b[33m20\u001b[0m\u001b[33m-\u001b[0m\u001b[33m065\u001b[0m\u001b[33m.html\u001b[0m\u001b[33m)\n",
      "\u001b[0m\u001b[33m-\u001b[0m\u001b[33m G\u001b[0m\u001b[33mottes\u001b[0m\u001b[33mman\u001b[0m\u001b[33m,\u001b[0m\u001b[33m D\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33m202\u001b[0m\u001b[33m2\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.\"\u001b[0m\u001b[33m In\u001b[0m\u001b[33m Encyclopedia\u001b[0m\u001b[33m of\u001b[0m\u001b[33m Complexity\u001b[0m\u001b[33m and\u001b[0m\u001b[33m Systems\u001b[0m\u001b[33m Science\u001b[0m\u001b[33m (\u001b[0m\u001b[33mpp\u001b[0m\u001b[33m.\u001b[0m\u001b[33m \u001b[0m\u001b[33m1\u001b[0m\u001b[33m-\u001b[0m\u001b[33m13\u001b[0m\u001b[33m).\u001b[0m\u001b[33m Springer\u001b[0m\u001b[33m,\u001b[0m\u001b[33m New\u001b[0m\u001b[33m York\u001b[0m\u001b[33m,\u001b[0m\u001b[33m NY\u001b[0m\u001b[33m.\u001b[0m\u001b[33m (\u001b[0m\u001b[33mhttps\u001b[0m\u001b[33m://\u001b[0m\u001b[33mlink\u001b[0m\u001b[33m.spring\u001b[0m\u001b[33mer\u001b[0m\u001b[33m.com\u001b[0m\u001b[33m/reference\u001b[0m\u001b[33mwork\u001b[0m\u001b[33mentry\u001b[0m\u001b[33m/\u001b[0m\u001b[33m10\u001b[0m\u001b[33m.\u001b[0m\u001b[33m100\u001b[0m\u001b[33m7\u001b[0m\u001b[33m/\u001b[0m\u001b[33m978\u001b[0m\u001b[33m-\u001b[0m\u001b[33m0\u001b[0m\u001b[33m-\u001b[0m\u001b[33m387\u001b[0m\u001b[33m-\u001b[0m\u001b[33m758\u001b[0m\u001b[33m88\u001b[0m\u001b[33m-\u001b[0m\u001b[33m6\u001b[0m\u001b[33m_\u001b[0m\u001b[33m447\u001b[0m\u001b[33m)\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",
    "    return await create_tool_agent(\n",
    "        client=client,\n",
    "        tools=[search_tool],\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=\"http://localhost:5001\")\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": 27,
   "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"
     ]
    },
    {
     "ename": "AttributeError",
     "evalue": "'WeatherTool' object has no attribute 'run'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[27], line 113\u001b[0m\n\u001b[1;32m    110\u001b[0m nest_asyncio\u001b[38;5;241m.\u001b[39mapply()\n\u001b[1;32m    112\u001b[0m \u001b[38;5;66;03m# Run the example\u001b[39;00m\n\u001b[0;32m--> 113\u001b[0m \u001b[38;5;28;01mawait\u001b[39;00m weather_example()\n",
      "Cell \u001b[0;32mIn[27], line 105\u001b[0m, in \u001b[0;36mweather_example\u001b[0;34m()\u001b[0m\n\u001b[1;32m     98\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m-\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m*\u001b[39m \u001b[38;5;241m50\u001b[39m)\n\u001b[1;32m    100\u001b[0m response \u001b[38;5;241m=\u001b[39m agent\u001b[38;5;241m.\u001b[39mcreate_turn(\n\u001b[1;32m    101\u001b[0m     messages\u001b[38;5;241m=\u001b[39m[{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrole\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muser\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcontent\u001b[39m\u001b[38;5;124m\"\u001b[39m: query}],\n\u001b[1;32m    102\u001b[0m     session_id\u001b[38;5;241m=\u001b[39msession_id,\n\u001b[1;32m    103\u001b[0m )\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m log \u001b[38;5;129;01min\u001b[39;00m EventLogger()\u001b[38;5;241m.\u001b[39mlog(response):\n\u001b[1;32m    106\u001b[0m     log\u001b[38;5;241m.\u001b[39mprint()\n",
      "File \u001b[0;32m~/new_task/llama-stack-client-python/src/llama_stack_client/lib/agents/event_logger.py:55\u001b[0m, in \u001b[0;36mEventLogger.log\u001b[0;34m(self, event_generator)\u001b[0m\n\u001b[1;32m     52\u001b[0m previous_event_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     53\u001b[0m previous_step_type \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 55\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mfor\u001b[39;00m chunk \u001b[38;5;129;01min\u001b[39;00m event_generator:\n\u001b[1;32m     56\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(chunk, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mevent\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m     57\u001b[0m         \u001b[38;5;66;03m# Need to check for custom tool first\u001b[39;00m\n\u001b[1;32m     58\u001b[0m         \u001b[38;5;66;03m# since it does not produce event but instead\u001b[39;00m\n\u001b[1;32m     59\u001b[0m         \u001b[38;5;66;03m# a Message\u001b[39;00m\n\u001b[1;32m     60\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(chunk, ToolResponseMessage):\n",
      "File \u001b[0;32m~/new_task/llama-stack-client-python/src/llama_stack_client/lib/agents/agent.py:76\u001b[0m, in \u001b[0;36mAgent.create_turn\u001b[0;34m(self, messages, attachments, session_id)\u001b[0m\n\u001b[1;32m     74\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m     75\u001b[0m     tool \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcustom_tools[tool_call\u001b[38;5;241m.\u001b[39mtool_name]\n\u001b[0;32m---> 76\u001b[0m     result_messages \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mexecute_custom_tool(tool, message)\n\u001b[1;32m     77\u001b[0m     next_message \u001b[38;5;241m=\u001b[39m result_messages[\u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m     79\u001b[0m \u001b[38;5;28;01myield\u001b[39;00m next_message\n",
      "File \u001b[0;32m~/new_task/llama-stack-client-python/src/llama_stack_client/lib/agents/agent.py:84\u001b[0m, in \u001b[0;36mAgent.execute_custom_tool\u001b[0;34m(self, tool, message)\u001b[0m\n\u001b[1;32m     81\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mexecute_custom_tool\u001b[39m(\n\u001b[1;32m     82\u001b[0m     \u001b[38;5;28mself\u001b[39m, tool: CustomTool, message: Union[UserMessage, ToolResponseMessage]\n\u001b[1;32m     83\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m List[Union[UserMessage, ToolResponseMessage]]:\n\u001b[0;32m---> 84\u001b[0m     result_messages \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mawait\u001b[39;00m \u001b[43mtool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m([message])\n\u001b[1;32m     85\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m result_messages\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'WeatherTool' object has no attribute 'run'"
     ]
    }
   ],
   "source": [
    "from typing import TypedDict, Optional, Dict, Any\n",
    "from datetime import datetime\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",
    "    \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",
    "        return {\n",
    "            \"temperature\": 72.5,\n",
    "            \"conditions\": \"partly cloudy\",\n",
    "            \"humidity\": 65.0\n",
    "        }\n",
    "\n",
    "async def create_weather_agent(client: LlamaStackClient) -> Agent:\n",
    "    \"\"\"Create an agent with weather tool capability.\"\"\"\n",
    "    agent_config = AgentConfig(\n",
    "        model=\"Llama3.1-8B-Instruct\",\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=\"http://localhost:5001\")\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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