{
 "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": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: llama-stack-client in ./.conda/envs/quick/lib/python3.13/site-packages (0.0.48)\n",
      "Requirement already satisfied: anyio<5,>=3.5.0 in ./.conda/envs/quick/lib/python3.13/site-packages (from llama-stack-client) (4.6.2.post1)\n",
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      "Requirement already satisfied: httpcore==1.* in ./.conda/envs/quick/lib/python3.13/site-packages (from httpx<1,>=0.23.0->llama-stack-client) (1.0.6)\n",
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      "Requirement already satisfied: annotated-types>=0.6.0 in ./.conda/envs/quick/lib/python3.13/site-packages (from pydantic<3,>=1.9.0->llama-stack-client) (0.7.0)\n",
      "Requirement already satisfied: pydantic-core==2.23.4 in ./.conda/envs/quick/lib/python3.13/site-packages (from pydantic<3,>=1.9.0->llama-stack-client) (2.23.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install llama-stack-client --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Agent' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[4], line 23\u001b[0m\n\u001b[1;32m     15\u001b[0m load_dotenv()\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m# Helper function to create an agent with tools\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;28;01masync\u001b[39;00m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mcreate_tool_agent\u001b[39m(\n\u001b[1;32m     19\u001b[0m     client: LlamaStackClient,\n\u001b[1;32m     20\u001b[0m     tools: List[Dict],\n\u001b[1;32m     21\u001b[0m     instructions: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mYou are a helpful assistant\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m     22\u001b[0m     model: \u001b[38;5;28mstr\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mLlama3.1-8B-Instruct\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[0;32m---> 23\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[43mAgent\u001b[49m:\n\u001b[1;32m     24\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Create an agent with specified tools.\"\"\"\u001b[39;00m\n\u001b[1;32m     25\u001b[0m     agent_config \u001b[38;5;241m=\u001b[39m AgentConfig(\n\u001b[1;32m     26\u001b[0m         model\u001b[38;5;241m=\u001b[39mmodel,\n\u001b[1;32m     27\u001b[0m         instructions\u001b[38;5;241m=\u001b[39minstructions,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     38\u001b[0m         enable_session_persistence\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[1;32m     39\u001b[0m     )\n",
      "\u001b[0;31mNameError\u001b[0m: name 'Agent' is not defined"
     ]
    }
   ],
   "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",
    "        input_shields=[\"llama_guard\"],\n",
    "        output_shields=[\"llama_guard\"],\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": null,
   "metadata": {},
   "outputs": [],
   "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=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:8000\")\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": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import TypedDict, Optional\n",
    "from datetime import datetime\n",
    "\n",
    "# Define tool types\n",
    "class WeatherInput(TypedDict):\n",
    "    location: str\n",
    "    date: Optional[str]\n",
    "\n",
    "class WeatherOutput(TypedDict):\n",
    "    temperature: float\n",
    "    conditions: str\n",
    "    humidity: float\n",
    "\n",
    "class WeatherTool:\n",
    "    \"\"\"Example custom tool for weather information.\"\"\"\n",
    "\n",
    "    def __init__(self, api_key: Optional[str] = None):\n",
    "        self.api_key = api_key\n",
    "\n",
    "    async def get_weather(self, location: str, date: Optional[str] = None) -> WeatherOutput:\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 __call__(self, input_data: WeatherInput) -> WeatherOutput:\n",
    "        \"\"\"Make the tool callable with structured input.\"\"\"\n",
    "        return await self.get_weather(\n",
    "            location=input_data[\"location\"],\n",
    "            date=input_data.get(\"date\")\n",
    "        )\n",
    "\n",
    "async def create_weather_agent(client: LlamaStackClient) -> Agent:\n",
    "    \"\"\"Create an agent with weather tool capability.\"\"\"\n",
    "    weather_tool = {\n",
    "        \"type\": \"function\",\n",
    "        \"function\": {\n",
    "            \"name\": \"get_weather\",\n",
    "            \"description\": \"Get weather information for a location\",\n",
    "            \"parameters\": {\n",
    "                \"type\": \"object\",\n",
    "                \"properties\": {\n",
    "                    \"location\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"City or location name\"\n",
    "                    },\n",
    "                    \"date\": {\n",
    "                        \"type\": \"string\",\n",
    "                        \"description\": \"Optional date (YYYY-MM-DD)\",\n",
    "                        \"format\": \"date\"\n",
    "                    }\n",
    "                },\n",
    "                \"required\": [\"location\"]\n",
    "            }\n",
    "        },\n",
    "        \"implementation\": WeatherTool()\n",
    "    }\n",
    "\n",
    "    return await create_tool_agent(\n",
    "        client=client,\n",
    "        tools=[weather_tool],\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",
    "    )\n",
    "\n",
    "# Example usage\n",
    "async def weather_example():\n",
    "    client = LlamaStackClient(base_url=\"http://localhost:8000\")\n",
    "    agent = await create_weather_agent(client)\n",
    "\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",
    "# Run the example\n",
    "await weather_example()"
   ]
  }
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