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  {
   "cell_type": "markdown",
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   "source": [
    "# The Price is Right\n",
    "\n",
    "First we'll polish off 2 more simple agents:\n",
    "\n",
    "The **Messaging Agent** to send push notifications\n",
    "\n",
    "The **Planning Agent** to coordinate activities\n",
    "\n",
    "Then we'll put it all together into an Agent Framework.\n",
    "\n",
    "For the Push Notification, we will be using a nifty platform called Pushover.  \n",
    "You'll need to set up a free account and add 2 tokens to your `.env` file:\n",
    "\n",
    "```\n",
    "PUSHOVER_USER=xxx\n",
    "PUSHOVER_TOKEN=xxx\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80d683d9-9e92-44ae-af87-a413ca84db21",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dotenv import load_dotenv\n",
    "from agents.messaging_agent import MessagingAgent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ba769cc-5301-4810-b01f-cab584cfb3b3",
   "metadata": {},
   "outputs": [],
   "source": [
    "load_dotenv(override=True)\n",
    "DB = \"products_vectorstore\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e05cc427-3d2c-4792-ade1-d356f95a82a9",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent = MessagingAgent()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ec518f5-dae4-44b1-a185-d7eaf853ec00",
   "metadata": {},
   "outputs": [],
   "source": [
    "agent.push(\"MASSIVE NEWS!!!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f2781ad-e122-4570-8fad-a2fe6452414e",
   "metadata": {},
   "source": [
    "<table style=\"margin: 0; text-align: left;\">\n",
    "    <tr>\n",
    "        <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
    "            <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
    "        </td>\n",
    "        <td>\n",
    "            <h2 style=\"color:#f71;\">Additional resource: more sophisticated planning agent</h2>\n",
    "            <span style=\"color:#f71;\">The Planning Agent that we use in the next cell is simply a python script that calls the other Agents; frankly that's all we require for this project. But if you're intrigued to see a more Autonomous version in which we give the Planning Agent tools and allow it to decide which Agents to call, see my implementation of <a href=\"https://github.com/ed-donner/agentic/blob/main/workshop/agents/autonomous_planning_agent.py\">AutonomousPlanningAgent</a> in my related repo, <a href=\"https://github.com/ed-donner/agentic\">Agentic</a>. This is an example with multiple tools that dynamically decides which function to call.\n",
    "            </span>\n",
    "        </td>\n",
    "    </tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57b3a014-0b15-425a-a29b-6fefc5006dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import chromadb\n",
    "DB = \"products_vectorstore\"\n",
    "client = chromadb.PersistentClient(path=DB)\n",
    "collection = client.get_or_create_collection('products')\n",
    "from agents.planning_agent import PlanningAgent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5c31c39-e357-446e-9cec-b4775c298941",
   "metadata": {},
   "outputs": [],
   "source": [
    "planner = PlanningAgent(collection)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9ac771b-ea12-41c0-a7ce-05f12e27ad9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "planner.plan()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8dd94a70-3202-452b-9ef0-551d6feb159b",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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