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{
"cells": [
{
"cell_type": "markdown",
"id": "23f53670-1a73-46ba-a754-4a497e8e0e64",
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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()\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": [],
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}
],
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"display_name": "Python 3 (ipykernel)",
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"name": "python3"
},
"language_info": {
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},
"file_extension": ".py",
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