From the uDemy course on LLM engineering.
https://www.udemy.com/course/llm-engineering-master-ai-and-large-language-models
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152 lines
4.2 KiB
152 lines
4.2 KiB
{ |
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"cells": [ |
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{ |
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"id": "23f53670-1a73-46ba-a754-4a497e8e0e64", |
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"source": [ |
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"# The Price is Right\n", |
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"\n", |
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"First we'll polish off 2 more simple agents:\n", |
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"\n", |
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"The **Messaging Agent** to send push notifications\n", |
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"\n", |
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"The **Planning Agent** to coordinate activities\n", |
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"\n", |
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"Then we'll put it all together into an Agent Framework.\n", |
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"\n", |
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"For the Push Notification, we will be using a nifty platform called Pushover. \n", |
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"You'll need to set up a free account and add 2 tokens to your `.env` file:\n", |
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"\n", |
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"```\n", |
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"PUSHOVER_USER=xxx\n", |
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"PUSHOVER_TOKEN=xxx\n", |
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"```" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "80d683d9-9e92-44ae-af87-a413ca84db21", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"from dotenv import load_dotenv\n", |
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"from agents.messaging_agent import MessagingAgent" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "5ba769cc-5301-4810-b01f-cab584cfb3b3", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"load_dotenv()\n", |
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"DB = \"products_vectorstore\"" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "e05cc427-3d2c-4792-ade1-d356f95a82a9", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"agent = MessagingAgent()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "5ec518f5-dae4-44b1-a185-d7eaf853ec00", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"agent.push(\"MASSIVE NEWS!!!\")" |
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] |
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}, |
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{ |
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"cell_type": "markdown", |
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"id": "7f2781ad-e122-4570-8fad-a2fe6452414e", |
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"metadata": {}, |
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"source": [ |
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"<table style=\"margin: 0; text-align: left;\">\n", |
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" <tr>\n", |
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" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n", |
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" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n", |
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" </td>\n", |
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" <td>\n", |
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" <h2 style=\"color:#f71;\">Additional resource: more sophisticated planning agent</h2>\n", |
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" <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", |
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" </span>\n", |
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" </td>\n", |
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" </tr>\n", |
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"</table>" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "57b3a014-0b15-425a-a29b-6fefc5006dee", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"import chromadb\n", |
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"DB = \"products_vectorstore\"\n", |
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"client = chromadb.PersistentClient(path=DB)\n", |
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"collection = client.get_or_create_collection('products')\n", |
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"from agents.planning_agent import PlanningAgent" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "a5c31c39-e357-446e-9cec-b4775c298941", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"planner = PlanningAgent(collection)" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "d9ac771b-ea12-41c0-a7ce-05f12e27ad9e", |
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"metadata": {}, |
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"outputs": [], |
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"source": [ |
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"planner.plan()" |
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] |
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}, |
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{ |
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"cell_type": "code", |
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"execution_count": null, |
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"id": "8dd94a70-3202-452b-9ef0-551d6feb159b", |
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"metadata": {}, |
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"outputs": [], |
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} |
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], |
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"display_name": "Python 3 (ipykernel)", |
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