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Chapter_02.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Chapter 2: Principles of Agentic Systems\n",
+    "---\n",
+    "\n",
+    "### Implementing Algorithm 1: Travel Booking Assistant Algorithm with Agency and Autonomy\n",
+    "\n",
+    "This is a simple Python implementation of Algorithm 1 in Chapter 2."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Agent TripPlanner is booking travel from SAN to SEA\n",
+      "Goal set: Book flight from SAN to SEA\n",
+      "Knowledge updated with 3 flight options\n",
+      "Decision made: Selected flight JetBlue\n",
+      "Booking confirmed: BOOK-JetBlue-TRIPPLANNER\n",
+      "\n",
+      "----------- Final Agent State: -----------\n",
+      "Name: TripPlanner\n",
+      "Goals: ['Book flight from SAN to SEA']\n",
+      "Knowledge Base: {'flight_options': [{'airline': 'Delta', 'departure_airport': 'SAN', 'destination_airport': 'SEA', 'flight_number': 'JB204', 'departure_time': datetime.datetime(2024, 9, 26, 4, 8, 40, 745425), 'arrival_time': datetime.datetime(2024, 9, 30, 8, 57, 27, 119850), 'price': 276.94, 'score': 3.610890445583881}, {'airline': 'United', 'departure_airport': 'SAN', 'destination_airport': 'SEA', 'flight_number': 'AA5319', 'departure_time': datetime.datetime(2024, 9, 8, 23, 9, 41, 529017), 'arrival_time': datetime.datetime(2024, 9, 26, 5, 22, 43, 911100), 'price': 275.79, 'score': 3.625947278726567}, {'airline': 'JetBlue', 'departure_airport': 'SAN', 'destination_airport': 'SEA', 'flight_number': 'DL188', 'departure_time': datetime.datetime(2024, 9, 20, 19, 44, 22, 403049), 'arrival_time': datetime.datetime(2024, 9, 26, 1, 50, 45, 132833), 'price': 202.88, 'score': 4.929022082018927}], 'booking_confirmation': 'BOOK-JetBlue-TRIPPLANNER'}\n",
+      "Booking Confirmation: BOOK-JetBlue-TRIPPLANNER\n"
+     ]
+    }
+   ],
+   "source": [
+    "from travel_provider import travel_provider\n",
+    "from typing import List, Dict, Any\n",
+    "\n",
+    "class TravelAgent:\n",
+    "    def __init__(self, name: str):\n",
+    "        self.name = name\n",
+    "        self.goals: List[str] = []\n",
+    "        self.knowledge_base: Dict[str, Any] = {}\n",
+    "\n",
+    "    def set_goal(self, goal: str):\n",
+    "        \"\"\"Agency: Defining objectives\"\"\"\n",
+    "        self.goals.append(goal)\n",
+    "        print(f\"Goal set: {goal}\")\n",
+    "\n",
+    "    def update_knowledge(self, departure: str, destination: str):\n",
+    "        \"\"\"Agency: Acquiring information from an API, and scoring\"\"\"\n",
+    "        # Simulating API call to get flight options\n",
+    "        response = travel_provider.flight_lookup(departure, destination)\n",
+    "        if response['status_code'] == 200:\n",
+    "            flight_options = response['flight_options']\n",
+    "            # Simple scoring based on price (lower is better)\n",
+    "            scored_options = [\n",
+    "                {**flight, 'score': 1000 / flight['price']} \n",
+    "                for flight in flight_options\n",
+    "            ]\n",
+    "            self.knowledge_base['flight_options'] = scored_options\n",
+    "            print(f\"Knowledge updated with {len(scored_options)} flight options\")\n",
+    "        else:\n",
+    "            print(\"Failed to fetch flight information\")\n",
+    "\n",
+    "    def make_decision(self) -> Dict[str, Any]:\n",
+    "        \"\"\"Autonomy: Independent decision-making\"\"\"\n",
+    "        if 'flight_options' not in self.knowledge_base:\n",
+    "            raise ValueError(\"No flight options available for decision-making\")\n",
+    "        best_option = max(self.knowledge_base['flight_options'], key=lambda x: x['score'])\n",
+    "        print(f\"Decision made: Selected flight {best_option['airline']}\")\n",
+    "        return best_option\n",
+    "\n",
+    "    def book_travel(self, departure: str, destination: str):\n",
+    "        \"\"\"Agency: Execute action on behalf of user\"\"\"\n",
+    "        print(f\"Agent {self.name} is booking travel from {departure} to {destination}\")\n",
+    "        \n",
+    "        self.set_goal(f\"Book flight from {departure} to {destination}\")\n",
+    "        self.update_knowledge(departure, destination)\n",
+    "        \n",
+    "        try:\n",
+    "            best_flight = self.make_decision()\n",
+    "            # Simulating booking process\n",
+    "            booking_confirmation = f\"BOOK-{best_flight['airline']}-{self.name.upper()}\"\n",
+    "            self.knowledge_base['booking_confirmation'] = booking_confirmation\n",
+    "            print(f\"Booking confirmed: {booking_confirmation}\")\n",
+    "        except Exception as e:\n",
+    "            print(f\"Booking failed: {str(e)}\")\n",
+    "\n",
+    "        return self\n",
+    "\n",
+    "# Usage example\n",
+    "if __name__ == \"__main__\":\n",
+    "    agent = TravelAgent(\"TripPlanner\")\n",
+    "    agent.book_travel(\"SAN\", \"SEA\")\n",
+    "    print(\"\\n----------- Final Agent State: -----------\")\n",
+    "    print(f\"Name: {agent.name}\")\n",
+    "    print(f\"Goals: {agent.goals}\")\n",
+    "    print(f\"Knowledge Base: {agent.knowledge_base}\")\n",
+    "    if 'booking_confirmation' in agent.knowledge_base:\n",
+    "        print(f\"Booking Confirmation: {agent.knowledge_base['booking_confirmation']}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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