AI Email

package.json

@@ -23,6 +23,7 @@
     "trigger:alerts": "bun scripts/trigger-alerts.ts"
   },
   "dependencies": {
+    "@ai-sdk/react": "^2.0.76",
     "@hookform/resolvers": "^5.2.2",
     "@radix-ui/react-accordion": "^1.2.12",
     "@radix-ui/react-alert-dialog": "^1.1.15",
@@ -63,6 +64,7 @@
     "@trpc/react": "^9",
     "@trpc/server": "^9",
     "@vercel/analytics": "^1.5.0",
+    "@vercel/functions": "^3.1.4",
     "@xyflow/react": "^12.8.6",
     "ai": "^5.0.76",
     "ai-elements": "^1.1.2",

scripts/send-email-to-user.ts

@@ -0,0 +1,72 @@
+#!/usr/bin/env bun
+
+import { getArgs } from "./utils/args";
+
+function parseArgs() {
+  const args = getArgs();
+  const [userId, ...rest] = args;
+
+  if (!userId) {
+    console.error(
+      "Usage: bun scripts/send-email-to-user.ts <user-id> [--force] [--local]",
+    );
+    process.exit(1);
+  }
+
+  const config: { userId: string; force?: boolean; local?: boolean } = {
+    userId,
+  };
+
+  for (const option of rest) {
+    if (option === "--force") {
+      config.force = true;
+    } else if (option === "--local") {
+      config.local = true;
+    }
+  }
+
+  return config;
+}
+
+async function main() {
+  const { userId, force, local } = parseArgs();
+
+  const workerUrl = local ? "http://localhost:8787" : process.env.WORKER_URL;
+
+  if (!workerUrl) {
+    console.error(
+      "WORKER_URL is required unless --local is specified. Set WORKER_URL environment variable.",
+    );
+    process.exit(1);
+  }
+
+  const apiKey = process.env.WORKER_API_KEY;
+
+  const response = await fetch(`${workerUrl}/trigger/process-user-alerts`, {
+    method: "POST",
+    headers: {
+      "Content-Type": "application/json",
+      ...(apiKey ? { Authorization: `Bearer ${apiKey}` } : {}),
+    },
+    body: JSON.stringify({ userId, forceSend: force }),
+  });
+
+  if (!response.ok) {
+    console.error(
+      `Failed to trigger workflow: ${response.status} ${response.statusText}`,
+    );
+    const text = await response.text();
+    console.error(text);
+    process.exit(1);
+  }
+
+  const result = await response.json();
+  console.log("Workflow triggered", result);
+}
+
+if (import.meta.main) {
+  main().catch((error) => {
+    console.error(error instanceof Error ? error.message : String(error));
+    process.exit(1);
+  });
+}

scripts/send-email.ts

@@ -1,8 +1,11 @@
-import type {
-  FlightOptionSummary,
-  NotificationEmailPayload,
-} from "@/lib/notifications";
 import { sendNotificationEmail } from "@/lib/notifications";
+import {
+  generateDailyDigestBlueprint,
+  generatePriceDropBlueprint,
+} from "@/lib/notifications/ai-email-agent";
+import { buildDailyDigestBlueprintContext } from "@/lib/notifications/ai-email-context";
+import { renderDailyPriceUpdateEmail } from "@/lib/notifications/templates/daily-price-update";
+import { renderPriceDropAlertEmail } from "@/lib/notifications/templates/price-drop-alert";
 
 const [recipientEmail, templateArg] = process.argv.slice(2);
 
@@ -15,77 +18,71 @@ if (!recipientEmail) {
 
 const template = templateArg === "price-drop" ? "price-drop" : "daily";
 
-const now = new Date();
-
-const sampleFlight: FlightOptionSummary = {
-  totalPrice: 219,
-  currency: "USD",
-  slices: [
-    {
-      durationMinutes: 360,
-      stops: 0,
-      price: 219,
-      legs: [
-        {
-          airlineCode: "AA",
-          airlineName: "American Airlines",
-          flightNumber: "100",
-          departureAirportCode: "JFK",
-          departureAirportName: "John F. Kennedy International",
-          departureDateTime: now.toISOString(),
-          arrivalAirportCode: "LAX",
-          arrivalAirportName: "Los Angeles International",
-          arrivalDateTime: new Date(
-            now.getTime() + 5 * 60 * 60 * 1000,
-          ).toISOString(),
-          durationMinutes: 300,
-        },
-      ],
-    },
-  ],
-};
-
-const baseAlert = {
-  id: "alt-demo",
-  label: "Weekend NYC → LA",
-  origin: "JFK",
-  destination: "LAX",
-  seatType: "Economy",
-  stops: "Nonstop",
-  airlines: ["AA"],
-  priceLimit: { amount: 250, currency: "USD" },
-};
-
-const payload: NotificationEmailPayload =
-  template === "price-drop"
-    ? {
-        type: "price-drop-alert",
-        alert: baseAlert,
-        flights: [sampleFlight],
-        detectedAt: now.toISOString(),
-        previousLowestPrice: { amount: 299, currency: "USD" },
-        newLowestPrice: { amount: 219, currency: "USD" },
-      }
-    : {
-        type: "daily-price-update",
-        summaryDate: now.toISOString(),
-        alerts: [
-          {
-            alert: baseAlert,
-            flights: [sampleFlight],
-            generatedAt: now.toISOString(),
-          },
-        ],
-      };
-
-await sendNotificationEmail({
-  recipient: { email: recipientEmail },
-  payload,
-})
-  .then((response) => {
-    console.log("Email queued:", response.data?.id);
-  })
-  .catch((error) => {
-    console.error("Failed to send email:", error);
-    process.exitCode = 1;
+async function main() {
+  const baseAlert = {
+    id: "alt-demo",
+    label: "Weekend NYC → LA",
+    origin: "JFK",
+    destination: "LAX",
+    seatType: "Economy",
+    stops: "Nonstop",
+    airlines: ["AA"],
+    priceLimit: { amount: 250, currency: "USD" },
+  };
+
+  const now = new Date().toISOString();
+
+  if (template === "price-drop") {
+    const payload = {
+      type: "price-drop-alert" as const,
+      alert: baseAlert,
+      flights: [],
+      detectedAt: now,
+    };
+
+    const context = buildDailyDigestBlueprintContext({
+      type: "daily-price-update",
+      summaryDate: now,
+      alerts: [],
+    });
+
+    const blueprint = await generatePriceDropBlueprint({
+      alert: context.alerts[0]?.alert ?? baseAlert,
+      flights: [],
+    });
+
+    const email = renderPriceDropAlertEmail(payload, { blueprint });
+
+    await sendNotificationEmail({
+      recipient: { email: recipientEmail },
+      payload,
+    });
+
+    console.log("AI-generated price drop email sent to", recipientEmail);
+    console.log("Subject:", email.subject);
+    return;
+  }
+
+  const payload = {
+    type: "daily-price-update" as const,
+    summaryDate: now,
+    alerts: [],
+  };
+
+  const context = buildDailyDigestBlueprintContext(payload);
+  const blueprint = await generateDailyDigestBlueprint(context);
+  const email = renderDailyPriceUpdateEmail(payload, { blueprint });
+
+  await sendNotificationEmail({
+    recipient: { email: recipientEmail },
+    payload,
   });
+
+  console.log("AI-generated daily digest email sent to", recipientEmail);
+  console.log("Subject:", email.subject);
+}
+
+await main().catch((error) => {
+  console.error("Failed to send email:", error);
+  process.exit(1);
+});

scripts/utils/args.ts

@@ -0,0 +1,3 @@
+export function getArgs(): string[] {
+  return process.argv.slice(2);
+}

src/ai/index.ts

@@ -0,0 +1,3 @@
+export * from "./planner-agent";
+export * from "./planner-prompt";
+export * from "./types";

src/ai/planner-agent.ts

@@ -0,0 +1,64 @@
+import {
+  Experimental_Agent as Agent,
+  type InferUITools,
+  stepCountIs,
+  type UIDataTypes,
+  type UIMessage,
+} from "ai";
+
+import { getSystemPrompt } from "./planner-prompt";
+import { controlSceneTool, searchDatesTool, searchFlightsTool } from "./tools";
+import type { PlannerContext } from "./types";
+
+const PlannerAgentTools = {
+  searchFlights: searchFlightsTool,
+  searchDates: searchDatesTool,
+  controlScene: controlSceneTool,
+};
+
+/**
+ * Create a Flight Planner Agent instance with user context.
+ *
+ * This factory function creates a new agent for each request with
+ * personalized system prompt including current date/time, user info,
+ * and scene state.
+ *
+ * Capabilities:
+ * - Search for specific flights with detailed filters
+ * - Find cheapest dates to fly across date ranges
+ * - Control UI scene to show maps or search results
+ * - Multi-turn conversations with context awareness
+ * - Parallel tool calls for efficient planning
+ *
+ * Tools:
+ * - searchFlights: Find one-way flights for specific dates
+ * - searchDates: Find best prices across date ranges
+ * - controlScene: Switch between map and search views
+ *
+ * Configuration:
+ * - Max steps: 15 (prevents infinite loops while allowing complex planning)
+ * - Auto tool choice: Agent decides when to use tools
+ * - Error recovery: Tools return graceful errors for agent to handle
+ *
+ * @param context - The planner context with user and scene information
+ * @returns A configured Agent instance
+ */
+export function createPlannerAgent(context: PlannerContext) {
+  return new Agent({
+    model: "openai/gpt-5-mini",
+    system: getSystemPrompt(context),
+    tools: PlannerAgentTools,
+    stopWhen: stepCountIs(25), // Allow multiple tool calls for complex planning
+    maxRetries: 3, // Retry on transient failures
+  });
+}
+
+/**
+ * Type-safe UI message for the planner agent.
+ * Includes all tool types with proper inference.
+ */
+export type PlannerAgentUIMessage = UIMessage<
+  never,
+  UIDataTypes,
+  InferUITools<typeof PlannerAgentTools>
+>;