feat: certificates, landing pages, ai tutor, website update, compliance

packages/jobs/src/enqueue/emails.ts

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+import type { JobsOptions } from 'bullmq';
+
+import { JOB_NAMES, QUEUE_NAMES } from '../queues/names';
+import { QUEUE_DEFAULTS } from '../queues/defaults';
+import { getQueue } from '../queues/factories';
+import type { TSendEmailPayload } from '../payloads/emails';
+
+/**
+ * BullMQ rejects `:` in custom job ids (it uses `:` as a separator in its
+ * Redis key scheme). Callers tend to pass keys like `welcome:<userId>` for
+ * readability, so we normalize the boundary here instead of forcing every
+ * caller to remember.
+ */
+function toJobId(idempotencyKey: string): string {
+  return `email-${idempotencyKey.replace(/:/g, '-')}`;
+}
+
+/**
+ * Enqueue a single `emails:send` job. BullMQ deduplicates on `jobId`, so
+ * pass an `idempotencyKey` (e.g. `welcome:<userId>`) when the same domain
+ * action might fire more than once.
+ */
+export async function enqueueEmailSend(
+  payload: TSendEmailPayload,
+  options: { idempotencyKey?: string } & JobsOptions = {}
+): Promise<string | undefined> {
+  const { idempotencyKey, ...jobOptions } = options;
+
+  const job = await getQueue(QUEUE_NAMES.emails).add(JOB_NAMES.emails.send, payload, {
+    ...QUEUE_DEFAULTS[QUEUE_NAMES.emails],
+    ...(idempotencyKey ? { jobId: toJobId(idempotencyKey) } : {}),
+    ...jobOptions
+  });
+
+  return job.id;
+}

packages/jobs/src/enqueue/index.ts

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+export * from './media';
+export * from './emails';

packages/jobs/src/enqueue/media.ts

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+import { JOB_NAMES, QUEUE_NAMES } from '../queues/names';
+import { QUEUE_DEFAULTS } from '../queues/defaults';
+import { getFlowProducer, getQueue } from '../queues/factories';
+import type { TActorContext } from '../payloads/media';
+
+export interface EnqueueLessonVideoPipelineInput {
+  mediaJobId: string;
+  assetId: string;
+  storageKey: string;
+  actorContext: TActorContext;
+  /** Skip transcription (e.g. when no OpenAI key is configured). */
+  withTranscription?: boolean;
+}
+
+export interface EnqueueLessonVideoPipelineResult {
+  /** BullMQ job id of the parent (transcribe-audio if enabled, otherwise generate-thumbnail). */
+  rootJobId: string;
+  /** All BullMQ job ids enqueued, keyed by job name. */
+  jobIds: Record<string, string>;
+}
+
+function collectFlowJobIds(
+  tree: Awaited<ReturnType<ReturnType<typeof getFlowProducer>['add']>>
+): Record<string, string> {
+  const jobIds: Record<string, string> = {};
+  const visit = (node: typeof tree | undefined): void => {
+    if (!node) return;
+
+    jobIds[node.job.name] = node.job.id ?? '';
+    node.children?.forEach(visit);
+  };
+
+  visit(tree);
+  return jobIds;
+}
+
+/**
+ * Enqueue the lesson-video post-processing pipeline.
+ *
+ * The flow looks like this:
+ *
+ *   transcribe-audio (media-transcribe)            <- parent
+ *     └── extract-audio  (media)                   <- child
+ *           └── generate-thumbnail (media)         <- grandchild (sequential)
+ *                 └── probe-metadata (media)       <- runs first
+ *
+ * BullMQ runs children before parents, so probe runs first, then thumbnail,
+ * then audio extract, then the transcription provider call. Each step is
+ * retried independently. If transcription is disabled we drop the
+ * transcribe + extract-audio nodes and use generate-thumbnail as the root.
+ *
+ * Each node payload is intentionally small — workers re-fetch the media_job
+ * + asset rows from the DB to get the latest state.
+ */
+export async function enqueueLessonVideoPipeline(
+  input: EnqueueLessonVideoPipelineInput
+): Promise<EnqueueLessonVideoPipelineResult> {
+  const { mediaJobId, assetId, storageKey, actorContext, withTranscription = true } = input;
+
+  const sharedData = { mediaJobId, assetId, storageKey, actorContext };
+  const flow = getFlowProducer();
+
+  if (!withTranscription) {
+    const job = await getQueue(QUEUE_NAMES.media).add(
+      JOB_NAMES.media.generateThumbnail,
+      sharedData,
+      QUEUE_DEFAULTS[QUEUE_NAMES.media]
+    );
+    const probe = await getQueue(QUEUE_NAMES.media).add(
+      JOB_NAMES.media.probeMetadata,
+      sharedData,
+      QUEUE_DEFAULTS[QUEUE_NAMES.media]
+    );
+
+    return {
+      rootJobId: job.id ?? '',
+      jobIds: {
+        [JOB_NAMES.media.generateThumbnail]: job.id ?? '',
+        [JOB_NAMES.media.probeMetadata]: probe.id ?? ''
+      }
+    };
+  }
+
+  const tree = await flow.add({
+    name: JOB_NAMES.mediaTranscribe.transcribeAudio,
+    queueName: QUEUE_NAMES.mediaTranscribe,
+    data: { mediaJobId, assetId, actorContext },
+    opts: QUEUE_DEFAULTS[QUEUE_NAMES.mediaTranscribe],
+    children: [
+      {
+        name: JOB_NAMES.media.extractAudio,
+        queueName: QUEUE_NAMES.media,
+        data: sharedData,
+        opts: QUEUE_DEFAULTS[QUEUE_NAMES.media],
+        children: [
+          {
+            name: JOB_NAMES.media.generateThumbnail,
+            queueName: QUEUE_NAMES.media,
+            data: sharedData,
+            opts: QUEUE_DEFAULTS[QUEUE_NAMES.media],
+            children: [
+              {
+                name: JOB_NAMES.media.probeMetadata,
+                queueName: QUEUE_NAMES.media,
+                data: sharedData,
+                opts: QUEUE_DEFAULTS[QUEUE_NAMES.media]
+              }
+            ]
+          }
+        ]
+      }
+    ]
+  });
+
+  return {
+    rootJobId: tree.job.id ?? '',
+    jobIds: collectFlowJobIds(tree)
+  };
+}
+
+/**
+ * Enqueue only extract-audio → transcribe-audio (new `media_job` row required).
+ * Used for manual “generate transcript” when the initial pipeline had no OpenAI key.
+ */
+export async function enqueueTranscriptionOnly(input: {
+  mediaJobId: string;
+  assetId: string;
+  storageKey: string;
+  actorContext: TActorContext;
+}): Promise<EnqueueLessonVideoPipelineResult> {
+  const { mediaJobId, assetId, storageKey, actorContext } = input;
+  const sharedData = { mediaJobId, assetId, storageKey, actorContext };
+  const flow = getFlowProducer();
+
+  const tree = await flow.add({
+    name: JOB_NAMES.mediaTranscribe.transcribeAudio,
+    queueName: QUEUE_NAMES.mediaTranscribe,
+    data: { mediaJobId, assetId, actorContext },
+    opts: QUEUE_DEFAULTS[QUEUE_NAMES.mediaTranscribe],
+    children: [
+      {
+        name: JOB_NAMES.media.extractAudio,
+        queueName: QUEUE_NAMES.media,
+        data: sharedData,
+        opts: QUEUE_DEFAULTS[QUEUE_NAMES.media]
+      }
+    ]
+  });
+
+  return {
+    rootJobId: tree.job.id ?? '',
+    jobIds: collectFlowJobIds(tree)
+  };
+}