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chore(ai): remove all experimental embed events (#13693)
## Background the changes from #13478 were backported when they shouldn't have been. ## Summary cherry pick and revert all the changes ## Manual Verification na ## Checklist - [x] Tests have been added / updated (for bug fixes / features) - [x] Documentation has been added / updated (for bug fixes / features) - [x] A _patch_ changeset for relevant packages has been added (for bug fixes / features - run `pnpm changeset` in the project root) - [x] I have reviewed this pull request (self-review)
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‎.changeset/cyan-months-do.md‎

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'ai': patch
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chore(ai): remove all experimental embed events

‎content/docs/03-ai-sdk-core/65-event-listeners.mdx‎

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---
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title: Event Callbacks
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description: Subscribe to lifecycle events in generateText, streamText, embed, and embedMany calls
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description: Subscribe to lifecycle events in generateText and streamText calls
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---
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# Event Callbacks
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The AI SDK provides per-call event callbacks that you can pass to `generateText`, `streamText`, `embed`, and `embedMany` to observe lifecycle events. This is useful for building observability tools, logging systems, analytics, and debugging utilities.
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The AI SDK provides per-call event callbacks that you can pass to `generateText` and `streamText` to observe lifecycle events. This is useful for building observability tools, logging systems, analytics, and debugging utilities.
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## Basic Usage
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Pass callbacks directly to `generateText`, `streamText`, `embed`, or `embedMany`:
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Pass callbacks directly to `generateText` or `streamText`:
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```ts
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import { generateText } from 'ai';
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## Available Callbacks
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### `generateText` / `streamText`
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<PropertiesTable
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content={[
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{
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]}
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/>
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### `embed` / `embedMany`
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<PropertiesTable
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content={[
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{
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name: 'experimental_onStart',
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type: '(event: EmbedOnStartEvent) => void | Promise<void>',
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description:
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'Called when the embedding operation begins, before the embedding model is called.',
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},
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{
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name: 'experimental_onFinish',
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type: '(event: EmbedOnFinishEvent) => void | Promise<void>',
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description:
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'Called when the embedding operation completes, after the embedding model returns.',
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},
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]}
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/>
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## Event Reference
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### `generateText` / `streamText`
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#### `experimental_onStart`
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### `experimental_onStart`
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Called when the generation operation begins, before any LLM calls are made.
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]}
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/>
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#### `experimental_onStepStart`
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### `experimental_onStepStart`
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Called before each step (LLM call) begins. Useful for tracking multi-step generations.
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]}
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/>
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#### `experimental_onToolCallStart`
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### `experimental_onToolCallStart`
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Called before a tool's `execute` function runs.
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]}
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/>
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#### `experimental_onToolCallFinish`
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### `experimental_onToolCallFinish`
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Called after a tool's `execute` function completes or errors. Uses a discriminated union on the `success` field.
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]}
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/>
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#### `onStepFinish`
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### `onStepFinish`
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Called after each step (LLM call) completes. Provides the full `StepResult`.
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/>
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#### `onFinish`
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### `onFinish`
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Called when the entire generation completes (all steps finished). Includes aggregated data.
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]}
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/>
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### `embed` / `embedMany`
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#### `experimental_onStart`
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Called when the embedding operation begins, before the embedding model is called. Both `embed` and `embedMany` share the same event interface; the `operationId` field distinguishes them (`'ai.embed'` vs `'ai.embedMany'`), and the `value` field is a single string for `embed` or an array of strings for `embedMany`.
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```ts
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import { embed } from 'ai';
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const result = await embed({
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model: openai.embedding('text-embedding-3-small'),
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value: 'sunny day at the beach',
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experimental_onStart: event => {
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console.log('Operation:', event.operationId);
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console.log('Model:', event.model.modelId);
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},
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});
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```
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<PropertiesTable
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content={[
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{
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name: 'callId',
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type: 'string',
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description: 'Unique identifier for this embed call.',
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},
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{
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name: 'operationId',
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type: 'string',
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description:
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"Identifies the operation type ('ai.embed' or 'ai.embedMany').",
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},
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{
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name: 'model',
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type: '{ provider: string; modelId: string }',
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description: 'The embedding model being used.',
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},
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{
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name: 'value',
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type: 'string | Array<string>',
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description:
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'The value(s) being embedded. A single string for embed, or an array for embedMany.',
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},
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{
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name: 'maxRetries',
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type: 'number',
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description: 'Maximum number of retries for failed requests.',
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},
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{
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name: 'abortSignal',
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type: 'AbortSignal | undefined',
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description: 'Abort signal for cancelling the operation.',
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},
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{
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name: 'headers',
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type: 'Record<string, string | undefined> | undefined',
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description: 'Additional HTTP headers sent with the request.',
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},
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{
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name: 'providerOptions',
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type: 'ProviderOptions | undefined',
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description: 'Additional provider-specific options.',
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},
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{
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name: 'functionId',
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type: 'string | undefined',
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description:
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'Identifier from telemetry settings for grouping related operations.',
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},
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{
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name: 'metadata',
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type: 'Record<string, JSONValue> | undefined',
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description: 'Additional metadata from telemetry settings.',
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},
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]}
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/>
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#### `experimental_onFinish`
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Called when the embedding operation completes. For `embed`, `embedding` is a single vector and `response` is a single response object. For `embedMany`, `embedding` is an array of vectors and `response` is an array of response objects (one per chunk).
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```ts
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import { embedMany } from 'ai';
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const result = await embedMany({
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model: openai.embedding('text-embedding-3-small'),
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values: ['sunny day at the beach', 'rainy afternoon in the city'],
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experimental_onFinish: event => {
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console.log('Operation:', event.operationId);
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console.log('Usage:', event.usage);
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},
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});
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```
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<PropertiesTable
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content={[
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{
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name: 'callId',
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type: 'string',
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description: 'Unique identifier for this embed call.',
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},
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{
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name: 'operationId',
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type: 'string',
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description:
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"Identifies the operation type ('ai.embed' or 'ai.embedMany').",
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},
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{
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name: 'model',
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type: '{ provider: string; modelId: string }',
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description: 'The embedding model that was used.',
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},
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{
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name: 'value',
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type: 'string | Array<string>',
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description: 'The value(s) that were embedded.',
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},
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{
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name: 'embedding',
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type: 'Embedding | Array<Embedding>',
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description:
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'The resulting embedding(s). A single vector for embed, or an array for embedMany.',
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},
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{
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name: 'usage',
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type: 'EmbeddingModelUsage',
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description: 'Token usage for the embedding operation.',
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},
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{
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name: 'warnings',
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type: 'Array<Warning>',
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description: 'Warnings from the embedding model.',
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},
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{
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name: 'providerMetadata',
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type: 'ProviderMetadata | undefined',
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description: 'Optional provider-specific metadata.',
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},
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{
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name: 'response',
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type: '{ headers?: Record<string, string>; body?: unknown } | Array<{ headers?: Record<string, string>; body?: unknown } | undefined> | undefined',
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description:
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'Response data. A single response for embed, or an array for embedMany (one per chunk).',
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},
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{
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name: 'functionId',
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type: 'string | undefined',
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description:
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'Identifier from telemetry settings for grouping related operations.',
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},
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{
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name: 'metadata',
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type: 'Record<string, JSONValue> | undefined',
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description: 'Additional metadata from telemetry settings.',
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},
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]}
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/>
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## Use Cases
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### Logging and Debugging
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```
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### Embedding Observability
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```ts
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import { embedMany } from 'ai';
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const result = await embedMany({
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model: openai.embedding('text-embedding-3-small'),
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values: ['sunny day at the beach', 'rainy afternoon in the city'],
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experimental_onStart: event => {
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console.log(`Embedding started (${event.operationId})`, {
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model: event.model.modelId,
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valueCount: Array.isArray(event.value) ? event.value.length : 1,
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});
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},
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experimental_onFinish: event => {
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console.log(`Embedding complete (${event.operationId})`, {
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tokens: event.usage.tokens,
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});
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},
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});
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```
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## Error Handling
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Errors thrown inside callbacks are caught and do not break the generation or embedding flow. This ensures that monitoring code cannot disrupt your application:
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Errors thrown inside callbacks are caught and do not break the generation flow. This ensures that monitoring code cannot disrupt your application:
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```ts
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const result = await generateText({

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