Documentation Index
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Embeds several values using an embedding model.
embedMany automatically splits large requests into smaller chunks if the model has a limit on how many embeddings can be generated in a single call.
import { embedMany } from 'ai';
import { openai } from '@ai-sdk/openai';
const result = await embedMany({
model: openai.embedding('text-embedding-3-small'),
values: [
'sunny day at the beach',
'rainy day in the mountains',
'snowy day in the city',
],
});
console.log(result.embeddings);
Parameters
The embedding model to use.
The values that should be embedded.
Maximum number of retries per embedding model call. Set to 0 to disable retries.
Abort signal for canceling the embedding request.
Additional headers to include in the request. Only applicable for HTTP-based providers.
Maximum number of concurrent requests. When the model supports parallel calls and there are multiple chunks,
this limits how many requests can run simultaneously.
Additional provider-specific options. They are passed through to the provider from the AI SDK
and enable provider-specific functionality that can be fully encapsulated in the provider.
Optional telemetry configuration (experimental).
Returns
The values that were embedded.
The embedding vectors as an array of number arrays. Each embedding corresponds to the input value at the same index.
The total token usage for all embedding operations.
Warnings from the embedding model provider (e.g., unsupported settings).
Additional provider-specific metadata aggregated from all requests.
responses
Array<EmbeddingModelResponseMetadata>
Response metadata from all embedding model calls.
Examples
Basic batch embedding
import { embedMany } from 'ai';
import { openai } from '@ai-sdk/openai';
const result = await embedMany({
model: openai.embedding('text-embedding-3-small'),
values: [
'sunny day at the beach',
'rainy day in the mountains',
'snowy day in the city',
],
});
console.log(result.embeddings);
// [
// [0.123, -0.456, 0.789, ...],
// [0.234, -0.567, 0.890, ...],
// [0.345, -0.678, 0.901, ...]
// ]
With usage tracking
import { embedMany } from 'ai';
import { openai } from '@ai-sdk/openai';
const result = await embedMany({
model: openai.embedding('text-embedding-3-small'),
values: [
'sunny day at the beach',
'rainy day in the mountains',
'snowy day in the city',
],
});
console.log('Total tokens used:', result.usage.tokens);
console.log('Number of embeddings:', result.embeddings.length);
console.log('Embedding dimensions:', result.embeddings[0].length);
Large batch with parallel processing
import { embedMany } from 'ai';
import { openai } from '@ai-sdk/openai';
// Generate 1000 values to embed
const values = Array.from({ length: 1000 }, (_, i) => `Document ${i}`);
const result = await embedMany({
model: openai.embedding('text-embedding-3-small'),
values,
maxParallelCalls: 5, // Process up to 5 chunks in parallel
});
console.log('Embedded', result.embeddings.length, 'documents');
console.log('Total tokens used:', result.usage.tokens);
Semantic search
import { embed, embedMany } from 'ai';
import { openai } from '@ai-sdk/openai';
const embeddingModel = openai.embedding('text-embedding-3-small');
// Embed a corpus of documents
const documents = [
'The beach was sunny and warm',
'Mountains covered in snow',
'City lights at night',
'Forest trail in autumn',
];
const { embeddings: documentEmbeddings } = await embedMany({
model: embeddingModel,
values: documents,
});
// Embed a search query
const { embedding: queryEmbedding } = await embed({
model: embeddingModel,
value: 'warm weather',
});
// Calculate cosine similarity
function cosineSimilarity(a: number[], b: number[]): number {
const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0));
const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0));
return dotProduct / (magnitudeA * magnitudeB);
}
// Find most similar document
const similarities = documentEmbeddings.map((docEmbedding, index) => ({
document: documents[index],
similarity: cosineSimilarity(queryEmbedding, docEmbedding),
}));
similarities.sort((a, b) => b.similarity - a.similarity);
console.log('Most similar:', similarities[0]);