Embeddings
@xsai/embed turns text into vectors. It needs a service with an embeddings endpoint.
pnpm add @xsai/embedimport { embed, embeddings } from '@xsai/embed'
const model = embeddings({
apiKey: process.env.OPENAI_API_KEY,
baseURL: 'https://api.openai.com/v1/',
model: 'YOUR_EMBEDDING_MODEL_ID',
})
const { embedding } = await embed(model, { input: 'A quiet forest.' })
console.log(embedding.length)embed takes one string and returns { embedding, usage? }. embedMany takes an array of strings and returns { embeddings, usage? }, with one vector for each input in the same order.
import { embedMany } from '@xsai/embed'
const { embeddings } = await embedMany(model, {
input: ['A quiet forest.', 'A busy city.'],
})embedMany sends all inputs in one request. It does not split large batches or retry, so batch the input yourself when the service limits it.
Reference
| Option | Description |
|---|---|
input | A string for embed, or a string array for embedMany. |
providerOptions.embeddings.dimensions | The number of output dimensions. The endpoint decides the support and the limits. |
signal | An AbortSignal. |
embeddings({ baseURL, model, apiKey?, headers?, fetch? }) sends POST embeddings. The adapter sorts response entries by index before it returns the vectors. usage has inputTokens and totalTokens, and it is absent when the service reports none. embed throws invalid-response if the model returns no embeddings. HTTP failures throw HttpError, and network failures throw network-error.
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