Get started

Install the adapter with Core:

pnpm add @anvia/core @anvia/transformers

Initialize a local Transformers.js feature-extraction pipeline:

import { createTransformersEmbeddingModel } from '@anvia/transformers'

const embeddings = await createTransformersEmbeddingModel()
const vectors = await embeddings.embedTexts([
  'Password reset links expire after thirty minutes.',
  'Enterprise customers receive priority support.',
])

The default is Xenova/all-MiniLM-L6-v2 with mean pooling, normalization, and a batch-size metadata value of 16.

Use with a vector store

import { embedDocuments } from '@anvia/core/embeddings'
import { InMemoryVectorStore } from '@anvia/core/vector-store'

const embedded = await embedDocuments(embeddings, documents, {
  id: (document) => document.id,
  content: (document) => document.text,
})

const index = InMemoryVectorStore.fromDocuments(embedded).index(embeddings)
const results = await index.search({ query: 'priority support', topK: 5 })

Before production

• Warm model loading and cache assets.
• Verify the selected model supports feature-extraction.
• Keep model, pooling, and normalization identical for indexing and querying.
• Rebuild the index after any of those values changes.
• Measure local CPU, memory, startup, and request latency.
• Add application concurrency limits where needed.