Anvia Docs

Embeddings turn text into vectors so you can search by meaning instead of exact keywords. Retrieval starts by choosing an embedding model.

1. Create an Embedding Model

import { OpenAIClient } from "@anvia/openai";

const client = new OpenAIClient({ apiKey });
const embeddings = client.embeddingModel("text-embedding-3-small");

Use the same embedding model for indexing documents and searching with user queries.

2. Embed One Text

import { embedText } from "@anvia/core";

const embedding = await embedText(embeddings, "Password reset links expire after 30 minutes.");

console.log(embedding.vector.length);

3. Embed Many Texts

import { embedTexts } from "@anvia/core";

const results = await embedTexts(embeddings, [
  "Password reset links expire after 30 minutes.",
  "Enterprise customers receive priority support.",
]);

Anvia respects the model's batch size when available.

4. Use Embeddings Through Higher-Level Helpers

Most retrieval workflows should use Embed Documents instead of calling embedText(...) directly. Document embedding preserves ids, metadata, and the original document shape.