Embeddings

Generate vector embeddings with Blazen in Node.js

Blazen provides a unified EmbeddingModel interface for generating vector embeddings across multiple providers. The API mirrors CompletionModel: create a model with a static factory method, then call embed().

Create an Embedding Model

import { EmbeddingModel } from "blazen";

// OpenAI (default: text-embedding-3-small, 1536 dimensions)
const model = EmbeddingModel.openai("sk-...");

// Together AI
const model = EmbeddingModel.together("tok-...");

// Cohere
const model = EmbeddingModel.cohere("co-...");

// Fireworks AI
const model = EmbeddingModel.fireworks("fw-...");

Generate Embeddings

Pass an array of strings to embed(). It returns an EmbeddingResponse with one vector per input text.

const response = await model.embed(["Hello, world!", "Goodbye, world!"]);

console.log(response.embeddings.length);       // 2
console.log(response.embeddings[0].length);    // 1536 (dimensionality)
console.log(response.model);                   // "text-embedding-3-small"

EmbeddingResponse

The response object has the following fields:

Property	Type	Description
`.embeddings`	`number[][]`	One vector per input text.
`.model`	`string`	Model that produced the embeddings.
`.usage`	`TokenUsage \| undefined`	Token usage statistics.
`.cost`	`number \| undefined`	Estimated cost in USD.
`.timing`	`RequestTiming \| undefined`	Request timing breakdown.

Model Properties

console.log(model.modelId);     // "text-embedding-3-small"
console.log(model.dimensions);  // 1536

Use Cases

Embeddings are the building block for semantic search, RAG pipelines, clustering, and classification. A typical pattern inside a workflow step:

import { EmbeddingModel } from "blazen";

const embedModel = EmbeddingModel.openai("sk-...");

wf.addStep("embed_documents", ["DocumentsReady"], async (event, ctx) => {
  const response = await embedModel.embed(event.documents);
  await ctx.set("vectors", response.embeddings);
  return { type: "SearchEvent" };
});