Embeddings

Generate vector embeddings with OpenAI and Google providers

Embedding providers convert text into high-dimensional vectors for semantic search. RadarOS provides a unified EmbeddingProvider interface with OpenAI and Google implementations.

EmbeddingProvider Interface

interface EmbeddingProvider {
  embed(text: string): Promise<number[]>;
  embedBatch(texts: string[]): Promise<number[][]>;
}

OpenAI Embeddings

npm install openai

import { OpenAIEmbedding } from "@radaros/core";

const embedder = new OpenAIEmbedding({
  apiKey: process.env.OPENAI_API_KEY,  // optional, uses env var by default
  model: "text-embedding-3-small",      // optional, this is the default
});

const vector = await embedder.embed("Hello world");
console.log(vector.length); // 1536

const vectors = await embedder.embedBatch(["Hello", "World"]);
console.log(vectors.length); // 2

Available Models

Model	Dimensions	Best For
text-embedding-3-small	1536	General use, cost-effective
text-embedding-3-large	3072	Higher accuracy
text-embedding-ada-002	1536	Legacy

apiKey · string

OpenAI API key. Falls back to OPENAI_API_KEY env var.

model · string · default: "text-embedding-3-small"

Embedding model name.

Google Embeddings

npm install @google/genai

import { GoogleEmbedding } from "@radaros/core";

const embedder = new GoogleEmbedding({
  apiKey: process.env.GOOGLE_API_KEY,  // optional, uses env var by default
  model: "text-embedding-004",         // optional, this is the default
});

const vector = await embedder.embed("Hello world");
const vectors = await embedder.embedBatch(["Hello", "World"]);

Available Models

Model	Dimensions	Best For
text-embedding-004	768	General use
embedding-001	768	Legacy

apiKey · string

Google API key. Falls back to GOOGLE_API_KEY env var.

model · string · default: "text-embedding-004"

Embedding model name.

Using with KnowledgeBase

Embedding providers are passed to KnowledgeBase via the vector store. Most vector stores accept an EmbeddingProvider in their configuration or the KnowledgeBase handles embedding internally.

import { KnowledgeBase, InMemoryVectorStore, OpenAIEmbedding } from "@radaros/core";

const embedder = new OpenAIEmbedding();
const vectorStore = new InMemoryVectorStore(1536);

const kb = new KnowledgeBase({
  name: "docs",
  vectorStore,
});

RAG Example

See a complete end-to-end RAG implementation using embeddings.