BGE-M3

Custom
Embeddings

BAAI's versatile embedding model supporting dense, sparse, and multi-vector retrieval. Open-source and highly effective.

Try BGE-M3

Vector output (1536 dimensions):

-0.50160.6105-0.1935-0.2598-0.46570.12340.40420.93710.21320.01020.09430.6004-0.8775-0.3592-0.34060.9162-0.6251-0.8002-0.45590.1086-0.87890.69330.3503-0.9404... 1512 more
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Pricing

Price per Generation
Per generationFree

API Integration

Use our OpenAI-compatible API to integrate BGE-M3 into your application.

Install
npm install railwail
JavaScript / TypeScript
import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

const vectors = await rw.run("bge-m3", "Hello world", { type: "embed" });
console.log(vectors[0].length); // embedding dimensions

// Or use the embed() method for full control
const res = await rw.embed("bge-m3", ["Hello", "World"]);
for (const item of res.data) {
  console.log(item.embedding.length);
}
Specifications
Provider
xAI
Category
Embeddings
Tags
open-source
multi-retrieval
BAAI
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