BGE-M3 (Multilingual)
BAAI multilingual embedding model covering 100+ languages with an 8192-token context. M3 stands for its multi-functionality (dense, sparse and ColBERT-style multi-vector retrieval), multilinguality and multi-granularity over long documents. Returns 1024-dim dense vectors and is a strong open choice for cross-lingual and long-text retrieval.
BGE-M3 (Multilingual) is embeddings AI model from huggingface, priced at €0.000 per 1M input tokens with a 8.2K tokens context window.
About this model
Pricing
API Integration
Use our OpenAI-compatible API to integrate BGE-M3 (Multilingual) into your application.
npm install railwailimport railwail from "railwail";
const rw = railwail("YOUR_API_KEY");
const vectors = await rw.run("bge-m3-multilingual", "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-multilingual", ["Hello", "World"]);
for (const item of res.data) {
console.log(item.embedding.length);
}Frequently asked questions
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