bge-m3

by BAAI

Hybrid retrieval in one model -- dense, sparse, and ColBERT embeddings from a single forward pass

8.2Mdl/month

568Mparams

HuggingFace Use in Pipeline

Identifiers

Model ID

BAAI/bge-m3

Feature URI

mixpeek://text_extractor@v1/baai_bge_m3_v1

Overview

BGE-M3 is BAAI's multi-functionality embedding model that produces three types of embeddings simultaneously: dense vectors for semantic search, sparse vectors for lexical matching, and ColBERT-style multi-vector representations for fine-grained late interaction. This eliminates the need to run separate models for different retrieval strategies.

The model supports 100+ languages and handles up to 8192 tokens of input, making it suitable for long documents. On Mixpeek, BGE-M3 powers hybrid retrieval pipelines where a single ingest pass produces all three representation types, and the retriever fuses them at query time for higher recall than any single strategy alone.

Architecture

XLM-RoBERTa backbone, 568M parameters. Produces dense embeddings (1024d), sparse term-weight vectors, and ColBERT multi-vector representations from one forward pass. Trained with self-knowledge distillation across 100+ languages.

Mixpeek SDK Integration

import { Mixpeek } from "mixpeek";

const mx = new Mixpeek({ apiKey: "API_KEY" });

await mx.collections.ingest({
  collection_id: "my-collection",
  source: { url: "https://example.com/report.pdf" },
  feature_extractors: [{
    name: "text_embedding",
    version: "v1",
    params: {
      model_id: "BAAI/bge-m3"
    }
  }]
});