BGE-M3 embeddings for RAG over long documents

BGE-M3 for RAG over long documents: 1024 dimensions, up to 8,192 tokens, 100+ languages, no prefix needed. MIT license.

Notes

No prefix needed for queries or passages.
Handles long inputs (8,192 tokens): chunks of about 1,000 words work well.
The model can also produce sparse vectors (keyword search) with the FlagEmbedding library.
Published by BAAI under the MIT license.

Estimated cost

Input tokens per call : ≈ 225

Input cost only, excluding the model's answer. Providers' public standard prices (Mistral AI, DeepSeek, OpenAI, Anthropic) converted from USD to euros at the ECB rate of September 29, 2026. Estimate: 1 token ≈ 3.6 characters.

Identifier

contextetech--bge-m3-long-documents

Type

Embeddings

File

bge-m3-long-documents.embedding.json

Format

JSON (application/json)

Size

629 bytes

Encoding

UTF-8

Estimated tokens

≈ 175

Language

English

License

MIT

Commercial use

Allowed

Personal data

None

Version

1.0

Published on

October 2, 2026 at 9:54 PM

Updated on

October 2, 2026 at 9:54 PM

Storage

in database

Uses

0

Likes

0

bge-m3-long-documents.embedding.json

{
  "format": "contextetech/embedding/v1",
  "id": "contextetech/bge-m3-long-documents",
  "description": "BGE-M3 for RAG over long documents: 1024 dimensions, up to 8,192 tokens, 100+ languages, no prefix needed. MIT license.",
  "tags": [
    "rag",
    "multilingual",
    "long-documents"
  ],
  "license": "MIT",
  "language": "en",
  "model": "BAAI/bge-m3",
  "dimensions": 1024,
  "maxTokens": 8192,
  "distance": "cosine",
  "languages": [
    "en",
    "es",
    "fr",
    "de",
    "it",
    "pt",
    "zh",
    "ja"
  ],
  "prefixes": {
    "query": "",
    "passage": ""
  },
  "chunkSize": 1000,
  "benchmarks": []
}

Embed (Python)

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("BAAI/bge-m3")
q = model.encode(["your input here"], normalize_embeddings=True)
docs = model.encode(["…"], normalize_embeddings=True)
print(q.shape)  # (1, 1024)
print(q @ docs.T)  # similarité