Model Library/BAAI:BGE-M3
BAAI:BGE-M3

BAAI:BGE-M3

baai/bge-m3
by BAAI
API Documentation
Input price
$0.01/ M tokens
Output price
$0.01/ M tokens
Context window
8Ktokens
Max output
94Ktokens

The BGE-M3 model is a multi-functional, multi-lingual, and multi-granularity text embedding framework that integrates three retrieval methods: dense retrieval, multi-vector retrieval, and sparse retrieval. Capable of processing over 100 languages and inputs ranging from short sentences to lengthy documents (up to 8,192 tokens), it delivers state-of-the-art performance in multilingual and cross-lingual tasks, topping benchmarks like MIRACL and MKQA. Additionally, BGE-M3 excels in long-document retrieval, achieving strong results on datasets such as MLDR and NarrativeQA, solidifying its versatility across diverse text granularities and applications.

Features

Serverless API

Docs

baai/bge-m3 is available via Novita's serverless API, where you pay per token. There are several ways to call the API, including OpenAI-compatible endpoints with exceptional reasoning performance.

Available Serverless

Run queries immediately, pay only for usage

Input$0.01 / M Tokens
Output$0.01 / M Tokens

  1. Get your API key

    Sign up and create a key to run the code below. The API is OpenAI-compatible.

    Log inGet your API key

  2. Make your first request

    The API is OpenAI-compatible: point any OpenAI SDK at the base URL and pass this model ID.

    Base URL
    https://api.novita.ai/openai
    Model ID
    baai/bge-m3
    API Endpoint
    embeddings
    1from openai import OpenAI
2
3client = OpenAI(
4    api_key="<Your API Key>",
5    base_url="https://api.novita.ai/openai"
6)
7
8response = client.embeddings.create(
9    model="baai/bge-m3",
10    input="The quick brown fox jumped over the lazy dog."
11)
12
13print(response.data[0].embedding)