BAAI:BGE-M3
baai/bge-m3
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
Use the following code examples to integrate with our API:
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)Info
Provider
BAAI
Quantization
-
Supported Functionality
Context Length
8K
Max Output
94K
Serverless
Supported
Input Capabilities
text
Output Capabilities
text
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