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Documentation Index

Fetch the complete documentation index at: https://docs.aihubmix.com/llms.txt

Use this file to discover all available pages before exploring further.

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Usage Guide

AiHubMix embedding models efficiently convert text or document content into searchable vector data, widely used in RAG Q&A systems and intelligent customer support. Whether for plain text or full documents, you can generate embeddings with a single call to significantly improve semantic processing. 
from openai import OpenAI
import os

client = OpenAI(
    api_key="sk-***", # Replace with the key you generated in the AIHUBMIX dashboard
    base_url="https://aihubmix.com/v1"
)

response = client.embeddings.create(
    input="Your text string goes here",
    model="gemini-embedding-001"
)

print(response.data[0].embedding)

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Available Models

  • gemini-embedding-001
  • gemini-embedding-exp-03-07
  • text-embedding-3-large
  • text-embedding-3-small
  • text-embedding-ada-002
  • jina-embeddings-v4
  • jina-embeddings-v3
  • jina-embeddings-v2-base-code
  • text-embedding-v4
  • Qwen/Qwen3-Embedding-0.6B
  • doubao-embedding-large-text-240915
  • doubao-embedding-text-240715