Embedding - AIHubMix

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)

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"
)

# Read file
def read_whimery_file():
    try:
        with open('yourpath/file.md', 'r', encoding='utf-8') as file:
            return file.read()
    except Exception as e:
        print(f"Error reading file: {e}")
        return None

# Read the content and create embeddings
content = read_whimery_file()
if content:
    response = client.embeddings.create(
        input=content,
        model="gemini-embedding-001"
    )
    
    print("File content successfully processed into embeddings")
    print(f"Embedding dimensions: {len(response.data[0].embedding)}")
    print("First 10 embedding values:", response.data[0].embedding)
else:
    print("Failed to read file content")

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

Last updated: 2026-06-01