> ## 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.

# Embedding

> AiHubMix propose une solution d'embedding vectoriel haute performance compatible avec OpenAI.

## Guide d'utilisation

Les modèles d'embedding d'AiHubMix convertissent efficacement le texte ou le contenu de documents en données vectorielles interrogeables, largement utilisés dans les systèmes de questions-réponses RAG et le support client intelligent. Que ce soit pour du texte brut ou des documents complets, vous pouvez générer des embeddings en un seul appel pour améliorer significativement le traitement sémantique.

<CodeGroup>
  ```py Universal Embedding theme={null}
  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)
  ```

  ```py Read Document and Embed theme={null}
  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")

  ```
</CodeGroup>

## Modèles disponibles

* 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

***

Dernière mise à jour : 2026-06-01