Compare y pruebe modelos open weight para texto, código, voz y visión, sin registrarse. Después, pase a producción con la misma API compatible con OpenAI.
BGE-Multilingual-Gemma2 es un modelo de «embeddings» multilingüe basado en LLM. Está entrenado en una amplia gama de idiomas y tareas. BGE-Multilingual-Gemma2 demuestra principalmente los siguientes avances: Datos de entrenamiento diversificados: Los datos de entrenamiento del modelo abarcan una amplia variedad de idiomas, incluidos inglés, chino, japonés, coreano, francés...¡y más!. Además, los datos cubren una variedad de tipos de tareas, como recuperación, clasificación y agrupamiento. Rendimiento excepcional El modelo presenta resultados de última generación («state-of-the-art») en benchmarks multilingües como MIRACL, MTEB-pl y MTEB-fr. También logra un excelente rendimiento en otras evaluaciones importantes, incluidas MTEB, C-MTEB y AIR-Bench.
The Embeddings Converter API provides a single unified endpoint that can handle both single sentence and batch requests.
In NLP, word embeddings are often used to represent individual words as vectors in a high dimensional space, where the vectors capture semantic and syntactic relationships between words. These embedding models allow you to perform tasks such as word analogy or sentence similarity. To do this, the difference between vectors in the embedding space must be calculated to identify relationships between words.
AI Endpoints makes it easy, with ready-to-use inference APIs. Discover how to use them:
The embedding APIs endpoint allows you to access state-of-the-art Open-Source models that transform raw text input into high-dimensional embedding vectors suitable for a variety of multilingual and monolingual applications:
The request payload must contain a model field to indicate which model you plan to use and an input field. input can be a single string or a JSON array of strings (limited to the max batch size samples indicated above, depending on which embedding model you are using).
For Qwen3-Embedding-8B, you can optionally add a dimensions field (integer between 32 and 4096) to customize the size of the output vectors. For example:
{
"model": "Qwen3-Embedding-8B",
"input": "Paris is the capital of France",
"dimensions": 1024
}First install the requests library:
pip install requestsNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.
Finally, run the following Python code:
import os
import requests
texts = [
"Paris is the capital of France",
"Paris is the capital of France",
"Berlin is the capital of Germany",
"This endpoint converts input sentence into a vector embeddings"
]
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/embeddings"
headers = {
"Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
"Content-Type": "application/json"
}
payload = {
"model": "bge-multilingual-gemma2",
"input": texts
}
response = requests.post(url, headers=headers, json=payload)
if response.status_code == 200:
data = response.json()
print(f"✅ Received {len(data['data'])} embeddings")
# Show a preview of the first embedding (first 5 values)
print("First embedding preview:", data['data'][0]['embedding'][:5])
else:
print("❌ Error:", response.status_code, response.text)The bge-multilingual-gemma2 API is compatible with the OpenAI specification.
First install the openai library:
pip install openaiNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>Finally, run the following Python code:
import os
from openai import OpenAI
client = OpenAI(
base_url="https://oai.endpoints.kepler.ai.cloud.ovh.net/v1",
api_key=os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')
)
MODEL = "bge-multilingual-gemma2"
texts = [
"Paris is the capital of France",
"Paris is the capital of France",
"Berlin is the capital of Germany",
"This endpoint converts input sentence into a vector embeddings"
]
response = client.embeddings.create(
model=MODEL,
input=texts
)
print(f"✅ Received {len(response.data)} embeddings")
# Show a preview of the first embedding (first 5 values)
print("First embedding preview:", response.data[0].embedding[:5])curl -X POST "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/embeddings" \
-H "Authorization: Bearer $OVH_AI_ENDPOINTS_ACCESS_TOKEN" \
-H "Content-Type: application/json" \
-d '{
"model": "bge-multilingual-gemma2",
"input": [
"Paris is the capital of France",
"Paris is the capital of France",
"Berlin is the capital of Germany",
"This endpoint converts input sentence into a vector embeddings"
]
}'After obtaining the embeddings, you can assess how semantically similar the input sentences are. The following example shows how to compute cosine similarity between the first embedding and the others.
Install the numpy library:
pip install numpyThen run the following code:
import numpy as np
def cosine_similarity(vec_a, vec_b):
"""Return cosine similarity between two vectors."""
return np.dot(vec_a, vec_b) / (np.linalg.norm(vec_a) * np.linalg.norm(vec_b))
# In this code, `data` is the JSON response from the `requests` example above
data = response.json()
embeddings = [item["embedding"] for item in data["data"]]
base = embeddings[0]
# Compute similarities to the first embedding
similarities = {
f"Similarity with sentence {i}": f"{cosine_similarity(base, vec):.3f}"
for i, vec in enumerate(embeddings[1:], start=1)
}
print("Sentence similarities:", similarities)If you exceed this limit, a 429 error code will be returned.
If you require higher usage, please get in touch with us to discuss increasing your rate limits.
For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.
Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.
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