bge large en v1.5
unofficialembedding
bge large en v1.5 specifications
| Context window | 512 |
|---|---|
| Max output tokens | — |
| Release date | — |
| Input | Text |
| Output | Vector |
bge large en v1.5 pricing in rubles
You pay for what you use, per token and per unit. The final request cost is returned in the API response.
| Type | Price |
|---|---|
| Input tokens | 26,52 ₽ per 1M tokens |
Prices are in rubles and may change with exchange rates.
What bge large en v1.5 can do
- Streaming responses
bge large en v1.5 API: connection and code examples
The service exposes a single OpenAI-compatible API. Set our base_url and the key from your dashboard.
OpenAI Python SDK
from openai import OpenAI
client = OpenAI(api_key="YOUR_API_KEY", base_url="https://api.neuralbridge.ru/v1")
result = client.embeddings.create(model="baai/bge-large-en-v1.5-08130", input="Текст для векторизации")
print(len(result.data[0].embedding))cURL
curl https://api.neuralbridge.ru/v1/embeddings \
-H "Authorization: Bearer YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{"model": "baai/bge-large-en-v1.5-08130", "input": "Текст для векторизации"}'API endpoints
POST /v1/embeddingsprimary | Embeddings Représentation vectorielle du texte pour la recherche, le clustering et l'évaluation de similarité. |
Supported request parameters
| Parameter | Default | Allowed values | Description |
|---|---|---|---|
dimensions | — | — | Dimension du vector embedding : un vecteur plus court occupe moins d'espace et accélère la recherche, mais la similarité est plus grossière. |
encoding_format | float | — | Comment fournir le vecteur : sous forme de tableau de nombres ou de chaîne base64. |