llama-nemotron-embed-vl-1b-v2 - API لـمصر، بالجنيه المصري

from seg import SEG  # pip install seg-sdk

client = SEG(api_key="YOUR_API_KEY")

response = client.embeddings.create(
    model="llama-nemotron-embed-vl-1b-v2",
    input="The food was delicious and the waiter...",
    encoding_format="float",
)

vector = response.data[0].embedding
print(f"dim={len(vector)}, first 8 dims: {vector[:8]}")

from openai import OpenAI

client = OpenAI(
    base_url="https://backend.sovereigneg.com/v1",
    api_key="YOUR_API_KEY",
)

response = client.embeddings.create(
    model="llama-nemotron-embed-vl-1b-v2",
    input="The food was delicious and the waiter...",
    encoding_format="float",
)

vector = response.data[0].embedding
print(f"dim={len(vector)}, first 8 dims: {vector[:8]}")

import OpenAI from "openai"

const client = new OpenAI({
  baseURL: "https://backend.sovereigneg.com/v1",
  apiKey: "YOUR_API_KEY",
})

const response = await client.embeddings.create({
  model: "llama-nemotron-embed-vl-1b-v2",
  input: "The food was delicious and the waiter...",
  encoding_format: "float",
})

const vector = response.data[0].embedding
console.log(`dim=${vector.length}, first 8 dims:`, vector.slice(0, 8))

curl https://backend.sovereigneg.com/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-nemotron-embed-vl-1b-v2",
    "input": "The food was delicious and the waiter...",
    "encoding_format": "float"
  }'