Output Formats

NagaAI supports two embedding output encodings:

float for directly usable JSON arrays
base64 for more compact transport or custom binary decoding workflows

Use float unless you have a specific reason to optimize transport size or handle your own decoding path.

Format comparison

Format	Best for
`float`	direct use in application code, quick debugging, JSON-first workflows
`base64`	lower payload size, custom binary pipelines, delayed decoding

Float Example

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="hello world",
    encoding_format="float",
)

print(type(response.data[0].embedding))

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.naga.ac/v1',
  apiKey: 'YOUR_API_KEY',
});

const response = await client.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'hello world',
  encoding_format: 'float',
});

console.log(Array.isArray(response.data[0].embedding));

curl https://api.naga.ac/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "hello world",
    "encoding_format": "float"
  }'

Base64 Example

from openai import OpenAI

client = OpenAI(
    base_url="https://api.naga.ac/v1",
    api_key="YOUR_API_KEY",
)

response = client.embeddings.create(
    model="text-embedding-3-small",
    input="hello world",
    encoding_format="base64",
)

print(response.data[0].embedding)

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.naga.ac/v1',
  apiKey: 'YOUR_API_KEY',
});

const response = await client.embeddings.create({
  model: 'text-embedding-3-small',
  input: 'hello world',
  encoding_format: 'base64',
});

console.log(response.data[0].embedding);

curl https://api.naga.ac/v1/embeddings \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "text-embedding-3-small",
    "input": "hello world",
    "encoding_format": "base64"
  }'

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Format comparison

Float Example

Base64 Example

​Format comparison

​Float Example

​Base64 Example

​Related Docs

Format comparison

Float Example

Base64 Example

Related Docs