Batch Inputs

The input field can be a single string or an array of inputs. Batching is useful when you are indexing many chunks and want to reduce request overhead.

Batch 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=["semantic search", "vector database", "hybrid retrieval"],
)

print(response.data[0].index)

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: ['semantic search', 'vector database', 'hybrid retrieval'],
});

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

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": [
      "semantic search",
      "vector database",
      "hybrid retrieval"
    ]
  }'

The response returns one embedding per input with an index field, so you can map results back to the original batch order.

How to read the result

data[0] is the embedding for the first input
data[1] is the embedding for the second input
the index field lets you map embeddings back to the original batch even if you do additional processing later

When To Batch

indexing many documents or chunks
reducing per-request overhead
building offline retrieval corpora

Common mistakes

batching inputs from different workflows when you need separate metadata
assuming the response order can be inferred without using index
sending a giant batch without thinking about request size, retries, and indexing checkpoints

Reference

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Batch Example

How to read the result

When To Batch

Common mistakes

Reference

​Batch Example

​How to read the result

​When To Batch

​Common mistakes

​Reference

Batch Example

How to read the result

When To Batch

Common mistakes

Reference