MapleAI

Free embeddings API. No key, no account.

OpenAI-compatible text embeddings powered by NVIDIA Nemotron Embed 1B. Send text, get back a 2048-dimensional vector. No sign-up, no API key, no credit card — the endpoint is free to call.

Endpoint POST https://polygon.mapleai.shop/v1/embeddings
OpenAI-compatible • $0.00 • No auth
$0per request, forever
2048vector dimensions
128inputs per request
1BNVIDIA Nemotron Embed

Quickstart

One POST request. Works with the OpenAI SDK out of the box.

cURL

curl https://polygon.mapleai.shop/v1/embeddings \
  -H 'content-type: application/json' \
  -d '{
    "input": "MapleAI sells GPT access per request",
    "input_type": "query",
    "encoding_format": "float"
  }'

JavaScript (OpenAI SDK)

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://polygon.mapleai.shop/v1',
  apiKey: 'not-required'
});

const res = await client.embeddings.create({
  model: 'nvidia/nemotron-3-embed-1b',
  input: 'MapleAI sells GPT access per request'
});
console.log(res.data[0].embedding.length); // 2048

Python (OpenAI SDK)

from openai import OpenAI

client = OpenAI(
    base_url="https://polygon.mapleai.shop/v1",
    api_key="not-required"
)

res = client.embeddings.create(
    model="nvidia/nemotron-3-embed-1b",
    input="MapleAI sells GPT access per request"
)
print(len(res.data[0].embedding))  # 2048

Batch up to 128 inputs

curl https://polygon.mapleai.shop/v1/embeddings \
  -H 'content-type: application/json' \
  -d '{
    "input": ["first document", "second document"],
    "input_type": "passage"
  }'

Use input_type: "query" for search questions and "passage" for documents — asymmetric retrieval works best when the two sides are marked differently.

Reference

Request and response format.

FieldTypeDescription
input (required)string | string[]One text or an array of 1–128 texts
modelstringOptional; any value is accepted and routed to nvidia/nemotron-3-embed-1b
input_typequery | passageRetrieval role of the text
encoding_formatfloat | base64Vector encoding, default float

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "index": 0,
      "embedding": [0.0123, -0.0456, ...]
    }
  ],
  "model": "nvidia/nemotron-3-embed-1b"
}

Errors carry the schema

Invalid requests return HTTP 400 with a machine-readable details.schema describing the exact valid shape, so an agent can self-correct on the first try.

Use cases

What people build with free embeddings.

Semantic search

Embed your document corpus once, embed the query at search time, rank by cosine similarity. The query/passage input types are built for exactly this.

RAG pipelines

Retrieve relevant chunks with free embeddings, then answer with a paid GPT model — you only pay for the final generation. See the runnable embeddings-to-chat example.

Clustering and dedup

Group near-duplicate tickets, reviews or crawler output before sending anything to a paid model. 2048-dim float vectors work with FAISS, pgvector, Qdrant and friends.

Fair use

Free means free, with common-sense limits.

What is included

Unauthenticated access for individuals, agents and prototypes. No API key, no account, no payment. The model is fixed to nvidia/nemotron-3-embed-1b.

What is not

Bulk corpus re-embedding at datacenter scale. If you push sustained high volume, we may rate-limit by source IP. Need guaranteed throughput? Talk to us: [email protected].