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Embed

by Quartermaster Embeddings in AI models & inference

x402 APIPassing, checked 5 min ago

Text embeddings for semantic search and RAG: one vector per call, 1024 dimensions.

GET https://quartermaster.surewhynot.app/v1/embed

Last 30 days

All checks passedSome failedAll failedNot checked
Uptime
100%
Response time
680 ms typical, 1.0 s slowest 5%
Last check
5 min ago
Next check
in 23 min

How to call it

# See the payment challenge (nothing is charged)
curl -i -X GET "https://quartermaster.surewhynot.app/v1/embed?text=The+quick+brown+fox"
import { wrapFetchWithPayment } from "@x402/fetch";
import { x402Client } from "@x402/core/client";
import { ExactEvmScheme } from "@x402/evm/exact/client";
import { privateKeyToAccount } from "viem/accounts";

const client = new x402Client().register(
  "eip155:8453",
  new ExactEvmScheme(privateKeyToAccount(process.env.AGENT_KEY)),
);
const pay = wrapFetchWithPayment(fetch, client);

// Not sure it's safe to pay? Preflight it first for $0.005:
// GET https://toolvet.app/api/v1/check?url=https%3A%2F%2Fquartermaster.surewhynot.app%2Fv1%2Fembed
const res = await pay("https://quartermaster.surewhynot.app/v1/embed?text=The+quick+brown+fox");
console.log(await res.json());

Example input

{
  "text": "The quick brown fox"
}

Example output

{
  "dimensions": 1024,
  "embedding": [
    0.0123,
    -0.0456
  ],
  "model": "@cf/qwen/qwen3-embedding-0.6b"
}

Security scan

  • No findings. We scan names, descriptions and tool definitions for hidden instructions and other prompt-injection patterns.

Recent checks

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