70trust / 100

Embed

by Busker Embeddings in AI models & inference

x402 APIPassing, checked 1 h ago

Generate sentence embeddings (all-MiniLM-L6-v2, 384-dim, mean-pooled + L2-normalized) for up to 96 texts per call. Drop-in compatible with any all-MiniLM-L6-v2 index. Pairs with /chunk for a complete RAG pipeline.

POST https://workbot1.oddsys.org/embed

Last 30 days

All checks passedSome failedAll failedNot checked
Uptime
100%
Response time
2.2 s typical, 2.2 s slowest 5%
Last check
1 h ago
Next check
any minute now

How to call it

# See the payment challenge (nothing is charged)
curl -i -X POST "https://workbot1.oddsys.org/embed" \
  -H "content-type: application/json" \
  -d '{"texts":["first passage","second passage"]}'
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%2Fworkbot1.oddsys.org%2Fembed
const res = await pay("https://workbot1.oddsys.org/embed", {
  method: "POST",
  headers: { "content-type": "application/json" },
  body: JSON.stringify({"texts":["first passage","second passage"]}),
});
console.log(await res.json());

Example input

{
  "texts": [
    "first passage",
    "second passage"
  ]
}

Example output

{
  "embeddings": [
    [
      0.021,
      -0.043,
      0.118,
      "…381 more"
    ]
  ],
  "meta": {
    "count": 1,
    "dimensions": 384,
    "inferenceMs": 34,
    "model": "all-minilm-l6-v2",
    "normalized": true,
    "totalTokens": 9
  }
}

Security scan

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

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