Api Endpoint

x402ai

Convert text into a 768-dimension embedding vector (nomic-embed-text) for semantic search, RAG retrieval, clustering, deduplication or similarity scoring. POST JSON {"input": string}, up to 3000 characters; returns {embedding: number[768], model, dimensions, elapsed_ms}. One text per call; English works best.

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Offering

Provider: api.x402ai.dev
Method: POST
Offering type: Api Endpoint
Transport type: http
Category: Development
Delivery: Not provided
Pricing model: Not provided
x402 version: 2

Endpoint context

URL: https://api.x402ai.dev/api/embed
MIME type: Not provided
First seen: 2026-10-16T01:50:24.663Z
Last seen: 2026-11-01T14:57:12.185Z
Tags: embeddings, vector-search, RAG, semantic-search, NLP

Website

Payment pricing

x402 payment terms are the authoritative price for calling this endpoint.

Payment option 1: 0.01 USDC
Atomic price: 10000 · Network: eip155:8453 · Asset: USDC · Scheme: exact

Raw x402 terms

[
  {
    "scheme": "exact",
    "network": "eip155:8453",
    "asset": "0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913",
    "amount": "10000",
    "payTo": "0xecE637d74aB1a000abcf69956e22f1131501ae8b"
  }
]

Observed health

Status: Healthy
Latest latency: 239 ms
Average latency: 1007 ms
HTTP status: 204
Last checked: 2026-11-01T23:16:37.887Z
Reachability: 100% over 14 checks

Latency is measured to the first response headers. A reachable result does not verify response accuracy or merchant identity.

Input schema

{
  "additionalProperties": false,
  "properties": {
    "body": {
      "properties": {
        "input": {
          "description": "Text to embed. 1-3000 characters; longer input is rejected with HTTP 400.",
          "maxLength": 3000,
          "minLength": 1,
          "type": "string"
        }
      },
      "required": [
        "input"
      ]
    },
    "bodyType": {
      "enum": [
        "json",
        "form-data",
        "text"
      ],
      "type": "string"
    },
    "method": {
      "enum": [
        "POST"
      ],
      "type": "string"
    },
    "type": {
      "const": "http",
      "type": "string"
    }
  },
  "required": [
    "type",
    "method",
    "bodyType",
    "body"
  ],
  "type": "object"
}

Output schema

{
  "properties": {
    "example": {
      "properties": {
        "dimensions": {
          "description": "Always 768",
          "type": "integer"
        },
        "elapsed_ms": {
          "type": "integer"
        },
        "embedding": {
          "description": "768-dimension embedding vector",
          "items": {
            "type": "number"
          },
          "type": "array"
        },
        "model": {
          "type": "string"
        }
      },
      "required": [
        "embedding",
        "dimensions"
      ],
      "type": "object"
    },
    "type": {
      "type": "string"
    }
  },
  "required": [
    "type"
  ],
  "type": "object"
}