Drop-in OpenAI Embeddings endpoint
Drop-in OpenAI Embeddings endpoint: POST {input: string | string[], model, encoding_format: float|base64, dimensions} and get {object: "list", data: [{embedding, index}], model, usage}. Models (Cloudflare Workers AI): bge-m3 (1024-d, multilingual (100+ languages), 8192-token context); bge-large-en-v1.5 (1024-d, English); bge-base-en-v1.5 (768-d, English); bge-small-en-v1.5 (384-d, English, fastest); embeddinggemma-300m (768-d, multilingual (Google EmbeddingGemma)). Any other model name (e.g....
Paid test badge: not yet. The checks above are free: we call the tool without paying and read the payment request it sends back. The Verified badge needs paid calls whose answers match the promised output, and nobody can buy a badge.
Endpoint
GET https://quickbuild.quynguyenbac.workers.dev/v1/embeddings
| Category | Market data |
|---|---|
| Provider host | quickbuild.quynguyenbac.workers.dev |
| Networks | eip155:8453 |
| Payment schemes | exact |
| Self-reported calls, 30 days | 1 from 1 payers (the provider's figure, not ours) |
Our checks, last 30 days
| Day | Result | HTTP | Asked | Time |
|---|---|---|---|---|
| 2026-10-24 | valid payment request | 402 | $0.001 | 59 ms |
Example input (from the provider)
{
"method": "GET",
"queryParams": {
"input": [
"x402 lets AI agents pay for APIs per call.",
"Vector search over documents"
],
"model": "bge-m3"
},
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"method": {
"enum": [
"GET"
],
"type": "string"
},
"queryParams": {
"properties": {
"dimensions": {
"description": "Optional output size; vectors are truncated and re-normalized.",
"maximum": 1024,
"minimum": 1,
"type": "integer"
},
"encoding_format": {
"description": "float (default) or base64 (little-endian float32).",
"enum": [
"float",
"base64"
],
"type": "string"
},
"input": {
"description": "Text or array of texts to embed (max 100). Optional: defaults to a sample sentence.",
"items": {
"type": "string"
},
"type": [
"string",
"array"
]
},
"model": {
"description": "Embedding model alias (default bge-m3).",
"enum": [
"bge-m3",
"bge-large-en-v1.5",
"bge-base-en-v1.5",
"bge-small-en-v1.5",
"embeddinggemma-300m"
],
"type": "string"
}
},
"required": [],
"type": "object"
},
"type": {
"const": "http",
"type": "string"
}
},
"required": [
"type",
"method"
],
"type": "object"
},
"output": {
"properties": {
"example": {
"properties": {
"data": {
"items": {
"properties": {
"embedding": {
"items": {
"type": "number"
},
"type": [
"array",
"string"
]
},
"index": {
"type": "integer"
},
"object": {
"type": "string"
}
},
"type": "object"
},
"type": "array"
},
"dimensions": {
"type": "integer"
},
"model": {
"type": "string"
},
"modelId": {
"type": "string"
},
"object": {
"type": "string"
},
"usage": {
"properties": {
"prompt_tokens": {
"type": "integer"
},
"total_tokens": {
"type": "integer"
}
},
"type": "object"
}
},
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}