Embeddings, OpenAI compatible
Embeddings, OpenAI compatible. A 1,024-dimension embedding vector for up to 4,000 characters of text in 100+ languages, from BAAI's BGE-M3. Use for semantic search, RAG retrieval, clustering and deduplication.
Answeringour last check, 2026-10-24
1 of 1checks answered this week
27 msmedian answer time
$0.01listed price per call
$0.01price it asked us
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://agent-data-tools.revmesh2074.workers.dev/ai/embed
| Category | Search and research |
|---|---|
| Provider host | agent-data-tools.revmesh2074.workers.dev |
| Networks | eip155:137, eip155:42161, eip155:8453, solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp |
| Payment schemes | exact |
| Self-reported calls, 30 days | 4 from 2 payers (the provider's figure, not ours) |
Our checks, last 30 days
| Day | Result | HTTP | Asked | Time |
|---|---|---|---|---|
| 2026-10-24 | valid payment request | 402 | $0.01 | 27 ms |
Example input (from the provider)
{
"method": "GET",
"queryParams": {
"text": "pay-per-call APIs for AI agents"
},
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"method": {
"enum": [
"GET"
],
"type": "string"
},
"queryParams": {
"properties": {
"text": {
"description": "Text to embed, up to 4,000 characters",
"examples": [
"pay-per-call APIs for AI agents"
],
"type": "string"
}
},
"required": [
"text"
],
"type": "object"
},
"type": {
"const": "http",
"type": "string"
}
},
"required": [
"type",
"method"
],
"type": "object"
},
"output": {
"properties": {
"example": {
"properties": {
"dimensions": {
"type": "number"
},
"embedding": {
"items": {
"type": "number"
},
"type": "array"
},
"model": {
"type": "string"
}
},
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}