Vector embeddings API
Vector embeddings API: converts up to 64 pieces of text per call into 768-dimensional numeric vectors suitable for similarity comparison, no separate provider account or key needed. Pay per call in USDC, and nothing submitted is retained afterward. Use it to build semantic search indexes, cluster related documents, or de-duplicate near-identical text at scale.
Answeringour last check, 2026-10-10
1 of 1checks answered this week
237 msmedian answer time
$0.002listed price per call
$0.002price 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
POST https://x402.forgemesh.io/text-vectorizer
| Category | Search and research |
|---|---|
| Provider host | x402.forgemesh.io |
| Networks | eip155:8453 |
| Payment schemes | exact |
| Self-reported calls, 30 days | 2 from 2 payers (the provider's figure, not ours) |
Our checks, last 30 days
| Day | Result | HTTP | Asked | Time |
|---|---|---|---|---|
| 2026-10-10 | valid payment request | 402 | $0.002 | 237 ms |
Example input (from the provider)
{
"body": {
"texts": [
"agent payments",
"x402 protocol"
]
},
"bodyType": "json",
"method": "POST",
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"body": {
"properties": {
"text": {
"description": "single text alternative to texts[]",
"type": "string"
},
"texts": {
"description": "1-64 texts to embed",
"items": {
"type": "string"
},
"type": "array"
}
},
"type": "object"
},
"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": {
"properties": {
"example": {
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}