Semantic similarity ranking — send a query plus up to 100 candidate texts, get t
Semantic similarity ranking — send a query plus up to 100 candidate texts, get the candidates back ranked by semantic similarity with scores. No vectors, no cosine math, no embedding model to manage: one call, one price, ranked results. Flat $0.02 per call in USDC via x402, no API key. For RAG retrieval, FAQ matching, and search result ordering.
Not tested: has real-world effectsour last check, 2026-09-24
0 of 0checks answered this week
n/amedian answer time
$0.02listed price per call
n/aprice it asked us
Paid test badge: not yet. The checks above are free: we call the tool without paying and read the payment request it sends back. The Verified badge needs paid calls whose answers match the promised output, and nobody can buy a badge.
Endpoint
POST https://netintel.dev/semantic/rank
| Category | Market data |
|---|---|
| Provider host | netintel.dev |
| Networks | eip155:8453, solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp |
| Payment schemes | exact |
| Self-reported calls, 30 days | 1 from 1 payers (the provider's figure, not ours) |
Our checks, last 30 days
We never call tools that send, buy, move money or file anything, not even without paying.
Example input (from the provider)
{
"body": {
"candidates": [
"Use the Forgot Password link to recover your account.",
"Refunds are accepted within 30 days.",
"Our offices open at 9:00 AM."
],
"query": "How do I reset my password?",
"top_k": 2
},
"bodyType": "json",
"method": "POST",
"type": "http"
}
Promised output schema (from the provider)
{
"$schema": "https://json-schema.org/draft/2020-12/schema",
"properties": {
"input": {
"additionalProperties": false,
"properties": {
"body": {
"properties": {
"candidates": {
"description": "1-100 candidate strings to rank (64000 chars total on the default model).",
"items": {
"type": "string"
},
"type": "array"
},
"min_score": {
"description": "Drop results scoring below this (-1 to 1). Optional.",
"type": "number"
},
"model": {
"description": "text-embedding-3-small (default) or text-embedding-3-large",
"type": "string"
},
"query": {
"description": "The text to rank candidates against.",
"type": "string"
},
"top_k": {
"description": "Return only the best N results. Optional.",
"type": "number"
}
},
"required": [
"query",
"candidates"
],
"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": {
"properties": {
"dimensions": {
"type": "number"
},
"method": {
"description": "Always 'embedding_similarity'",
"type": "string"
},
"model": {
"type": "string"
},
"normalized": {
"type": "boolean"
},
"provider": {
"type": "string"
},
"results": {
"description": "Ranked best-first: [{index (original position), score, text}]",
"type": "array"
},
"usage": {
"description": "prompt_tokens (input-only)",
"type": "object"
}
},
"type": "object"
},
"type": {
"type": "string"
}
},
"required": [
"type"
],
"type": "object"
}
},
"required": [
"input"
],
"type": "object"
}