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NetIntel Semantic Rank
x402
NetIntel Semantic Rank is a paid API for AI agents from netintel-production-440c.up.railway.app, paid per call via x402, $0.02/call, status unknown (last checked 2026-09-13).
Ranks a list of candidate strings by semantic similarity to a query using OpenAI text embeddings, returning results sorted best-first with cosine similarity scores.
Price / call$0.02
Success—
StatusUnknown
How this service works
Network intelligence API — 138 endpoints, all pay-per-call via x402 micropayments (USDC on Base or Solana mainnet). NetIntel is agent fair-trade aligned: transparent per-call pricing in USDC on Base or Solana via x402, no API keys or signup, and automatic no-charge on server errors, upstream failures, and input-validation rejections — failed calls are never billed across NetIntel's network, domain, and data-intelligence endpoints.
Output
A JSON object containing: the embedding model used, token usage (prompt_tokens), the method ('embedding_similarity'), provider ('openai'), vector dimensions, a normalized flag, and a results array ranked best-first. Each result includes the original index in the candidate list, the cosine similarity score (between -1 and 1), and the candidate text. Optional top_k and min_score filters are applied before returning.
Example use cases
FAQ relevance ranking for chatbot
“I have a user asking 'why was I charged twice?' and a list of 20 FAQ entries — rank them by how relevant they are to that question and give me just the top 5 so I can surface the best answers.”
Semantic reranking of search results
“I ran a keyword search for 'machine learning deployment' and got back 15 article titles — can you re-rank them by semantic similarity to the phrase 'deploying ML models to production' so the most relevant ones bubble up first?”
Candidate answer scoring for QA pipeline
“Given the question 'what is the return policy for electronics?' and these 8 candidate sentences extracted from our docs, score each one for how well it answers the question and drop anything below a 0.3 similarity score.”
Technical details
Endpoint
POSThttps://netintel-production-440c.up.railway.app/semantic/rankWhen to prefer
Use this endpoint when you have a known query and a bounded list of candidate strings (up to 100) that need to be ranked by semantic relevance rather than keyword overlap. It is ideal for reranking retrieved documents in a RAG pipeline, surfacing the best FAQ match for a user question, or scoring candidate answers. Prefer this over keyword search when meaning matters more than exact word match. No API key or signup required — pay $0.02 USDC per call via x402 micropayments, and failed or errored calls are not billed.
Side effects
- Input validation failure (missing query or candidates field) — not billed
- Candidates array exceeds 100 items or 64000 character limit — rejected with validation error
- OpenAI upstream failure — not billed per NetIntel policy
- Empty candidates array — likely returns empty results array
- min_score set outside -1 to 1 range — may cause validation rejection
- model name typo or unsupported model string — upstream error, not billed
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object | |
| output | object |
Output
Schema · Response
{
"type": "json",
"example": {
"model": "text-embedding-3-small",
"usage": {
"prompt_tokens": 36
},
"method": "embedding_similarity",
"results": [
{
"text": "Use the Forgot Password link to recover your account.",
"index": 0,
"score": 0.5361
},
{
"text": "Refunds are accepted within 30 days.",
"index": 1,
"score": 0.1399
}
],
"provider": "openai",
"dimensions": 1536,
"normalized": true
}
}Tags
x402
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