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Pocket Network Semantic Similarity Scoring
x402
Pocket Network Semantic Similarity Scoring is a paid API for AI agents from agent.pocket.network, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-30).
Computes a cosine similarity score between two text inputs using a fixed embedding model, returning a deterministic JSON similarity value per request.
Price / call$0.005
Success—
StatusUnknown
How this service works
Text and embedding similarity scoring: cosine match and dedupe decisions at fixed model precision. POST /v1/semantic with {text_a, text_b} and get a similarity score as one JSON object. Deterministic. Pay per request in USDC; no account, no API key.
Output
A single JSON object containing a cosine similarity score (a float between 0 and 1) representing how semantically related the two input texts are. Higher values indicate greater similarity. The result is deterministic — identical inputs always yield the same score.
Example use cases
Deduplicating support ticket queue
“I have two customer support tickets — ticket A says 'My payment failed at checkout' and ticket B says 'I couldn't complete my purchase, the transaction didn't go through.' Are these describing the same issue? Give me a similarity score.”
RAG retrieval relevance check
“Before passing this chunk to the user, I need to know how relevant it is to their query. The query is 'best practices for container security' and the chunk starts with 'Docker hardening techniques include minimizing image layers and using non-root users.' Score their semantic similarity.”
Plagiarism detection in submissions
“Check whether these two essay paragraphs are essentially the same content: the first says 'Climate change accelerates the melting of polar ice caps, raising sea levels globally' and the second says 'The warming climate is causing Arctic and Antarctic ice to melt faster, leading to rising oceans worldwide.'”
Technical details
Endpoint
POSThttps://agent.pocket.network/v1/semantic-similarity?utm_source=zero.xyzWhen to prefer
Prefer this endpoint when you need a fast, deterministic, pay-per-call semantic similarity score with no account setup or API key — ideal for deduplication pipelines, RAG relevance checks, or any workflow where reproducible cosine similarity at a fixed model precision is required. Choose this over OpenAI embeddings or other similarity APIs when you want stateless, per-request USDC micropayment billing without subscriptions.
Side effects
- Missing text_a or text_b fields returns a 400 validation error
- Empty string inputs may return a zero or undefined similarity score
- Extremely long texts may be truncated or rejected depending on model token limits
- Payment failure (insufficient USDC) results in a 402 Payment Required response
- Network errors or provider downtime return 5xx responses
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text_a | string | First text. |
| text_b | string | Second text. |
Output
Schema · Response
{
"type": "json",
"example": {
"data": {},
"portal": {
"serviceId": "semantic-similarity",
"provenance": "third-party-supplier",
"schemaCheck": "passed"
}
}
}Tags
x402
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