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KiHustle Word Embedding Similarity Proxy
x402
KiHustle Word Embedding Similarity Proxy is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-13).
Computes word or text embedding similarity by proxying a text input through a vector embedding model and returning a similarity result
Price / call$0.002
Success—
StatusUnknown
How this service works
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Output
Returns a JSON object with a 'result' field indicating the processed similarity outcome and a 'status' field confirming success. The exact similarity score or embedding vector details depend on the underlying model, but the response confirms whether the text was successfully processed and compared.
Example use cases
Semantic duplicate content detection
“I have two sentences — 'AI helps automate tasks' and 'Artificial intelligence streamlines workflows' — can you check how semantically similar they are using a word embedding comparison?”
Keyword relevance scoring for SEO
“Check how semantically close the phrase 'side hustle income ideas' is to 'online earning strategies' so I can decide if they're interchangeable for my content.”
Matching user queries to FAQ answers
“I want to see if the user question 'how do I make money with AI tools' is semantically similar enough to my FAQ answer 'using AI automation for digital income' to be a valid match — can you run an embedding similarity check?”
Technical details
Endpoint
POSThttps://kihustle.tech/nexus/api/v1/word-embed-similarity-proxyWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-call semantic text similarity computation without setting up your own embedding infrastructure. It is suitable for single-text embedding lookups or similarity proxying at low cost ($0.002/call). Prefer alternatives if you need batch processing, a specific named embedding model with guaranteed output dimensions, or raw embedding vectors rather than a similarity result.
Side effects
- Empty or missing 'text' field returns an error or undefined behavior
- Text that is too long may be truncated or rejected
- Underlying embedding model unavailability causes proxy failure
- Non-string input types may cause schema validation errors
- Network timeout if the proxied embedding service is slow
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string |
Output
Schema · Response
{
"type": "json",
"example": {
"result": "processed",
"status": "success"
}
}Tags
x402
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