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PennyRail AI Embed Small – Embedding Vector
x402
PennyRail AI Embed Small – Embedding Vector is a paid API for AI agents from pennyrail.vercel.app, paid per call via x402, $0.005/call, status unknown (last checked 2026-09-15).
Generates a small embedding vector representation for a given input object, accessible via a pay-per-call settlement API
Price / call$0.005
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns a JSON object containing the embedding vector (a dense array of floating-point numbers) representing the input. The vector can be used for downstream tasks such as semantic search, clustering, classification, or retrieval-augmented generation.
Example use cases
Semantic search index builder
“Take this product description — 'Lightweight running shoes with breathable mesh and responsive foam sole' — and generate an embedding vector so I can store it in my semantic search index.”
RAG document chunk embedding
“I'm building a retrieval-augmented generation pipeline. Embed this paragraph from my docs: 'API rate limits reset every 60 seconds per token.' I need the vector to store in my vector database.”
Clustering user feedback by topic
“Vectorize this customer feedback entry: 'The checkout process is confusing and took way too long.' I want to cluster it with similar complaints using cosine similarity.”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/intel/ai.embed-small--embedding-vectorWhen to prefer
Prefer this endpoint when you need a lightweight, pay-per-call embedding generation with no subscription commitment and x402-compatible micropayment settlement. It is ideal for agents that need to embed individual text chunks on-demand without managing API keys or monthly quotas. The 'small' model variant is best when speed and cost-efficiency are prioritised over maximum embedding dimensionality.
Side effects
- Missing required 'input' field returns a validation error
- Malformed input object may return a 400 bad request
- Payment not settled or insufficient USDC balance may result in a 402 Payment Required response
- Model overload or cold-start latency on Vercel serverless may cause timeouts
- Overly large input objects may exceed size limits and return an error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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