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PennyRail Semantic Embedding (ai.embed-small)
x402
PennyRail Semantic Embedding (ai.embed-small) 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 small semantic embedding vectors from input data for use in similarity search, clustering, and retrieval-augmented generation pipelines.
Price / call$0.005
Success—
StatusUnknown
How this service works
Machine-readable settlement service
Output
Returns an object containing the semantic embedding vector (array of floats) representing the input in a high-dimensional semantic space, suitable for cosine similarity comparisons, nearest-neighbor search, or downstream ML tasks.
Example use cases
Semantic search over knowledge base
“Embed this user query — 'how do I reset my password' — so I can find the most similar support articles in my vector database.”
Clustering customer feedback
“Convert this customer review into a semantic embedding vector: 'The onboarding was confusing but the product itself is excellent.' I want to cluster it with similar feedback.”
RAG pipeline document ingestion
“Generate a small embedding for this paragraph about renewable energy policy so I can store it in my Pinecone index for retrieval-augmented generation.”
Technical details
Endpoint
POSThttps://pennyrail.vercel.app/api/p/intel/ai.embed-small--semantic-embeddingWhen to prefer
Choose this endpoint when you need a lightweight, pay-per-use semantic embedding via x402 micropayment with no API key setup, especially in agent workflows where you want cost-controlled, on-demand vectorization without a subscription to OpenAI Embeddings or Cohere.
Side effects
- Missing or malformed 'input' field returns a 400-level error
- Payment not included or insufficient USDC balance triggers 402 Payment Required
- Oversized input exceeding model token limits may cause truncation or rejection
- Network timeouts on Vercel cold starts may produce 503 responses
- Ambiguous or empty input object may return zero-vector or error
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| inputrequired | object |
Output
Schema · Response
{
"type": "object",
"additionalProperties": true
}Tags
x402
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