{"uid":"cap_5NTNJRoguh01K7hD3d4YN","slug":"pennyrail-ai-embed-small-b9818c57","name":"PennyRail AI Embed Small","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/intel/ai.embed-small--proven-embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","required":["input"],"properties":{"input":{"type":"object"}}},"responseSchema":{"type":"object","additionalProperties":true},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"down","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_1CEFS2KEFF7LXSpDtD2tR","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates small-sized vector embeddings for input data via a paid micropayment-gated API endpoint","exampleAgentPrompt":"Embed this text using the small embedding model so I can store it in my vector database: 'The quick brown fox jumped over the lazy dog.'","exampleUseCases":[{"title":"RAG document indexing pipeline","prompt":"I need to embed each chunk of my documentation so I can store them in Pinecone and do retrieval-augmented generation — can you vectorize this chunk: 'Our refund policy allows returns within 30 days of purchase.'"},{"title":"Semantic similarity scoring","prompt":"Embed both of these sentences so I can compute their cosine similarity and check if they mean the same thing: 'How do I reset my password?' and 'Steps to recover account access.'"},{"title":"Product description clustering","prompt":"Convert this product description to a small embedding vector so I can cluster it with similar items in my catalog: 'Wireless noise-cancelling over-ear headphones with 30-hour battery life.'"}],"resultDescription":"Returns an object containing the embedding vector (dense numerical array) representing the semantic content of the input, suitable for use in vector databases, similarity search, or downstream ML tasks.","failureModes":["Missing or malformed 'input' field returns a validation error","Payment not settled via x402 protocol returns 402 Payment Required","Oversized input payload may exceed model token limits and return an error","Service unavailability on Vercel hosting returns 5xx errors","Empty input object may produce a zero-length or degenerate embedding"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, cost-effective embedding model (small variant) accessible via micropayment per call with no subscription required, ideal for low-volume or pay-as-you-go agent workflows using x402 payment rails.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-04T10:27:27.144Z","isFirstParty":false,"canonicalSlug":"pennyrail-ai-embed-small-b9818c57"}