{"uid":"cap_gdjorJuSS2LSUApaRyuLx","slug":"pennyrail-ai-embed-small-6f64bc06","name":"PennyRail AI Embed Small","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/intel/ai.embed-small--embed","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":"unknown","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__Z6p_xTAYO0GqMBwXcVVj","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-scale vector embeddings for text or structured input via a paid x402 micropayment-gated API endpoint","exampleAgentPrompt":"Embed this sentence using the small embedding model: 'The quick brown fox jumps over the lazy dog' — I need the vector output for a semantic similarity lookup.","exampleUseCases":[{"title":"Semantic search index builder","prompt":"Take this list of product descriptions and embed each one using the small embedding model so I can index them for semantic search."},{"title":"Document similarity comparison","prompt":"Embed these two paragraphs with the small model and give me their vector representations so I can compute how similar they are."},{"title":"Clustering user feedback","prompt":"I have a batch of short customer reviews — embed each one using the small embedding model so I can cluster them by topic."}],"resultDescription":"Returns a JSON object containing the vector embedding (numerical array) representing the semantic content of the input, suitable for downstream tasks like similarity search, clustering, or retrieval-augmented generation.","failureModes":["Missing or malformed 'input' field returns a validation error","Payment not provided or insufficient USDC (x402 payment required before processing)","Input object too large or unsupported type may return a 400 error","Service unavailability on Vercel deployment returns 5xx","Ambiguous or empty input may return a zero-vector or error"],"whenToPreferThis":"Choose this endpoint when you need lightweight, cost-efficient vector embeddings via a micropayment x402 model and do not require a large embedding dimension. It is ideal for agents operating in pay-per-call pipelines where cost control per embedding call matters ($0.005 USDC), and when integrating with Coinbase-compatible x402 payment flows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T13:00:36.380Z","isFirstParty":false}