{"uid":"cap_66r3nUNtY0Jbm91N7AsPC","slug":"pennyrail-ai-embeddings-v1-31c54e78","name":"PennyRail AI Embeddings v1","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--v1-embeddings","method":"POST","headers":{},"bodySchema":{"type":"object","required":["input"],"properties":{"input":{"type":"object"}}},"responseSchema":{"type":"object","additionalProperties":true},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_UTaeS5qYbgxILCpyeTzoh","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates vector embeddings for input data via a pay-per-call settlement endpoint using the x402 micropayment protocol","exampleAgentPrompt":"Generate a vector embedding for this text: 'The quick brown fox jumps over the lazy dog' — I need it for a semantic similarity search pipeline.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"Embed this document chunk so I can store it in my vector database for retrieval-augmented generation: 'Our return policy allows customers to return items within 30 days of purchase.'"},{"title":"Semantic search query encoding","prompt":"Convert my user's search query 'best noise-cancelling headphones under $200' into an embedding vector so I can find the closest matching products in my catalog."},{"title":"Text similarity comparison","prompt":"I need embeddings for both of these sentences so I can measure how semantically similar they are: 'The patient has a fever' and 'The individual is running a high temperature.'"}],"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 storage in a vector database.","failureModes":["Missing or malformed 'input' field returns a validation error","Payment not settled via x402 protocol returns HTTP 402 Payment Required","Oversized input exceeding token limits may return a 400 or truncation error","Network or provider backend failures return 5xx errors","Invalid content type or malformed JSON body returns 400"],"whenToPreferThis":"Choose this endpoint when you need on-demand, pay-per-call text embeddings without a subscription or API key commitment, especially in agentic workflows where micropayments via x402 are already supported. Ideal for low-volume or variable-load embedding needs billed per request in USDC.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:57:32.522Z","isFirstParty":false}