{"uid":"cap_E0ts9El7nD8IClUFKee9v","slug":"pennyrail-batch-text-embeddings-0c70027c","name":"PennyRail Batch Text Embeddings","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--batch-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_6bz91zIjkPhUrPTq4uggv","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 a batch of text inputs, payable per-call via x402 micropayment protocol","exampleAgentPrompt":"Generate batch embeddings for these five product descriptions so I can index them for semantic search: 'wireless headphones', 'noise-cancelling earbuds', 'Bluetooth speaker', 'USB-C audio adapter', 'studio monitor speakers'.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"Take these 20 knowledge base paragraphs and turn them into embeddings so I can store them in my vector database for retrieval-augmented generation."},{"title":"Semantic search for e-commerce","prompt":"Vectorize this batch of 50 product titles so I can run similarity search when customers type in a natural language query on our store."},{"title":"Text clustering for research","prompt":"I have 200 customer feedback comments — can you get embeddings for all of them at once so I can cluster them by topic?"}],"resultDescription":"Returns an object containing vector embedding arrays corresponding to each input text, suitable for downstream tasks such as semantic similarity, clustering, classification, or retrieval.","failureModes":["Missing or malformed 'input' object returns a 400-level validation error","Payment not attached or insufficient USDC results in 402 Payment Required response","Oversized batch may exceed compute limits and return a timeout or payload-too-large error","Invalid x402 payment header causes settlement failure and request rejection","Empty input object may return empty embeddings or an error depending on server handling"],"whenToPreferThis":"Choose this endpoint when you need to embed multiple texts in a single call using a pay-per-use micropayment model (x402/USDC), especially in agent pipelines where spinning up your own embedding infrastructure is overkill and you want to pay only for what you use. Prefer over self-hosted alternatives when low operational overhead and per-call billing matter more than throughput volume.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T07:21:47.084Z","isFirstParty":false}