{"uid":"cap_bmqi28Ip1PxTHWpQamlOS","slug":"pennyrail-large-embedding-model-ec3932ae","name":"PennyRail Large Embedding Model","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/standard/ai.embed-large--proven-embed-large","method":"POST","headers":{},"bodySchema":{"type":"object","required":["input"],"properties":{"input":{"type":"object"}}},"responseSchema":{"type":"object","additionalProperties":true},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_N3OzN0ANYM66j0cLtgEUv","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates large-scale vector embeddings for input data using a proven embedding model via PennyRail's pay-per-call settlement infrastructure","exampleAgentPrompt":"Embed this product description using the large embedding model so I can store it in my vector database for semantic search: 'Ergonomic mesh office chair with lumbar support and adjustable armrests.'","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"I need to vectorize this paragraph so I can index it in my vector store for retrieval-augmented generation: 'The quarterly earnings report showed a 12% increase in revenue driven by strong cloud segment growth.'"},{"title":"Semantic similarity comparison","prompt":"Can you embed both of these customer support queries so I can compare how semantically similar they are? First: 'How do I reset my password?' Second: 'I forgot my login credentials and can't get in.'"},{"title":"Clustering user feedback","prompt":"Generate a large-model embedding for this user review so I can cluster it with similar feedback: 'The onboarding was confusing but once I figured it out the product was really powerful.'"}],"resultDescription":"Returns an object containing the embedding vector (a dense array of floating-point numbers) representing the semantic content of the input, produced by the proven-embed-large model. The response schema is open-ended and may include the vector array, model metadata, and token usage information.","failureModes":["Missing required 'input' field returns a validation error","Malformed input object may return a 400 or schema error","Payment failure via x402 protocol results in 402 Payment Required before processing begins","Oversized input exceeding model token limits may return an error or truncation","Network timeout on the Vercel-hosted endpoint under high load"],"whenToPreferThis":"Choose this endpoint when you need large-dimension, high-quality embeddings and are comfortable with per-call micropayment billing via x402/USDC. Prefer it over smaller embedding models when semantic fidelity and coverage matter more than latency or cost. It is well-suited for production RAG pipelines, semantic search indexing, and clustering tasks where embedding quality is critical.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T07:21:51.975Z","isFirstParty":false}