{"uid":"cap_lCELnFeTCZwdCVirk2dpG","slug":"pennyrail-openai-compatible-embeddings-99632d7d","name":"PennyRail OpenAI-Compatible Embeddings","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--openai-compatible-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_DmgFb2jOcj9trEJvG3Hug","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 text or data using an OpenAI-compatible embeddings API, accessible via micropayment settlement","exampleAgentPrompt":"Convert this text into an embedding vector using PennyRail's OpenAI-compatible embeddings endpoint: 'The quick brown fox jumps over the lazy dog'.","exampleUseCases":[{"title":"Semantic search index population","prompt":"Generate an embedding vector for this product description so I can store it in my vector database for semantic search: 'Lightweight hiking boots with waterproof membrane and ankle support, ideal for trail running.'"},{"title":"RAG pipeline document ingestion","prompt":"I need to embed this paragraph for my retrieval-augmented generation pipeline — can you vectorize it? 'Climate change refers to long-term shifts in global temperatures and weather patterns, primarily driven by human activity since the mid-20th century.'"},{"title":"Sentence similarity comparison","prompt":"Get me the embedding vectors for these two sentences so I can compute their cosine similarity: 'Machine learning is a subset of artificial intelligence' and 'AI encompasses techniques like deep learning and neural networks'."}],"resultDescription":"Returns an object containing one or more embedding vectors (arrays of floating-point numbers) representing the semantic content of the input, in a format compatible with the OpenAI embeddings API response schema.","failureModes":["Missing or malformed 'input' field returns a validation error","Payment of 0.002 USDC not settled via x402 protocol results in 402 Payment Required","Empty input object may produce zero-length or error response","Rate limiting or upstream OpenAI API errors may cause 5xx responses","Oversized input exceeding token limits may be rejected"],"whenToPreferThis":"Choose this endpoint when you need OpenAI-format embeddings with per-call micropayment billing (0.002 USDC) rather than a subscription or API-key-based model. It is ideal for agents operating in x402-enabled payment ecosystems, serverless or ephemeral environments where managing API keys is undesirable, or when you need drop-in OpenAI embeddings compatibility without provisioning an OpenAI account.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:54:09.594Z","isFirstParty":false}