{"uid":"cap_9-A3jQy9TsoSjHqkYrDKh","slug":"pennyrail-embedding-api-ai-v1-embeddings-8a8e835d","name":"PennyRail Embedding API (ai.v1-embeddings)","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/mini/ai.v1-embeddings--embedding-api?utm_source=zero.xyz","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_cOfo_siMNzJSGHnXh1AwT","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 paid micropayment-gated API endpoint on PennyRail","exampleAgentPrompt":"Can you generate a vector embedding for this text: 'The quick brown fox jumps over the lazy dog' — I need it for a semantic similarity pipeline I'm building?","exampleUseCases":[{"title":"Semantic search index population","prompt":"I need to embed a product description — 'Wireless noise-cancelling headphones with 30-hour battery life' — into a vector so I can store it in my semantic search index."},{"title":"RAG pipeline document encoding","prompt":"Take this paragraph from my knowledge base and turn it into an embedding vector so my retrieval-augmented generation system can do similarity lookups against it."},{"title":"Duplicate content detection","prompt":"Can you generate embeddings for these two support ticket summaries so I can measure their cosine similarity and flag them as duplicates if they're close enough?"}],"resultDescription":"Returns an object containing the computed embedding vector (numeric float array) representing the semantic content of the input, usable directly in vector databases, similarity computations, or ML pipelines. The response schema is open-ended, so additional metadata fields may be included alongside the embedding.","failureModes":["Payment not provided or insufficient USDC (402 Payment Required)","Malformed or missing 'input' field in request body (400 Bad Request)","Input object type not supported by the underlying embedding model","Service unavailable or cold-start latency on Vercel serverless (503/504)","Response schema is fully open-ended, so embedding field name must be inferred at runtime"],"whenToPreferThis":"Choose this endpoint when you need a micropayment-gated, x402-compatible embedding API that integrates natively with Coinbase's payment infrastructure. It is well-suited for AI agents that already operate within the x402 pay-per-call ecosystem and need to generate embeddings on demand without a pre-provisioned API key or subscription. Prefer alternatives like OpenAI or Cohere embeddings if you need guaranteed SLAs, well-documented schemas, or high-volume batch processing.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-01T12:37:47.835Z","isFirstParty":false,"canonicalSlug":"pennyrail-embedding-api-ai-v1-embeddings-8a8e835d"}