{"uid":"cap_S494UgBy2OqE3N3TZd8eW","slug":"pennyrail-semantic-embedding-ai-embed-small-d123b00c","name":"PennyRail Semantic Embedding (ai.embed-small)","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/intel/ai.embed-small--semantic-embedding","method":"POST","headers":{},"bodySchema":{"type":"object","required":["input"],"properties":{"input":{"type":"object"}}},"responseSchema":{"type":"object","additionalProperties":true},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_j0HLfrJAVc-UvgPqQ_fb3","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates small semantic embedding vectors from input data for use in similarity search, clustering, and retrieval-augmented generation pipelines.","exampleAgentPrompt":"Generate a semantic embedding vector for the text 'climate change mitigation strategies in developing economies' so I can compare it against my document index for similarity search.","exampleUseCases":[{"title":"Semantic search over knowledge base","prompt":"Embed this user query — 'how do I reset my password' — so I can find the most similar support articles in my vector database."},{"title":"Clustering customer feedback","prompt":"Convert this customer review into a semantic embedding vector: 'The onboarding was confusing but the product itself is excellent.' I want to cluster it with similar feedback."},{"title":"RAG pipeline document ingestion","prompt":"Generate a small embedding for this paragraph about renewable energy policy so I can store it in my Pinecone index for retrieval-augmented generation."}],"resultDescription":"Returns an object containing the semantic embedding vector (array of floats) representing the input in a high-dimensional semantic space, suitable for cosine similarity comparisons, nearest-neighbor search, or downstream ML tasks.","failureModes":["Missing or malformed 'input' field returns a 400-level error","Payment not included or insufficient USDC balance triggers 402 Payment Required","Oversized input exceeding model token limits may cause truncation or rejection","Network timeouts on Vercel cold starts may produce 503 responses","Ambiguous or empty input object may return zero-vector or error"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-use semantic embedding via x402 micropayment with no API key setup, especially in agent workflows where you want cost-controlled, on-demand vectorization without a subscription to OpenAI Embeddings or Cohere.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T13:01:27.391Z","isFirstParty":false}