{"uid":"cap_7lRZxafnX8-6zP0nYtYfy","slug":"pennyrail-ai-embed-small-embedding-vector-1ababfb8","name":"PennyRail AI Embed Small – Embedding Vector","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/intel/ai.embed-small--embedding-vector","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__9sEMQf8rtNzCelIBg8lO","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 a small embedding vector representation for a given input object, accessible via a pay-per-call settlement API","exampleAgentPrompt":"Can you generate a small embedding vector for this text: 'The quick brown fox jumps over the lazy dog'? I need the vector to power a semantic search index.","exampleUseCases":[{"title":"Semantic search index builder","prompt":"Take this product description — 'Lightweight running shoes with breathable mesh and responsive foam sole' — and generate an embedding vector so I can store it in my semantic search index."},{"title":"RAG document chunk embedding","prompt":"I'm building a retrieval-augmented generation pipeline. Embed this paragraph from my docs: 'API rate limits reset every 60 seconds per token.' I need the vector to store in my vector database."},{"title":"Clustering user feedback by topic","prompt":"Vectorize this customer feedback entry: 'The checkout process is confusing and took way too long.' I want to cluster it with similar complaints using cosine similarity."}],"resultDescription":"Returns a JSON object containing the embedding vector (a dense array of floating-point numbers) representing the input. The vector can be used for downstream tasks such as semantic search, clustering, classification, or retrieval-augmented generation.","failureModes":["Missing required 'input' field returns a validation error","Malformed input object may return a 400 bad request","Payment not settled or insufficient USDC balance may result in a 402 Payment Required response","Model overload or cold-start latency on Vercel serverless may cause timeouts","Overly large input objects may exceed size limits and return an error"],"whenToPreferThis":"Prefer this endpoint when you need a lightweight, pay-per-call embedding generation with no subscription commitment and x402-compatible micropayment settlement. It is ideal for agents that need to embed individual text chunks on-demand without managing API keys or monthly quotas. The 'small' model variant is best when speed and cost-efficiency are prioritised over maximum embedding dimensionality.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:59:30.470Z","isFirstParty":false}