{"uid":"cap_CCf6aleLtyJd30fheyI9p","slug":"pennyrail-ai-small-text-embeddings-c370e5bc","name":"PennyRail AI Small Text Embeddings","description":"Machine-readable settlement service","url":"https://pennyrail.vercel.app/api/p/intel/ai.embed-small--text-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":"0.005","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_jObhHE-MwjJZIw6doR85y","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 dense vector embeddings for text input using a small embedding model, accessible via pay-per-call settlement.","exampleAgentPrompt":"Embed this text using the ai.embed-small model: 'Renewable energy adoption is accelerating globally due to falling solar panel costs.'","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"Turn this paragraph into a vector embedding so I can store it in my vector database for retrieval-augmented generation: 'The Federal Reserve raised interest rates by 25 basis points in its latest meeting.'"},{"title":"Semantic similarity search","prompt":"Embed this user query so I can find the most semantically similar documents in my knowledge base: 'What are the side effects of ibuprofen?'"},{"title":"Text clustering for topic modeling","prompt":"Convert each of these customer feedback snippets into embeddings so I can cluster them by topic — start with: 'Your checkout process is way too complicated and takes forever.'"}],"resultDescription":"Returns a JSON object containing the dense vector embedding (float array) representing the semantic content of the input text, generated by the small embedding model. The response schema is open-ended and may include the embedding array, model metadata, and token usage.","failureModes":["Missing or malformed 'input' object returns a 400 validation error","Payment not fulfilled via x402 protocol results in a 402 Payment Required response","Input text too long for the small model context window may cause truncation or error","Upstream model unavailability on Vercel infrastructure causes 503 errors","Empty input object may return a zero-vector or error depending on implementation"],"whenToPreferThis":"Choose this endpoint when you need cost-effective, low-latency text embeddings at $0.005 USDC per call and can tolerate a smaller model's representational capacity. Ideal for high-volume embedding pipelines (RAG, semantic search, clustering) where per-call cost matters and a 'small' model quality tier is sufficient. Prefer over larger embedding models when budget or throughput is the primary constraint.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T07:21:55.837Z","isFirstParty":false}