{"uid":"cap_MTPEbmfDLqrQnmaa2spMB","slug":"ciel-m2m-api-marketplace-vectorize-endpoint-dd7d023d","name":"CIEL M2M API Marketplace – Vectorize Endpoint","description":"50+ micro-services for AI agents: data parsing, crypto analytics, security scanning, code tools, and more. Pay per call via x402 (USDC on Base) or RapidAPI subscription.","url":"https://169.58.38.67.sslip.io/api/vectorize","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"properties":{"type":"string"}}},"responseSchema":{"type":"json","example":{"result":"data"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.03","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":"$0.03/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.03","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.03","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_yc18Cf8VcSS2-OjzJRQPE","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.03","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts text or structured data into vector embeddings for downstream semantic search, similarity, or ML pipelines.","exampleAgentPrompt":"Vectorize this text for me so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog' — I need the embedding array back.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I'm building a retrieval-augmented generation system — can you vectorize this paragraph so I can store it in Pinecone: 'Our refund policy allows returns within 30 days of purchase with a valid receipt'?"},{"title":"Semantic similarity for product search","prompt":"Convert this product description into a vector embedding so I can compare it against my catalog for similarity matching: 'lightweight waterproof hiking boots with ankle support'."},{"title":"Clustering customer feedback","prompt":"I need to vectorize this customer review so I can run clustering on it later: 'The onboarding flow was confusing but the core product is excellent once you figure it out.'"}],"resultDescription":"Returns a JSON object with a 'result' field containing the vector embedding or processed data representation of the input content, suitable for use in vector databases, similarity computations, or ML inference pipelines.","failureModes":["Missing or malformed 'properties' field in request body returns a validation error","Empty or null input string may return a generic result or error","Oversized input exceeding token limits may be truncated or rejected","Payment failure via x402 (insufficient USDC balance) results in 402 Payment Required","Network timeout on the raw-IP sslip.io host may occur under load"],"whenToPreferThis":"Choose this endpoint when you need a pay-per-call vectorization service with no subscription commitment, payable in USDC on Base via x402. It fits agentic workflows where embeddings are generated on-demand per document or query, and is suitable when you want to avoid managing embedding model infrastructure yourself.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-18T06:59:53.513Z","isFirstParty":false}