{"uid":"cap_TIPvaPqM1XDBCVOBRm7GX","slug":"pagos-andreax-dev-text-embeddings-ec37c1a6","name":"pagos.andreax.dev Text Embeddings","description":"Convierte un texto en un vector (embedding) para búsqueda semántica, RAG, clustering o memoria. El bloque base que todo agente de IA necesita. Alto volumen. input=texto.","url":"https://pagos.andreax.dev/api/taller/peaje/embeddings","method":"GET","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method"],"properties":{"type":{"type":"string","const":"http"},"method":{"enum":["GET","HEAD","DELETE"],"type":"string"},"queryParams":{"type":"object","required":["input"],"properties":{"input":{"type":"string","description":"Texto a convertir en vector"}}}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_igYuVMbX-ICNIPMX3_S_y","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts a text string into a numerical embedding vector for use in semantic search, RAG pipelines, clustering, and AI memory.","exampleAgentPrompt":"Convert this sentence into an embedding vector so I can store it in my vector database for semantic search: 'The quick brown fox jumps over the lazy dog.'","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"Take this paragraph and turn it into an embedding vector so I can index it in my vector DB for retrieval-augmented generation: 'Our return policy allows customers to return any item within 30 days of purchase for a full refund.'"},{"title":"Semantic similarity search for support tickets","prompt":"Embed this customer support query so I can find the most similar past tickets in my system: 'My order never arrived and the tracking page says it was delivered.'"},{"title":"Clustering user feedback by topic","prompt":"Convert this user review into a vector embedding so I can cluster it with similar feedback: 'The onboarding flow was confusing and I couldn't figure out how to connect my bank account.'"}],"resultDescription":"Returns a numerical vector (array of floats) representing the semantic content of the input text, suitable for storage in a vector database, cosine similarity comparisons, clustering algorithms, or as memory in an AI agent pipeline.","failureModes":["Missing required 'input' query parameter returns an error","Empty string input may return a zero vector or error","Excessively long text may be truncated or rejected","Network timeout for high-volume concurrent requests","x402 payment failure if wallet balance is insufficient"],"whenToPreferThis":"Choose this endpoint when you need a lightweight, pay-per-call embedding service at $0.001 USDC per call without managing your own embedding model infrastructure. Ideal for agents doing semantic search, RAG, or clustering tasks that require high-volume embedding at low cost.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T12:30:33.983Z","isFirstParty":false}