{"uid":"cap_8ieFLrdfJPm5yWJlpyiR6","slug":"relaystation-embed-1b22f015","name":"Relaystation Embed","description":"$0.002 per 10 KB. Generate text embeddings, single or batch — pairs with the baton vector index for pay-per-query RAG memory. 1¢ x402 min; remainder auto-credits — relaystation.ai/penny","url":"https://api.relaystation.ai/v1/llm/embed","method":"POST","headers":{},"bodySchema":{"type":"object","$schema":"https://json-schema.org/draft/2020-12/schema","required":["input"],"properties":{"input":{"type":"object","required":["type","method","bodyType","body"],"properties":{"body":{"type":"object","properties":{"input":{"anyOf":[{"type":"string","minLength":1},{"type":"array","items":{"type":"string","minLength":1},"maxItems":100,"minItems":1}]},"normalize":{"type":"boolean"},"dimensions":{"enum":[256,512,1024],"type":"integer"}}},"type":{"type":"string","const":"http"},"method":{"enum":["POST","PUT","PATCH"],"type":"string"},"bodyType":{"enum":["json","form-data","text"],"type":"string"}},"additionalProperties":false},"output":{"type":"object","required":["type"],"properties":{"type":{"type":"string"},"example":{"type":"object"}}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_KAtDw37L7fle1V0op-X-S","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generate single or batch text embeddings at $0.002/10KB, designed to pair with the Baton vector index for pay-per-query RAG memory pipelines.","exampleAgentPrompt":"Embed the following text chunks so I can store them in a vector index for semantic search — here are my 5 paragraphs from the product documentation: [text]. Use batch mode and return the embedding vectors.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"I have a set of 50 support articles I want to chunk and embed so I can do semantic retrieval later — can you batch embed all these text chunks and hand me back the vectors to store in the Baton index?"},{"title":"Semantic similarity comparison","prompt":"Take these two customer feedback strings and embed them both, then I'll compute the cosine similarity myself — string one is 'the onboarding was smooth and intuitive' and string two is 'getting started was really easy'."},{"title":"AI agent memory store","prompt":"My agent needs to remember conversation snippets — embed this text block so I can store it as a memory entry in our vector store: 'User prefers concise bullet-point summaries and works in the Pacific timezone.'"}],"resultDescription":"Returns one or more dense float vector arrays representing the semantic content of the input text, suitable for cosine similarity search, nearest-neighbor lookup, or storage in a compatible vector index such as Baton. Each vector captures the meaning of its corresponding input string.","failureModes":["Input text too large for a single call — need to chunk before submitting","Batch payload exceeds size limits resulting in a 413 error","Malformed JSON body returns a 400 bad request","Insufficient x402 payment balance causes payment rejection","Network timeout on very large batch submissions","Empty string inputs may return zero vectors or errors"],"whenToPreferThis":"Choose this endpoint when you need cost-transparent, pay-per-use text embeddings that integrate natively with the Relaystation/Baton ecosystem for RAG memory. It is especially suited for agentic workflows where embedding costs need to be metered per query via x402 micropayments, and where batch embedding of document chunks is required. Prefer it over managed embedding services when you want itemized billing at $0.002/10KB with no monthly subscription.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:53:09.307Z","isFirstParty":false}