{"uid":"cap_ehxWRd2IaZkhw_brtAIdE","slug":"jarvisclaw-embeddings-api-195b7d9f","name":"JarvisClaw Embeddings API","description":"AI API Gateway with smart routing, pay per call via x402. OpenAI-compatible. Settled in USDC on Base & Solana.","url":"https://api.jarvisclaw.ai/v1/embeddings","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.045","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":"$0.045/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.045","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.045","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_FVwu2Curj3cGwkFnvihtq","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.045","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates vector embeddings from text using AI models via a pay-per-call OpenAI-compatible gateway settled in USDC","exampleAgentPrompt":"Using JarvisClaw, generate an embedding for the text 'The quick brown fox jumps over the lazy dog' with the gpt-4.1-nano model — I'll pay per call in USDC.","exampleUseCases":[{"title":"Building a semantic search engine","prompt":"I need to embed all my product descriptions so customers can search by meaning instead of keywords. Can you generate embeddings for each product using JarvisClaw's gpt-4.1-nano model and pay per call in USDC?"},{"title":"Indexing documents for RAG retrieval","prompt":"I'm setting up a knowledge base for customer support. Help me convert our FAQ documents into embeddings using JarvisClaw so I can pull the most relevant answers based on semantic similarity without managing API subscriptions."},{"title":"Finding similar user queries in real-time","prompt":"Our agent needs to cluster incoming support tickets by meaning to route them efficiently. Can you embed each ticket using JarvisClaw's trustless pay-per-call model so we can find similar issues and batch them together?"}],"resultDescription":"A numerical vector (embedding) representing the semantic meaning of the input text, returned in OpenAI-compatible format, suitable for downstream similarity search, clustering, or RAG indexing.","failureModes":["Payment not included or insufficient USDC — returns 402 Payment Required","Invalid or unsupported model ID — returns 400 Bad Request","Messages array malformed or missing content — returns 422 Unprocessable Entity","Rate limit exceeded — returns 429 Too Many Requests","Upstream model provider unavailable — returns 502 Bad Gateway"],"whenToPreferThis":"Choose this endpoint when you need OpenAI-compatible embeddings with no subscription or API key setup, paying only per call in USDC on Base or Solana via x402 protocol — ideal for agentic workflows requiring trustless, metered AI access.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T07:07:04.113Z","isFirstParty":false}