{"uid":"cap_un-x2gGLE-i1lskqGxmYl","slug":"nexusapi-ai-embeddings-ffe5d01f","name":"NexusAPI AI Embeddings","description":"Crypto, AI, Social Media, Finance, Weather, Tools & more. Pay per call in USDC on Base. No API keys.","url":"https://nexus-api-virid.vercel.app/api/ai/embeddings","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"query":{"type":"string","description":"Search query or input parameter"}}},"responseSchema":{"type":"object","description":"API response data"},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.021615","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":"$0.021615/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.021615","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.021615","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_A8tZglpLJkKOEC726Uoeq","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.021615","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates vector embeddings for a given text query or input string, enabling semantic search and similarity computation.","exampleAgentPrompt":"Generate an embedding vector for the text 'What are the best practices for machine learning model deployment?' so I can use it for semantic similarity search.","exampleUseCases":[{"title":"RAG pipeline query embedding","prompt":"I need to embed this user query — 'how do I cancel my subscription?' — into a vector so I can search my knowledge base for relevant documents."},{"title":"Semantic similarity for product search","prompt":"Turn the phrase 'wireless noise-cancelling headphones for travel' into an embedding vector so I can find the most similar products in my catalog."},{"title":"Document clustering and indexing","prompt":"Generate an embedding for this paragraph: 'Climate change is accelerating glacier melt worldwide.' I want to index it in my vector database for later retrieval."}],"resultDescription":"Returns a numerical vector (array of floats) representing the semantic meaning of the input text, suitable for use in similarity search, clustering, or retrieval-augmented generation pipelines.","failureModes":["Empty or missing query string returns an error or malformed response","Very long input text may be truncated or rejected","Payment failure (insufficient USDC balance) results in HTTP 402","Ambiguous or unsupported input types may return a generic error","Rate limiting or server overload may cause timeouts"],"whenToPreferThis":"Choose this endpoint when you need a pay-per-call, no-API-key embedding service that accepts USDC on Base, especially useful in agent pipelines that handle micro-payments natively via x402. Prefer it over managed embedding APIs when you want frictionless onboarding without credential management.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:49:56.226Z","isFirstParty":false}