{"uid":"cap_UKYSPwzymNtxKAVi7rvMZ","slug":"cryptorisk-api-text-embeddings-jina-embeddings-v3-328cde9f","name":"CryptoRisk API - Text Embeddings (jina-embeddings-v3)","description":"Embeddings: text -> vectors (jina-embeddings-v3). Batch up to 64.","url":"https://cryptorisk-api.vercel.app/api/embed","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/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_Frwp4k0_l_WDsD_wmfhkX","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts text into dense vector embeddings using jina-embeddings-v3, supporting batch processing of up to 64 texts per call.","exampleAgentPrompt":"Convert these 3 product descriptions into vector embeddings using jina-embeddings-v3 so I can index them for semantic search: 'Wireless noise-cancelling headphones', 'Bluetooth portable speaker', 'USB-C wired earbuds'.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"I need to embed these 10 chunks of my knowledge base into vectors so I can store them in Pinecone for retrieval-augmented generation — can you batch them through the jina-embeddings-v3 endpoint?"},{"title":"Semantic similarity scoring","prompt":"I have two sentences — 'The cat sat on the mat' and 'A feline rested on a rug' — can you get embeddings for both so I can compute their cosine similarity?"},{"title":"Pay-per-call embedding without API account","prompt":"I don't want to sign up for Jina AI or OpenAI — just convert this list of 20 customer reviews into vector embeddings right now, paying per call."}],"resultDescription":"An array of dense float vectors (one per input text), each corresponding to a jina-embeddings-v3 embedding of the submitted text. Vectors can be used directly for similarity search, clustering, classification, or indexing into a vector database.","failureModes":["Batch size exceeds 64 texts — request rejected","Empty or missing text input — validation error","Payment not attached or insufficient USDC — 402 Payment Required","Text too long for model context window — truncation or error","Vercel cold start causing occasional latency spike"],"whenToPreferThis":"Choose this endpoint when you need pay-per-call text embeddings using jina-embeddings-v3 with no API key or account registration required, especially for low-volume or sporadic workloads where a subscription is not cost-effective. Ideal for agents operating in x402 micropayment environments that need to embed batches of up to 64 texts on demand.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:34:39.337Z","isFirstParty":false}