{"uid":"cap_g_5sPYUCtLY3_v1o1NrQm","slug":"uxus-finance-text-embedding-api-f59d2c80","name":"uxus.finance Text Embedding API","description":"Text embeddings for AI agents — no API key, no account, no signup; pay per call in USDC on Base. POST JSON { input: string or string[] (max 64 per call) }. Model is jina-embeddings-v3, 1024 dimensions, retrieval.passage task. Returns { model, embeddings (one vector per input), dimensions, count, usage, latency_ms }. For agents building a vector index or doing semantic search without a Jina or OpenAI key.","url":"https://uxus.finance/api/embed","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"input":{"type":"string | string[]","description":"Text to embed, or an array of up to 64 strings."}}},"responseSchema":{"type":"json","example":{"count":1,"model":"jina-embeddings-v3","usage":{"total_tokens":8},"dimensions":1024,"embeddings":[[0.013,-0.021,0.005]],"latency_ms":300}},"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_ON6EIXVmayyhI0DYB_F_y","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":"Generates a 1024-dimensional jina-embeddings-v3 vector for each input string (up to 64 strings per call), returning embeddings with dimension count and token usage.","exampleAgentPrompt":"Embed these 3 product descriptions into jina-embeddings-v3 vectors so I can store them in my vector database: 'Wireless noise-cancelling headphones', 'Bluetooth over-ear headset with ANC', 'Premium audio headphones with active noise reduction'.","exampleUseCases":[{"title":"Semantic search index population","prompt":"Take these 50 FAQ answers and turn them into 1024-dim embeddings so I can index them in my vector store for semantic search retrieval."},{"title":"Duplicate product detection","prompt":"Embed these two product titles — 'Men's Running Sneakers Size 10' and 'Athletic Running Shoes for Men Size 10' — and give me their vectors so I can check how similar they are."},{"title":"RAG document chunking pipeline","prompt":"I have these 20 text chunks from my knowledge base — can you convert each one to a jina-embeddings-v3 vector so I can load them into Pinecone?"}],"resultDescription":"Returns an array of 1024-dimensional float vectors (one per input string), along with the dimension count (1024) and token usage statistics for the batch call.","failureModes":["Batch exceeds 64 strings — request rejected with error","Empty input array or null strings — validation error returned","Input strings too long for model token limit — truncation or error","Payment failure via x402 protocol — 402 response with payment details","Network timeout on large batches — partial or no response"],"whenToPreferThis":"Choose this endpoint when you need jina-embeddings-v3 specifically (1024-dim, strong multilingual and retrieval performance), can batch up to 64 strings per call, and want a pay-per-call model via x402/USDC without subscription overhead. Prefer over OpenAI or Cohere embeddings when you want jina's model quality and the x402 micropayment model fits your stack.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:34:39.336Z","isFirstParty":false}