{"uid":"cap_uWnVsbAqbqOeDx3huq0p8","slug":"dev-5090-text-embeddings-3ab67221","name":"dev-5090 Text Embeddings","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://biz-integral-pull-earrings.trycloudflare.com/v1/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"usage":{"type":"object"},"choices":{"type":"array"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.005","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":"$0.005/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.005","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_or36eaETtKYBZ03buh_IF","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.005","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Converts text input into 768-dimensional vector embeddings for semantic similarity, search, and downstream ML tasks","exampleAgentPrompt":"Can you generate a 768-dimensional embedding vector for the sentence 'The quick brown fox jumps over the lazy dog' so I can store it in my vector database?","exampleUseCases":[{"title":"Semantic search index population","prompt":"Embed these 5 product descriptions so I can index them in my vector database for semantic search: 'Wireless noise-cancelling headphones', 'Bluetooth portable speaker', 'USB-C wired earbuds', 'Over-ear studio monitor headphones', 'True wireless sport earbuds'."},{"title":"RAG query embedding","prompt":"I need to embed the user's query 'what are the refund policies for digital products?' into a vector so I can do a nearest-neighbor lookup against my knowledge base."},{"title":"Document similarity comparison","prompt":"Convert these two paragraphs into embeddings so I can compute their cosine similarity and check if they're semantically related: 'Machine learning is a subset of AI that learns from data' and 'Artificial intelligence systems improve performance through experience'."}],"resultDescription":"Returns a 768-dimensional float vector (or array of vectors for batch input) representing the semantic content of the provided text, along with usage metadata indicating token counts or processing statistics.","failureModes":["Empty or missing input field returns an error","Excessively long input text may exceed token limits and return a truncation or overflow error","Malformed JSON body returns a 400 bad request","Service unavailable due to Cloudflare tunnel instability on ephemeral trycloudflare.com domain","Payment not processed correctly via x402 protocol results in 402 response"],"whenToPreferThis":"Choose this endpoint when you need fast, affordable 768-dimensional text embeddings via a pay-per-call x402 micropayment model, especially for ad-hoc or low-volume embedding tasks where spinning up a dedicated embedding service is overkill. Ideal for RAG pipelines, semantic search indexing, or similarity computation where 768-dim vectors are sufficient. Note that the domain is ephemeral (trycloudflare.com), so prefer this for experimental or short-lived workflows rather than production systems requiring high availability.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T06:42:48.337Z","isFirstParty":false,"canonicalSlug":"dev-5090-text-embeddings-3ab67221"}