{"uid":"cap_8MNuW8HLaJW6HjVAN_Jja","slug":"text-embedding-api-768-dim-1f89a459","name":"Text Embedding API (768-dim)","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://released-mph-process-lib.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_wsz1BfkhppuSIHYvhNLGH","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 input text or arrays of text into 768-dimensional vector embeddings for semantic similarity, search, and classification tasks.","exampleAgentPrompt":"Can you embed this sentence into a 768-dimensional vector: 'The quick brown fox jumps over the lazy dog'?","exampleUseCases":[{"title":"Semantic search over document corpus","prompt":"Take these 10 product descriptions and embed them all so I can store them in my vector database for semantic search later."},{"title":"Duplicate detection in support tickets","prompt":"I have two customer support messages — embed both of them so I can compute their cosine similarity and check if they're asking the same thing."},{"title":"Clustering user feedback","prompt":"I've got a batch of 50 user survey responses. Can you embed each one so I can run k-means clustering to group similar feedback together?"}],"resultDescription":"Returns a 768-dimensional floating-point vector (or array of vectors for batch input) representing the semantic content of the input text, suitable for similarity comparison, nearest-neighbor search, or downstream ML tasks.","failureModes":["Empty or null input string returns an error","Input array exceeds maximum batch size limit","Network timeout if input text is extremely long","Payment failure via x402 protocol blocks the request","Malformed JSON body returns 400 error"],"whenToPreferThis":"Choose this endpoint when you need fast, affordable 768-dimensional text embeddings via a simple REST call with per-call micropayment pricing ($0.005 USDC). Ideal for agents that need to embed text on-the-fly without managing API keys or subscriptions, especially in agentic pipelines using the x402 payment protocol.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-20T12:34:20.834Z","isFirstParty":false,"canonicalSlug":"text-embedding-api-768-dim-1f89a459"}