{"uid":"cap_VVx0yeSLm7XUXtSdGSoCP","slug":"dev-5090-text-embedding-api-2fd08859","name":"dev-5090 Text Embedding API","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://over-arabia-specifically-luxury.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_lYiBcJaeo6CYG0ajEfH9u","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":"Generates 768-dimensional text embeddings for single strings or batches of strings","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 all these product descriptions for me so I can store them in my Pinecone index for semantic search: ['Wireless noise-cancelling headphones', 'Bluetooth portable speaker', 'USB-C charging cable']."},{"title":"Sentence similarity comparison","prompt":"I need to compare how similar these two sentences are — can you get embeddings for 'machine learning is transforming healthcare' and 'AI is revolutionizing medicine' so I can compute cosine similarity?"},{"title":"RAG document chunking pipeline","prompt":"Vectorize this paragraph for me so I can add it to my retrieval-augmented generation knowledge base: 'Photosynthesis is the process by which plants convert sunlight into chemical energy stored as glucose.'"}],"resultDescription":"Returns an object with an array of 768-dimensional float vectors (one per input string), along with token usage statistics. Each embedding captures the semantic meaning of the input text in a dense numerical representation suitable for similarity search, clustering, or downstream ML tasks.","failureModes":["Empty input string returns an error or zero vector","Extremely long inputs may be truncated or rejected","Batch inputs exceeding token limits may fail","Service unavailability due to ephemeral Cloudflare tunnel going offline","Malformed JSON body returns a 400 error","Rate limiting or quota exhaustion from the x402 payment layer"],"whenToPreferThis":"Choose this endpoint when you need fast, affordable 768-dimensional text embeddings without a commercial API account. It is well-suited for prototyping semantic search pipelines, populating vector databases, or batch-embedding small corpora at low cost ($0.005/call). Prefer it over OpenAI or Cohere embedding APIs when you want a pay-per-call x402 micropayment model with no subscription required. The 768-dim output is compatible with many standard vector stores and similarity libraries.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T12:35:41.339Z","isFirstParty":false,"canonicalSlug":"dev-5090-text-embedding-api-2fd08859"}