{"uid":"cap_qQy3HDRCgz2xPJqNRFQSV","slug":"gedx402-text-embedding-api-33c76033","name":"GEDX402 Text Embedding API","description":"Generate text embeddings with BGE Base EN V1.5 (@cf/baai/bge-base-en-v1.5). POST text or texts[] for semantic search, RAG retrieval, and clustering; billed by input character volume.","url":"https://embed.gedx402.com/v1/embed","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"text":{"type":"string"},"texts":{"type":"array","items":{"type":"string"}}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.0073","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":"$0.0073/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.0073","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.0073","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_je6baUYO2wZyz01WQSWcZ","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.0073","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates vector embeddings for text strings (single or batch) using Workers AI, paid per-call via USDC cryptocurrency with no API key required","exampleAgentPrompt":"Embed this sentence into a vector using GEDX402's Workers AI — no API key, just pay with USDC on Base: 'The quick brown fox jumps over the lazy dog'","exampleUseCases":[{"title":"Build semantic search for product catalog","prompt":"I need to embed all our product descriptions so customers can search by meaning instead of keywords. Can you vectorize them using GEDX402 and store the embeddings for similarity matching?"},{"title":"Power RAG retrieval without API signup","prompt":"Set up embeddings for our knowledge base documents using GEDX402 so our agent can find relevant passages. Pay with USDC on Base and cache the vectors for repeated queries."},{"title":"Batch vectorize customer support tickets","prompt":"Embed our last 500 support tickets into vectors using GEDX402 so we can cluster similar issues and identify common problems. Just use crypto payment, no API key needed."}],"resultDescription":"Returns a JSON object with a 'data' array where each element contains an 'index' (position in batch) and 'embedding' (array of floats representing the vector for that input string)","failureModes":["Payment not received or insufficient USDC — 402 Payment Required response","Invalid or missing 'text' and 'texts' fields — 400 Bad Request","Text input too long for model context window","Network timeout on Cloudflare Workers edge","Unsupported blockchain network for payment"],"whenToPreferThis":"Choose this endpoint when you need text embeddings without traditional API key registration, prefer paying per-call with USDC on Base, Polygon, Arbitrum, World, or Solana, or want to use Cloudflare Workers AI infrastructure via the x402 payment protocol in a crypto-native agent workflow.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T18:30:53.706Z","isFirstParty":false}