{"uid":"cap_2t-EIOFJSA0w7k6mItSJD","slug":"inference402-text-embeddings-1f83d1fb","name":"Inference402 Text Embeddings","description":"Pay-per-call AI inference for agents: chat (Llama 3.3 70B / 3.1 8B), structured JSON, summarize, translate (incl. Egyptian Arabic), extract, classify, sentiment, keywords, rewrite, embeddings, moderation, vision and speech-to-text. USDC on Base mainnet over x402. No API key, no account.","url":"https://inference402.worlddata402.workers.dev/v1/embed?utm_source=zero.xyz","method":"GET","headers":{},"bodySchema":{"type":"object","properties":{"texts":{"type":"array","items":{"type":"string","maxLength":2000},"maxItems":32,"minItems":1}}},"responseSchema":{"type":"json","example":{"count":2,"model":"@cf/baai/bge-m3","dimensions":1024,"embeddings":[[0.012,-0.034,"..."],[0.011,-0.03,"..."]]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","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":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ku0OAxcP8r1fOk-4TkVJ9","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates dense vector embeddings for up to 32 text strings using BGE-M3 (1024 dimensions), paid per-call with USDC on Base via x402.","exampleAgentPrompt":"Generate BGE-M3 embeddings for these two sentences: 'The quick brown fox jumps over the lazy dog' and 'Machine learning transforms industries' — I need the 1024-dimensional vectors to store in my vector database.","exampleUseCases":[{"title":"RAG pipeline document ingestion","prompt":"Embed these 5 product descriptions so I can store them in my Pinecone index for semantic search: 'Wireless noise-cancelling headphones with 30hr battery', 'Ergonomic standing desk with height memory', 'Mechanical keyboard with RGB backlight', 'Ultrawide curved monitor 34 inch', 'USB-C hub with 7 ports'."},{"title":"Semantic similarity scoring","prompt":"I need vector embeddings for both of these sentences so I can compute their cosine similarity: 'How do I reset my password?' and 'Steps to recover account access' — convert them to BGE-M3 vectors."},{"title":"Customer feedback clustering","prompt":"Turn these three customer reviews into embeddings so I can cluster them by topic: 'Shipping was incredibly fast', 'The product broke after one week', 'Customer support resolved my issue quickly'."}],"resultDescription":"Returns a JSON object with the embedding count, model name ('cf/baai/bge-m3'), number of dimensions (1024), and an array of float arrays — one 1024-dimensional embedding vector per input text string.","failureModes":["Payment failure: 402 returned if USDC payment on Base is missing or insufficient","Input too large: array exceeds 32 items or individual text exceeds 2000 characters","Empty input: minItems:1 violated returns validation error","Network/worker timeout: Cloudflare Worker cold-start or inference latency spike","Invalid JSON body: malformed texts array causes 400 error"],"whenToPreferThis":"Choose this endpoint when you need keywordless semantic embeddings (BGE-M3, 1024-dim) without API keys or account setup — ideal for agents that need pay-as-you-go vector generation via x402/USDC on Base. Prefer over OpenAI or Cohere embeddings when you want accountless micropayment access, multilingual coverage (BGE-M3 supports 100+ languages), or batch embedding up to 32 texts per call.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:37:03.517Z","isFirstParty":false,"canonicalSlug":"inference402-text-embeddings-1f83d1fb"}