{"uid":"cap_z7gWIURcd6SKck4S5L8L-","slug":"sitecheck-api-text-embeddings-2500a025","name":"SiteCheck API — Text Embeddings","description":"Pay-per-call tools for AI agents: image generation, speech-to-text, text-to-speech, embeddings, LLM chat, website audits and contact enrichment. Plus prediction-market search, briefs, quotes and unsigned buy transactions, powered by Panta. Payment: x402, USDC on Base, Solana or Arc. No signup, no API key.","url":"https://api.sitecheck-api.workers.dev/api/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"text":{"description":"A string or an array of up to 100 strings"}}},"responseSchema":{"type":"json","example":{"model":"@cf/baai/bge-m3","dimensions":1024,"embeddings":[[0.012,-0.034]]}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.001","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":"$0.001/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.001","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_1mcVgtuRpFNsVkrnTmhUv","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.001","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 one or more text strings using the BAAI BGE-M3 model, returning 1024-dimensional float arrays","exampleAgentPrompt":"Can you generate text embeddings for these three product descriptions so I can store them in my vector database for semantic search: 'Wireless noise-cancelling headphones', 'Bluetooth over-ear audio with ANC', 'Premium sound isolation earphones'?","exampleUseCases":[{"title":"Semantic search index population","prompt":"I have a list of 50 customer support FAQ answers — can you generate embeddings for all of them so I can index them in my vector database for semantic search?"},{"title":"Duplicate content detection","prompt":"Can you embed these two product descriptions and compare their vectors to tell me how similar they are? First: 'Stainless steel insulated water bottle 32oz', second: 'Double-wall vacuum thermos 32 fluid ounces'."},{"title":"RAG document chunking pipeline","prompt":"I'm building a RAG pipeline — please generate a 1024-dimensional embedding for this paragraph so I can store it alongside its source text: 'Our return policy allows customers to return items within 30 days of purchase for a full refund.'"}],"resultDescription":"Returns a JSON object containing the model name ('@cf/baai/bge-m3'), the number of dimensions (1024), and an 'embeddings' array — one float array per input string, each containing 1024 floating-point values representing the semantic content of that text.","failureModes":["Input exceeds 100 strings in the array — API may reject or truncate the batch","Text string is too long for the model's token limit — may produce truncated or degraded embeddings","Payment via x402/USDC on Base fails or is insufficient — request rejected before processing","Empty string or null input — may return zero vectors or an error","Network timeout on Cloudflare Workers edge — transient 5xx error"],"whenToPreferThis":"Choose this endpoint when you need pay-per-call text embeddings with no signup, no API key, and instant access via x402 micropayments on Base. It is ideal for agents that need to embed text on demand in small batches (up to 100 strings) without managing API credentials. The BGE-M3 model produces high-quality multilingual 1024-dimensional embeddings suitable for semantic search, RAG, and similarity tasks. Prefer it over OpenAI or Cohere embedding APIs when you want keyless, per-call billing and are already operating in a Web3/x402 payment context.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-02T01:18:15.471Z","isFirstParty":false,"canonicalSlug":"sitecheck-api-text-embeddings-2500a025"}