{"uid":"cap_gbxXgdLF2HE7zpD8DkJnx","slug":"autopilot-studio-text-embedding-9707baac","name":"Autopilot Studio Text Embedding","description":"AI-made art, prompts, stories and guides — fresh every day.","url":"https://autopilot-studio.kariguetoazul.workers.dev/v1/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"texts":{"type":"array","items":{"type":"string"},"description":"up to 16 texts, 2000 chars each"}}},"responseSchema":{"type":"json","example":{"count":2,"model":"bge-m3","dimensions":1024,"embeddings":[[0.012345,-0.045678],[0.011111,-0.022222]]}},"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_iLq82BoOzvrybi9BquJ1b","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":"Converts up to 16 text strings into high-dimensional vector embeddings using the BGE-M3 model (1024 dimensions).","exampleAgentPrompt":"I need to embed these two texts into vectors for semantic search: 'The quick brown fox jumps over the lazy dog' and 'A fast auburn fox leaps over a sleepy hound' — give me their 1024-dimensional BGE-M3 embeddings.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"Embed these three product descriptions so I can store them in my vector database for semantic search: 'Lightweight running shoes with breathable mesh', 'Heavy-duty hiking boots with ankle support', and 'Casual canvas sneakers for everyday wear'."},{"title":"Semantic similarity scoring","prompt":"I want to check how semantically similar these two customer support tickets are — can you get embeddings for 'My payment failed at checkout' and 'I was charged but the order didn't go through'?"},{"title":"Text clustering for content organization","prompt":"I have a batch of article headlines I need to cluster by topic — please generate vector embeddings for all of these so I can run k-means on them: 'Fed raises interest rates', 'Bitcoin hits new high', 'S&P 500 drops 2%', 'Ethereum upgrade complete'."}],"resultDescription":"A JSON object containing the count of embedded texts, the model name ('bge-m3'), the vector dimensions (1024), and an array of float arrays — one embedding vector per input text. Each embedding is a 1024-dimensional dense vector suitable for cosine similarity, dot-product search, or storage in a vector database.","failureModes":["More than 16 texts submitted — input array exceeds the supported batch size","Individual text exceeds 2000 characters — text is too long and may be truncated or rejected","Empty texts array — no input provided","Payment failure (x402) — insufficient USDC balance or wallet not configured","Network timeout on the Cloudflare Workers edge — request may need retry"],"whenToPreferThis":"Choose this endpoint when you need fast, multilingual, high-quality 1024-dimensional text embeddings from the BGE-M3 model at very low cost ($0.001 per call) via a pay-per-use x402 micropayment model. It supports batches of up to 16 texts per request, making it efficient for small-to-medium embedding jobs. Prefer it over subscription-based embedding APIs when you want no monthly commitment and per-call billing.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T18:38:33.059Z","isFirstParty":false,"canonicalSlug":"autopilot-studio-text-embedding-9707baac"}