{"uid":"cap_X2FBXs4-a75k47tnkNo-f","slug":"dev-5090-text-embedding-api-08585c61","name":"dev-5090 Text Embedding API","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://dev-5090-box.tailfb4c1f.ts.net/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_WAqS8qp2n05cmD4d6py7-","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 vector embeddings for one or more input text strings","exampleAgentPrompt":"Turn this sentence into a 768-dimensional embedding vector so I can store it in my vector database: 'The quick brown fox jumps over the lazy dog.'","exampleUseCases":[{"title":"Semantic search over document corpus","prompt":"Embed this user query so I can find the most similar passages in my vector database: 'What are the side effects of ibuprofen?'"},{"title":"Duplicate detection in support tickets","prompt":"Generate embeddings for these two support tickets so I can check if they're about the same issue: ticket A says 'my payment failed at checkout' and ticket B says 'I got an error when trying to purchase'."},{"title":"Batch vectorization for RAG pipeline","prompt":"I need embeddings for all these product descriptions so I can load them into my Pinecone index — here's the list: ['Wireless noise-cancelling headphones', 'Over-ear Bluetooth headset', 'USB-C wired earbuds']."}],"resultDescription":"Returns a 768-dimensional floating-point vector (or array of vectors for batch input) representing the semantic content of the input text, along with token usage metadata. The embeddings can be used directly for cosine similarity, nearest-neighbor search, clustering, or as features in downstream ML tasks.","failureModes":["Empty or missing 'input' field returns a validation error","Input text exceeding model context window may be truncated or rejected","Network unreachability of the Tailscale private host returns a connection timeout","Payment of $0.005 USDC not sent or rejected causes HTTP 402 response","Batch array with too many strings may cause OOM on the RTX 5090 GPU"],"whenToPreferThis":"Choose this endpoint when you need 768-dimensional dense embeddings from a self-hosted, uncensored model on private infrastructure (Tailscale network) and are comfortable with per-call micropayments via x402. Prefer this over hosted OpenAI/Cohere embeddings when privacy, censorship resistance, or cost predictability at small scale matters. Less suitable for high-throughput production workloads requiring SLA guarantees.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-12T12:30:04.421Z","isFirstParty":false,"canonicalSlug":"dev-5090-text-embedding-api-08585c61"}