{"uid":"cap_89mpUNlVqvqHqvMXYb0ZR","slug":"text-embedding-api-768-dim-1a54c097","name":"Text Embedding API (768-dim)","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://river-guestbook-newark-fails.trycloudflare.com/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_jIeOngFdQIDBGyPX6l3fq","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":"Converts text input (single string or batch) into 768-dimensional vector embeddings for semantic tasks","exampleAgentPrompt":"Turn this sentence into a 768-dimensional embedding vector so I can compare it semantically against my document database: 'The quick brown fox jumps over the lazy dog.'","exampleUseCases":[{"title":"Semantic search over knowledge base","prompt":"Embed this user query so I can find the most relevant documents in my vector database: 'What are the side effects of ibuprofen?'"},{"title":"Sentence similarity scoring","prompt":"Get embeddings for both of these sentences so I can measure how similar they are: 'How do I cancel my subscription?' and 'Steps to unsubscribe from a plan.'"},{"title":"Batch vectorization for RAG pipeline","prompt":"Embed all of these product descriptions at once so I can index them for retrieval-augmented generation: ['Wireless noise-cancelling headphones', 'Bluetooth over-ear headset with mic', 'Wired studio monitor headphones']."}],"resultDescription":"Returns a JSON object containing a 'choices' array with the embedding vector(s) (768-dimensional float arrays) for each input text, along with a 'usage' object reporting token counts consumed.","failureModes":["Empty or missing 'input' field returns an error","Input text exceeding token limit may be truncated or rejected","Malformed JSON body returns 400-level error","Temporary Cloudflare tunnel unavailability returns 5xx or connection timeout","Payment failure or insufficient USDC balance blocks the request"],"whenToPreferThis":"Choose this endpoint when you need lightweight, fast 768-dimensional text embeddings for semantic search, clustering, or similarity tasks, especially in agentic pipelines that support x402 micropayment authentication. The 768-dim output is compatible with many standard vector stores (Pinecone, Weaviate, Qdrant). Avoid if you need higher-dimensional embeddings (e.g. 1536-dim OpenAI style) or if you require a guaranteed SLA from a named commercial provider.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-16T12:36:11.733Z","isFirstParty":false,"canonicalSlug":"text-embedding-api-768-dim-1a54c097"}