{"uid":"cap_GAvwR2psr6xReQWVSlmN8","slug":"text-embedding-api-768-dim-b705adf7","name":"Text Embedding API (768-dim)","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://pearl-valium-stickers-upc.trycloudflare.com/v1/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"data":{"type":"array"},"model":{"type":"string"},"usage":{"type":"object"},"object":{"type":"string"}}},"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":"down","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_x5NCUK4l-4ziiWVxyXLBP","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 strings into 768-dimensional vector embeddings for semantic similarity, search, and downstream ML tasks.","exampleAgentPrompt":"Embed this sentence for me using the local 768-dim model: 'The quick brown fox jumps over the lazy dog' — I need the vector to do a similarity search.","exampleUseCases":[{"title":"Semantic search over documentation","prompt":"Convert this list of FAQ answers into 768-dimensional embeddings so I can do cosine similarity search when users ask questions: ['Returns take 30 days.', 'We ship worldwide.', 'Contact support at help@example.com.']"},{"title":"Clustering customer feedback","prompt":"Embed each of these 50 customer reviews as 768-dim vectors so I can cluster them by topic and find the main themes — here's the list: ['Great product', 'Shipping was slow', ...]"},{"title":"RAG pipeline document encoding","prompt":"I'm building a retrieval-augmented generation pipeline — can you embed this paragraph into a 768-dim vector so I can store it in my vector database? The text is: 'Our refund policy allows customers to return any item within 30 days of purchase.'"}],"resultDescription":"Returns an object containing one or more 768-dimensional float vectors corresponding to the input text(s), along with model name, object type, and token usage statistics. Each embedding is a dense array of 768 floating-point numbers suitable for cosine similarity or dot-product comparisons.","failureModes":["Empty input string returns a zero vector or error","Oversized input exceeding model context window may be truncated or rejected","Transient Cloudflare tunnel downtime causes 502/503 errors","Malformed JSON body returns 400 bad request","Model identifier mismatch returns error if specified model is unavailable"],"whenToPreferThis":"Choose this endpoint when you need 768-dimensional embeddings generated locally (privacy-preserving, no data sent to third-party APIs), particularly for RAG pipelines, semantic search indexes, or clustering tasks. Prefer this over OpenAI or Cohere embeddings when data privacy is critical, when you need consistent offline availability, or when cost per call matters at scale. The 768-dim output is compatible with most standard vector databases (Pinecone, Weaviate, Qdrant).","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-11-04T13:12:32.388Z","isFirstParty":false,"canonicalSlug":"text-embedding-api-768-dim-b705adf7"}