{"uid":"cap_kT9Lkh0598GztnU9W14IO","slug":"dev-5090-text-embeddings-c63ecc4f","name":"dev-5090 Text Embeddings","description":"Text embeddings (768-dim). Send {\"input\": \"text\"} or {\"input\": [\"a\",\"b\"]}.","url":"https://linked-aimed-reproduced-changing.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_XR0vIwV7e6SbAt4U-13J-","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 dense vector embeddings for semantic similarity, search, and ML tasks.","exampleAgentPrompt":"Turn this sentence into a 768-dimensional embedding vector: 'The quick brown fox jumps over the lazy dog' — I need it for a semantic similarity search pipeline.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"Embed this paragraph for me so I can store it in my Pinecone index: 'Photosynthesis is the process by which plants convert sunlight into chemical energy stored as glucose.'"},{"title":"Semantic similarity between two sentences","prompt":"Generate embeddings for both of these sentences so I can measure how similar they are: 'Dogs make great pets' and 'Canines are wonderful companions.'"},{"title":"Batch encoding for clustering","prompt":"I have a list of customer feedback snippets I need to cluster — can you embed all of these for me: ['shipping was late', 'product quality is excellent', 'packaging was damaged', 'fast delivery and great service']?"}],"resultDescription":"Returns a JSON object containing a 768-dimensional float vector (or array of vectors for batch input) representing the semantic content of the input text, along with usage metadata such as token counts.","failureModes":["Empty or missing 'input' field returns an error","Oversized input text may exceed token limits and return a 4xx error","Service may be temporarily unavailable if the Cloudflare tunnel is down","Batch input with too many strings may hit payload size limits","Malformed JSON body returns a 400 error"],"whenToPreferThis":"Choose this endpoint when you need fast, low-cost 768-dimensional text embeddings without needing OpenAI or Cohere accounts — particularly useful for prototyping RAG systems, semantic search, or clustering pipelines where cost per call matters and you can tolerate a tunneled/ephemeral infrastructure.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T06:33:34.129Z","isFirstParty":false,"canonicalSlug":"dev-5090-text-embeddings-c63ecc4f"}