{"uid":"cap_wH6vZEOojQhaZV0WnPwN6","slug":"dt0ur-online-text-embedding-api-fc6b270b","name":"dt0ur.online Text Embedding API","description":"Generates 384-dimensional normalized dense vector embeddings from input text using local all-MiniLM-L6-v2 transformer models for RAG indexing.","url":"https://dt0ur.online/api/inference/embed?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":null,"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.05","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":"$0.05/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.05","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_UubMEvMwOJRmd4z71dlht","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.05","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Generates 384-dimensional normalized dense vector embeddings from input text using the all-MiniLM-L6-v2 transformer model, suitable for RAG indexing and semantic search.","exampleAgentPrompt":"Can you embed this text into a dense vector for me: 'The mitochondria is the powerhouse of the cell' — I need a 384-dimensional normalized embedding to index it in my RAG pipeline.","exampleUseCases":[{"title":"RAG pipeline document indexing","prompt":"I have a new batch of support articles I want to index into my vector database — can you generate embeddings for this paragraph: 'To reset your password, click the forgot password link on the login page and follow the email instructions.'"},{"title":"Semantic similarity search query","prompt":"Before I run a nearest-neighbor lookup in my vector store, I need to embed the user's query: 'What are the best practices for securing cloud infrastructure?' — can you convert it to a 384-dim vector?"},{"title":"Clustering text by topic","prompt":"I'm grouping customer feedback by topic and need to vectorize each review to compare them — can you embed this one: 'The onboarding flow was confusing but customer support was very responsive and helpful.'"}],"resultDescription":"Returns a 384-dimensional array of normalized floating-point values representing the semantic embedding of the input text, generated by the all-MiniLM-L6-v2 model. The vector is ready for insertion into vector databases (e.g. Pinecone, Weaviate, pgvector) or cosine similarity comparisons.","failureModes":["Empty or missing 'text' field returns a validation error","Extremely long input text may be truncated or cause an error if it exceeds model token limits (typically 256-512 tokens for MiniLM)","Network latency or model cold-start may cause timeouts","Payment failure (x402) if USDC balance is insufficient","Non-string input types in the 'text' field may return a 400 error"],"whenToPreferThis":"Choose this endpoint when you need fast, lightweight 384-dimensional sentence embeddings using the all-MiniLM-L6-v2 model for RAG indexing, semantic search, or clustering tasks. Prefer it over larger embedding models (e.g. text-embedding-ada-002) when latency and cost matter more than maximum embedding dimensionality, and when MiniLM-quality representations are sufficient for your retrieval task.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-03T07:26:18.150Z","isFirstParty":false,"canonicalSlug":"dt0ur-online-text-embedding-api-fc6b270b"}