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dt0ur.online Text Embedding API
x402
dt0ur.online Text Embedding API is a paid API for AI agents from dt0ur.online, paid per call via x402, $0.05/call, status down (last checked 2026-10-03).
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.
Price / call$0.05
Success—
StatusDown
How this service works
Generates 384-dimensional normalized dense vector embeddings from input text using local all-MiniLM-L6-v2 transformer models for RAG indexing.
Output
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.
Example use cases
RAG pipeline document indexing
“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.'”
Semantic similarity search query
“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?”
Clustering text by topic
“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.'”
Technical details
Endpoint
POSThttps://dt0ur.online/api/inference/embed?utm_source=zero.xyzWhen to prefer
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.
Side effects
- 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
Tags
x402
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