# dt0ur.online Text Embedding API

> 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.

## Facts

- Endpoint: POST https://dt0ur.online/api/inference/embed?utm_source=zero.xyz
- Price: $0.05/call
- Payment: x402
- Status: down
- Last checked: 2026-10-03
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/dt0ur-online-text-embedding-api-fc6b270b
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_wH6vZEOojQhaZV0WnPwN6

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability dt0ur-online-text-embedding-api-fc6b270b -d '<json body>'
```

Example prompt: 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.

## When to prefer this

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.

## Known failure modes

- 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

## 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.

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/dt0ur-online-text-embedding-api-fc6b270b/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from dt0ur.online](https://www.zero.xyz/host/dt0ur.online/llms.txt)