# Vektorwerk Text Embedding

> Vektorwerk Text Embedding is a paid API for AI agents from vektor.halowerk.com, paid per call via x402, $0.001/call, status unknown (last checked 2026-09-14).

Embeds up to 32 texts into vector representations using a local Ollama runtime with nomic or bge_m3 models, returning the model name and embedding dimensions.

## Facts

- Endpoint: POST https://vektor.halowerk.com/embed
- Price: $0.001/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-14
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/vektorwerk-text-embedding-e33616df
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_SnpOUC43FuzbYc8paIjtH

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 vektorwerk-text-embedding-e33616df -d '<json body>'
```

Example prompt: Embed these 5 product descriptions into vectors using the nomic model so I can store them in my vector database: 'Wireless headphones with noise cancellation', 'Bluetooth speaker for outdoor use', 'USB-C charging cable 6ft', 'Laptop stand adjustable height', 'Mechanical keyboard with RGB lighting'.

## When to prefer this

Prefer this endpoint when you need local, privacy-preserving text embeddings without calling paid third-party APIs like OpenAI or Cohere. Ideal for batch embedding up to 32 texts per call with a choice between nomic (general purpose) and bge-m3 (multilingual) models. Best suited for RAG pipelines, semantic search indexing, and clustering tasks where data privacy or cost is a concern.

## Known failure modes

- More than 32 texts submitted — maxItems constraint violated, request rejected
- Invalid model name provided — only 'nomic' and 'bge_m3' are accepted
- Empty texts array or minItems not met — at least 1 text required
- Local Ollama runtime unavailable — service may be temporarily down
- Text inputs too long for the selected model's context window — truncation or error possible

## How this service works

Embeds up to 32 texts on the local Ollama runtime. Returns explicit model and dimensions; no paid third-party API is called.

## Output

Returns embedding vectors for each input text along with the model name used and the dimensionality of the embeddings, enabling downstream vector database storage, similarity search, or clustering.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "text": {
   "type": "string"
  },
  "model": {
   "enum": [
    "nomic",
    "bge_m3"
   ],
   "type": "string",
   "default": "nomic"
  },
  "texts": {
   "type": "array",
   "items": {
    "type": "string"
   },
   "maxItems": 32,
   "minItems": 1
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/vektorwerk-text-embedding-e33616df/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from vektor.halowerk.com](https://www.zero.xyz/host/vektor.halowerk.com/llms.txt)