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Vektorwerk Text Embedding
x402
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.
Price / call$0.001
Success—
StatusUnknown
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.
Example use cases
RAG document ingestion pipeline
“Convert these 10 knowledge base paragraphs into embeddings using the bge_m3 model so I can index them in my vector store for retrieval-augmented generation.”
Semantic similarity scoring setup
“I need to embed these 20 customer support tickets using the nomic model so I can cluster them by topic — can you turn them all into vectors at once?”
Privacy-safe local embedding
“Embed this batch of 8 internal confidential memos into vectors using the nomic model — I need this done locally without sending anything to a third-party API.”
Technical details
Endpoint
POSThttps://vektor.halowerk.com/embedWhen to prefer
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.
Side effects
- 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
Input
Schema · Request body
| Field | Type | Description |
|---|---|---|
| text | string | |
| model | string | |
| texts | array |
Tags
x402
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