Individual skill
venice-embeddings
Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), text-only input (token arrays rejected), per-input and batch limits, per-model dimensions/privacy, the text-embedding-ada-002 alias, API-key privacy gating, OpenAI/LangChain compatibility, response compression (gzip/br), and practical usage for retrieval, clustering, and RAG.
skills/venice-embeddings/SKILL.md
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# Venice Embeddings
`POST /api/v1/embeddings` returns vector embeddings for strings. It's OpenAI-compatible: request and response match `https://api.openai.com/v1/embeddings` closely enough that the OpenAI SDK works with `baseURL: "https://api.venice.ai/api/v1"`, with one exception: **token-ID arrays are not accepted** (see below).
Auth: Bearer API key or x402 wallet (`SIGN-IN-WITH-X`) — see [`venice-auth`](../venice-auth/SKILL.md).
## Use when
- You're building retrieval / RAG / similarity search.
- You need text clustering, classification, deduplication, or reranking features.
- You want embeddings from a model Venice runs as **Private** (most of the catalog) rather than an anonymized third-party one — check `model_spec.privacy` on each model.
Text-only: `input` must be a string or an array of strings. For images, run them through a vision chat model and embed the description.
