Venice Embeddings

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.

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.

Minimal request

curl https://api.venice.ai/api/v1/embeddings \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  --compressed \
  -d '{
    "model": "text-embedding-bge-m3",
    "input": "Why is the sky blue?"
  }'

{
  "object": "list",
  "model": "text-embedding-bge-m3",
  "data": [
    { "object": "embedding", "index": 0, "embedding": [0.0023, -0.0093, 0.0158, ...] }
  ],
  "usage": { "prompt_tokens": 8, "total_tokens": 8 }
}

Request schema