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.
Install
npx skills add veniceai/skills --skill venice-embeddingsgit clone --depth 1 https://github.com/veniceai/skills.git /tmp/skills
mkdir -p ~/.claude/skills
cp -r /tmp/skills/skills/venice-embeddings ~/.claude/skills/venice-embeddingsPersonal skills live in ~/.claude/skills. Use .claude/skills in a project folder to share a skill with your team.
From the SKILL.md
# 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.
## Minimal request
```bash
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?"
}'
```
```json
{
"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 schemaDetails
- Skill name
venice-embeddings- Repository
- veniceai/skills
- Path
skills/venice-embeddings/SKILL.md- License
- MIT
- Repo updated
- 30 October 2026
- Listed in
- VoltAgent/awesome-agent-skills
FAQ
What does the Venice Embeddings skill do?
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.
How do I install the Venice Embeddings skill?
skills CLI (Claude Code, Cursor, Codex and more): npx skills add veniceai/skills --skill venice-embeddings. Skills go in ~/.claude/skills (personal) or .claude/skills (project), and Claude loads them automatically when a task matches the description.
Is Venice Embeddings popular?
It lives in veniceai/skills, which has 160 GitHub stars and 27 forks, and is included in 1 curated skill list.
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