# venice-embeddings

Guides AI agents through querying the Venice POST /api/v1/embeddings endpoint, handling strict schemas, and batching texts properly.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · ★ 160
- Author: veniceai
- License: MIT · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/veniceai/skills/tree/HEAD/skills/venice-embeddings/SKILL.md
- Updated: 2026-10-23
- HTML: https://affpapa.org/en/nevibecode/skills/venice-embeddings

## What it does

This skill instructs coding agents on requesting vector embeddings from Venice. The endpoint matches OpenAI's format at `https://api.venice.ai/api/v1`, allowing standard SDK clients to run without custom wrappers. Venice enforces strict payload validation; unknown keys such as `input_type` or `truncate` trigger an immediate `400` status.

Token-ID arrays are rejected by the API. If you use LangChain's `OpenAIEmbeddings`, you must set `check_embedding_ctx_length=False` because it tokenizes text into numbers beforehand. Requests accept up to 2048 strings per batch, with each item capped at 8192 estimated tokens.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add veniceai/skills --skill venice-embeddings -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/venice-embeddings/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/venice-embeddings/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add veniceai/skills --skill venice-embeddings -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/venice-embeddings/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add veniceai/skills --skill venice-embeddings -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/venice-embeddings/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install veniceai/skills venice-embeddings
```

2. **Manually: put the files in place**
- File: `.github/skills/venice-embeddings/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add veniceai/skills --skill venice-embeddings -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/veniceai/skills --path skills/venice-embeddings
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/venice-embeddings/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add veniceai/skills --skill venice-embeddings -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/veniceai/skills/tree/HEAD/skills/venice-embeddings/SKILL.md

## When to use it
- generate vector embeddings for RAG and search pipelines
- configure OpenAI SDK and LangChain to hit Venice endpoints
- batch text embedding requests up to 2048 items safely

## Example prompts
- Write a TypeScript batch embedding helper for Venice using text-embedding-bge-m3.
- Configure LangChain OpenAIEmbeddings for Venice without triggering 400 token array errors.
- Draft a curl command to call Venice embeddings with base64 encoding and compression.

## Source text · SKILL.md · MIT

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: d39c0d6ea70ce39d.

BEGIN THIRD-PARTY FILE d39c0d6ea70ce39d
````text
---
name: venice-embeddings
description: 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.
---

# 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 schema

The body is **strict** — unknown top-level fields (e.g. `input_type`, `task`, `truncate`) are rejected with `400`.

| Field | Type | Notes |
|---|---|---|
| `model` | string | **Required.** Model ID from `GET /models?type=embedding`. `text-embedding-ada-002` is accepted as an alias for `text-embedding-bge-m3` (the response `model` then reads `text-embedding-bge-m3`). |
| `input` | string \| string[] | **Required.** A non-empty string, or an array of 1–2048 strings. Token-ID arrays (`number[]` / `number[][]`) are rejected with `400` — see Gotchas. |
| `encoding_format` | `"float"` \| `"base64"` | Default `"float"`. `"base64"` returns each vector as a base64-encoded string — a much smaller payload; decode client-side. |
| `dimensions` | integer ≥ 1 | Optional. Requested output size. Only honoured by models whose `model_spec.supportsCustomDimensions` is `true`; it is forwarded as-is otherwise, and the provider may ignore or reject it. |
| `user` | string | Accepted for OpenAI compatibility; not used for inference, but it does split the error budget per value (see [`venice-errors`](../venice-errors/SKILL.md#error-budget)). |

### Input limits
````
END THIRD-PARTY FILE d39c0d6ea70ce39d

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First 3017 of 9465 bytes. © veniceai · MIT · https://github.com/veniceai/skills/tree/HEAD/skills/venice-embeddings/SKILL.md

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