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venice-embeddings

Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenAI compatibility, response compression (gzip/br), and practical usage for retrieval, clustering, and RAG.

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Venice Embeddings

POST /api/v1/embeddings returns vector embeddings for strings. It's OpenAI-compatible: the request and response match https://api.openai.com/v1/embeddings closely enough that the OpenAI SDK works out of the box with baseURL: "https://api.venice.ai/api/v1".

Use when

You're building retrieval / RAG / similarity search.
You need text clustering, classification, deduplication, or reranking.
You want Venice's "no-training, no-retention" stance on inference inputs — embeddings are generated and returned; the API does not publish E2EE semantics on /embeddings the way it does on selected chat models.

Text-only. For image/multimodal signals, either run images through a vision chat model and embed the description, or pick a multimodal-capable embedding model from GET /models?type=embedding (the catalog changes; inspect model_spec on each row).

Minimal request

curl https://api.venice.ai/api/v1/embeddings \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Accept-Encoding: gzip, br" \
  -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

Field	Type	Notes
`model`	string	Required. Model ID from `GET /models?type=embedding`.
`input`	string \| string[] \| number[] \| number[][]	Required. Single string, array of strings (≤ 2048 entries), or pre-tokenized arrays.
`encoding_format`	`"float"` \| `"base64"`	Default `"float"`. Use `"base64"` for ~4× payload shrinkage; decode client-side.
`dimensions`	integer	Optional. Truncate output dimensions. Only meaningful when the model's `model_spec.supportsCustomDimensions === true` — behavior on non-supporting models is model-dependent; test a small call before relying on it.
`user`	string	Accepted for OpenAI compat. Discarded by Venice.

input max tokens per string is capped at the model's model_spec.maxInputTokens (typically 8192). Batch arrays are capped at 2048 items. Venice returns one embedding per element, in order, with matching index.

Response headers & compression

Request Accept-Encoding: gzip, br. The response will include Content-Encoding accordingly. For long batches this matters — vectors are large.

For x402 auth, X-Balance-Remaining reports your remaining USDC credits.

Using the OpenAI SDK

import OpenAI from 'openai'

const client = new OpenAI({
  apiKey: process.env.VENICE_API_KEY,
  baseURL: 'https://api.venice.ai/api/v1',
})

const res = await client.embeddings.create({
  model: 'text-embedding-bge-m3',
  input: ['first doc', 'second doc'],
})

const vec0 = res.data[0].embedding

Batch-embedding pattern

async function embedBatch(texts: string[], batchSize = 64) {
  const out: number[][] = []
  for (let i = 0; i < texts.length; i += batchSize) {
    const slice = texts.slice(i, i + batchSize)
    const res = await client.embeddings.create({
      model: 'text-embedding-bge-m3',
      input: slice,
      encoding_format: 'float',
    })
    for (const row of res.data) out[i + row.index] = row.embedding
  }
  return out
}

Keep batches ≤ model context limit total tokens.
On 429, back off exponentially and halve the batch — see venice-errors.

Choosing a model

Query GET /models?type=embedding for the current catalog. Each entry exposes:

model_spec.embeddingDimensions — native output dimension (e.g. 1024 for BGE-M3).
model_spec.maxInputTokens — max tokens per input string.
model_spec.supportsCustomDimensions — whether dimensions can truncate the output.
model_spec.pricing.input.usd / .diem — cost per million input tokens.

Built-in options include text-embedding-bge-m3, text-embedding-bge-en-icl, text-embedding-qwen3-8b, text-embedding-qwen3-0-6b, text-embedding-multilingual-e5-large-instruct, text-embedding-3-small, text-embedding-3-large, gemini-embedding-2-preview, text-embedding-nemotron-embed-vl-1b-v2.

Always pin the model ID — cosine distances are not comparable across different embedding models.

Error handling

Code	Meaning
`400`	Validation error. Check `details` in the response for the exact field.
`401`	Auth / Pro-only model.
`402`	Insufficient balance. Bearer → `INSUFFICIENT_BALANCE`. x402 → structured `PAYMENT_REQUIRED`.
`415`	Wrong `Content-Type` — must be `application/json`.
`429`	Rate limited.
`500`	Inference failed; retry with jitter.
`503`	Model at capacity; retry later.

Gotchas

dimensions is only meaningful when model_spec.supportsCustomDimensions === true. Behavior on other models is model-dependent — test with a small request before relying on it.
input must not be empty; Venice rejects empty strings with 400.
Whether the returned vectors are L2-normalized depends on the model — verify with Math.hypot(...v) ≈ 1 before assuming.
For RAG, store model alongside the vector so you can re-embed on upgrade.

Ver fuente ↗

Metadatos del archivo

name: venice-embeddings
description: Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenAI compatibility, response compression (gzip/br), and practical usage for retrieval, clustering, and RAG.

Ver texto original

---
name: venice-embeddings
description: Call POST /embeddings on Venice. Covers request shape (input, model, encoding_format, dimensions, user), OpenAI 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: the request and response match `https://api.openai.com/v1/embeddings` closely enough that the OpenAI SDK works out of the box with `baseURL: "https://api.venice.ai/api/v1"`.

## Use when

- You're building retrieval / RAG / similarity search.
- You need text clustering, classification, deduplication, or reranking.
- You want Venice's "no-training, no-retention" stance on inference inputs — embeddings are generated and returned; the API does not publish E2EE semantics on `/embeddings` the way it does on selected chat models.

Text-only. For image/multimodal signals, either run images through a vision chat model and embed the description, or pick a multimodal-capable embedding model from `GET /models?type=embedding` (the catalog changes; inspect `model_spec` on each row).

## Minimal request

```bash
curl https://api.venice.ai/api/v1/embeddings \
  -H "Authorization: Bearer $VENICE_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Accept-Encoding: gzip, br" \
  -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

| Field | Type | Notes |
|---|---|---|
| `model` | string | **Required.** Model ID from `GET /models?type=embedding`. |
| `input` | string \| string[] \| number[] \| number[][] | **Required.** Single string, array of strings (≤ 2048 entries), or pre-tokenized arrays. |
| `encoding_format` | `"float"` \| `"base64"` | Default `"float"`. Use `"base64"` for ~4× payload shrinkage; decode client-side. |
| `dimensions` | integer | Optional. Truncate output dimensions. Only meaningful when the model's `model_spec.supportsCustomDimensions === true` — behavior on non-supporting models is model-dependent; test a small call before relying on it. |
| `user` | string | Accepted for OpenAI compat. Discarded by Venice. |

`input` max tokens per string is capped at the model's `model_spec.maxInputTokens` (typically 8192). Batch arrays are capped at **2048 items**. Venice returns one embedding per element, in order, with matching `index`.

## Response headers & compression

Request `Accept-Encoding: gzip, br`. The response will include `Content-Encoding` accordingly. For long batches this matters — vectors are large.

For x402 auth, `X-Balance-Remaining` reports your remaining USDC credits.

## Using the OpenAI SDK

```ts
import OpenAI from 'openai'

const client = new OpenAI({
  apiKey: process.env.VENICE_API_KEY,
  baseURL: 'https://api.venice.ai/api/v1',
})

const res = await client.embeddings.create({
  model: 'text-embedding-bge-m3',
  input: ['first doc', 'second doc'],
})

const vec0 = res.data[0].embedding
```

## Batch-embedding pattern

```ts
async function embedBatch(texts: string[], batchSize = 64) {
  const out: number[][] = []
  for (let i = 0; i < texts.length; i += batchSize) {
    const slice = texts.slice(i, i + batchSize)
    const res = await client.embeddings.create({
      model: 'text-embedding-bge-m3',
      input: slice,
      encoding_format: 'float',
    })
    for (const row of res.data) out[i + row.index] = row.embedding
  }
  return out
}
```

- Keep batches ≤ model context limit total tokens.
- On `429`, back off exponentially and halve the batch — see [`venice-errors`](../venice-errors/SKILL.md).

## Choosing a model

Query `GET /models?type=embedding` for the current catalog. Each entry exposes:

- `model_spec.embeddingDimensions` — native output dimension (e.g. 1024 for BGE-M3).
- `model_spec.maxInputTokens` — max tokens per input string.
- `model_spec.supportsCustomDimensions` — whether `dimensions` can truncate the output.
- `model_spec.pricing.input.usd` / `.diem` — cost per **million** input tokens.

Built-in options include `text-embedding-bge-m3`, `text-embedding-bge-en-icl`, `text-embedding-qwen3-8b`, `text-embedding-qwen3-0-6b`, `text-embedding-multilingual-e5-large-instruct`, `text-embedding-3-small`, `text-embedding-3-large`, `gemini-embedding-2-preview`, `text-embedding-nemotron-embed-vl-1b-v2`.

Always pin the model ID — cosine distances are **not** comparable across different embedding models.

## Error handling

| Code | Meaning |
|---|---|
| `400` | Validation error. Check `details` in the response for the exact field. |
| `401` | Auth / Pro-only model. |
| `402` | Insufficient balance. Bearer → `INSUFFICIENT_BALANCE`. x402 → structured `PAYMENT_REQUIRED`. |
| `415` | Wrong `Content-Type` — must be `application/json`. |
| `429` | Rate limited. |
| `500` | Inference failed; retry with jitter. |
| `503` | Model at capacity; retry later. |

## Gotchas

- `dimensions` is only meaningful when `model_spec.supportsCustomDimensions === true`. Behavior on other models is model-dependent — test with a small request before relying on it.
- `input` must not be empty; Venice rejects empty strings with `400`.
- Whether the returned vectors are L2-normalized depends on the model — verify with `Math.hypot(...v) ≈ 1` before assuming.
- For RAG, store `model` alongside the vector so you can re-embed on upgrade.

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Licencia: MIT

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Quality score needs review
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Stars/forks activity: 139 stars, 20 forks; issue activity unavailable in current metadata
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Indexado

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Repositorio fuente: veniceai/skills
Licencia: MIT
Versión: 1.0.0
Último push de GitHub: 31 ago 2026
Registro actualizado: 6 sept 2026
Ruta de instrucciones: skills/venice-embeddings/SKILL.md @ be69bebc4703

Versión declarada en el registro; consulta las versiones de la fuente.

Calidad

66/100

Prometedor

Confianza

64/100

Solo sandbox

Auditoría

75/100

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Dependency or permission surface needs review
Permission surface may require sandboxing
Financial research output is not financial advice; require human review before any live investment decision
Financial research output is not financial advice; require human review before any live investment decision.
Quality score needs review
Permission surface needs review: secrets or environment access, shell or command execution
Stars/forks activity: 139 stars, 20 forks; issue activity unavailable in current metadata
Dependency/runtime risk: command execution surface, credential or environment access
Permission surface: secrets or environment access, shell or command execution

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    "track": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [],
  "do_not_use_when": [
    "teams that need a vendor-supported SLA",
    "high-compliance environments without internal security review",
    "No major risk signals from current metadata",
    "High-risk permission hints: Shell or command execution, Secrets or environment access",
    "Dependency or permission surface needs review",
    "Permission surface may require sandboxing",
    "Financial research output is not financial advice; require human review before any live investment decision",
    "Financial research output is not financial advice; require human review before any live investment decision."
  ],
  "agent_contract": {
    "task_input": "Use venice-embeddings in an agent workflow",
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first.",
    "install_policy": "block",
    "minimum_review_before_use": [
      "Trust: 72/100 Strong shortlist",
      "Audit: 75/100 Needs review",
      "Safety: 35/100 Avoid automatic install",
      "Review repository, license, install command, and permission surface before production use."
    ],
    "expected_agent_output": {
      "selected_skill": "veniceai-venice-embeddings (venice-embeddings)",
      "install_command": "npx skills add veniceai/skills --skill venice-embeddings",
      "risk_summary": "Needs review; Blocked for auto-install; Review before production",
      "verification_result": "Report the smallest successful task, files touched, warnings, and any missing setup."
    }
  },
  "outcome_feedback": {
    "endpoint": "https://www.openagentskill.com/api/agent/outcome",
    "method": "POST",
    "requires_resolve_event_id": true,
    "event_id_source": "Use install_receipt.outcome_feedback.event_id or feedback.event_id returned by /api/agent/resolve for the current task.",
    "expected_outcomes": [
      "success",
      "failed",
      "not_relevant",
      "blocked_by_risk",
      "setup_required"
    ],
    "payload_template": {
      "event_id": "<install_receipt.outcome_feedback.event_id or feedback.event_id from /api/agent/resolve>",
      "skill_slug": "veniceai-venice-embeddings",
      "task": "Use venice-embeddings in an agent workflow",
      "agent": "codex",
      "outcome": "success",
      "install_used": true,
      "risk_blocked": false,
      "setup_required": false,
      "task_success": true,
      "output_quality": 4,
      "error_type": null,
      "human_review_required": false,
      "workspace": "sandbox",
      "time_to_useful_ms": 120000,
      "notes": "Report the smallest successful task, setup friction, files touched, and risk notes."
    }
  },
  "endpoints": {
    "web": "https://www.openagentskill.com/skills/veniceai-venice-embeddings",
    "api": "https://www.openagentskill.com/api/agent/skills/veniceai-venice-embeddings",
    "audit": "https://www.openagentskill.com/skills/veniceai-venice-embeddings/audit",
    "eval": "https://www.openagentskill.com/api/agent/evals?slug=veniceai-venice-embeddings&task=Use%20venice-embeddings%20in%20an%20agent%20workflow&max_risk=medium",
    "resolve": "https://www.openagentskill.com/api/agent/resolve?task=Use%20venice-embeddings%20in%20an%20agent%20workflow&agent=codex&max_risk=medium",
    "receipt": "https://www.openagentskill.com/api/agent/receipt?task=Use%20venice-embeddings%20in%20an%20agent%20workflow&agent=codex&max_risk=medium&format=text",
    "install": "https://www.openagentskill.com/api/skills/veniceai-venice-embeddings/install",
    "manifest": "https://www.openagentskill.com/api/registry/manifest/veniceai-venice-embeddings"
  }
}

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Creador: veniceai
Fuente: veniceai/skills
Indexado por: Índice comunitario de OpenAgentSkill

La atribución enlaza al repositorio público o al perfil del creador. Los creadores pueden reclamar la ficha para actualizar las señales de propiedad.

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