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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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Preis unbestätigt★ 139 GitHub-StarsVerzeichnis aktualisiert · 6. Sept. 2026agent-skill

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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.

Quelle ansehen ↗

Dateimetadaten

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.

Originaltext anzeigen

---
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.

Quelle prüfen

Preis und Betriebskosten

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Lizenz: MIT
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Vor Installation prüfen: Automatische Installation vermeiden

Lizenz: MIT

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

Vollständiges Audit öffnen

Tools sind Metadatenhinweise, keine getestete Kompatibilität. Prompts sind Vorschläge.

Mit einer kleinen Aufgabe beginnen

1Quelle lesen und Eingaben, Ergebnisse, Abhängigkeiten sowie Berechtigungen prüfen.
2Agent um einen Plan bitten. Einrichtung und Kosten vor einem isolierten Test genehmigen.
3Ergebnisse und geänderte Dateien prüfen. Nur tatsächliche Ausführungen melden und die Quellrevision aufbewahren.

Prüfe Abhängigkeiten, API-Schlüssel und externe Kosten in der Quelle. Öffentliche Repositories bedeuten nicht, dass alle Dienste kostenlos sind.

Quelle und Nutzungshinweise

Erfasst

Metadaten und Prüfungen dienen der Orientierung. Beliebtheit, Quellenerfassung und erfolgreiche Ausführung sind verschiedene Fakten.

Quell-Repository: veniceai/skills
Lizenz: MIT
Version: 1.0.0
Letzter GitHub-Push: 31. Aug. 2026
Verzeichnis aktualisiert: 6. Sept. 2026
Anleitungspfad: skills/venice-embeddings/SKILL.md @ be69bebc4703

Version aus den Verzeichnismetadaten; Releases der Quelle prüfen.

Qualität

66/100

Vielversprechend

Vertrauen

64/100

Nur Sandbox

Audit

75/100

Prüfung nötig

Vollständiges Audit öffnen Eval-Bericht ansehen

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

Verified installs: —
Ergebnisse: —

Kopieren ist keine Installation. Zahlen benötigen eine Erfolgsmeldung und garantieren keine allgemeine Qualität.

Agent-Zugang

Die Registry API stellt Entscheidungs-, Vertrauens-, Audit-, Use-Case- und Installationssignale ohne UI-Scraping bereit.

Installations-API öffnen LLM-Textformat Manifest öffnen Auto-Auflösungsplan

Weitere Details

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    "human_review_required": true,
    "blocked": true,
    "recommended_action": "Do not auto-install. Inspect the source, dependencies, and permission surface first."
  },
  "quality": {
    "score": 66,
    "label": "Promising"
  },
  "supply": {
    "track": "Research and knowledge work",
    "scenario": "RAG and knowledge",
    "maintenance": "1mo since push",
    "risk": "Needs review"
  },
  "alternative_skills": [
    {
      "slug": "amd-quark-torch-llm-ptq",
      "name": "quark-torch-llm-ptq",
      "url": "https://www.openagentskill.com/skills/amd-quark-torch-llm-ptq",
      "stars": 395,
      "install_command": "npx skills add amd/skills --skill quark-torch-llm-ptq",
      "trust_score": 73,
      "audit_score": 77
    }
  ],
  "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"
  }
}

Für Ersteller

Quelle des Eintrags

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Dieser Eintrag wurde aus öffentlichen Quellen indexiert und ist erst nach Genehmigung eines Maintainer-Anspruchs offiziell.

Ersteller: veniceai
Quelle: veniceai/skills
Indexiert von: OpenAgentSkill Community-Index

Die Zuordnung verlinkt auf das öffentliche Repository oder Creator-Profil. Creator können den Eintrag beanspruchen, um Eigentümersignale zu aktualisieren.

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Dieser Registry-indexiert-Eintrag wird veniceai zugeschrieben, ist aber noch nicht offiziell markiert. Beanspruche ihn, um ein verifiziertes Eigentümersignal hinzuzufügen und künftige Launch-, Installations- und Audit-Updates vertrauenswürdiger zu machen.

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