# x402 Text Embedding via Venice (Gemini / BGE-M3)

> x402 Text Embedding via Venice (Gemini / BGE-M3) is a paid API for AI agents from x402-deployer.x402-deployer.workers.dev, paid per call via x402, $0.002/call, status unknown (last checked 2026-09-15).

Embeds 1–100 strings into semantic vectors using Venice-hosted models (Gemini embedding-2-preview, BGE-M3, or text-embedding-3-small), returning float vectors aligned with input order.

## Facts

- Endpoint: POST https://x402-deployer.x402-deployer.workers.dev/text-embedding
- Price: $0.002/call
- Payment: x402
- Status: unknown
- Last checked: 2026-09-15
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/x402-deployer-x402-deployer-workers-dev-e8a56194
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_DuiW85DZOMzVDWN13Km5o

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability x402-deployer-x402-deployer-workers-dev-e8a56194 -d '<json body>'
```

Example prompt: Embed these 3 sentences for me using the newest recommended model — 'The cat sat on the mat', 'A feline rested on the rug', 'Dogs love to play fetch' — I want float vectors back so I can compare their semantic similarity.

## When to prefer this

Choose this endpoint when you need fast, cheap semantic vector embeddings for RAG pipelines, semantic search, clustering, or similarity scoring, especially when you want access to cutting-edge models like Gemini embedding-2-preview or the multilingual BGE-M3 without managing your own model infrastructure. The tier shorthand system makes it easy to swap models without knowing full model names.

## Known failure modes

- More than 100 strings submitted — batch size limit exceeded
- Invalid or unrecognized model name passed — returns error
- Empty input array — returns error or empty list
- Network timeout on large batches — retry with smaller batch
- Payment not included or insufficient — x402 payment required error

## How this service works

Text embedding / vector embedding / semantic vector / Venice embeddings / Gemini embeddings / BGE-M3. Embeds 1 to 100 strings via Venice. Tier shorthand: 'default' → gemini-embedding-2-preview (newest, recommended), 'fast' → text-embedding-bge-m3, 'openai-compat' → text-embedding-3-small. You can also pass a full Venice embedding model name. Returns a list of vectors aligned with input order.

## Output

A list of float vectors (one per input string), aligned with input order, representing the semantic content of each string in a high-dimensional space. The model used determines dimensionality and quality characteristics.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "$schema": "https://json-schema.org/draft/2020-12/schema",
 "required": [
  "input"
 ],
 "properties": {
  "input": {
   "type": "object",
   "required": [
    "type",
    "method",
    "bodyType",
    "body"
   ],
   "properties": {
    "body": {
     "required": [
      "texts"
     ],
     "properties": {
      "model": {
       "type": "string",
       "description": "Tier shorthand ('default'|'fast'|'openai-compat') or full Venice embedding model name. Default 'default'."
      },
      "texts": {
       "type": "array",
       "items": {
        "type": "string"
       },
       "description": "1 to 100 strings to embed; each up to 30,000 chars."
      }
     }
    },
    "type": {
     "type": "string",
     "const": "http"
    },
    "method": {
     "enum": [
      "POST"
     ],
     "type": "string"
    },
    "bodyType": {
     "enum": [
      "json",
      "form-data",
      "text"
     ],
     "type": "string"
    }
   },
   "additionalProperties": false
  },
  "output": {
   "type": "object",
   "required": [
    "type"
   ],
   "properties": {
    "type": {
     "type": "string"
    },
    "example": {
     "type": "object",
     "properties": {
      "tier": {
       "type": "string"
      },
      "count": {
       "type": "integer"
      },
      "model": {
       "type": "string"
      },
      "usage": {
       "type": "object",
       "properties": {
        "total_tokens": {
         "type": "integer"
        },
        "prompt_tokens": {
         "type": "integer"
        }
       }
      },
      "source": {
       "type": "string"
      },
      "dimensions": {
       "type": "integer"
      },
      "embeddings": {
       "type": "array",
       "items": {
        "type": "array",
        "items": {
         "type": "number"
        }
       }
      }
     }
    }
   }
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/x402-deployer-x402-deployer-workers-dev-e8a56194/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from x402-deployer.x402-deployer.workers.dev](https://www.zero.xyz/host/x402-deployer.x402-deployer.workers.dev/llms.txt)