bge-m3

Convert text into 1024-dimensional vectors for semantic search and retrieval.

POSThttps://orux.top/api/v1/embeddings

Authorization

Pass your Orux API key in the request header. Keep keys on your server.

Authorization: Bearer YOUR_API_KEY
Content-Type: application/json

Request body

model: bge-m3. Place model parameters directly in the request body.

Examples use configured defaults. Replace placeholders in angle brackets with actual values.

Parameters

Fields accepted in the request body for this model.

inputobject

Required

encoding_formatenum

Optional

Values: floatDefault: float

Response examples

Illustrative response structures. Actual content depends on the request.

Open playground

Code examples

Request body

curl 'https://orux.top/api/v1/embeddings' \
  -H 'Authorization: Bearer YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
  "input": [
    "社区咖啡馆提供安静的阅读空间。",
    "想找一家可以看书喝咖啡的店。",
    "明天的航班将在上午起飞。"
  ],
  "encoding_format": "float",
  "model": "bge-m3"
}'

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Response examples

{
  "object": "list",
  "data": [{ "object": "embedding", "index": 0, "embedding": [0.0023, -0.009, "..."] }],
  "model": "bge-m3",
  "usage": { "prompt_tokens": 8, "total_tokens": 8 }
}

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