Embeddings — OneHub

Generate embeddings. Currently routes to OpenAI.

Docs

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Embeddings

POST https://www.onehub.design/v1/embeddings

Request

curl https://www.onehub.design/v1/embeddings \
  -H "Authorization: Bearer sk-onehub-xxx" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/text-embedding-3-small",
    "input": ["Hello world", "Another document"]
  }'

Response

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [-0.0055, 0.0212, -0.0301, 0.0124],
      "index": 0
    },
    {
      "object": "embedding",
      "embedding": [0.0031, -0.0114, 0.0278, -0.0029],
      "index": 1
    }
  ],
  "model": "openai/text-embedding-3-small",
  "usage": { "prompt_tokens": 8, "total_tokens": 8 }
}

Parameters

Field	Type	Description
`model`	string	Embedding model ID, e.g. `openai/text-embedding-3-small`
`input`	string / array	Text or array of strings to embed
`dimensions`	number	Optional output dimensionality
`encoding_format`	string	`float` (default) or `base64`

Embeddings output is a dense vector you can store and index for semantic search and RAG (retrieval-augmented generation) pipelines.