Embeddings

The Embeddings API turns text into vectors you can use for semantic search, retrieval, clustering, and ranking.

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POST https://api.meshapi.ai/v1/embeddings
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Authorization: Bearer rsk_<your_key>

Quick start

$
curl https://api.meshapi.ai/v1/embeddings \
>
  -H "Authorization: Bearer <YOUR_RSK_KEY>" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "openai/text-embedding-3-small",
>
    "input": "The quick brown fox jumps over the lazy dog."
>
  }'

Request fields

Batch input in a single request

You can embed multiple strings in one API call:

$
curl https://api.meshapi.ai/v1/embeddings \
>
  -H "Authorization: Bearer <YOUR_RSK_KEY>" \
>
  -H "Content-Type: application/json" \
>
  -d '{
>
    "model": "openai/text-embedding-3-small",
>
    "input": [
>
      "first sentence",
>
      "second sentence",
>
      "third sentence"
>
    ]
>
  }'

The returned data array matches the order of your input array.

Response shape

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{
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  "object": "list",
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  "data": [
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    {
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      "object": "embedding",
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      "index": 0,
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      "embedding": [-0.0123, 0.0456]
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    }
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  ],
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  "model": "openai/text-embedding-3-small",
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  "usage": {
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    "prompt_tokens": 12,
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    "total_tokens": 12
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  }
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}

Choosing a model

Use GET /v1/models to inspect available embedding models and their pricing. For embeddings, the most useful fields are:

• model_type
• context_length
• pricing
• is_free

SDK coverage

The official SDKs all expose first-class embeddings resources:

• Node: client.embeddings.create(...)
• Python: client.embeddings.create(...)
• Go: client.Embeddings.Create(...)
• Java: client.embeddings().create(...)

Common errors