import { Callout, Tabs } from "@/docs/components";

Embeddings

<div className="status-badge status-stable">Stable</div>

Generate vector embeddings for text to power semantic search and RAG applications.

Create Embedding

<div className="api-endpoint"> <span className="method-badge method-badge-post">POST</span> <span className="path">/api/v1/embeddings</span> </div>

Generate vector embeddings for the provided text.

Request

bash

curl -X POST "https://elizacloud.ai/api/v1/embeddings" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "The quick brown fox jumps over the lazy dog",
    "model": "text-embedding-3-small"
  }'

</Tabs.Tab> <Tabs.Tab>

javascript

const response = await fetch('https://elizacloud.ai/api/v1/embeddings', {
  method: 'POST',
  headers: {
    'Authorization': 'Bearer YOUR_API_KEY',
    'Content-Type': 'application/json',
  },
  body: JSON.stringify({
    input: 'The quick brown fox jumps over the lazy dog',
    model: 'text-embedding-3-small',
  }),
});

const data = await response.json();
console.log(data.data[0].embedding);

</Tabs.Tab> <Tabs.Tab>

python

import requests

response = requests.post(
    'https://elizacloud.ai/api/v1/embeddings',
    headers={
        'Authorization': 'Bearer YOUR_API_KEY',
        'Content-Type': 'application/json',
    },
    json={
        'input': 'The quick brown fox jumps over the lazy dog',
        'model': 'text-embedding-3-small',
    }
)

data = response.json()
print(data['data'][0]['embedding'])

</Tabs.Tab> </Tabs>

Parameters

Parameter	Type	Required	Description
`input`	string/array	✓	Text to embed (string or array of strings)
`model`	string		Embedding model. Default: `text-embedding-3-small`
`encoding_format`	string		Output format: `float` or `base64`
`dimensions`	integer		Number of dimensions for output

Response

json

{
  "object": "list",
  "data": [
    {
      "object": "embedding",
      "embedding": [0.0023, -0.0145, 0.0312, ...],
      "index": 0
    }
  ],
  "model": "text-embedding-3-small",
  "usage": {
    "prompt_tokens": 10,
    "total_tokens": 10
  }
}

Batch Embeddings

Embed multiple texts in a single request:

json

{
  "input": [
    "First sentence to embed",
    "Second sentence to embed",
    "Third sentence to embed"
  ],
  "model": "text-embedding-3-small"
}

<Callout type="info"> Batch requests are more efficient. You can embed up to 100 texts per request. </Callout>

Available Models

Embedding model availability and dimensions are deployment-specific. Use /api/v1/models, the API Explorer, or the embeddings response metadata for the current catalog.

Use Cases

Semantic Search

javascript

// Embed your query
const queryResponse = await fetch("https://elizacloud.ai/api/v1/embeddings", {
  method: "POST",
  headers: {
    Authorization: "Bearer YOUR_API_KEY",
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    input: "How do I reset my password?",
    model: "text-embedding-3-small",
  }),
});

const queryEmbedding = (await queryResponse.json()).data[0].embedding;
// Find similar documents using cosine similarity

Cosine Similarity

javascript

function cosineSimilarity(a, b) {
  const dotProduct = a.reduce((sum, val, i) => sum + val * b[i], 0);
  const magnitudeA = Math.sqrt(a.reduce((sum, val) => sum + val * val, 0));
  const magnitudeB = Math.sqrt(b.reduce((sum, val) => sum + val * val, 0));
  return dotProduct / (magnitudeA * magnitudeB);
}