Text Embedding 3 Small
text-embedding-3-smallText Embedding 3 Small for semantic search, retrieval, ranking, and vector analytics.
Text Embedding 3 Small for semantic search, retrieval, ranking, and vector analytics.
Vector generation for semantic search, RAG, retrieval, clustering, ranking, and analytics. Endpoint: https://www.omixa.cloud/api/v1/embeddings
Text Embedding 3 Small for semantic search, retrieval, ranking, and vector analytics.
Use Omixa's unified endpoint and your workspace API key. Provider routing, billing, failover, and usage records are handled by Omixa.
https://www.omixa.cloud/api/v1/embeddings
Only send options supported by this model. Required fields and accepted values are listed below.
| Field | Type | Required | Accepted values | Description |
|---|---|---|---|---|
model |
string | Yes | text-embedding-3-small | Use `text-embedding-3-small`. Omixa resolves the active provider route and failover key automatically. |
input |
string|array | Yes | Any valid value | Text or array of texts to embed. |
dimensions |
integer | No | Any valid value | Output vector dimensions for models that support truncation. |
encoding_format |
string | No | float, base64 | OpenAI-compatible embedding encoding. |
user |
string | No | Any valid value | Optional end-user identifier for audit or provider policy forwarding. |
Start with this model-safe payload and expect the normalized Omixa response shape shown beside it.
{
"model": "text-embedding-3-small",
"input": "Customer support knowledge base paragraph.",
"dimensions": 1024
}
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
0.0123,
-0.0456
],
"index": 0
}
],
"usage": {
"prompt_tokens": 12,
"total_tokens": 12
}
}
Replace the example API key with a workspace key and keep model-specific fields unchanged unless the table above marks them optional.
curl -X POST https://www.omixa.cloud/api/v1/embeddings \
-H "Authorization: Bearer omx_live_xxx" \
-H "Content-Type: application/json" \
-d '{
"model": "text-embedding-3-small",
"input": "Customer support knowledge base paragraph.",
"dimensions": 1024
}'
const response = await fetch('https://www.omixa.cloud/api/v1/embeddings', {
method: 'POST',
headers: {
'Authorization': 'Bearer omx_live_xxx',
'Content-Type': 'application/json'
},
body: "{\n \"model\": \"text-embedding-3-small\",\n \"input\": \"Customer support knowledge base paragraph.\",\n \"dimensions\": 1024\n}"
});
const data = await response.json();
import requests
response = requests.post(
'https://www.omixa.cloud/api/v1/embeddings',
headers={'Authorization': 'Bearer omx_live_xxx', 'Content-Type': 'application/json'},
json={
"model": "text-embedding-3-small",
"input": "Customer support knowledge base paragraph.",
"dimensions": 1024
}
)
print(response.json())
$ch = curl_init('https://www.omixa.cloud/api/v1/embeddings');
curl_setopt_array($ch, [
CURLOPT_POST => true,
CURLOPT_HTTPHEADER => ['Authorization: Bearer omx_live_xxx', 'Content-Type: application/json'],
CURLOPT_POSTFIELDS => '{
"model": "text-embedding-3-small",
"input": "Customer support knowledge base paragraph.",
"dimensions": 1024
}',
CURLOPT_RETURNTRANSFER => true,
]);
$response = curl_exec($ch);
using var client = new HttpClient();
client.DefaultRequestHeaders.Authorization = new System.Net.Http.Headers.AuthenticationHeaderValue("Bearer", "omx_live_xxx");
var json = @"{
""model"": ""text-embedding-3-small"",
""input"": ""Customer support knowledge base paragraph."",
""dimensions"": 1024
}";
var response = await client.PostAsync("https://www.omixa.cloud/api/v1/embeddings", new StringContent(json, System.Text.Encoding.UTF8, "application/json"));
var body = await response.Content.ReadAsStringAsync();
payload := []byte(`{
"model": "text-embedding-3-small",
"input": "Customer support knowledge base paragraph.",
"dimensions": 1024
}`)
req, _ := http.NewRequest("POST", "https://www.omixa.cloud/api/v1/embeddings", bytes.NewReader(payload))
req.Header.Set("Authorization", "Bearer omx_live_xxx")
req.Header.Set("Content-Type", "application/json")
resp, err := http.DefaultClient.Do(req)