Embeddings

const embedding = await client.generateEmbedding({
  model: "openai/text-embedding-3-large",
  input: "Sample text",
});

embedding = client.generate_embedding({
    "model": "openai/text-embedding-3-large",
    "input": "Sample text",
})

resp, err := client.GenerateEmbedding(context.Background(), map[string]interface{}{
  "model": "openai/text-embedding-3-large",
  "input": "Sample text",
})
if err != nil {
  panic(err)
}
fmt.Println(resp["data"])

var response = await client.CreateEmbedding(new Dictionary<string, object>
{
    ["model"] = "openai/text-embedding-3-large",
    ["input"] = "Sample text",
});

$response = $client->generateEmbedding([
    'model' => 'openai/text-embedding-3-large',
    'input' => 'Sample text',
]);

response = client.generate_embedding(
  model: 'openai/text-embedding-3-large',
  input: 'Sample text'
)

String body = """
{
  "model": "openai/text-embedding-3-large",
  "input": "Sample text"
}
""";

Object response = client.createEmbedding(new com.fasterxml.jackson.databind.ObjectMapper().readTree(body));
System.out.println(String.valueOf(response));

// لا توجد دالة للتضمينات بعد: ترسل post() بيانات JSON إلى أي مسار في Phaseo.
let response = client.post("/embeddings", &json!({
    "model": "openai/text-embedding-3-large",
    "input": "Sample text"
}))?;