Amazon Titan Embed v2 API nasıl kullanılır?

Amazon Titan Embed v2 modelini Parel API ile kullanmak için OpenAI SDK'sını yükleyin, base_url olarak https://api.parel.cloud/v1 belirleyin ve model parametresine "titan-embed-v2" yazın. Python, Node.js, Java, C# ve cURL desteklenir.

Amazon Titan Embed v2 KVKK uyumlu mu?

Amazon Titan Embed v2 AWS Frankfurt (eu-central-1) sunucularında çalışır. KVKK uyumlu alternatifler için Parel edge modellerine bakabilirsiniz.

Amazon

Amazon Titan Embed v2

Semantik arama ve RAG pipeline'ları için

Input 3 kredi/1M token

Output 0 kredi/1M token

Playground'da Dene

Embedding 8K token

Hakkında

Amazon cok dilli embedding. 1024 boyut, 8K token. En uygun fiyat.

Kullanım Alanları

› Semantik arama ve belge erişimi
› RAG (Retrieval-Augmented Generation) pipeline'ları
› Metin benzerliği ve kümeleme analizi
› Öneri sistemleri ve kişiselleştirme
› Anlamsal metin sınıflandırması

Aile

Titan Embed

Mimari

Transformer

Veri Merkezi

AWS Frankfurt (eu-central-1)

Provider: Amazon
Tür: Embedding
Boyut: 8K token
Model ID: titan-embed-v2
Input Fiyat: 3 kredi/1M token
Output Fiyat: 0 kredi/1M token

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OpenAI SDK ile dakikalar içinde entegre edin.

Ücretsiz Başla Tüm Modeller

Authentication #

API anahtarınızı ortam değişkeni olarak tanımlayın.

python

import os
os.environ["OPENAI_API_KEY"] = "pk-..."

Kurulum #

OpenAI SDK'yi yükleyin.

python

pip install openai

Embedding Oluştur #

Tek bir metin için vektörel temsil oluşturun.

python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.parel.cloud/v1",
    api_key="pk-...",
)

response = client.embeddings.create(
    model="titan-embed-v2",
    input="Ornek metin",
)

embedding = response.data[0].embedding
print(f"Boyut: {len(embedding)}")
print(embedding[:5])

javascript

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.parel.cloud/v1",
  apiKey: "pk-...",
});

const response = await client.embeddings.create({
  model: "titan-embed-v2",
  input: "Ornek metin",
});

const embedding = response.data[0].embedding;
console.log("Boyut:", embedding.length);
console.log(embedding.slice(0, 5));

java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.*;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .baseUrl("https://api.parel.cloud/v1")
    .apiKey("pk-...")
    .build();

EmbeddingCreateParams params = EmbeddingCreateParams.builder()
    .model("titan-embed-v2")
    .input("Ornek metin")
    .build();

CreateEmbeddingResponse response = client.embeddings().create(params);

List<Double> embedding = response.data().get(0).embedding();
System.out.println("Boyut: " + embedding.size());
System.out.println(embedding.subList(0, 5));

csharp

using OpenAI;
using OpenAI.Embeddings;

EmbeddingClient client = new(
    model: "titan-embed-v2",
    credential: new ApiKeyCredential("pk-..."),
    options: new OpenAIClientOptions {
        Endpoint = new Uri("https://api.parel.cloud/v1") }
);

OpenAIEmbedding embedding = client.GenerateEmbedding("Ornek metin");

ReadOnlyMemory<float> vector = embedding.ToFloats();
Console.WriteLine($"Boyut: {vector.Length}");
Console.WriteLine(string.Join(", ", vector.Span.Slice(0, 5).ToArray()));

bash

curl -X POST https://api.parel.cloud/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer pk-..." \
  -d '{
    "model": "titan-embed-v2",
    "input": "Ornek metin"
  }'

Toplu Embedding #

Birden fazla metin icin tek istekte embedding alin.

python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.parel.cloud/v1",
    api_key="pk-...",
)

response = client.embeddings.create(
    model="titan-embed-v2",
    input=[
        "Birinci metin",
        "Ikinci metin",
        "Ucuncu metin",
    ],
)

for i, item in enumerate(response.data):
    print(f"[{i}] Boyut: {len(item.embedding)}")

javascript

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.parel.cloud/v1",
  apiKey: "pk-...",
});

const response = await client.embeddings.create({
  model: "titan-embed-v2",
  input: [
    "Birinci metin",
    "Ikinci metin",
    "Ucuncu metin",
  ],
});

response.data.forEach((item, i) => {
  console.log(`[${i}] Boyut: ${item.embedding.length}`);
});

java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.*;
import java.util.List;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .baseUrl("https://api.parel.cloud/v1")
    .apiKey("pk-...")
    .build();

EmbeddingCreateParams params = EmbeddingCreateParams.builder()
    .model("titan-embed-v2")
    .input(List.of("Birinci metin", "Ikinci metin", "Ucuncu metin"))
    .build();

CreateEmbeddingResponse response = client.embeddings().create(params);

for (int i = 0; i < response.data().size(); i++) {
    System.out.printf("[%d] Boyut: %d%n", i, response.data().get(i).embedding().size());
}

csharp

using OpenAI;
using OpenAI.Embeddings;

EmbeddingClient client = new(
    model: "titan-embed-v2",
    credential: new ApiKeyCredential("pk-..."),
    options: new OpenAIClientOptions {
        Endpoint = new Uri("https://api.parel.cloud/v1") }
);

OpenAIEmbeddingCollection result = client.GenerateEmbeddings(
    new List<string> { "Birinci metin", "Ikinci metin", "Ucuncu metin" }
);

for (int i = 0; i < result.Count; i++)
{
    Console.WriteLine($"[{i}] Boyut: {result[i].ToFloats().Length}");
}

bash

curl -X POST https://api.parel.cloud/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer pk-..." \
  -d '{
    "model": "titan-embed-v2",
    "input": ["Birinci metin", "Ikinci metin", "Ucuncu metin"]
  }'

Cosine Similarity #

İki metin arasındaki benzerlik skorunu hesaplayın.

python

import numpy as np
from openai import OpenAI

client = OpenAI(
    base_url="https://api.parel.cloud/v1",
    api_key="pk-...",
)

def get_embedding(text: str) -> list[float]:
    response = client.embeddings.create(
        model="titan-embed-v2",
        input=text,
    )
    return response.data[0].embedding

def cosine_similarity(a, b):
    a, b = np.array(a), np.array(b)
    return np.dot(a, b) / (np.linalg.norm(a) * np.linalg.norm(b))

e1 = get_embedding("Istanbul'un nufusu kac?")
e2 = get_embedding("Istanbul'da kac kisi yasiyor?")
e3 = get_embedding("Bugun hava nasil?")

print(f"Benzer: {cosine_similarity(e1, e2):.4f}")
print(f"Farkli: {cosine_similarity(e1, e3):.4f}")

javascript

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.parel.cloud/v1",
  apiKey: "pk-...",
});

async function getEmbedding(text) {
  const res = await client.embeddings.create({
    model: "titan-embed-v2",
    input: text,
  });
  return res.data[0].embedding;
}

function cosineSimilarity(a, b) {
  let dot = 0, normA = 0, normB = 0;
  for (let i = 0; i < a.length; i++) {
    dot += a[i] * b[i];
    normA += a[i] * a[i];
    normB += b[i] * b[i];
  }
  return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}

const e1 = await getEmbedding("Istanbul'un nufusu kac?");
const e2 = await getEmbedding("Istanbul'da kac kisi yasiyor?");
const e3 = await getEmbedding("Bugun hava nasil?");

console.log("Benzer:", cosineSimilarity(e1, e2).toFixed(4));
console.log("Farkli:", cosineSimilarity(e1, e3).toFixed(4));

java

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.*;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .baseUrl("https://api.parel.cloud/v1")
    .apiKey("pk-...")
    .build();

List<Double> getEmbedding(String text) {
    EmbeddingCreateParams params = EmbeddingCreateParams.builder()
        .model("titan-embed-v2")
        .input(text)
        .build();
    return client.embeddings().create(params).data().get(0).embedding();
}

double cosineSimilarity(List<Double> a, List<Double> b) {
    double dot = 0, normA = 0, normB = 0;
    for (int i = 0; i < a.size(); i++) {
        dot += a.get(i) * b.get(i);
        normA += a.get(i) * a.get(i);
        normB += b.get(i) * b.get(i);
    }
    return dot / (Math.sqrt(normA) * Math.sqrt(normB));
}

var e1 = getEmbedding("Istanbul'un nufusu kac?");
var e2 = getEmbedding("Istanbul'da kac kisi yasiyor?");
var e3 = getEmbedding("Bugun hava nasil?");

System.out.printf("Benzer: %.4f%n", cosineSimilarity(e1, e2));
System.out.printf("Farkli: %.4f%n", cosineSimilarity(e1, e3));

csharp

using OpenAI;
using OpenAI.Embeddings;

EmbeddingClient client = new(
    model: "titan-embed-v2",
    credential: new ApiKeyCredential("pk-..."),
    options: new OpenAIClientOptions {
        Endpoint = new Uri("https://api.parel.cloud/v1") }
);

float[] GetEmbedding(string text) =>
    client.GenerateEmbedding(text).ToFloats().ToArray();

float CosineSimilarity(float[] a, float[] b)
{
    float dot = 0, normA = 0, normB = 0;
    for (int i = 0; i < a.Length; i++)
    {
        dot += a[i] * b[i];
        normA += a[i] * a[i];
        normB += b[i] * b[i];
    }
    return dot / (MathF.Sqrt(normA) * MathF.Sqrt(normB));
}

var e1 = GetEmbedding("Istanbul'un nufusu kac?");
var e2 = GetEmbedding("Istanbul'da kac kisi yasiyor?");
var e3 = GetEmbedding("Bugun hava nasil?");

Console.WriteLine($"Benzer: {CosineSimilarity(e1, e2):F4}");
Console.WriteLine($"Farkli: {CosineSimilarity(e1, e3):F4}");

bash

# 1. Embedding'leri alin
curl -X POST https://api.parel.cloud/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer pk-..." \
  -d '{
    "model": "titan-embed-v2",
    "input": ["Istanbul nufusu kac?", "Istanbul kac kisi?"]
  }'

# 2. Cosine similarity hesabini kendi kodunuzda yapin

Kayıt olun, API anahtarınızı alın.

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