Mulai Cepat - DashVector

Topik ini membantu Anda memulai dengan cepat menggunakan DashVector.

Prasyarat

Anda telah berhasil membuat kluster. Untuk informasi lebih lanjut, lihat Buat kluster.
Anda telah berhasil membuat Kunci API. Untuk informasi lebih lanjut, lihat Pengelolaan Kunci API.
Anda telah menginstal SDK DashVector versi terbaru. Untuk informasi lebih lanjut, lihat Instal SDK DashVector.

Catatan

Ganti YOUR_API_KEY dengan Kunci API Anda dan YOUR_CLUSTER_ENDPOINT dengan Titik akhir kluster Anda dalam contoh berikut.
Titik akhir kluster dapat ditemukan di halaman Detail Kluster pada konsol DashVector.

Langkah 1. Buat klien

Langkah ini dapat dilewati jika Anda menggunakan HTTP API.

Python

import dashvector

client = dashvector.Client(
    api_key='YOUR_API_KEY',
    endpoint='YOUR_CLUSTER_ENDPOINT'
)
assert client

Java

import com.aliyun.dashvector.DashVectorClient;
import com.aliyun.dashvector.common.DashVectorException;


DashVectorClient client = new DashVectorClient("YOUR_API_KEY", "YOUR_CLUSTER_ENDPOINT");

Langkah 2. Buat koleksi

Buat koleksi bernama quickstart dengan empat dimensi vektor.

Python

client.create(name='quickstart', dimension=4)

collection = client.get('quickstart')
assert collection

Java

import com.aliyun.dashvector.models.responses.Response;
import com.aliyun.dashvector.DashVectorCollection;


Response<Void> response = client.create("quickstart", 4);
System.out.println(response);
DashVectorCollection collection = client.get("quickstart");

assert collection.isSuccess();

HTTP

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "name": "quickstart", 
    "dimension": 4
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections

Catatan

Jika metode pengukuran jarak tidak ditentukan, nilai default Cosine akan digunakan.
Jika tipe data vektor tidak ditentukan, nilai default Float akan digunakan.

Langkah 3. Sisipkan Dokumen

Python

from dashvector import Doc

# Gunakan objek dashvector.Doc untuk menyisipkan satu Dok.
collection.insert(Doc(id='1', vector=[0.1, 0.2, 0.3, 0.4]))

# Gunakan objek dashvector.Doc untuk menyisipkan dua Dok sekaligus.
collection.insert(
    [
        Doc(id='2', vector=[0.2, 0.3, 0.4, 0.5], fields={'age': 20, 'name': 'zhangsan'}),
        Doc(id='3', vector=[0.3, 0.4, 0.5, 0.6], fields={'anykey': 'anyvalue'})    
    ]
)

Java

import com.aliyun.dashvector.models.Vector;
import com.aliyun.dashvector.models.Doc;
import com.aliyun.dashvector.models.requests.InsertDocRequest;
import com.aliyun.dashvector.models.responses.Response;

import java.util.Arrays;
import java.util.HashMap;


Doc doc1 = Doc.builder()
    .id("1")
    .vector(
        Vector.builder()
            .value(Arrays.asList(0.1f, 0.2f, 0.3f, 0.4f))
            .build()
    ).build();

Doc doc2 = Doc.builder()
    .id("2")
    .vector(
        Vector.builder()
            .value(Arrays.asList(0.2f, 0.3f, 0.4f, 0.5f))
            .build()
    ).fields(new HashMap<String, Object>(){{
        put("age", 20);
        put("name", "zhangsan");
    }}).build();

Doc doc3 = Doc.builder()
    .id("3")
    .field("anykey", "anyvalue")
    .vector(
        Vector.builder()
            .value(Arrays.asList(0.3f, 0.4f, 0.5f, 0.6f))
            .build()
    ).build();

InsertDocRequest request = InsertDocRequest.builder()
    .docs(Arrays.asList(doc1, doc2, doc3))
    .build();

Response<Void> response = collection.insert(request);

HTTP

# Sisipkan tiga Dok.

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "docs": [
      {"id": "1", "vector": [0.1, 0.2, 0.3, 0.4]},
      {"id": "2", "vector": [0.2, 0.3, 0.4, 0.5], "fields": {"age": 20, "name": "zhangsan"}},
      {"id": "3", "vector": [0.3, 0.4, 0.5, 0.6], "fields": {"anykey": "anyvalue"}}
    ]
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/docs

Langkah 4. Lakukan pencarian kesamaan

Python

rets = collection.query([0.1, 0.2, 0.3, 0.4], topk=2)

print(rets)

Java

import com.aliyun.dashvector.models.Vector;
import com.aliyun.dashvector.models.Doc;
import com.aliyun.dashvector.models.requests.QueryDocRequest;
import com.aliyun.dashvector.models.responses.Response;

import java.util.Arrays;
import java.util.List;


Vector vector = Vector.builder().value(Arrays.asList(0.1f, 0.2f, 0.3f, 0.4f)).build();
      	
QueryDocRequest request = QueryDocRequest.builder()
    .vector(vector)
    .topk(2)
    .build();

Response<List<Doc>> response = collection.query(request);

HTTP

curl -XPOST \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{
    "vector": [0.1, 0.2, 0.3, 0.4],
    "topk": 2
  }' https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/query

Langkah 5. Hapus Dokumen

Python

# Hapus sebuah Dok.
collection.delete(ids=['1'])

Java

import com.aliyun.dashvector.models.Doc;
import com.aliyun.dashvector.models.requests.DeleteDocRequest;
import com.aliyun.dashvector.models.responses.Response;


DeleteDocRequest request = DeleteDocRequest.builder()
    .id("1")
    .build();
      
Response<List<Doc>> response = collection.delete(request);

HTTP

curl -XDELETE \
  -H 'dashvector-auth-token: YOUR_API_KEY' \
  -H 'Content-Type: application/json' \
  -d '{"ids": ["1"]}' \
  https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/docs

Langkah 6. Lihat statistik koleksi

Python

stats = collection.stats()

print(stats)

Java

import com.aliyun.dashvector.models.CollectionStats;
import com.aliyun.dashvector.models.responses.Response;


Response<CollectionStats> response = collection.stats();

HTTP

curl -H 'dashvector-auth-token: YOUR_API_KEY' \
  https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart/stats

Langkah 7. Hapus koleksi

Python

client.delete('quickstart')

Java

import com.aliyun.dashvector.models.responses.Response;


Response<Void> response = client.delete("quickstart");

HTTP

curl -XDELETE -H 'dashvector-auth-token: YOUR_API_KEY' \
  https://YOUR_CLUSTER_ENDPOINT/v1/collections/quickstart