Guía de inicio rápido de DashVector - DashVector - Centro de documentación de Alibaba Cloud

Aprenda a crear una colección, insertar vectores y ejecutar búsquedas por similitud con DashVector.

Requisitos previos

A cluster is created. Cree un clúster
An API key is obtained. Gestión de claves de API
Instale la versión más reciente del SDK de DashVector. Para obtener más información, consulte Install the DashVector SDK.

Nota

Reemplace YOUR_API_KEY y YOUR_CLUSTER_ENDPOINT con sus valores reales.
Encuentre el endpoint de su clúster en la página Cluster Detail de la consola de DashVector.

Paso 1. Cree un cliente

Si utiliza la API HTTP, omita este paso.

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");

Paso 2. Cree una colección

Cree una colección llamada quickstart con cuatro dimensiones de vector.

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

Nota

La métrica de distancia predeterminada es Cosine.
El tipo de datos de vector predeterminado es Float.

Paso 3. Inserte documentos

Python

from dashvector import Doc

# Use the dashvector.Doc object to insert a single Doc.
collection.insert(Doc(id='1', vector=[0.1, 0.2, 0.3, 0.4]))

# Use the dashvector.Doc object to insert two Docs at a time.
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

# Insert three Docs.

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

Paso 4. Ejecute una búsqueda por similitud

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

Paso 5. Elimine un documento

Python

# Delete a Doc.
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

Paso 6. Consulte las estadísticas de la colección

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

Paso 7. Elimine una colección

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