标准标记符

Milvus 中的standard 令牌分割器根据空格和标点符号分割文本，适用于大多数语言。

配置

要配置使用standard 令牌转换器的分析器，请在analyzer_params 中将tokenizer 设置为standard 。

analyzer_params = {
    "tokenizer": "standard",
}

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");

const analyzer_params = {
    "tokenizer": "standard",
};

analyzerParams = map[string]any{"tokenizer": "standard"}

# restful
analyzerParams='{
  "tokenizer": "standard"
}'

standard 标记符号分析器可与一个或多个过滤器结合使用。例如，以下代码定义了一个使用standard 标记器和lowercase 过滤器的分析器：

analyzer_params = {
    "tokenizer": "standard",
    "filter": ["lowercase"]
}

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");
analyzerParams.put("filter", Collections.singletonList("lowercase"));

const analyzer_params = {
    "tokenizer": "standard",
    "filter": ["lowercase"]
};

analyzerParams = map[string]any{"tokenizer": "standard", "filter": []any{"lowercase"}}

# restful
analyzerParams='{
  "tokenizer": "standard",
  "filter": [
    "lowercase"
  ]
}'

定义analyzer_params 后，可以在定义 Collections Schema 时将其应用到VARCHAR 字段。这样，Milvus 就能使用指定的分析器对该字段中的文本进行处理，从而实现高效的标记化和过滤。有关详情，请参阅示例使用。

示例

在将分析器配置应用到 Collections 模式之前，请使用run_analyzer 方法验证其行为。

分析器配置

analyzer_params = {
    "tokenizer": "standard",
    "filter": ["lowercase"]
}

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");
analyzerParams.put("filter", Collections.singletonList("lowercase"));

// javascript

analyzerParams = map[string]any{"tokenizer": "standard", "filter": []any{"lowercase"}}

# restful

验证使用run_analyzer

from pymilvus import (
    MilvusClient,
)

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

# Sample text to analyze
sample_text = "The Milvus vector database is built for scale!"

# Run the standard analyzer with the defined configuration
result = client.run_analyzer(sample_text, analyzer_params)
print("English analyzer output:", result)

import io.milvus.v2.client.ConnectConfig;
import io.milvus.v2.client.MilvusClientV2;
import io.milvus.v2.service.vector.request.RunAnalyzerReq;
import io.milvus.v2.service.vector.response.RunAnalyzerResp;

ConnectConfig config = ConnectConfig.builder()
        .uri("http://localhost:19530")
        .token("root:Milvus")
        .build();
MilvusClientV2 client = new MilvusClientV2(config);

List<String> texts = new ArrayList<>();
texts.add("The Milvus vector database is built for scale!");

RunAnalyzerResp resp = client.runAnalyzer(RunAnalyzerReq.builder()
        .texts(texts)
        .analyzerParams(analyzerParams)
        .build());
List<RunAnalyzerResp.AnalyzerResult> results = resp.getResults();

// javascript

import (
    "context"
    "encoding/json"
    "fmt"

    "github.com/milvus-io/milvus/client/v2/milvusclient"
)

client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "localhost:19530",
    APIKey:  "root:Milvus",
})
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

bs, _ := json.Marshal(analyzerParams)
texts := []string{"The Milvus vector database is built for scale!"}
option := milvusclient.NewRunAnalyzerOption(texts).
    WithAnalyzerParams(string(bs))

result, err := client.RunAnalyzer(ctx, option)
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

# restful

预期输出

['the', 'milvus', 'vector', 'database', 'is', 'built', 'for', 'scale']