Stemmer
stemmer
过滤器将单词简化为其基本或根形式(称为词干提取),使得匹配不同变化形式中具有相似意义的单词变得更加容易。stemmer
过滤器支持多种语言,允许在各种语言环境中有效地进行搜索和索引。
配置
stemmer
过滤器是 Zilliz Cloud 中的自定义过滤器,通过在过滤器配置中设置 "type": "stemmer"
并将 language
参数设置为您需要的值的方式来指定。
- Python
- Java
- NodeJS
- Go
- cURL
analyzer_params = {
"tokenizer": "standard",
"filter":[{
"type": "stemmer", # Specifies the filter type as stemmer
"language": "english", # Sets the language for stemming to English
}],
}
Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");
analyzerParams.put("filter",
Collections.singletonList(
new HashMap<String, Object>() {{
put("type", "stemmer");
put("language", "english");
}}
)
);
const analyzer_params = {
"tokenizer": "standard",
"filter":[{
"type": "stemmer", // Specifies the filter type as stop
"language": "english",
}],
};
analyzerParams = map[string]any{"tokenizer": "standard",
"filter": []any{map[string]any{
"type": "stemmer",
"language": "english",
}}}
# restful
analyzerParams='{
"tokenizer": "standard",
"filter": [
{
"type": "stemmer",
"language": "english"
}
]
}'
stemmer
过滤器接受以下可选参数。
参数 | 描述 |
---|---|
| 指定词干提取过程的语言。支持的语言包括: |
stemmer
过滤器作用于分词器生成的词项,因此必须与分词器结合使用。有关 Zilliz Cloud 中可用的分词器列表,请参阅分词器参考。
定义 analyzer_params
后,您可以在定义 Collection Schema 时将其应用于 VARCHAR 字段。这使得 Zilliz Cloud 能够使用指定的分析器处理该字段中的文本,以实现高效的分词和过滤。更多信息,请参阅使用示例。
示例输出
在完成 Analyzer 配置后,您可以使用 run_analyzer
方法来验证分词效果是否符合预期。
Analyzer 配置
- Python
- Java
- NodeJS
- Go
- cURL
analyzer_params = {
"tokenizer": "standard",
"filter":[{
"type": "stemmer", # Specifies the filter type as stemmer
"language": "english", # Sets the language for stemming to English
}],
}
Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");
analyzerParams.put("filter",
Collections.singletonList(
new HashMap<String, Object>() {{
put("type", "stemmer");
put("language", "english");
}}
)
);
// javascript
analyzerParams = map[string]any{"tokenizer": "standard",
"filter": []any{map[string]any{
"type": "stemmer",
"language": "english",
}}}
# restful
analyzerParams='{
"tokenizer": "standard",
"filter": [
{
"type": "stemmer",
"language": "english"
}
]
}'
使用 run_analyzer 验证效果
- Python
- Java
- NodeJS
- Go
- cURL
from pymilvus import (
MilvusClient,
)
client = MilvusClient(uri="YOUR_CLUSTER_ENDPOINT")
# Sample text to analyze
sample_text = "running runs looked ran runner"
# Run the standard analyzer with the defined configuration
result = client.run_analyzer(sample_text, analyzer_params)
print("Standard 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("YOUR_CLUSTER_ENDPOINT")
.build();
MilvusClientV2 client = new MilvusClientV2(config);
List<String> texts = new ArrayList<>();
texts.add("running runs looked ran runner");
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: "YOUR_CLUSTER_TOKEN",
})
if err != nil {
fmt.Println(err.Error())
// handle error
}
bs, _ := json.Marshal(analyzerParams)
texts := []string{"running runs looked ran runner"}
option := milvusclient.NewRunAnalyzerOption(texts).
WithAnalyzerParams(string(bs))
result, err := client.RunAnalyzer(ctx, option)
if err != nil {
fmt.Println(err.Error())
// handle error
}
# restful
not support yet
预期结果
['run', 'run', 'look', 'ran', 'runner']