词干
stemmer 过滤器可将单词还原为其基本形式或词根形式(称为词干化),从而更容易匹配不同词性中含义相似的单词。stemmer 过滤器支持多种语言,可在各种语言环境中进行有效搜索和索引。
配置
stemmer 过滤器是 Milvus 的自定义过滤器。要使用它,请在过滤器配置中指定"type": "stemmer" ,并使用language 参数选择所需的语言进行词干处理。
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 过滤器接受以下可配置参数。
参数 |
参数 |
|---|---|
|
指定词干处理的语言。支持的语言包括 |
stemmer 过滤器对标记符生成的术语进行操作,因此必须与标记符结合使用。
定义analyzer_params 后,可以在定义 Collections Schema 时将它们应用到VARCHAR 字段。这样,Milvus 就可以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":[{
"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"
}
]
}'
验证使用run_analyzerCompatible with Milvus 2.5.11+
from pymilvus import (
MilvusClient,
)
client = MilvusClient(uri="http://localhost:19530")
# 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("http://localhost:19530")
.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: "root:Milvus",
})
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
预期输出
['run', 'run', 'look', 'ran', 'runner']