LinderaPublic Preview
The lindera tokenizer performs dictionary-based morphological analysis. It is a good choice for languages—such as Japanese, Korean, and Chinese—whose words are not separated by spaces.
The lindera tokenizer preserves punctuation marks as separate tokens in the output. For example, "こんにちは！" becomes ["こんにちは", "！"]. To remove these standalone punctuation tokens, use the removepunct filter.
Configuration
To configure an analyzer using the lindera tokenizer, set tokenizer.type to lindera and choose a dictionary with dict_kind.
- Python
- Java
- Go
- NodeJS
- cURL
analyzer_params = {
"tokenizer": {
"type": "lindera",
"dict_kind": "ipadic"
}
}
Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer",
new HashMap<String, Object>() {{
put("type", "lindera");
put("dict_kind", "ipadic");
}});
analyzerParams = map[string]any{"tokenizer": map[string]any{"type": "lindera", "dict_kind": "ipadic"}}
// node.js
# restful
Parameter | Description |
|---|---|
| The type of tokenizer. This is fixed to |
| A dictionary used to define vocabulary. Possible values:
|
After defining analyzer_params, you can apply them to a VARCHAR field when defining a collection schema. This allows Zilliz Cloud to process the text in that field using the specified analyzer for efficient tokenization and filtering. For details, refer to Example use.
Examples
Before applying the analyzer configuration to your collection schema, verify its behavior using the run_analyzer method.
Analyzer configuration
- Python
- Java
- Go
- NodeJS
- cURL
analyzer_params = {
"tokenizer": {
"type": "lindera",
"dict_kind": "ipadic"
}
}
Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer",
new HashMap<String, Object>() {{
put("type", "lindera");
put("dict_kind", "ipadic");
}});
analyzerParams = map[string]any{"tokenizer": map[string]any{"type": "lindera", "dict_kind": "ipadic"}}
// nodejs
# restful
Verification using run_analyzer
- Python
- Java
- Go
- NodeJS
- cURL
from pymilvus import (
MilvusClient,
)
client = MilvusClient(
uri="YOUR_CLUSTER_ENDPOINT",
token="YOUR_CLUSTER_TOKEN"
)
# Sample text to analyze
sample_text = "東京スカイツリーの最寄り駅はとうきょうスカイツリー駅で"
# 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")
.token("YOUR_CLUSTER_TOKEN")
.build();
MilvusClientV2 client = new MilvusClientV2(config);
List<String> texts = new ArrayList<>();
texts.add("東京スカイツリーの最寄り駅はとうきょうスカイツリー駅で");
RunAnalyzerResp resp = client.runAnalyzer(RunAnalyzerReq.builder()
.texts(texts)
.analyzerParams(analyzerParams)
.build());
List<RunAnalyzerResp.AnalyzerResult> results = resp.getResults();
import (
"context"
"encoding/json"
"fmt"
"github.com/milvus-io/milvus/client/v2/milvusclient"
)
client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
Address: "YOUR_CLUSTER_ENDPOINT",
APIKey: "YOUR_CLUSTER_TOKEN",
})
if err != nil {
fmt.Println(err.Error())
// handle error
}
bs, _ := json.Marshal(analyzerParams)
texts := []string{"東京スカイツリーの最寄り駅はとうきょうスカイツリー駅で"}
option := milvusclient.NewRunAnalyzerOption(texts).
WithAnalyzerParams(string(bs))
result, err := client.RunAnalyzer(ctx, option)
if err != nil {
fmt.Println(err.Error())
// handle error
}
// node.js
# restful
Expected output
{tokens: ['東京', 'スカイ', 'ツリー', 'の', '最寄り駅', 'は', 'とう', 'きょう', 'スカイ', 'ツリー', '駅', 'で']}