Whitespace

The whitespace tokenizer divides text into terms whenever there is a space between words.

Configuration

To configure an analyzer using the whitespace tokenizer, set tokenizer to whitespace in analyzer_params.

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

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

const analyzer_params = {
    "tokenizer": "whitespace"
};

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

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

The whitespace tokenizer can work in conjunction with one or more filters. For example, the following code defines an analyzer that uses the whitespace tokenizer and lowercase filter:

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

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

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

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

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

After defining analyzer_params, you can apply them to a VARCHAR field when defining a collection schema. This allows Milvus 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

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

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

// javascript

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

# restful

Verification using run_analyzer

from pymilvus import (
    MilvusClient,
)

client = MilvusClient(uri="http://localhost:19530")

# 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("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("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

// go

# restful

Expected output

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