ASCII folding

The asciifolding filter converts characters outside the Basic Latin Unicode block (the first 127 ASCII characters) into their ASCII equivalents. For instance, it transforms characters like í to i, making text processing simpler and more consistent, especially for multilingual content.

Configuration

The asciifolding filter is built into Milvus. To use it, simply specify its name in the filter section within analyzer_params.

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

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

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

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

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

The asciifolding filter operates on the terms generated by the tokenizer, so it must be used in combination with a tokenizer. For a list of tokenizers available in Milvus, refer to Standard Tokenizer and its sibling pages.

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": "standard",
    "filter": ["asciifolding"],
}

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

// javascript

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

# restful

Verification using run_analyzer

from pymilvus import (
    MilvusClient,
)

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

# Sample text to analyze
sample_text = "Café Möller serves crème brûlée and piñatas."

# 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("Café Möller serves crème brûlée and piñatas.");

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

// javascript

// go

# restful

Expected output

['Cafe', 'Moller', 'serves', 'creme', 'brulee', 'and', 'pinatas']