Standard Tokenizer

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The standard tokenizer in Zilliz Cloud splits text based on spaces and punctuation marks, making it suitable for most languages.

Configuration

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

Python

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

Java

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

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

Python

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

Java

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

📘Notes

For simpler setup, you may choose to use the standard analyzer, which combines the standard tokenizer with the lowercase filter.

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.

Example output

Here’s an example of how the standard tokenizer processes text:

Original text:

"The Milvus vector database is built for scale!"

Expected output:

["The", "Milvus", "vector", "database", "is", "built", "for", "scale"]