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");

NodeJS

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

Go

// go

cURL

# restful
analyzerParams='{
  "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"));

NodeJS

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

Go

// go

cURL

# restful
analyzerParams='{
  "tokenizer": "standard",
  "filter": [
    "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.

Examples

Before applying the analyzer configuration to your collection schema, verify its behavior using the run_analyzer method.

Analyzer configuration:

Python

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

Java

// java

NodeJS

// javascript

Go

// go

cURL

# restful

**Verification using run_analyzer:

Python

# Sample text to analyze
sample_text = "The Milvus vector database is built for scale!"

# Run the standard analyzer with the defined configuration
result = MilvusClient.run_analyzer(sample_text, analyzer_params)
print(result)

Java

// java

NodeJS

// javascript

Go

// go

cURL

# restful

Expected output:

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