Alphanumonly

The alphanumonly filter removes tokens that contain non-ASCII characters, keeping only alphanumeric terms. This filter is useful for processing text where only basic letters and numbers are relevant, excluding any special characters or symbols.

Configuration

The alphanumonly filter is built into Zilliz Cloud. To use it, simply specify its name in the filter section within analyzer_params.

Python

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

Java

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

NodeJS

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

Go

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

cURL

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

The alphanumonly 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 Zilliz Cloud, refer to Tokenizer Reference.

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": ["alphanumonly"],
}

Java

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

NodeJS

// javascript

Go

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

cURL

# restful

Verification using run_analyzer

Python

from pymilvus import (
    MilvusClient,
)

client = MilvusClient(
    uri="YOUR_CLUSTER_ENDPOINT",
    token="YOUR_CLUSTER_TOKEN"
)

# Sample text to analyze
sample_text = "Milvus 2.0 @ Scale! #AI #Vector_Databasé"

# Run the standard analyzer with the defined configuration
result = client.run_analyzer(sample_text, analyzer_params)
print("Standard analyzer output:", result)

Java

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("Milvus 2.0 @ Scale! #AI #Vector_Databasé");

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

NodeJS

// javascript

Go

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{"Milvus 2.0 @ Scale! #AI #Vector_Databasé"}
option := milvusclient.NewRunAnalyzerOption(texts).
    WithAnalyzerParams(string(bs))

result, err := client.RunAnalyzer(ctx, option)
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

cURL

# restful

Expected output

['Milvus', '2', '0', 'Scale', 'AI', 'Vector']