Cncharonly

The cncharonly filter removes tokens that contain any non-Chinese characters. This filter is useful when you want to focus solely on Chinese text, filtering out any tokens that contain other scripts, numbers, or symbols.

Configuration

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

Python

python

analyzer_params = {
    "tokenizer": "jieba",
    "filter": ["cncharonly"],
}

Java

java

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

NodeJS

javascript

const analyzer_params = {
    "tokenizer": "jieba",
    "filter": ["cncharonly"],
};

Go

go

analyzerParams = map[string]any{"tokenizer": "jieba", "filter": []any{"cncharonly"}}

cURL

bash

# restful
analyzerParams='{
  "tokenizer": "jieba",
  "filter": [
    "cncharonly"
  ]
}'

C++

c++

nlohmann::json analyzer_params = {
    {"tokenizer", "jieba"},
    {"filter", {"cncharonly"}}
};

The cncharonly 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 Jieba and its sibling pages.

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

python

analyzer_params = {
    "tokenizer": "jieba",
    "filter": ["cncharonly"],
}

Java

java

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

NodeJS

javascript

// javascript

Go

go

analyzerParams = map[string]any{"tokenizer": "jieba", "filter": []any{"cncharonly"}}

cURL

bash

# restful

C++

c++

nlohmann::json analyzer_params = {
    {"tokenizer", "jieba"},
    {"filter", {"cncharonly"}}
};

Verification using run_analyzer

Python

python

from pymilvus import (
    MilvusClient,
)

client = MilvusClient(uri="YOUR_CLUSTER_ENDPOINT")

# Sample text to analyze
sample_text = "Milvus 是 LF AI & Data Foundation 下的一个开源项目，以 Apache 2.0 许可发布。"

# Run the jieba tokenizer with the defined configuration
result = client.run_analyzer(sample_text, analyzer_params)
print("Analyzer output:", result)

Java

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")
        .build();
MilvusClientV2 client = new MilvusClientV2(config);

List<String> texts = new ArrayList<>();
texts.add("Milvus 是 LF AI & Data Foundation 下的一个开源项目，以 Apache 2.0 许可发布。");

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

NodeJS

javascript

// javascript

Go

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 是 LF AI & Data Foundation 下的一个开源项目，以 Apache 2.0 许可发布。"}
option := milvusclient.NewRunAnalyzerOption(texts).
    WithAnalyzerParams(string(bs))

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

cURL

bash

# restful

C++

c++

#include "milvus/MilvusClientV2.h"

auto client = milvus::MilvusClientV2::Create();

milvus::ConnectParam connect_param{"YOUR_CLUSTER_ENDPOINT"};
auto status = client->Connect(connect_param);
if (!status.IsOk()) {
    std::cout << status.Message() << std::endl;
}

std::string text = "Milvus 是 LF AI & Data Foundation 下的一个开源项目，以 Apache 2.0 许可发布。";
auto request = milvus::RunAnalyzerRequest()
                       .AddText(text)
                       .WithAnalyzerParams(analyzer_params);

milvus::RunAnalyzerResponse response;
status = client->RunAnalyzer(request, response);
if (!status.IsOk()) {
    std::cout << status.Message() << std::endl;
}

Expected output

python

['是', '下的一个开源项目', '以', '许可发布']