Remove Punct

The removepunct filter removes tokens that contain punctuation or whitespace from the token stream. Use it when you want cleaner text processing that focuses on meaningful content words rather than punctuation marks.

Notes

This filter is most effective with jieba, lindera, and icu tokenizers, which preserve punctuation as separate tokens (e.g., "Hello!" → ["Hello", "!"]). The standard tokenizer discards punctuation during tokenization. The whitespace tokenizer preserves punctuation, including punctuation within a token. When combined with whitespace, removepunct removes the entire token if it contains punctuation or whitespace; it does not strip individual characters from the token.

Configuration

The removepunct 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": ["removepunct"]
}

Java

java

import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

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

Go

go

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

Rust

rust

use serde_json::json;

let analyzer_params = json!({
    "tokenizer": "jieba",
    "filter": ["removepunct"]
});

C++

c++

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

NodeJS

javascript

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

cURL

bash

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

The removepunct filter operates on the terms generated by the tokenizer, so it must be used in combination with a tokenizer.

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": "icu",
    "filter": ["removepunct"]
}

Java

java

import java.util.Collections;
import java.util.HashMap;
import java.util.Map;

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

Go

go

analyzerParams := map[string]any{"tokenizer": "icu", "filter": []string{"removepunct"}}

Rust

rust

use serde_json::json;

let analyzer_params = json!({
    "tokenizer": "icu",
    "filter": ["removepunct"]
});

C++

c++

nlohmann::json analyzer_params = {
    {"tokenizer", "icu"},
    {"filter", {"removepunct"}}
};

NodeJS

javascript

const analyzer_params = {
    "tokenizer": "icu",
    "filter": ["removepunct"],
};

cURL

bash

# restful
analyzerParams='{
  "tokenizer": "icu",
  "filter": [
    "removepunct"
  ]
}'

Verification using run_analyzer

Python

python

from pymilvus import (
    MilvusClient,
)

client = MilvusClient(uri="YOUR_CLUSTER_ENDPOINT")

# Sample text to analyze
sample_text = "Привет! Как дела?"

# Run the standard analyzer with the defined configuration
result = client.run_analyzer(sample_text, analyzer_params)
print("Standard 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;
import java.util.ArrayList;
import java.util.List;

ConnectConfig config = ConnectConfig.builder()
        .uri("YOUR_CLUSTER_ENDPOINT")
        .build();
MilvusClientV2 client = new MilvusClientV2(config);

List<String> texts = new ArrayList<>();
texts.add("Привет! Как дела?");

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

Go

go

import (
    "context"
    "fmt"

    "github.com/milvus-io/milvus/client/v3/milvusclient"
)

ctx := context.Background()

client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "YOUR_CLUSTER_ENDPOINT",
    APIKey:  "YOUR_CLUSTER_TOKEN",
})
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

texts := []string{"Привет! Как дела?"}
option := milvusclient.NewRunAnalyzerOption(texts...).
    WithAnalyzerParams(analyzerParams)

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

Rust

rust

use milvus::v2::prelude::*;
use serde_json::json;

let text = "Привет! Как дела?";

let response = client
    .run_analyzer(
        RunAnalyzerRequest::builder()
            .texts([text])
            .analyzer_params(json!({"tokenizer": "icu", "filter": ["removepunct"]}))
            .build()?,
    )
    .await?;

for result in response.results() {
    for token in result.get_tokens() {
        println!("{}", token.get_text());
    }
}

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 = "Привет! Как дела?";
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;
}

NodeJS

javascript

import { MilvusClient } from "@zilliz/milvus2-sdk-node";

const sampleText = "Привет! Как дела?";

const client = new MilvusClient({
  address: "YOUR_CLUSTER_ENDPOINT",
});

const result = await client.runAnalyzer({
  analyzer_params: analyzer_params,
  text: sampleText,
});

cURL

bash

# restful
curl -X POST "YOUR_CLUSTER_ENDPOINT/v2/vectordb/common/run_analyzer" \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_CLUSTER_TOKEN" \
  -d '{
    "analyzerParams": "{\"tokenizer\": \"icu\", \"filter\": [\"removepunct\"]}",
    "text": ["Привет! Как дела?"]
  }'

Expected output

python

['Привет', 'Как', 'дела']