Cnalphanumonly

Filter ` cnalphanumonly ` menghapus token yang mengandung karakter apa pun selain karakter Tionghoa, huruf Inggris, atau angka.

Konfigurasi

Filter ` cnalphanumonly ` sudah terintegrasi dalam Milvus. Untuk menggunakannya, cukup tentukan namanya di bagian ` filter ` dalam berkas ` analyzer_params`.

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

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("cnalphanumonly"));

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

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

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

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

using System.Collections.Generic;

var analyzerParams = new Dictionary<string, object>
{
    ["tokenizer"] = "jieba",
    ["filter"] = new[] { "cnalphanumonly" }
};

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

Filter ` cnalphanumonly ` beroperasi pada istilah yang dihasilkan oleh tokenizer, sehingga harus digunakan bersama dengan tokenizer. Untuk daftar tokenizer yang tersedia di Milvus, lihat Jieba dan halaman-halaman terkaitnya.

Setelah mendefinisikan analyzer_params, Anda dapat menerapkannya ke bidang VARCHAR saat mendefinisikan skema koleksi. Hal ini memungkinkan Milvus memproses teks di bidang tersebut menggunakan analyzer yang ditentukan untuk tokenisasi dan penyaringan yang efisien. Untuk detailnya, lihat Contoh penggunaan.

Contoh

Sebelum menerapkan konfigurasi penganalisis ke skema koleksi Anda, verifikasi perilakunya menggunakan metode run_analyzer.

Konfigurasi penganalisis

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

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("cnalphanumonly"));

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

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

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

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

using System.Collections.Generic;

var analyzerParams = new Dictionary<string, object>
{
    ["tokenizer"] = "jieba",
    ["filter"] = new[] { "cnalphanumonly" }
};

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

Verifikasi menggunakan run_analyzer

from pymilvus import (
    MilvusClient,
)

client = MilvusClient(
    uri="http://localhost:19530",
    token="root:Milvus"
)

# 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)

import java.util.*;
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("http://localhost:19530")
        .token("root:Milvus")
        .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();

import (
    "context"
    "fmt"

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

ctx := context.Background()

client, err := milvusclient.New(ctx, &milvusclient.ClientConfig{
    Address: "http://localhost:19530",
    APIKey:  "root:Milvus",
})
if err != nil {
    fmt.Println(err.Error())
    // handle error
}

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(analyzerParams)

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

for _, r := range result {
    for _, token := range r.Tokens {
        fmt.Println("Analyzer output:", token.Text)
    }
}

use milvus::v2::error::Result;
use milvus::v2::prelude::*;

let config = ConnectConfig::new().uri("http://localhost:19530").token("root:Milvus");
let client = ClientV2::new(&config).await?;

let sample_text = "Milvus 是 LF AI & Data Foundation 下的一个开源项目，以 Apache 2.0 许可发布。";
let result = client
    .run_analyzer(
        RunAnalyzerRequest::builder()
            .texts(vec![sample_text])
            .analyzer_params(analyzer_params)
            .build()?,
    )
    .await?;

println!("Analyzer output: {:?}", result);

#include "milvus/MilvusClientV2.h"
#include <iostream>
#include <string>

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

milvus::ConnectParam connect_param{"http://localhost:19530", "root:Milvus"};
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;
}

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

const client = new MilvusClient({
    address: "http://localhost:19530",
    token: "root:Milvus",
});

// Sample text to analyze
const 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
const result = await client.runAnalyzer({
    text: sample_text,
    analyzer_params: analyzer_params
});
console.log("Analyzer output:", result);

using Milvus.Client.V2;
using Milvus.Client.V2.Requests.Utility;
using Milvus.Client.V2.Responses.Utility;
using Milvus.Client.V2.Types;

var client = new MilvusClientV2(new ConnectConfig
{
    Uri = "http://localhost:19530",
    ApiKey = "root:Milvus"
});
await client.ConnectAsync();

// Sample text to analyze
var sampleText = "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
RunAnalyzerResp result = await client.RunAnalyzerAsync(new RunAnalyzerReq
{
    AnalyzerParams = analyzerParams,
    Texts = new[] { sampleText }
});
Console.WriteLine("Analyzer output: " + string.Join(", ", result.Results.SelectMany(r => r.Tokens).Select(t => t.Token)));

export CLUSTER_ENDPOINT="http://localhost:19530"
export TOKEN="root:Milvus"

curl --request POST \
--url "${CLUSTER_ENDPOINT}/v2/vectordb/common/run_analyzer" \
--header "Authorization: Bearer ${TOKEN}" \
--header "Content-Type: application/json" \
--header "Request-Timeout: 10" \
-d '{
    "text": ["Milvus 是 LF AI & Data Foundation 下的一个开源项目，以 Apache 2.0 许可发布。"],
    "analyzerParams": "{\"tokenizer\": \"jieba\", \"filter\": [\"cnalphanumonly\"]}"
}'

Hasil yang diharapkan

['Milvus', '是', 'LF', 'AI', 'Data', 'Foundation', '下', '的', '一个', '开源', '项目', '以', 'Apache', '2.0', '许可', '发布']