Cnalphanumonly

El filtro « cnalphanumonly » elimina los tokens que contengan cualquier carácter que no sea un carácter chino, una letra inglesa o un dígito.

Configuración

El filtro « cnalphanumonly » está integrado en Milvus. Para utilizarlo, basta con especificar su nombre en la sección « filter » dentro de « 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"
  ]
}'

El filtro « cnalphanumonly » opera sobre los términos generados por el tokenizador, por lo que debe utilizarse en combinación con un tokenizador. Para consultar la lista de tokenizadores disponibles en Milvus, consulta Jieba y sus páginas relacionadas.

Una vez definidos los « analyzer_params », puede aplicarlos a un campo « VARCHAR » al definir el esquema de una colección. Esto permite a Milvus procesar el texto de ese campo utilizando el analizador especificado para una tokenización y un filtrado eficientes. Para más detalles, consulte «Ejemplo de uso».

Ejemplos

Antes de aplicar la configuración del analizador al esquema de su colección, compruebe su comportamiento mediante el método run_analyzer.

Configuración del analizador

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"
  ]
}'

Verificación mediante 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\"]}"
}'

Resultado esperado

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