English

The english analyzer in Zilliz Cloud is designed to process English text, applying language-specific rules for tokenization and filtering.

Definition

The english analyzer uses the following components:

• Tokenizer: Uses the standard tokenizer to split text into discrete word units.

• Filters: Includes multiple filters for comprehensive text processing:

  • lowercase: Converts all tokens to lowercase, enabling case-insensitive searches.

  • stemmer: Reduces words to their root form to support broader matching (e.g., "running" becomes "run").

  • stop_words: Removes common English stop words to focus on key terms in text.

The functionality of the english analyzer is equivalent to the following custom analyzer configuration:

Python

python

analyzer_params = {
        "tokenizer": "standard",
        "filter": [
                "lowercase",
                {
                        "type": "stemmer",
                        "language": "english"
                }, {
                        "type": "stop",
                        "stop_words": "_english_"
                }
        ]
}

Java

java

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("tokenizer", "standard");
analyzerParams.put("filter",
        Arrays.asList("lowercase",
                new HashMap<String, Object>() {{
                    put("type", "stemmer");
                    put("language", "english");
                }},
                new HashMap<String, Object>() {{
                    put("type", "stop");
                    put("stop_words", Collections.singletonList("_english_"));
                }}
        )
);

NodeJS

javascript

const analyzer_params = {
    "type": "standard", // Specifies the standard analyzer type
    "stop_words", ["of"] // Optional: List of words to exclude from tokenization
}

Go

go

analyzerParams = map[string]any{"tokenizer": "standard",
        "filter": []any{"lowercase", map[string]any{
            "type":     "stemmer",
            "language": "english",
        }, map[string]any{
            "type":       "stop",
            "stop_words": "_english_",
        }}}

cURL

bash

# restful
analyzerParams='{
  "tokenizer": "standard",
  "filter": [
    "lowercase",
    {
      "type": "stemmer",
      "language": "english"
    },
    {
      "type": "stop",
      "stop_words": "_english_"
    }
  ]
}'

C++

c++

nlohmann::json analyzer_params = {
    {"tokenizer", "standard"},
    {"filter", {
        "lowercase", 
        {{"type", "stemmer"}, {"language", "english"}},
        {{"type", "stop"}, {"stop_words", "_english_"}}
    }}
};

Configuration

To apply the english analyzer to a field, simply set type to english in analyzer_params, and include optional parameters as needed.

Python

python

analyzer_params = {
    "type": "english",
}

Java

java

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("type", "english");

NodeJS

javascript

const analyzer_params = {
    "type": "english",
}

Go

go

analyzerParams = map[string]any{"type": "english"}

cURL

bash

# restful
analyzerParams='{
  "type": "english"
}'

C++

c++

nlohmann::json analyzer_params = {
    {"type", "english"}
};

The english analyzer accepts the following optional parameters:

Parameter	Description
stop_words	An array containing a list of stop words, which will be removed from tokenization. Defaults to _english_, a built-in set of common English stop words.

Example configuration with custom stop words:

Python

python

analyzer_params = {
    "type": "english",
    "stop_words": ["a", "an", "the"]
}

Java

java

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("type", "english");
analyzerParams.put("stop_words", Arrays.asList("a", "an", "the"));

NodeJS

javascript

const analyzer_params = {
    "type": "english",
    "stop_words": ["a", "an", "the"]
}

Go

go

analyzerParams = map[string]any{"type": "english", "stop_words": []string{"a", "an", "the"}}

cURL

bash

# restful
analyzerParams='{
  "type": "english",
  "stop_words": [
    "a",
    "an",
    "the"
  ]
}'

C++

c++

nlohmann::json analyzer_params = {
    {"type", "english"},
    {"stop_words", {"a", "an", "the"}}
};

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 = {
    "type": "english",
    "stop_words": ["a", "an", "the"]
}

Java

java

Map<String, Object> analyzerParams = new HashMap<>();
analyzerParams.put("type", "english");
analyzerParams.put("stop_words", Arrays.asList("a", "an", "the"));

NodeJS

javascript

// javascript

Go

go

analyzerParams = map[string]any{"type": "english", "stop_words": []string{"a", "an", "the"}}

cURL

bash

# restful
analyzerParams='{
  "type": "english",
  "stop_words": [
    "a",
    "an",
    "the"
  ]
}'

C++

c++

nlohmann::json analyzer_params = {
    {"type", "english"},
    {"stop_words", {"a", "an", "the"}}
};

Verification using run_analyzer

Python

python

from pymilvus import (
    MilvusClient,
)

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

# Sample text to analyze
sample_text = "Milvus is a vector database built for scale!"

# Run the standard analyzer with the defined configuration
result = client.run_analyzer(sample_text, analyzer_params)
print("English 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")
        .token("YOUR_CLUSTER_TOKEN")
        .build();
MilvusClientV2 client = new MilvusClientV2(config);

List<String> texts = new ArrayList<>();
texts.add("Milvus is a vector database built for scale!");

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 is a vector database built for scale!"}
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", "YOUR_CLUSTER_TOKEN"};
auto status = client->Connect(connect_param);
if (!status.IsOk()) {
    std::cout << status.Message() << std::endl;
}

std::string text = "Milvus is a vector database built for scale!";
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

English analyzer output: ['milvus', 'vector', 'databas', 'built', 'scale']